Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.
High-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); virtual assistants (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."
Various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.General intelligence—the ability to complete any task performed by a human on an at least equal level—is among the field's long-term goals. To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience, and other fields.
Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism throughout its history, followed by periods of disappointment and loss of funding, known as AI winters. Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture, and by the early 2020s many billions of dollars were being invested in AI and the field experienced rapid ongoing progress in what has become known as the AI boom. The emergence of advanced generative AI in the midst of the AI boom and its ability to create and modify content exposed several unintended consequences and harms in the present and raised concerns about the risks of AI and its long-term effects in the future, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.
Goals
The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.
Reasoning and problem-solving
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.
Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments. Accurate and efficient reasoning is an unsolved problem.
Knowledge representation
Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas.
A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects; situations, events, states, and time; causes and effects; knowledge about knowledge (what we know about what other people know);default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing); and many other aspects and domains of knowledge.
Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous); and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.
Planning and decision-making
An "agent" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal. In automated decision-making, the agent has preferences—there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the "utility") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.
In classical planning, the agent knows exactly what the effect of any action will be. In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.
In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences.Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.
A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g., by iteration), be heuristic, or it can be learned.
Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.
Learning
Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning.
There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.Supervised learning requires labeling the training data with the expected answers, and comes in two main varieties: classification (where the program must learn to predict what category the input belongs in) and regression (where the program must deduce a numeric function based on numeric input).
In reinforcement learning, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good".Transfer learning is when the knowledge gained from one problem is applied to a new problem.Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.
Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.
Natural language processing
Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.
Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation unless restricted to small domains called "micro-worlds" (due to the common sense knowledge problem). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the basis of computational language structure.
Modern deep learning techniques for NLP include word embedding (representing words, typically as vectors encoding their meaning),transformers (a deep learning architecture using an attention mechanism), and others. In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text, and by 2023, these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applications.
Perception
Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyze visual input.
The field includes speech recognition,image classification,facial recognition, object recognition,object tracking, and robotic perception.
Social intelligence
Affective computing is a field that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.
However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the effects displayed by a videotaped subject.
General intelligence
A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.
Techniques
AI research uses a wide variety of techniques to accomplish the goals above.
Search and optimization
AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search.
State space search
State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.
Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. "Heuristics" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.
Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and countermoves, looking for a winning position.
Local search
Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.
Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks, through the backpropagation algorithm.
Another type of local search is evolutionary computation, which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation.
Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).
Logic
Formal logic is used for reasoning and knowledge representation. Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies") and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as "Every X is a Y" and "There are some Xs that are Ys").
Deductive reasoning in logic is the process of proving a new statement (conclusion) from other statements that are given and assumed to be true (the premises). Proofs can be structured as proof trees, in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules.
Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms. In the case of Horn clauses, problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem. In the more general case of the clausal form of first-order logic, resolution is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.
Inference in both Horn clause logic and first-order logic is undecidable, and therefore intractable. However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog, is Turing complete. Moreover, its efficiency is competitive with computation in other symbolic programming languages.
Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.
Non-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning. Other specialized versions of logic have been developed to describe many complex domains.
Probabilistic methods for uncertain reasoning
Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks,game theory and mechanism design.
Bayesian networks are a tool that can be used for reasoning (using the Bayesian inference algorithm),learning (using the expectation–maximization algorithm),planning (using decision networks) and perception (using dynamic Bayesian networks).
Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception systems analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).
Classifiers and statistical learning methods
The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. Classifiers are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an "observation") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.
There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm.K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s. The naive Bayes classifier is reportedly the "most widely used learner" at Google, due in part to its scalability.Neural networks are also used as classifiers.
Artificial neural networks
An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.
Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm. Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.
In feedforward neural networks the signal passes in only one direction.Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks.Perceptrons use only a single layer of neurons; deep learning uses multiple layers. Convolutional neural networks strengthen the connection between neurons that are "close" to each other—this is especially important in image processing, where a local set of neurons must identify an "edge" before the network can identify an object.
Deep learning
Deep learning uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.
Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification, and others. The reason that deep learning performs so well in so many applications is not known as of 2023. The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.
GPT
Generative pre-trained transformers (GPT) are large language models (LLMs) that generate text based on the semantic relationships between words in sentences. Text-based GPT models are pretrained on a large corpus of text that can be from the Internet. The pretraining consists of predicting the next token (a token being usually a word, subword, or punctuation). Throughout this pretraining, GPT models accumulate knowledge about the world and can then generate human-like text by repeatedly predicting the next token. Typically, a subsequent training phase makes the model more truthful, useful, and harmless, usually with a technique called reinforcement learning from human feedback (RLHF). Current GPT models are prone to generating falsehoods called "hallucinations", although this can be reduced with RLHF and quality data. They are used in chatbots, which allow people to ask a question or request a task in simple text.
Current models and services include Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot, and LLaMA.Multimodal GPT models can process different types of data (modalities) such as images, videos, sound, and text.
Hardware and software
In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models' training. Specialized programming languages such as Prolog were used in early AI research, but general-purpose programming languages like Python have become predominant.
The transistor density in integrated circuits has been observed to roughly double every 18 months—a trend known as Moore's law, named after the Intel co-founder Gordon Moore, who first identified it. Improvements in GPUs have been even faster, a trend sometimes called Huang's law, named after Nvidia co-founder and CEO Jensen Huang.
Applications
AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple's Face ID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used by Facebook, Apple's iPhoto and TikTok). The deployment of AI may be overseen by a Chief automation officer (CAO).
Health and medicine
The application of AI in medicine and medical research has the potential to increase patient care and quality of life. Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.
For medical research, AI is an important tool for processing and integrating big data. This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication. It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research. New AI tools can deepen the understanding of biomedically relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours rather than months, the 3D structure of a protein. In 2023, it was reported that AI-guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria. In 2024, researchers used machine learning to accelerate the search for Parkinson's disease drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of alpha-synuclein (the protein that characterises Parkinson's disease). They were able to speed up the initial screening process ten-fold and reduce the cost by a thousand-fold.
Games
Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Then, in 2017, it defeated Ke Jie, who was the best Go player in the world. Other programs handle imperfect-information games, such as the poker-playing program Pluribus.DeepMind developed increasingly generalistic reinforcement learning models, such as with MuZero, which could be trained to play chess, Go, or Atari games. In 2019, DeepMind's AlphaStar achieved grandmaster level in StarCraft II, a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map. In 2021, an AI agent competed in a PlayStation Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning. In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen open-world video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions.
Mathematics
Large language models, such as GPT-4, Gemini, Claude, LLaMa or Mistral, are increasingly used in mathematics. These probabilistic models are versatile, but can also produce wrong answers in the form of hallucinations. They sometimes need a large database of mathematical problems to learn from, but also methods such as supervised fine-tuning or trained classifiers with human-annotated data to improve answers for new problems and learn from corrections. A February 2024 study showed that the performance of some language models for reasoning capabilities in solving math problems not included in their training data was low, even for problems with only minor deviations from trained data. One technique to improve their performance involves training the models to produce correct reasoning steps, rather than just the correct result. In January 2025, Microsoft proposed the technique rStar-Math that leverages Monte Carlo tree search and step-by-step reasoning, enabling a relatively small language model like Qwen-7B to solve 53% of the AIME 2024 and 90% of the MATH benchmark problems.
Alternatively, dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as AlphaTensor, AlphaGeometry and AlphaProof all from Google DeepMind,Llemma from EleutherAI or Julius.
When natural language is used to describe mathematical problems, converters can transform such prompts into a formal language such as Lean to define mathematical tasks.
Some models have been developed to solve challenging problems and reach good results in benchmark tests, others to serve as educational tools in mathematics.
Topological deep learning integrates various topological approaches.
Finance
Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years.
World Pensions experts like Nicolas Firzli insist it may be too early to see the emergence of highly innovative AI-informed financial products and services: "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I'm not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."
Military
Various countries are deploying AI military applications. The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams.
AI has been used in military operations in Iraq, Syria, Israel and Ukraine.
Generative AI
Generative artificial intelligence (generative AI, GenAI, or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. These models learn the underlying patterns and structures of their training data and use them to produce new data based on the input, which often comes in the form of natural language prompts.
Improvements in transformer-based deep neural networks, particularly large language models (LLMs), enabled an AI boom of generative AI systems in the early 2020s. These include chatbots such as ChatGPT, Copilot, Gemini, and LLaMA; text-to-image artificial intelligence image generation systems such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video AI generators such as Sora. Companies such as OpenAI, Anthropic, Microsoft, Google, and Baidu as well as numerous smaller firms have developed generative AI models.
Generative AI has uses across a wide range of industries, including software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, fashion, and product design. However, concerns have been raised about the potential misuse of generative AI such as cybercrime, the use of fake news or deepfakes to deceive or manipulate people, and the mass replacement of human jobs. Intellectual property law concerns also exist around generative models that are trained on and emulate copyrighted works of art.Agents
Artificial intelligent (AI) agents are software entities designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals. These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including virtual assistants, chatbots, autonomous vehicles, game-playing systems, and industrial robotics. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations. This means they are restricted to performing tasks within their defined scope and have finite memory and processing capabilities. In real-world applications, AI agents often face time constraints for decision-making and action execution. Many AI agents incorporate learning algorithms, enabling them to improve their performance over time through experience or training. Using machine learning, AI agents can adapt to new situations and optimise their behaviour for their designated tasks.
Sexuality
Applications of AI in this domain include AI-enabled menstruation and fertility trackers that analyze user data to offer prediction, AI-integrated sex toys (e.g., teledildonics), AI-generated sexual education content, and AI agents that simulate sexual and romantic partners (e.g., Replika). AI is also used for the production of non-consensual deepfake pornography, raising significant ethical and legal concerns.
AI technologies have also been used to attempt to identify online gender-based violence and online sexual grooming of minors.
Other industry-specific tasks
There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported having incorporated "AI" in some offerings or processes. A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management.
AI applications for evacuation and disaster management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS, videos or social media. Further, AI can provide real time information on the real time evacuation conditions.
In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water.
Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights." For example, it is used for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. Additionally, it could be used for activities in space, such as space exploration, including the analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation.
During the 2024 Indian elections, US$50 millions was spent on authorized AI-generated content, notably by creating deepfakes of allied (including sometimes deceased) politicians to better engage with voters, and by translating speeches to various local languages.
Ethics
AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of DeepMind hopes to "solve intelligence, and then use that to solve everything else". However, as the use of AI has become widespread, several unintended consequences and risks have been identified. In-production systems can sometimes not factor ethics and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable in deep learning.
Risks and harm
Privacy and copyright
Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright.
AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency.
Sensitive user data collected may include online activity records, geolocation data, video, or audio. For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.
AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy experts, such as Cynthia Dwork, have begun to view privacy in terms of fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."
Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "fair use". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work". Website owners who do not wish to have their content scraped can indicate it in a "robots.txt" file. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI. Another discussed approach is to envision a separate sui generis system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.
Dominance by tech giants
The commercial AI scene is dominated by Big Tech companies such as Alphabet Inc., Amazon, Apple Inc., Meta Platforms, and Microsoft. Some of these players already own the vast majority of existing cloud infrastructure and computing power from data centers, allowing them to entrench further in the marketplace.
Power needs and environmental impacts
In January 2024, the International Energy Agency (IEA) released Electricity 2024, Analysis and Forecast to 2026, forecasting electric power use. This is the first IEA report to make projections for data centers and power consumption for artificial intelligence and cryptocurrency. The report states that power demand for these uses might double by 2026, with additional electric power usage equal to electricity used by the whole Japanese nation.
Prodigious power consumption by AI is responsible for the growth of fossil fuels use, and might delay closings of obsolete, carbon-emitting coal energy facilities. There is a feverish rise in the construction of data centers throughout the US, making large technology firms (e.g., Microsoft, Meta, Google, Amazon) into voracious consumers of electric power. Projected electric consumption is so immense that there is concern that it will be fulfilled no matter the source. A ChatGPT search involves the use of 10 times the electrical energy as a Google search. The large firms are in haste to find power sources – from nuclear energy to geothermal to fusion. The tech firms argue that – in the long view – AI will be eventually kinder to the environment, but they need the energy now. AI makes the power grid more efficient and "intelligent", will assist in the growth of nuclear power, and track overall carbon emissions, according to technology firms.
A 2024 Goldman Sachs Research Paper, AI Data Centers and the Coming US Power Demand Surge, found "US power demand (is) likely to experience growth not seen in a generation...." and forecasts that, by 2030, US data centers will consume 8% of US power, as opposed to 3% in 2022, presaging growth for the electrical power generation industry by a variety of means. Data centers' need for more and more electrical power is such that they might max out the electrical grid. The Big Tech companies counter that AI can be used to maximize the utilization of the grid by all.
In 2024, the Wall Street Journal reported that big AI companies have begun negotiations with the US nuclear power providers to provide electricity to the data centers. In March 2024 Amazon purchased a Pennsylvania nuclear-powered data center for $650 Million (US).Nvidia CEO Jen-Hsun Huang said nuclear power is a good option for the data centers.
In September 2024, Microsoft announced an agreement with Constellation Energy to re-open the Three Mile Island nuclear power plant to provide Microsoft with 100% of all electric power produced by the plant for 20 years. Reopening the plant, which suffered a partial nuclear meltdown of its Unit 2 reactor in 1979, will require Constellation to get through strict regulatory processes which will include extensive safety scrutiny from the US Nuclear Regulatory Commission. If approved (this will be the first ever US re-commissioning of a nuclear plant), over 835 megawatts of power – enough for 800,000 homes – of energy will be produced. The cost for re-opening and upgrading is estimated at $1.6 billion (US) and is dependent on tax breaks for nuclear power contained in the 2022 US Inflation Reduction Act. The US government and the state of Michigan are investing almost $2 billion (US) to reopen the Palisades Nuclear reactor on Lake Michigan. Closed since 2022, the plant is planned to be reopened in October 2025. The Three Mile Island facility will be renamed the Crane Clean Energy Center after Chris Crane, a nuclear proponent and former CEO of Exelon who was responsible for Exelon spinoff of Constellation.
After the last approval in September 2023, Taiwan suspended the approval of data centers north of Taoyuan with a capacity of more than 5 MW in 2024, due to power supply shortages. Taiwan aims to phase out nuclear power by 2025. On the other hand, Singapore imposed a ban on the opening of data centers in 2019 due to electric power, but in 2022, lifted this ban.
Although most nuclear plants in Japan have been shut down after the 2011 Fukushima nuclear accident, according to an October 2024 Bloomberg article in Japanese, cloud gaming services company Ubitus, in which Nvidia has a stake, is looking for land in Japan near nuclear power plant for a new data center for generative AI. Ubitus CEO Wesley Kuo said nuclear power plants are the most efficient, cheap and stable power for AI.
On 1 November 2024, the Federal Energy Regulatory Commission (FERC) rejected an application submitted by Talen Energy for approval to supply some electricity from the nuclear power station Susquehanna to Amazon's data center. According to the Commission Chairman Willie L. Phillips, it is a burden on the electricity grid as well as a significant cost shifting concern to households and other business sectors.
Misinformation
YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem [citation needed].
In 2022, generative AI began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films, or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda. AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.
Algorithmic bias and fairness
Machine learning applications will be biased if they learn from biased data. The developers may not be aware that the bias exists. Bias can be introduced by the way training data is selected and by the way a model is deployed. If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination. The field of fairness studies how to prevent harms from algorithmic biases.
On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people, a problem called "sample size disparity". Google "fixed" this problem by preventing the system from labelling anything as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.
COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend. In 2017, several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.
A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender". Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."
Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as recommendations, some of these "recommendations" will likely be racist. Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is descriptive rather than prescriptive.
Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.
There are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is distributive fairness, which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases, but it may conflict with anti-discrimination laws.
At its 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022), the Association for Computing Machinery, in Seoul, South Korea, presented and published findings that recommend that until AI and robotics systems are demonstrated to be free of bias mistakes, they are unsafe, and the use of self-learning neural networks trained on vast, unregulated sources of flawed internet data should be curtailed.[dubious – discuss]
Lack of transparency
Many AI systems are so complex that their designers cannot explain how they reach their decisions. Particularly with deep neural networks, in which there are a large amount of non-linear relationships between inputs and outputs. But some popular explainability techniques exist.
It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to show the scale. Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading.
People who have been harmed by an algorithm's decision have a right to an explanation. Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists. Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used.
DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems.
Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output. LIME can locally approximate a model's outputs with a simpler, interpretable model.Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned.Deconvolution, DeepDream and other generative methods can allow developers to see what different layers of a deep network for computer vision have learned, and produce output that can suggest what the network is learning. For generative pre-trained transformers, Anthropic developed a technique based on dictionary learning that associates patterns of neuron activations with human-understandable concepts.
Bad actors and weaponized AI
Artificial intelligence provides a number of tools that are useful to bad actors, such as authoritarian governments, terrorists, criminals or rogue states.
A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision. Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and, if produced at scale, they are potentially weapons of mass destruction. Even when used in conventional warfare, they currently cannot reliably choose targets and could potentially kill an innocent person. In 2014, 30 nations (including China) supported a ban on autonomous weapons under the United Nations' Convention on Certain Conventional Weapons, however the United States and others disagreed. By 2015, over fifty countries were reported to be researching battlefield robots.
AI tools make it easier for authoritarian governments to efficiently control their citizens in several ways. Face and voice recognition allow widespread surveillance. Machine learning, operating this data, can classify potential enemies of the state and prevent them from hiding. Recommendation systems can precisely target propaganda and misinformation for maximum effect. Deepfakes and generative AI aid in producing misinformation. Advanced AI can make authoritarian centralized decision making more competitive than liberal and decentralized systems such as markets. It lowers the cost and difficulty of digital warfare and advanced spyware. All these technologies have been available since 2020 or earlier—AI facial recognition systems are already being used for mass surveillance in China.
There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours.
Technological unemployment
Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.
In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are redistributed. Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk". The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies. In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.
Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.
From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement.
Existential risk
It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, "spell the end of the human race". This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character. These sci-fi scenarios are misleading in several ways.
First, AI does not require human-like sentience to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a paperclip factory manager).Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead." In order to be safe for humanity, a superintelligence would have to be genuinely aligned with humanity's morality and values so that it is "fundamentally on our side".
Second, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are built on language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive.
The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI. Personalities such as Stephen Hawking, Bill Gates, and Elon Musk, as well as AI pioneers such as Yoshua Bengio, Stuart Russell, Demis Hassabis, and Sam Altman, have expressed concerns about existential risk from AI.
In May 2023, Geoffrey Hinton announced his resignation from Google in order to be able to "freely speak out about the risks of AI" without "considering how this impacts Google". He notably mentioned risks of an AI takeover, and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI.
In 2023, many leading AI experts endorsed the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war".
Some other researchers were more optimistic. AI pioneer Jürgen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier." While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors."Andrew Ng also argued that "it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests."Yann LeCun "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction." In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine. However, after 2016, the study of current and future risks and possible solutions became a serious area of research.
Ethical machines and alignment
Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk.
Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas. The field of machine ethics is also called computational morality, and was founded at an AAAI symposium in 2005.
Other approaches include Wendell Wallach's "artificial moral agents" and Stuart J. Russell's three principles for developing provably beneficial machines.
Open source
Active organizations in the AI open-source community include Hugging Face,Google,EleutherAI and Meta. Various AI models, such as Llama 2, Mistral or Stable Diffusion, have been made open-weight, meaning that their architecture and trained parameters (the "weights") are publicly available. Open-weight models can be freely fine-tuned, which allows companies to specialize them with their own data and for their own use-case. Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitate bioterrorism) and that once released on the Internet, they cannot be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.
Frameworks
Artificial Intelligence projects can have their ethical permissibility tested while designing, developing, and implementing an AI system. An AI framework such as the Care and Act Framework containing the SUM values—developed by the Alan Turing Institute tests projects in four main areas:
- Respect the dignity of individual people
- Connect with other people sincerely, openly, and inclusively
- Care for the wellbeing of everyone
- Protect social values, justice, and the public interest
Other developments in ethical frameworks include those decided upon during the Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others; however, these principles do not go without their criticisms, especially regards to the people chosen contributes to these frameworks.
Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers.
The UK AI Safety Institute released in 2024 a testing toolset called 'Inspect' for AI safety evaluations available under a MIT open-source licence which is freely available on GitHub and can be improved with third-party packages. It can be used to evaluate AI models in a range of areas including core knowledge, ability to reason, and autonomous capabilities.
Regulation
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone. Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology.Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years. In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics. In 2024, the Council of Europe created the first international legally binding treaty on AI, called the "Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law". It was adopted by the European Union, the United States, the United Kingdom, and other signatories.
In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks". A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".
In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks. 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence. In May 2024 at the AI Seoul Summit, 16 global AI tech companies agreed to safety commitments on the development of AI.
History
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of mathematical reasoning. This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an "electronic brain". They developed several areas of research that would become part of AI, such as McCullouch and Pitts design for "artificial neurons" in 1943, and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that "machine intelligence" was plausible.
The field of AI research was founded at a workshop at Dartmouth College in 1956. The attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English. Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s.
Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field. In 1965 Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". In 1967 Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects.Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The "AI winter", a period when obtaining funding for AI projects was difficult, followed.
In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.
Up to this point, most of AI's funding had gone to projects that used high-level symbols to represent mental objects like plans, goals, beliefs, and known facts. In the 1980s, some researchers began to doubt that this approach would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition, and began to look into "sub-symbolic" approaches.Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that move and survive.Judea Pearl, Lofti Zadeh, and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic. But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others. In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.
AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics). By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence" (a tendency known as the AI effect). However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or "AGI"), which had several well-funded institutions by the 2010s.
Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field. For many specific tasks, other methods were abandoned. Deep learning's success was based on both hardware improvements (faster computers,graphics processing units, cloud computing) and access to large amounts of data (including curated datasets, such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.
In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study.
In the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program taught only the game's rules and developed a strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text.ChatGPT, launched on November 30, 2022, became the fastest-growing consumer software application in history, gaining over 100 million users in two months. It marked what is widely regarded as AI's breakout year, bringing it into the public consciousness. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions of dollars in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI". About 800,000 "AI"-related U.S. job openings existed in 2022. According to PitchBook research, 22% of newly funded startups in 2024 claimed to be AI companies.
Philosophy
Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines. Another major focus has been whether machines can be conscious, and the associated ethical implications. Many other topics in philosophy are relevant to AI, such as epistemology and free will. Rapid advancements have intensified public discussions on the philosophy and ethics of AI.
Defining artificial intelligence
Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?" He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks."[377]
Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure. However, they are critical that the test requires the machine to imitate humans. "Aeronautical engineering texts", they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons.'" AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".
McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world". Another AI founder, Marvin Minsky, similarly describes it as "the ability to solve hard problems". The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals. These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible.
Another definition has been adopted by Google, a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.
Some authors have suggested in practice, that the definition of AI is vague and difficult to define, with contention as to whether classical algorithms should be categorised as AI, with many companies during the early 2020s AI boom using the term as a marketing buzzword, often even if they did "not actually use AI in a material way".
Evaluating approaches to AI
No established unifying theory or paradigm has guided AI research for most of its history. The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, soft and narrow. Critics argue that these questions may have to be revisited by future generations of AI researchers.
Symbolic AI and its limits
Symbolic AI (or "GOFAI") simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."
However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult. Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge. Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.
The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence, in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of neuro-symbolic artificial intelligence attempts to bridge the two approaches.
Neat vs. scruffy
"Neats" hope that intelligent behavior is described using simple, elegant principles (such as logic, optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s, but eventually was seen as irrelevant. Modern AI has elements of both.
Soft vs. hard computing
Finding a provably correct or optimal solution is intractable for many important problems. Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks.
Narrow vs. general AI
AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals. General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The sub-field of artificial general intelligence studies this area exclusively.
Machine consciousness, sentience, and mind
The philosophy of mind does not know whether a machine can have a mind, consciousness and mental states, in the same sense that human beings do. This issue considers the internal experiences of the machine, rather than its external behavior. Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field: to build machines that can solve problems using intelligence. Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on." However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction.
Consciousness
David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). While human information processing is easy to explain, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to know what red looks like.
Computationalism and functionalism
Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind–body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.
Philosopher John Searle characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle challenges this claim with his Chinese room argument, which attempts to show that even a computer capable of perfectly simulating human behavior would not have a mind.
AI welfare and rights
It is difficult or impossible to reliably evaluate whether an advanced AI is sentient (has the ability to feel), and if so, to what degree. But if there is a significant chance that a given machine can feel and suffer, then it may be entitled to certain rights or welfare protection measures, similarly to animals.Sapience (a set of capacities related to high intelligence, such as discernment or self-awareness) may provide another moral basis for AI rights.Robot rights are also sometimes proposed as a practical way to integrate autonomous agents into society.
In 2017, the European Union considered granting "electronic personhood" to some of the most capable AI systems. Similarly to the legal status of companies, it would have conferred rights but also responsibilities. Critics argued in 2018 that granting rights to AI systems would downplay the importance of human rights, and that legislation should focus on user needs rather than speculative futuristic scenarios. They also noted that robots lacked the autonomy to take part to society on their own.
Progress in AI increased interest in the topic. Proponents of AI welfare and rights often argue that AI sentience, if it emerges, would be particularly easy to deny. They warn that this may be a moral blind spot analogous to slavery or factory farming, which could lead to large-scale suffering if sentient AI is created and carelessly exploited.
Future
Superintelligence and the singularity
A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. If research into artificial general intelligence produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to what I. J. Good called an "intelligence explosion" and Vernor Vinge called a "singularity".
However, technologies cannot improve exponentially indefinitely, and typically follow an S-shaped curve, slowing when they reach the physical limits of what the technology can do.
Transhumanism
Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines may merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in the writings of Aldous Huxley and Robert Ettinger.
Edward Fredkin argues that "artificial intelligence is the next step in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his 1998 book Darwin Among the Machines: The Evolution of Global Intelligence.
Decomputing
Arguments for decomputing have been raised by Dan McQuillan (Resisting AI: An Anti-fascist Approach to Artificial Intelligence, 2022), meaning an opposition to the sweeping application and expansion of artificial intelligence. Similar to degrowth the approach criticizes AI as an outgrowth of the systemic issues and capitalist world we live in. Arguing that a different future is possible, in which distance between people is reduced and not increased to AI intermediaries.
In fiction
Thought-capable artificial beings have appeared as storytelling devices since antiquity, and have been a persistent theme in science fiction.
A common trope in these works began with Mary Shelley's Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.
Isaac Asimov introduced the Three Laws of Robotics in many stories, most notably with the "Multivac" super-intelligent computer. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek's R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.
See also
- Artificial intelligence and elections – Use and impact of AI on political elections
- Artificial intelligence content detection – Software to detect AI-generated content
- Behavior selection algorithm – Algorithm that selects actions for intelligent agents
- Business process automation – Automation of business processes
- Case-based reasoning – Process of solving new problems based on the solutions of similar past problems
- Computational intelligence – Ability of a computer to learn a specific task from data or experimental observation
- Digital immortality – Hypothetical concept of storing a personality in digital form
- Emergent algorithm – Algorithm exhibiting emergent behavior
- Female gendering of AI technologies – Gender biases in digital technology
- Glossary of artificial intelligence – List of definitions of terms and concepts commonly used in the study of artificial intelligence
- Intelligence amplification – Use of information technology to augment human intelligence
- Intelligent agent – Software agent which acts autonomously
- Mind uploading – Hypothetical process of digitally emulating a brain
- Organoid intelligence – Use of brain cells and brain organoids for intelligent computing
- Robotic process automation – Form of business process automation technology
- Wetware computer – Computer composed of organic material
Explanatory notes
- This list of intelligent traits is based on the topics covered by the major AI textbooks, including: Russell & Norvig (2021), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and Nilsson (1998)
- This list of tools is based on the topics covered by the major AI textbooks, including: Russell & Norvig (2021), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and Nilsson (1998)
- It is among the reasons that expert systems proved to be inefficient for capturing knowledge.
- "Rational agent" is general term used in economics, philosophy and theoretical artificial intelligence. It can refer to anything that directs its behavior to accomplish goals, such as a person, an animal, a corporation, a nation, or in the case of AI, a computer program.
- Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence". In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".
- See AI winter § Machine translation and the ALPAC report of 1966
- Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.
- Expectation–maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown latent variables.
- Some form of deep neural networks (without a specific learning algorithm) were described by: Warren S. McCulloch and Walter Pitts (1943)Alan Turing (1948);Karl Steinbuch and (1961). Deep or recurrent networks that learned (or used gradient descent) were developed by: Frank Rosenblatt(1957);Oliver Selfridge (1959);Alexey Ivakhnenko and (1965); (1971);Shun-Ichi Amari (1972);John Joseph Hopfield (1982). Precursors to backpropagation were developed by: Henry J. Kelley (1960);Arthur E. Bryson (1962);Stuart Dreyfus (1962);Arthur E. Bryson and Yu-Chi Ho (1969); Backpropagation was independently developed by: Seppo Linnainmaa (1970);Paul Werbos (1974).
- Geoffrey Hinton said, of his work on neural networks in the 1990s, "our labeled datasets were thousands of times too small. [And] our computers were millions of times too slow."
- In statistics, a bias is a systematic error or deviation from the correct value. But in the context of fairness, it refers to a tendency in favor or against a certain group or individual characteristic, usually in a way that is considered unfair or harmful. A statistically unbiased AI system that produces disparate outcomes for different demographic groups may thus be viewed as biased in the ethical sense.
- Including Jon Kleinberg (Cornell University), Sendhil Mullainathan (University of Chicago), Cynthia Chouldechova (Carnegie Mellon) and Sam Corbett-Davis (Stanford)
- Moritz Hardt (a director at the Max Planck Institute for Intelligent Systems) argues that machine learning "is fundamentally the wrong tool for a lot of domains, where you're trying to design interventions and mechanisms that change the world."
- When the law was passed in 2018, it still contained a form of this provision.
- This is the United Nations' definition, and includes things like land mines as well.
- See table 4; 9% is both the OECD average and the U.S. average.
- Sometimes called a "robopocalypse"
- "Electronic brain" was the term used by the press around this time.
- Daniel Crevier wrote, "the conference is generally recognized as the official birthdate of the new science."Russell and Norvig called the conference "the inception of artificial intelligence."
- Russell and Norvig wrote "for the next 20 years the field would be dominated by these people and their students."
- Russell and Norvig wrote, "it was astonishing whenever a computer did anything kind of smartish".
- The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU.
- Russell and Norvig write: "in almost all cases, these early systems failed on more difficult problems"
- Embodied approaches to AI were championed by Hans Moravec and Rodney Brooks and went by many names: Nouvelle AI.Developmental robotics.
- Matteo Wong wrote in The Atlantic: "Whereas for decades, computer-science fields such as natural-language processing, computer vision, and robotics used extremely different methods, now they all use a programming method called "deep learning". As a result, their code and approaches have become more similar, and their models are easier to integrate into one another."
- Jack Clark wrote in Bloomberg: "After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever", and noted that the number of software projects that use machine learning at Google increased from a "sporadic usage" in 2012 to more than 2,700 projects in 2015.
- Nils Nilsson wrote in 1983: "Simply put, there is wide disagreement in the field about what AI is all about."
- Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."
- Searle presented this definition of "Strong AI" in 1999. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." Strong AI is defined similarly by Russell and Norvig: "Stong AI – the assertion that machines that do so are actually thinking (as opposed to simulating thinking)."
References
- Russell & Norvig (2021), pp. 1–4.
- AI set to exceed human brain power Archived 2008-02-19 at the Wayback Machine CNN.com (July 26, 2006)
- Kaplan, Andreas; Haenlein, Michael (2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons. 62: 15–25. doi:10.1016/j.bushor.2018.08.004. ISSN 0007-6813. S2CID 158433736.
- Artificial general intelligence: Russell & Norvig (2021, pp. 32–33, 1020–1021)
Proposal for the modern version: Pennachin & Goertzel (2007)
Warnings of overspecialization in AI from leading researchers: Nilsson (1995), McCarthy (2007), Beal & Winston (2009) - Russell & Norvig (2021, §1.2).
- Dartmouth workshop: Russell & Norvig (2021, p. 18), McCorduck (2004, pp. 111–136), NRC (1999, pp. 200–201)
The proposal: McCarthy et al. (1955) - Successful programs of the 1960s: McCorduck (2004, pp. 243–252), Crevier (1993, pp. 52–107), Moravec (1988, p. 9), Russell & Norvig (2021, pp. 19–21)
- Funding initiatives in the early 1980s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): McCorduck (2004, pp. 426–441), Crevier (1993, pp. 161–162, 197–203, 211, 240), Russell & Norvig (2021, p. 23), NRC (1999, pp. 210–211), Newquist (1994, pp. 235–248)
- First AI Winter, Lighthill report, Mansfield Amendment: Crevier (1993, pp. 115–117), Russell & Norvig (2021, pp. 21–22), NRC (1999, pp. 212–213), Howe (1994), Newquist (1994, pp. 189–201)
- Second AI Winter: Russell & Norvig (2021, p. 24), McCorduck (2004, pp. 430–435), Crevier (1993, pp. 209–210), NRC (1999, pp. 214–216), Newquist (1994, pp. 301–318)
- Deep learning revolution, AlexNet: Goldman (2022), Russell & Norvig (2021, p. 26), McKinsey (2018)
- Toews (2023).
- Problem-solving, puzzle solving, game playing, and deduction: Russell & Norvig (2021, chpt. 3–5), Russell & Norvig (2021, chpt. 6) (constraint satisfaction), Poole, Mackworth & Goebel (1998, chpt. 2, 3, 7, 9), Luger & Stubblefield (2004, chpt. 3, 4, 6, 8), Nilsson (1998, chpt. 7–12)
- Uncertain reasoning: Russell & Norvig (2021, chpt. 12–18), Poole, Mackworth & Goebel (1998, pp. 345–395), Luger & Stubblefield (2004, pp. 333–381), Nilsson (1998, chpt. 7–12)
- Intractability and efficiency and the combinatorial explosion: Russell & Norvig (2021, p. 21)
- Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: Kahneman (2011), Dreyfus & Dreyfus (1986), Wason & Shapiro (1966), Kahneman, Slovic & Tversky (1982)
- Knowledge representation and knowledge engineering: Russell & Norvig (2021, chpt. 10), Poole, Mackworth & Goebel (1998, pp. 23–46, 69–81, 169–233, 235–277, 281–298, 319–345), Luger & Stubblefield (2004, pp. 227–243), Nilsson (1998, chpt. 17.1–17.4, 18)
- Smoliar & Zhang (1994).
- Neumann & Möller (2008).
- Kuperman, Reichley & Bailey (2006).
- McGarry (2005).
- Bertini, Del Bimbo & Torniai (2006).
- Russell & Norvig (2021), pp. 272.
- Representing categories and relations: Semantic networks, description logics, inheritance (including frames, and scripts): Russell & Norvig (2021, §10.2 & 10.5), Poole, Mackworth & Goebel (1998, pp. 174–177), Luger & Stubblefield (2004, pp. 248–258), Nilsson (1998, chpt. 18.3)
- Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): Russell & Norvig (2021, §10.3), Poole, Mackworth & Goebel (1998, pp. 281–298), Nilsson (1998, chpt. 18.2)
- Causal calculus: Poole, Mackworth & Goebel (1998, pp. 335–337)
- Representing knowledge about knowledge: Belief calculus, modal logics: Russell & Norvig (2021, §10.4), Poole, Mackworth & Goebel (1998, pp. 275–277)
- Default reasoning, Frame problem, default logic, non-monotonic logics, circumscription, closed world assumption, abduction: Russell & Norvig (2021, §10.6), Poole, Mackworth & Goebel (1998, pp. 248–256, 323–335), Luger & Stubblefield (2004, pp. 335–363), Nilsson (1998, ~18.3.3) (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning").
- Breadth of commonsense knowledge: Lenat & Guha (1989, Introduction), Crevier (1993, pp. 113–114), Moravec (1988, p. 13), Russell & Norvig (2021, pp. 241, 385, 982) (qualification problem)
- Newquist (1994), p. 296.
- Crevier (1993), pp. 204–208.
- Russell & Norvig (2021), p. 528.
- Automated planning: Russell & Norvig (2021, chpt. 11).
- Automated decision making, Decision theory: Russell & Norvig (2021, chpt. 16–18).
- Classical planning: Russell & Norvig (2021, Section 11.2).
- Sensorless or "conformant" planning, contingent planning, replanning (a.k.a online planning): Russell & Norvig (2021, Section 11.5).
- Uncertain preferences: Russell & Norvig (2021, Section 16.7) Inverse reinforcement learning: Russell & Norvig (2021, Section 22.6)
- Information value theory: Russell & Norvig (2021, Section 16.6).
- Markov decision process: Russell & Norvig (2021, chpt. 17).
- Game theory and multi-agent decision theory: Russell & Norvig (2021, chpt. 18).
- Learning: Russell & Norvig (2021, chpt. 19–22), Poole, Mackworth & Goebel (1998, pp. 397–438), Luger & Stubblefield (2004, pp. 385–542), Nilsson (1998, chpt. 3.3, 10.3, 17.5, 20)
- Turing (1950).
- Solomonoff (1956).
- Unsupervised learning: Russell & Norvig (2021, pp. 653) (definition), Russell & Norvig (2021, pp. 738–740) (cluster analysis), Russell & Norvig (2021, pp. 846–860) (word embedding)
- Supervised learning: Russell & Norvig (2021, §19.2) (Definition), Russell & Norvig (2021, Chpt. 19–20) (Techniques)
- Reinforcement learning: Russell & Norvig (2021, chpt. 22), Luger & Stubblefield (2004, pp. 442–449)
- Transfer learning: Russell & Norvig (2021, pp. 281), The Economist (2016)
- "Artificial Intelligence (AI): What Is AI and How Does It Work? | Built In". builtin.com. Retrieved 30 October 2023.
- Computational learning theory: Russell & Norvig (2021, pp. 672–674), Jordan & Mitchell (2015)
- Natural language processing (NLP): Russell & Norvig (2021, chpt. 23–24), Poole, Mackworth & Goebel (1998, pp. 91–104), Luger & Stubblefield (2004, pp. 591–632)
- Subproblems of NLP: Russell & Norvig (2021, pp. 849–850)
- Russell & Norvig (2021), pp. 856–858.
- Dickson (2022).
- Modern statistical and deep learning approaches to NLP: Russell & Norvig (2021, chpt. 24), Cambria & White (2014)
- Vincent (2019).
- Russell & Norvig (2021), pp. 875–878.
- Bushwick (2023).
- Computer vision: Russell & Norvig (2021, chpt. 25), Nilsson (1998, chpt. 6)
- Russell & Norvig (2021), pp. 849–850.
- Russell & Norvig (2021), pp. 895–899.
- Russell & Norvig (2021), pp. 899–901.
- Challa et al. (2011).
- Russell & Norvig (2021), pp. 931–938.
- MIT AIL (2014).
- Affective computing: Thro (1993), Edelson (1991), Tao & Tan (2005), Scassellati (2002)
- Waddell (2018).
- Poria et al. (2017).
- Search algorithms: Russell & Norvig (2021, chpts. 3–5), Poole, Mackworth & Goebel (1998, pp. 113–163), Luger & Stubblefield (2004, pp. 79–164, 193–219), Nilsson (1998, chpts. 7–12)
- State space search: Russell & Norvig (2021, chpt. 3)
- Russell & Norvig (2021), sect. 11.2.
- Uninformed searches (breadth first search, depth-first search and general state space search): Russell & Norvig (2021, sect. 3.4), Poole, Mackworth & Goebel (1998, pp. 113–132), Luger & Stubblefield (2004, pp. 79–121), Nilsson (1998, chpt. 8)
- Heuristic or informed searches (e.g., greedy best first and A*): Russell & Norvig (2021, sect. 3.5), Poole, Mackworth & Goebel (1998, pp. 132–147), Poole & Mackworth (2017, sect. 3.6), Luger & Stubblefield (2004, pp. 133–150)
- Adversarial search: Russell & Norvig (2021, chpt. 5)
- Local or "optimization" search: Russell & Norvig (2021, chpt. 4)
- Singh Chauhan, Nagesh (18 December 2020). "Optimization Algorithms in Neural Networks". KDnuggets. Retrieved 13 January 2024.
- Evolutionary computation: Russell & Norvig (2021, sect. 4.1.2)
- Merkle & Middendorf (2013).
- Logic: Russell & Norvig (2021, chpts. 6–9), Luger & Stubblefield (2004, pp. 35–77), Nilsson (1998, chpt. 13–16)
- Propositional logic: Russell & Norvig (2021, chpt. 6), Luger & Stubblefield (2004, pp. 45–50), Nilsson (1998, chpt. 13)
- First-order logic and features such as equality: Russell & Norvig (2021, chpt. 7), Poole, Mackworth & Goebel (1998, pp. 268–275), Luger & Stubblefield (2004, pp. 50–62), Nilsson (1998, chpt. 15)
- Logical inference: Russell & Norvig (2021, chpt. 10)
- logical deduction as search: Russell & Norvig (2021, sects. 9.3, 9.4), Poole, Mackworth & Goebel (1998, pp. ~46–52), Luger & Stubblefield (2004, pp. 62–73), Nilsson (1998, chpt. 4.2, 7.2)
- Resolution and unification: Russell & Norvig (2021, sections 7.5.2, 9.2, 9.5)
- Warren, D.H.; Pereira, L.M.; Pereira, F. (1977). "Prolog-the language and its implementation compared with Lisp". ACM SIGPLAN Notices. 12 (8): 109–115. doi:10.1145/872734.806939.
- Fuzzy logic: Russell & Norvig (2021, pp. 214, 255, 459), Scientific American (1999)
- Stochastic methods for uncertain reasoning: Russell & Norvig (2021, chpt. 12–18, 20), Poole, Mackworth & Goebel (1998, pp. 345–395), Luger & Stubblefield (2004, pp. 165–191, 333–381), Nilsson (1998, chpt. 19)
- decision theory and decision analysis: Russell & Norvig (2021, chpt. 16–18), Poole, Mackworth & Goebel (1998, pp. 381–394)
- Information value theory: Russell & Norvig (2021, sect. 16.6)
- Markov decision processes and dynamic decision networks: Russell & Norvig (2021, chpt. 17)
- Stochastic temporal models: Russell & Norvig (2021, chpt. 14) Hidden Markov model: Russell & Norvig (2021, sect. 14.3) Kalman filters: Russell & Norvig (2021, sect. 14.4) Dynamic Bayesian networks: Russell & Norvig (2021, sect. 14.5)
- Game theory and mechanism design: Russell & Norvig (2021, chpt. 18)
- Bayesian networks: Russell & Norvig (2021, sects. 12.5–12.6, 13.4–13.5, 14.3–14.5, 16.5, 20.2–20.3), Poole, Mackworth & Goebel (1998, pp. 361–381), Luger & Stubblefield (2004, pp. ~182–190, ≈363–379), Nilsson (1998, chpt. 19.3–19.4)
- Domingos (2015), chpt. 6.
- Bayesian inference algorithm: Russell & Norvig (2021, sect. 13.3–13.5), Poole, Mackworth & Goebel (1998, pp. 361–381), Luger & Stubblefield (2004, pp. ~363–379), Nilsson (1998, chpt. 19.4 & 7)
- Domingos (2015), p. 210.
- Bayesian learning and the expectation–maximization algorithm: Russell & Norvig (2021, chpt. 20), Poole, Mackworth & Goebel (1998, pp. 424–433), Nilsson (1998, chpt. 20), Domingos (2015, p. 210)
- Bayesian decision theory and Bayesian decision networks: Russell & Norvig (2021, sect. 16.5)
- Statistical learning methods and classifiers: Russell & Norvig (2021, chpt. 20),
- Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from data analysis to generative AI. Intellisemantic Editions. ISBN 978-8-8947-8760-3.
- Decision trees: Russell & Norvig (2021, sect. 19.3), Domingos (2015, p. 88)
- Non-parameteric learning models such as K-nearest neighbor and support vector machines: Russell & Norvig (2021, sect. 19.7), Domingos (2015, p. 187) (k-nearest neighbor)
- Domingos (2015, p. 88) (kernel methods)
- Domingos (2015), p. 152.
- Naive Bayes classifier: Russell & Norvig (2021, sect. 12.6), Domingos (2015, p. 152)
- Neural networks: Russell & Norvig (2021, chpt. 21), Domingos (2015, Chapter 4)
- Gradient calculation in computational graphs, backpropagation, automatic differentiation: Russell & Norvig (2021, sect. 21.2), Luger & Stubblefield (2004, pp. 467–474), Nilsson (1998, chpt. 3.3)
- Universal approximation theorem: Russell & Norvig (2021, p. 752) The theorem: Cybenko (1988), Hornik, Stinchcombe & White (1989)
- Feedforward neural networks: Russell & Norvig (2021, sect. 21.1)
- Recurrent neural networks: Russell & Norvig (2021, sect. 21.6)
- Perceptrons: Russell & Norvig (2021, pp. 21, 22, 683, 22)
- Deep learning: Russell & Norvig (2021, chpt. 21), Goodfellow, Bengio & Courville (2016), Hinton et al. (2016), Schmidhuber (2015)
- Convolutional neural networks: Russell & Norvig (2021, sect. 21.3)
- Deng & Yu (2014), pp. 199–200.
- Ciresan, Meier & Schmidhuber (2012).
- Russell & Norvig (2021), p. 751.
- Russell & Norvig (2021), p. 17.
- Russell & Norvig (2021), p. 785.
- Schmidhuber (2022), sect. 5.
- Schmidhuber (2022), sect. 6.
- Schmidhuber (2022), sect. 7.
- Schmidhuber (2022), sect. 8.
- Quoted in Christian (2020, p. 22)
- Smith (2023).
- "Explained: Generative AI". 9 November 2023.
- "AI Writing and Content Creation Tools". MIT Sloan Teaching & Learning Technologies. Archived from the original on 25 December 2023. Retrieved 25 December 2023.
- Marmouyet (2023).
- Kobielus (2019).
- Thomason, James (21 May 2024). "Mojo Rising: The resurgence of AI-first programming languages". VentureBeat. Archived from the original on 27 June 2024. Retrieved 26 May 2024.
- Wodecki, Ben (5 May 2023). "7 AI Programming Languages You Need to Know". AI Business. Archived from the original on 25 July 2024. Retrieved 5 October 2024.
- Plumb, Taryn (18 September 2024). "Why Jensen Huang and Marc Benioff see 'gigantic' opportunity for agentic AI". VentureBeat. Archived from the original on 5 October 2024. Retrieved 4 October 2024.
- Mims, Christopher (19 September 2020). "Huang's Law Is the New Moore's Law, and Explains Why Nvidia Wants Arm". Wall Street Journal. ISSN 0099-9660. Archived from the original on 2 October 2023. Retrieved 19 January 2025.
- Davenport, T; Kalakota, R (June 2019). "The potential for artificial intelligence in healthcare". Future Healthc J. 6 (2): 94–98. doi:10.7861/futurehosp.6-2-94. PMC 6616181. PMID 31363513.
- Lyakhova, U.A.; Lyakhov, P.A. (2024). "Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects". Computers in Biology and Medicine. 178: 108742. doi:10.1016/j.compbiomed.2024.108742. PMID 38875908. Archived from the original on 3 December 2024. Retrieved 10 October 2024.
- Alqudaihi, Kawther S.; Aslam, Nida; Khan, Irfan Ullah; Almuhaideb, Abdullah M.; Alsunaidi, Shikah J.; Ibrahim, Nehad M. Abdel Rahman; Alhaidari, Fahd A.; Shaikh, Fatema S.; Alsenbel, Yasmine M.; Alalharith, Dima M.; Alharthi, Hajar M.; Alghamdi, Wejdan M.; Alshahrani, Mohammed S. (2021). "Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities". IEEE Access. 9: 102327–102344. Bibcode:2021IEEEA...9j2327A. doi:10.1109/ACCESS.2021.3097559. ISSN 2169-3536. PMC 8545201. PMID 34786317.
- Bax, Monique; Thorpe, Jordan; Romanov, Valentin (December 2023). "The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence". Frontiers in Sensors. 4. doi:10.3389/fsens.2023.1294721. ISSN 2673-5067.
- Jumper, J; Evans, R; Pritzel, A (2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. Bibcode:2021Natur.596..583J. doi:10.1038/s41586-021-03819-2. PMC 8371605. PMID 34265844.
- "AI discovers new class of antibiotics to kill drug-resistant bacteria". 20 December 2023. Archived from the original on 16 September 2024. Retrieved 5 October 2024.
- "AI speeds up drug design for Parkinson's ten-fold". Cambridge University. 17 April 2024. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- Horne, Robert I.; Andrzejewska, Ewa A.; Alam, Parvez; Brotzakis, Z. Faidon; Srivastava, Ankit; Aubert, Alice; Nowinska, Magdalena; Gregory, Rebecca C.; Staats, Roxine; Possenti, Andrea; Chia, Sean; Sormanni, Pietro; Ghetti, Bernardino; Caughey, Byron; Knowles, Tuomas P. J.; Vendruscolo, Michele (17 April 2024). "Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning". Nature Chemical Biology. 20 (5). Nature: 634–645. doi:10.1038/s41589-024-01580-x. PMC 11062903. PMID 38632492.
- Grant, Eugene F.; Lardner, Rex (25 July 1952). "The Talk of the Town – It". The New Yorker. ISSN 0028-792X. Archived from the original on 16 February 2020. Retrieved 28 January 2024.
- Anderson, Mark Robert (11 May 2017). "Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution". The Conversation. Archived from the original on 17 September 2024. Retrieved 28 January 2024.
- Markoff, John (16 February 2011). "Computer Wins on 'Jeopardy!': Trivial, It's Not". The New York Times. ISSN 0362-4331. Archived from the original on 22 October 2014. Retrieved 28 January 2024.
- Byford, Sam (27 May 2017). "AlphaGo retires from competitive Go after defeating world number one 3–0". The Verge. Archived from the original on 7 June 2017. Retrieved 28 January 2024.
- Brown, Noam; Sandholm, Tuomas (30 August 2019). "Superhuman AI for multiplayer poker". Science. 365 (6456): 885–890. Bibcode:2019Sci...365..885B. doi:10.1126/science.aay2400. ISSN 0036-8075. PMID 31296650.
- "MuZero: Mastering Go, chess, shogi and Atari without rules". Google DeepMind. 23 December 2020. Retrieved 28 January 2024.
- Sample, Ian (30 October 2019). "AI becomes grandmaster in 'fiendishly complex' StarCraft II". The Guardian. ISSN 0261-3077. Archived from the original on 29 December 2020. Retrieved 28 January 2024.
- Wurman, P. R.; Barrett, S.; Kawamoto, K. (2022). "Outracing champion Gran Turismo drivers with deep reinforcement learning" (PDF). Nature. 602 (7896): 223–228. Bibcode:2022Natur.602..223W. doi:10.1038/s41586-021-04357-7. PMID 35140384.
- Wilkins, Alex (13 March 2024). "Google AI learns to play open-world video games by watching them". New Scientist. Archived from the original on 26 July 2024. Retrieved 21 July 2024.
- "Improving mathematical reasoning with process supervision". OpenAI. 31 May 2023. Retrieved 26 January 2025.
- Srivastava, Saurabh (29 February 2024). "Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap". arXiv:2402.19450 [cs.AI].
- Lightman, Hunter; Kosaraju, Vineet; Burda, Yura; Edwards, Harri; Baker, Bowen; Lee, Teddy; Leike, Jan; Schulman, John; Sutskever, Ilya; Cobbe, Karl (2023). "Let's Verify Step by Step". arXiv:2305.20050v1 [cs.LG].
- Franzen, Carl (9 January 2025). "Microsoft's new rStar-Math technique upgrades small models to outperform OpenAI's o1-preview at math problems". VentureBeat. Retrieved 26 January 2025.
- Roberts, Siobhan (25 July 2024). "AI achieves silver-medal standard solving International Mathematical Olympiad problems". The New York Times. Archived from the original on 26 September 2024. Retrieved 7 August 2024.
- Azerbayev, Zhangir; Schoelkopf, Hailey; Paster, Keiran; Santos, Marco Dos; McAleer', Stephen; Jiang, Albert Q.; Deng, Jia; Biderman, Stella; Welleck, Sean (16 October 2023). "Llemma: An Open Language Model For Mathematics". EleutherAI Blog. Retrieved 26 January 2025.
- "Julius AI". julius.ai.
- McFarland, Alex (12 July 2024). "8 Best AI for Math Tools (January 2025)". Unite.AI. Retrieved 26 January 2025.
- Matthew Finio & Amanda Downie: IBM Think 2024 Primer, "What is Artificial Intelligence (AI) in Finance?" 8 Dec. 2023
- M. Nicolas, J. Firzli: Pensions Age/European Pensions magazine, "Artificial Intelligence: Ask the Industry" May June 2024 https://videovoice.org/ai-in-finance-innovation-entrepreneurship-vs-over-regulation-with-the-eus-artificial-intelligence-act-wont-work-as-intended/ Archived 11 September 2024 at the Wayback Machine.
- Congressional Research Service (2019). Artificial Intelligence and National Security (PDF). Washington, DC: Congressional Research Service. Archived (PDF) from the original on 8 May 2020. Retrieved 25 February 2024.PD-notice
- Slyusar, Vadym (2019). Artificial intelligence as the basis of future control networks (Preprint). doi:10.13140/RG.2.2.30247.50087.
- Iraqi, Amjad (3 April 2024). "'Lavender': The AI machine directing Israel's bombing spree in Gaza". +972 Magazine. Archived from the original on 10 October 2024. Retrieved 6 April 2024.
- Davies, Harry; McKernan, Bethan; Sabbagh, Dan (1 December 2023). "'The Gospel': how Israel uses AI to select bombing targets in Gaza". The Guardian. Archived from the original on 6 December 2023. Retrieved 4 December 2023.
- Marti, J Werner (10 August 2024). "Drohnen haben den Krieg in der Ukraine revolutioniert, doch sie sind empfindlich auf Störsender – deshalb sollen sie jetzt autonom operieren". Neue Zürcher Zeitung (in German). Archived from the original on 10 August 2024. Retrieved 10 August 2024.
- Newsom, Gavin; Weber, Shirley N. (5 September 2023). "Executive Order N-12-23" (PDF). Executive Department, State of California. Archived (PDF) from the original on 21 February 2024. Retrieved 7 September 2023.
- Pinaya, Walter H. L.; Graham, Mark S.; Kerfoot, Eric; Tudosiu, Petru-Daniel; Dafflon, Jessica; Fernandez, Virginia; Sanchez, Pedro; Wolleb, Julia; da Costa, Pedro F.; Patel, Ashay (2023). "Generative AI for Medical Imaging: extending the MONAI Framework". arXiv:2307.15208 [eess.IV].
- "What is ChatGPT, DALL-E, and generative AI?". McKinsey. Retrieved 14 December 2024.
- "What is generative AI?". IBM. 22 March 2024.
- Pasick, Adam (27 March 2023). "Artificial Intelligence Glossary: Neural Networks and Other Terms Explained". The New York Times. ISSN 0362-4331. Archived from the original on 1 September 2023. Retrieved 22 April 2023.
- Karpathy, Andrej; Abbeel, Pieter; Brockman, Greg; Chen, Peter; Cheung, Vicki; Duan, Yan; Goodfellow, Ian; Kingma, Durk; Ho, Jonathan; Rein Houthooft; Tim Salimans; John Schulman; Ilya Sutskever; Wojciech Zaremba (16 June 2016). "Generative models". OpenAI. Archived from the original on 17 November 2023. Retrieved 15 March 2023.
- Griffith, Erin; Metz, Cade (27 January 2023). "Anthropic Said to Be Closing In on $300 Million in New A.I. Funding". The New York Times. Archived from the original on 9 December 2023. Retrieved 14 March 2023.
- Lanxon, Nate; Bass, Dina; Davalos, Jackie (10 March 2023). "A Cheat Sheet to AI Buzzwords and Their Meanings". Bloomberg News. Archived from the original on 17 November 2023. Retrieved 14 March 2023.
- Metz, Cade (14 March 2023). "OpenAI Plans to Up the Ante in Tech's A.I. Race". The New York Times. ISSN 0362-4331. Archived from the original on 31 March 2023. Retrieved 31 March 2023.
- Thoppilan, Romal; De Freitas, Daniel; Hall, Jamie; Shazeer, Noam; Kulshreshtha, Apoorv (20 January 2022). "LaMDA: Language Models for Dialog Applications". arXiv:2201.08239 [cs.CL].
- Roose, Kevin (21 October 2022). "A Coming-Out Party for Generative A.I., Silicon Valley's New Craze". The New York Times. Archived from the original on 15 February 2023. Retrieved 14 March 2023.
- Metz, Cade (15 February 2024). "OpenAI Unveils A.I. That Instantly Generates Eye-Popping Videos". The New York Times. ISSN 0362-4331. Archived from the original on 15 February 2024. Retrieved 16 February 2024.
- "The race of the AI labs heats up". The Economist. 30 January 2023. Archived from the original on 17 November 2023. Retrieved 14 March 2023.
- Yang, June; Gokturk, Burak (14 March 2023). "Google Cloud brings generative AI to developers, businesses, and governments". Archived from the original on 17 November 2023. Retrieved 15 March 2023.
- Brynjolfsson, Erik; Li, Danielle; Raymond, Lindsey R. (April 2023), Generative AI at Work (Working Paper), Working Paper Series, doi:10.3386/w31161, archived from the original on 28 March 2024, retrieved 21 January 2024
- "Don't fear an AI-induced jobs apocalypse just yet". The Economist. 6 March 2023. Archived from the original on 17 November 2023. Retrieved 14 March 2023.
- Coyle, Jake (27 September 2023). "In Hollywood writers' battle against AI, humans win (for now)". AP News. Associated Press. Archived from the original on 3 April 2024. Retrieved 26 January 2024.
- Harreis, H.; Koullias, T.; Roberts, Roger. "Generative AI: Unlocking the future of fashion". Archived from the original on 17 November 2023. Retrieved 14 March 2023.
- "How Generative AI Can Augment Human Creativity". Harvard Business Review. 16 June 2023. ISSN 0017-8012. Archived from the original on 20 June 2023. Retrieved 20 June 2023.
- Hendrix, Justin (16 May 2023). "Transcript: Senate Judiciary Subcommittee Hearing on Oversight of AI". techpolicy.press. Archived from the original on 17 November 2023. Retrieved 19 May 2023.
- Simon, Felix M.; Altay, Sacha; Mercier, Hugo (18 October 2023). "Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown". Harvard Kennedy School Misinformation Review. doi:10.37016/mr-2020-127. S2CID 264113883. Archived from the original on 17 November 2023. Retrieved 16 November 2023.
- "New AI systems collide with copyright law". BBC News. 1 August 2023. Retrieved 28 September 2024.
- Poole, David; Mackworth, Alan (2023). Artificial Intelligence, Foundations of Computational Agents (3rd ed.). Cambridge University Press. doi:10.1017/9781009258227. ISBN 978-1-0092-5819-7. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- Russell, Stuart; Norvig, Peter (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. ISBN 978-0-1346-1099-3.
- "Why agents are the next frontier of generative AI". McKinsey Digital. 24 July 2024. Archived from the original on 3 October 2024. Retrieved 10 August 2024.
- Figueiredo, Mayara Costa; Ankrah, Elizabeth; Powell, Jacquelyn E.; Epstein, Daniel A.; Chen, Yunan (12 January 2024). "Powered by AI: Examining How AI Descriptions Influence Perceptions of Fertility Tracking Applications". Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7 (4): 154:1–154:24. doi:10.1145/3631414.
- Power, Jennifer; Pym, Tinonee; James, Alexandra; Waling, Andrea (5 July 2024). "Smart Sex Toys: A Narrative Review of Recent Research on Cultural, Health and Safety Considerations". Current Sexual Health Reports. 16 (3): 199–215. doi:10.1007/s11930-024-00392-3. ISSN 1548-3592.
- Marcantonio, Tiffany L.; Avery, Gracie; Thrash, Anna; Leone, Ruschelle M. (10 September 2024). "Large Language Models in an App: Conducting a Qualitative Synthetic Data Analysis of How Snapchat's "My AI" Responds to Questions About Sexual Consent, Sexual Refusals, Sexual Assault, and Sexting". The Journal of Sex Research: 1–15. doi:10.1080/00224499.2024.2396457. ISSN 0022-4499. PMID 39254628. Archived from the original on 9 December 2024. Retrieved 9 December 2024.
- Hanson, Kenneth R.; Bolthouse, Hannah (2024). ""Replika Removing Erotic Role-Play Is Like Grand Theft Auto Removing Guns or Cars": Reddit Discourse on Artificial Intelligence Chatbots and Sexual Technologies". Socius: Sociological Research for a Dynamic World. 10. doi:10.1177/23780231241259627. ISSN 2378-0231.
- Mania, Karolina (1 January 2024). "Legal Protection of Revenge and Deepfake Porn Victims in the European Union: Findings From a Comparative Legal Study". Trauma, Violence, & Abuse. 25 (1): 117–129. doi:10.1177/15248380221143772. ISSN 1524-8380. PMID 36565267.
- Singh, Suyesha; Nambiar, Vaishnavi (2024). "Role of Artificial Intelligence in the Prevention of Online Child Sexual Abuse: A Systematic Review of Literature". Journal of Applied Security Research. 19 (4): 586–627. doi:10.1080/19361610.2024.2331885. ISSN 1936-1610. Archived from the original on 9 December 2024. Retrieved 9 December 2024.
- Razi, Afsaneh; Kim, Seunghyun; Alsoubai, Ashwaq; Stringhini, Gianluca; Solorio, Thamar; De Choudhury, Munmun; Wisniewski, Pamela J. (13 October 2021). "A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection". Proceedings of the ACM on Human-Computer Interaction. 5 (CSCW2): 1–38. doi:10.1145/3479609. ISSN 2573-0142. Archived from the original on 9 December 2024. Retrieved 9 December 2024.
- Ransbotham, Sam; Kiron, David; Gerbert, Philipp; Reeves, Martin (6 September 2017). "Reshaping Business With Artificial Intelligence". MIT Sloan Management Review. Archived from the original on 13 February 2024.
- Sun, Yuran; Zhao, Xilei; Lovreglio, Ruggiero; Kuligowski, Erica (1 January 2024), Naser, M. Z. (ed.), "8 – AI for large-scale evacuation modeling: promises and challenges", Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure, Woodhead Publishing Series in Civil and Structural Engineering, Woodhead Publishing, pp. 185–204, ISBN 978-0-1282-4073-1, archived from the original on 19 May 2024, retrieved 28 June 2024.
- Gomaa, Islam; Adelzadeh, Masoud; Gwynne, Steven; Spencer, Bruce; Ko, Yoon; Bénichou, Noureddine; Ma, Chunyun; Elsagan, Nour; Duong, Dana; Zalok, Ehab; Kinateder, Max (1 November 2021). "A Framework for Intelligent Fire Detection and Evacuation System". Fire Technology. 57 (6): 3179–3185. doi:10.1007/s10694-021-01157-3. ISSN 1572-8099. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- Zhao, Xilei; Lovreglio, Ruggiero; Nilsson, Daniel (1 May 2020). "Modelling and interpreting pre-evacuation decision-making using machine learning". Automation in Construction. 113: 103140. doi:10.1016/j.autcon.2020.103140. hdl:10179/17315. ISSN 0926-5805. Archived from the original on 19 May 2024. Retrieved 5 October 2024.
- "India's latest election embraced AI technology. Here are some ways it was used constructively". PBS News. 12 June 2024. Archived from the original on 17 September 2024. Retrieved 28 October 2024.
- Müller, Vincent C. (30 April 2020). "Ethics of Artificial Intelligence and Robotics". Stanford Encyclopedia of Philosophy Archive. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- Simonite (2016).
- Russell & Norvig (2021), p. 987.
- Laskowski (2023).
- GAO (2022).
- Valinsky (2019).
- Russell & Norvig (2021), p. 991.
- Russell & Norvig (2021), pp. 991–992.
- Christian (2020), p. 63.
- Vincent (2022).
- Kopel, Matthew. "Copyright Services: Fair Use". Cornell University Library. Archived from the original on 26 September 2024. Retrieved 26 April 2024.
- Burgess, Matt. "How to Stop Your Data From Being Used to Train AI". Wired. ISSN 1059-1028. Archived from the original on 3 October 2024. Retrieved 26 April 2024.
- Reisner (2023).
- Alter & Harris (2023).
- "Getting the Innovation Ecosystem Ready for AI. An IP policy toolkit" (PDF). WIPO.
- Hammond, George (27 December 2023). "Big Tech is spending more than VC firms on AI startups". Ars Technica. Archived from the original on 10 January 2024.
- Wong, Matteo (24 October 2023). "The Future of AI Is GOMA". The Atlantic. Archived from the original on 5 January 2024.
- "Big tech and the pursuit of AI dominance". The Economist. 26 March 2023. Archived from the original on 29 December 2023.
- Fung, Brian (19 December 2023). "Where the battle to dominate AI may be won". CNN Business. Archived from the original on 13 January 2024.
- Metz, Cade (5 July 2023). "In the Age of A.I., Tech's Little Guys Need Big Friends". The New York Times. Archived from the original on 8 July 2024. Retrieved 5 October 2024.
- "Electricity 2024 – Analysis". IEA. 24 January 2024. Retrieved 13 July 2024.
- Calvert, Brian (28 March 2024). "AI already uses as much energy as a small country. It's only the beginning". Vox. New York, New York. Archived from the original on 3 July 2024. Retrieved 5 October 2024.
- Halper, Evan; O'Donovan, Caroline (21 June 2024). "AI is exhausting the power grid. Tech firms are seeking a miracle solution". Washington Post.
- Davenport, Carly. "AI Data Centers and the Coming YS Power Demand Surge" (PDF). Goldman Sachs. Archived from the original (PDF) on 26 July 2024. Retrieved 5 October 2024.
- Ryan, Carol (12 April 2024). "Energy-Guzzling AI Is Also the Future of Energy Savings". Wall Street Journal. Dow Jones.
- Hiller, Jennifer (1 July 2024). "Tech Industry Wants to Lock Up Nuclear Power for AI". Wall Street Journal. Dow Jones. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- Kendall, Tyler (28 September 2024). "Nvidia's Huang Says Nuclear Power an Option to Feed Data Centers". Bloomberg.
- Halper, Evan (20 September 2024). "Microsoft deal would reopen Three Mile Island nuclear plant to power AI". Washington Post.
- Hiller, Jennifer (20 September 2024). "Three Mile Island's Nuclear Plant to Reopen, Help Power Microsoft's AI Centers". Wall Street Journal. Dow Jones. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- Niva Yadav (19 August 2024). "Taiwan to stop large data centers in the North, cites insufficient power". DatacenterDynamics. Archived from the original on 8 November 2024. Retrieved 7 November 2024.
- Mochizuki, Takashi; Oda, Shoko (18 October 2024). "エヌビディア出資の日本企業、原発近くでＡＩデータセンター新設検討". Bloomberg (in Japanese). Archived from the original on 8 November 2024. Retrieved 7 November 2024.
- Naureen S Malik and Will Wade (5 November 2024). "Nuclear-Hungry AI Campuses Need New Plan to Find Power Fast". Bloomberg.
- Nicas (2018).
- Rainie, Lee; Keeter, Scott; Perrin, Andrew (22 July 2019). "Trust and Distrust in America". Pew Research Center. Archived from the original on 22 February 2024.
- Williams (2023).
- Taylor & Hern (2023).
- Samuel, Sigal (19 April 2022). "Why it's so damn hard to make AI fair and unbiased". Vox. Archived from the original on 5 October 2024. Retrieved 24 July 2024.
- Rose (2023).
- CNA (2019).
- Goffrey (2008), p. 17.
- Berdahl et al. (2023); Goffrey (2008, p. 17); Rose (2023); Russell & Norvig (2021, p. 995)
- Christian (2020), p. 25.
- Russell & Norvig (2021), p. 995.
- Grant & Hill (2023).
- Larson & Angwin (2016).
- Christian (2020), p. 67–70.
- Christian (2020, pp. 67–70); Russell & Norvig (2021, pp. 993–994)
- Russell & Norvig (2021, p. 995); Lipartito (2011, p. 36); Goodman & Flaxman (2017, p. 6); Christian (2020, pp. 39–40, 65)
- Quoted in Christian (2020, p. 65).
- Russell & Norvig (2021, p. 994); Christian (2020, pp. 40, 80–81)
- Quoted in Christian (2020, p. 80)
- Dockrill (2022).
- Sample (2017).
- "Black Box AI". 16 June 2023. Archived from the original on 15 June 2024. Retrieved 5 October 2024.
- Christian (2020), p. 110.
- Christian (2020), pp. 88–91.
- Christian (2020, p. 83); Russell & Norvig (2021, p. 997)
- Christian (2020), p. 91.
- Christian (2020), p. 83.
- Verma (2021).
- Rothman (2020).
- Christian (2020), pp. 105–108.
- Christian (2020), pp. 108–112.
- Ropek, Lucas (21 May 2024). "New Anthropic Research Sheds Light on AI's 'Black Box'". Gizmodo. Archived from the original on 5 October 2024. Retrieved 23 May 2024.
- Russell & Norvig (2021), p. 989.
- Russell & Norvig (2021), pp. 987–990.
- Russell & Norvig (2021), p. 988.
- Robitzski (2018); Sainato (2015)
- Harari (2018).
- Buckley, Chris; Mozur, Paul (22 May 2019). "How China Uses High-Tech Surveillance to Subdue Minorities". The New York Times. Archived from the original on 25 November 2019. Retrieved 2 July 2019.
- "Security lapse exposed a Chinese smart city surveillance system". 3 May 2019. Archived from the original on 7 March 2021. Retrieved 14 September 2020.
- Urbina et al. (2022).
- E. McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2022), 51(3) Industrial Law Journal 511–559. Archived 27 May 2023 at the Wayback Machine.
- Ford & Colvin (2015);McGaughey (2022)
- IGM Chicago (2017).
- Arntz, Gregory & Zierahn (2016), p. 33.
- Lohr (2017); Frey & Osborne (2017); Arntz, Gregory & Zierahn (2016, p. 33)
- Zhou, Viola (11 April 2023). "AI is already taking video game illustrators' jobs in China". Rest of World. Archived from the original on 21 February 2024. Retrieved 17 August 2023.
- Carter, Justin (11 April 2023). "China's game art industry reportedly decimated by growing AI use". Game Developer. Archived from the original on 17 August 2023. Retrieved 17 August 2023.
- Morgenstern (2015).
- Mahdawi (2017); Thompson (2014)
- Tarnoff, Ben (4 August 2023). "Lessons from Eliza". The Guardian Weekly. pp. 34–39.
- Cellan-Jones (2014).
- Russell & Norvig 2021, p. 1001.
- Bostrom (2014).
- Russell (2019).
- Bostrom (2014); Müller & Bostrom (2014); Bostrom (2015).
- Harari (2023).
- Müller & Bostrom (2014).
- Leaders' concerns about the existential risks of AI around 2015: Rawlinson (2015), Holley (2015), Gibbs (2014), Sainato (2015)
- ""Godfather of artificial intelligence" talks impact and potential of new AI". CBS News. 25 March 2023. Archived from the original on 28 March 2023. Retrieved 28 March 2023.
- Pittis, Don (4 May 2023). "Canadian artificial intelligence leader Geoffrey Hinton piles on fears of computer takeover". CBC. Archived from the original on 7 July 2024. Retrieved 5 October 2024.
- "'50–50 chance' that AI outsmarts humanity, Geoffrey Hinton says". Bloomberg BNN. 14 June 2024. Archived from the original on 14 June 2024. Retrieved 6 July 2024.
- Valance (2023).
- Taylor, Josh (7 May 2023). "Rise of artificial intelligence is inevitable but should not be feared, 'father of AI' says". The Guardian. Archived from the original on 23 October 2023. Retrieved 26 May 2023.
- Colton, Emma (7 May 2023). "'Father of AI' says tech fears misplaced: 'You cannot stop it'". Fox News. Archived from the original on 26 May 2023. Retrieved 26 May 2023.
- Jones, Hessie (23 May 2023). "Juergen Schmidhuber, Renowned 'Father Of Modern AI,' Says His Life's Work Won't Lead To Dystopia". Forbes. Archived from the original on 26 May 2023. Retrieved 26 May 2023.
- McMorrow, Ryan (19 December 2023). "Andrew Ng: 'Do we think the world is better off with more or less intelligence?'". Financial Times. Archived from the original on 25 January 2024. Retrieved 30 December 2023.
- Levy, Steven (22 December 2023). "How Not to Be Stupid About AI, With Yann LeCun". Wired. Archived from the original on 28 December 2023. Retrieved 30 December 2023.
- Arguments that AI is not an imminent risk: Brooks (2014), Geist (2015), Madrigal (2015), Lee (2014)
- Christian (2020), pp. 67, 73.
- Yudkowsky (2008).
- Anderson & Anderson (2011).
- AAAI (2014).
- Wallach (2010).
- Russell (2019), p. 173.
- Stewart, Ashley; Melton, Monica. "Hugging Face CEO says he's focused on building a 'sustainable model' for the $4.5 billion open-source-AI startup". Business Insider. Archived from the original on 25 September 2024. Retrieved 14 April 2024.
- Wiggers, Kyle (9 April 2024). "Google open sources tools to support AI model development". TechCrunch. Archived from the original on 10 September 2024. Retrieved 14 April 2024.
- Heaven, Will Douglas (12 May 2023). "The open-source AI boom is built on Big Tech's handouts. How long will it last?". MIT Technology Review. Retrieved 14 April 2024.
- Brodsky, Sascha (19 December 2023). "Mistral AI's New Language Model Aims for Open Source Supremacy". AI Business. Archived from the original on 5 September 2024. Retrieved 5 October 2024.
- Edwards, Benj (22 February 2024). "Stability announces Stable Diffusion 3, a next-gen AI image generator". Ars Technica. Archived from the original on 5 October 2024. Retrieved 14 April 2024.
- Marshall, Matt (29 January 2024). "How enterprises are using open source LLMs: 16 examples". VentureBeat. Archived from the original on 26 September 2024. Retrieved 5 October 2024.
- Piper, Kelsey (2 February 2024). "Should we make our most powerful AI models open source to all?". Vox. Archived from the original on 5 October 2024. Retrieved 14 April 2024.
- Alan Turing Institute (2019). "Understanding artificial intelligence ethics and safety" (PDF). Archived (PDF) from the original on 11 September 2024. Retrieved 5 October 2024.
- Alan Turing Institute (2023). "AI Ethics and Governance in Practice" (PDF). Archived (PDF) from the original on 11 September 2024. Retrieved 5 October 2024.
- Floridi, Luciano; Cowls, Josh (23 June 2019). "A Unified Framework of Five Principles for AI in Society". Harvard Data Science Review. 1 (1). doi:10.1162/99608f92.8cd550d1. S2CID 198775713. Archived from the original on 7 August 2019. Retrieved 5 December 2023.
- Buruk, Banu; Ekmekci, Perihan Elif; Arda, Berna (1 September 2020). "A critical perspective on guidelines for responsible and trustworthy artificial intelligence". Medicine, Health Care and Philosophy. 23 (3): 387–399. doi:10.1007/s11019-020-09948-1. ISSN 1572-8633. PMID 32236794. S2CID 214766800. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- Kamila, Manoj Kumar; Jasrotia, Sahil Singh (1 January 2023). "Ethical issues in the development of artificial intelligence: recognizing the risks". International Journal of Ethics and Systems. ahead-of-print (ahead-of-print). doi:10.1108/IJOES-05-2023-0107. ISSN 2514-9369. S2CID 259614124. Archived from the original on 5 October 2024. Retrieved 5 October 2024.
- "AI Safety Institute releases new AI safety evaluations platform". UK Government. 10 May 2024. Archived from the original on 5 October 2024. Retrieved 14 May 2024.
- Regulation of AI to mitigate risks: Berryhill et al. (2019), Barfield & Pagallo (2018), Iphofen & Kritikos (2019), Wirtz, Weyerer & Geyer (2018), Buiten (2019)
- Law Library of Congress (U.S.). Global Legal Research Directorate (2019).
- Vincent (2023).
- Stanford University (2023).
- UNESCO (2021).
- Kissinger (2021).
- Altman, Brockman & Sutskever (2023).
- VOA News (25 October 2023). "UN Announces Advisory Body on Artificial Intelligence". Archived from the original on 18 September 2024. Retrieved 5 October 2024.
- "Council of Europe opens first ever global treaty on AI for signature". Council of Europe. 5 September 2024. Archived from the original on 17 September 2024. Retrieved 17 September 2024.
- Edwards (2023).
- Kasperowicz (2023).
- Fox News (2023).
- Milmo, Dan (3 November 2023). "Hope or Horror? The great AI debate dividing its pioneers". The Guardian Weekly. pp. 10–12.
Artificial intelligence AI in its broadest sense is intelligence exhibited by machines particularly computer systems It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals Such machines may be called AIs High profile applications of AI include advanced web search engines e g Google Search recommendation systems used by YouTube Amazon and Netflix virtual assistants e g Google Assistant Siri and Alexa autonomous vehicles e g Waymo generative and creative tools e g ChatGPT and AI art and superhuman play and analysis in strategy games e g chess and Go However many AI applications are not perceived as AI A lot of cutting edge AI has filtered into general applications often without being called AI because once something becomes useful enough and common enough it s not labeled AI anymore Various subfields of AI research are centered around particular goals and the use of particular tools The traditional goals of AI research include reasoning knowledge representation planning learning natural language processing perception and support for robotics General intelligence the ability to complete any task performed by a human on an at least equal level is among the field s long term goals To reach these goals AI researchers have adapted and integrated a wide range of techniques including search and mathematical optimization formal logic artificial neural networks and methods based on statistics operations research and economics AI also draws upon psychology linguistics philosophy neuroscience and other fields Artificial intelligence was founded as an academic discipline in 1956 and the field went through multiple cycles of optimism throughout its history followed by periods of disappointment and loss of funding known as AI winters Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques This growth accelerated further after 2017 with the transformer architecture and by the early 2020s many billions of dollars were being invested in AI and the field experienced rapid ongoing progress in what has become known as the AI boom The emergence of advanced generative AI in the midst of the AI boom and its ability to create and modify content exposed several unintended consequences and harms in the present and raised concerns about the risks of AI and its long term effects in the future prompting discussions about regulatory policies to ensure the safety and benefits of the technology GoalsThe general problem of simulating or creating intelligence has been broken into subproblems These consist of particular traits or capabilities that researchers expect an intelligent system to display The traits described below have received the most attention and cover the scope of AI research Reasoning and problem solving Early researchers developed algorithms that imitated step by step reasoning that humans use when they solve puzzles or make logical deductions By the late 1980s and 1990s methods were developed for dealing with uncertain or incomplete information employing concepts from probability and economics Many of these algorithms are insufficient for solving large reasoning problems because they experience a combinatorial explosion They become exponentially slower as the problems grow Even humans rarely use the step by step deduction that early AI research could model They solve most of their problems using fast intuitive judgments Accurate and efficient reasoning is an unsolved problem Knowledge representation An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real world facts Formal knowledge representations are used in content based indexing and retrieval scene interpretation clinical decision support knowledge discovery mining interesting and actionable inferences from large databases and other areas A knowledge base is a body of knowledge represented in a form that can be used by a program An ontology is the set of objects relations concepts and properties used by a particular domain of knowledge Knowledge bases need to represent things such as objects properties categories and relations between objects situations events states and time causes and effects knowledge about knowledge what we know about what other people know default reasoning things that humans assume are true until they are told differently and will remain true even when other facts are changing and many other aspects and domains of knowledge Among the most difficult problems in knowledge representation are the breadth of commonsense knowledge the set of atomic facts that the average person knows is enormous and the sub symbolic form of most commonsense knowledge much of what people know is not represented as facts or statements that they could express verbally There is also the difficulty of knowledge acquisition the problem of obtaining knowledge for AI applications Planning and decision making An agent is anything that perceives and takes actions in the world A rational agent has goals or preferences and takes actions to make them happen In automated planning the agent has a specific goal In automated decision making the agent has preferences there are some situations it would prefer to be in and some situations it is trying to avoid The decision making agent assigns a number to each situation called the utility that measures how much the agent prefers it For each possible action it can calculate the expected utility the utility of all possible outcomes of the action weighted by the probability that the outcome will occur It can then choose the action with the maximum expected utility In classical planning the agent knows exactly what the effect of any action will be In most real world problems however the agent may not be certain about the situation they are in it is unknown or unobservable and it may not know for certain what will happen after each possible action it is not deterministic It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked In some problems the agent s preferences may be uncertain especially if there are other agents or humans involved These can be learned e g with inverse reinforcement learning or the agent can seek information to improve its preferences Information value theory can be used to weigh the value of exploratory or experimental actions The space of possible future actions and situations is typically intractably large so the agents must take actions and evaluate situations while being uncertain of what the outcome will be A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function that supplies the utility of each state and the cost of each action A policy associates a decision with each possible state The policy could be calculated e g by iteration be heuristic or it can be learned Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents Learning Machine learning is the study of programs that can improve their performance on a given task automatically It has been a part of AI from the beginning There are several kinds of machine learning Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance Supervised learning requires labeling the training data with the expected answers and comes in two main varieties classification where the program must learn to predict what category the input belongs in and regression where the program must deduce a numeric function based on numeric input In reinforcement learning the agent is rewarded for good responses and punished for bad ones The agent learns to choose responses that are classified as good Transfer learning is when the knowledge gained from one problem is applied to a new problem Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning Computational learning theory can assess learners by computational complexity by sample complexity how much data is required or by other notions of optimization Natural language processing Natural language processing NLP allows programs to read write and communicate in human languages such as English Specific problems include speech recognition speech synthesis machine translation information extraction information retrieval and question answering Early work based on Noam Chomsky s generative grammar and semantic networks had difficulty with word sense disambiguation unless restricted to small domains called micro worlds due to the common sense knowledge problem Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages and that thesauri and not dictionaries should be the basis of computational language structure Modern deep learning techniques for NLP include word embedding representing words typically as vectors encoding their meaning transformers a deep learning architecture using an attention mechanism and others In 2019 generative pre trained transformer or GPT language models began to generate coherent text and by 2023 these models were able to get human level scores on the bar exam SAT test GRE test and many other real world applications Perception Machine perception is the ability to use input from sensors such as cameras microphones wireless signals active lidar sonar radar and tactile sensors to deduce aspects of the world Computer vision is the ability to analyze visual input The field includes speech recognition image classification facial recognition object recognition object tracking and robotic perception Social intelligence Kismet a robot head which was made in the 1990s it is a machine that can recognize and simulate emotions Affective computing is a field that comprises systems that recognize interpret process or simulate human feeling emotion and mood For example some virtual assistants are programmed to speak conversationally or even to banter humorously it makes them appear more sensitive to the emotional dynamics of human interaction or to otherwise facilitate human computer interaction However this tends to give naive users an unrealistic conception of the intelligence of existing computer agents Moderate successes related to affective computing include textual sentiment analysis and more recently multimodal sentiment analysis wherein AI classifies the effects displayed by a videotaped subject General intelligence A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence TechniquesAI research uses a wide variety of techniques to accomplish the goals above Search and optimization AI can solve many problems by intelligently searching through many possible solutions There are two very different kinds of search used in AI state space search and local search State space search State space search searches through a tree of possible states to try to find a goal state For example planning algorithms search through trees of goals and subgoals attempting to find a path to a target goal a process called means ends analysis Simple exhaustive searches are rarely sufficient for most real world problems the search space the number of places to search quickly grows to astronomical numbers The result is a search that is too slow or never completes Heuristics or rules of thumb can help prioritize choices that are more likely to reach a goal Adversarial search is used for game playing programs such as chess or Go It searches through a tree of possible moves and countermoves looking for a winning position Local search Illustration of gradient descent for 3 different starting points two parameters represented by the plan coordinates are adjusted in order to minimize the loss function the height Local search uses mathematical optimization to find a solution to a problem It begins with some form of guess and refines it incrementally Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function Variants of gradient descent are commonly used to train neural networks through the backpropagation algorithm Another type of local search is evolutionary computation which aims to iteratively improve a set of candidate solutions by mutating and recombining them selecting only the fittest to survive each generation Distributed search processes can coordinate via swarm intelligence algorithms Two popular swarm algorithms used in search are particle swarm optimization inspired by bird flocking and ant colony optimization inspired by ant trails Logic Formal logic is used for reasoning and knowledge representation Formal logic comes in two main forms propositional logic which operates on statements that are true or false and uses logical connectives such as and or not and implies and predicate logic which also operates on objects predicates and relations and uses quantifiers such as Every X is a Y and There are some Xs that are Ys Deductive reasoning in logic is the process of proving a new statement conclusion from other statements that are given and assumed to be true the premises Proofs can be structured as proof trees in which nodes are labelled by sentences and children nodes are connected to parent nodes by inference rules Given a problem and a set of premises problem solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms In the case of Horn clauses problem solving search can be performed by reasoning forwards from the premises or backwards from the problem In the more general case of the clausal form of first order logic resolution is a single axiom free rule of inference in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved Inference in both Horn clause logic and first order logic is undecidable and therefore intractable However backward reasoning with Horn clauses which underpins computation in the logic programming language Prolog is Turing complete Moreover its efficiency is competitive with computation in other symbolic programming languages Fuzzy logic assigns a degree of truth between 0 and 1 It can therefore handle propositions that are vague and partially true Non monotonic logics including logic programming with negation as failure are designed to handle default reasoning Other specialized versions of logic have been developed to describe many complex domains Probabilistic methods for uncertain reasoning A simple Bayesian network with the associated conditional probability tables Many problems in AI including in reasoning planning learning perception and robotics require the agent to operate with incomplete or uncertain information AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics Precise mathematical tools have been developed that analyze how an agent can make choices and plan using decision theory decision analysis and information value theory These tools include models such as Markov decision processes dynamic decision networks game theory and mechanism design Bayesian networks are a tool that can be used for reasoning using the Bayesian inference algorithm learning using the expectation maximization algorithm planning using decision networks and perception using dynamic Bayesian networks Probabilistic algorithms can also be used for filtering prediction smoothing and finding explanations for streams of data thus helping perception systems analyze processes that occur over time e g hidden Markov models or Kalman filters Expectation maximization clustering of Old Faithful eruption data starts from a random guess but then successfully converges on an accurate clustering of the two physically distinct modes of eruption Classifiers and statistical learning methods The simplest AI applications can be divided into two types classifiers e g if shiny then diamond on one hand and controllers e g if diamond then pick up on the other hand Classifiers are functions that use pattern matching to determine the closest match They can be fine tuned based on chosen examples using supervised learning Each pattern also called an observation is labeled with a certain predefined class All the observations combined with their class labels are known as a data set When a new observation is received that observation is classified based on previous experience There are many kinds of classifiers in use The decision tree is the simplest and most widely used symbolic machine learning algorithm K nearest neighbor algorithm was the most widely used analogical AI until the mid 1990s and Kernel methods such as the support vector machine SVM displaced k nearest neighbor in the 1990s The naive Bayes classifier is reportedly the most widely used learner at Google due in part to its scalability Neural networks are also used as classifiers Artificial neural networks A neural network is an interconnected group of nodes akin to the vast network of neurons in the human brain An artificial neural network is based on a collection of nodes also known as artificial neurons which loosely model the neurons in a biological brain It is trained to recognise patterns once trained it can recognise those patterns in fresh data There is an input at least one hidden layer of nodes and an output Each node applies a function and once the weight crosses its specified threshold the data is transmitted to the next layer A network is typically called a deep neural network if it has at least 2 hidden layers Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training The most common training technique is the backpropagation algorithm Neural networks learn to model complex relationships between inputs and outputs and find patterns in data In theory a neural network can learn any function In feedforward neural networks the signal passes in only one direction Recurrent neural networks feed the output signal back into the input which allows short term memories of previous input events Long short term memory is the most successful network architecture for recurrent networks Perceptrons use only a single layer of neurons deep learning uses multiple layers Convolutional neural networks strengthen the connection between neurons that are close to each other this is especially important in image processing where a local set of neurons must identify an edge before the network can identify an object Deep learning Deep learning uses several layers of neurons between the network s inputs and outputs The multiple layers can progressively extract higher level features from the raw input For example in image processing lower layers may identify edges while higher layers may identify the concepts relevant to a human such as digits letters or faces Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence including computer vision speech recognition natural language processing image classification and others The reason that deep learning performs so well in so many applications is not known as of 2023 The sudden success of deep learning in 2012 2015 did not occur because of some new discovery or theoretical breakthrough deep neural networks and backpropagation had been described by many people as far back as the 1950s but because of two factors the incredible increase in computer power including the hundred fold increase in speed by switching to GPUs and the availability of vast amounts of training data especially the giant curated datasets used for benchmark testing such as ImageNet GPT Generative pre trained transformers GPT are large language models LLMs that generate text based on the semantic relationships between words in sentences Text based GPT models are pretrained on a large corpus of text that can be from the Internet The pretraining consists of predicting the next token a token being usually a word subword or punctuation Throughout this pretraining GPT models accumulate knowledge about the world and can then generate human like text by repeatedly predicting the next token Typically a subsequent training phase makes the model more truthful useful and harmless usually with a technique called reinforcement learning from human feedback RLHF Current GPT models are prone to generating falsehoods called hallucinations although this can be reduced with RLHF and quality data They are used in chatbots which allow people to ask a question or request a task in simple text Current models and services include Gemini formerly Bard ChatGPT Grok Claude Copilot and LLaMA Multimodal GPT models can process different types of data modalities such as images videos sound and text Hardware and software In the late 2010s graphics processing units GPUs that were increasingly designed with AI specific enhancements and used with specialized TensorFlow software had replaced previously used central processing unit CPUs as the dominant means for large scale commercial and academic machine learning models training Specialized programming languages such as Prolog were used in early AI research but general purpose programming languages like Python have become predominant The transistor density in integrated circuits has been observed to roughly double every 18 months a trend known as Moore s law named after the Intel co founder Gordon Moore who first identified it Improvements in GPUs have been even faster a trend sometimes called Huang s law named after Nvidia co founder and CEO Jensen Huang ApplicationsAI and machine learning technology is used in most of the essential applications of the 2020s including search engines such as Google Search targeting online advertisements recommendation systems offered by Netflix YouTube or Amazon driving internet traffic targeted advertising AdSense Facebook virtual assistants such as Siri or Alexa autonomous vehicles including drones ADAS and self driving cars automatic language translation Microsoft Translator Google Translate facial recognition Apple s Face ID or Microsoft s DeepFace and Google s FaceNet and image labeling used by Facebook Apple s iPhoto and TikTok The deployment of AI may be overseen by a Chief automation officer CAO Health and medicine The application of AI in medicine and medical research has the potential to increase patient care and quality of life Through the lens of the Hippocratic Oath medical professionals are ethically compelled to use AI if applications can more accurately diagnose and treat patients For medical research AI is an important tool for processing and integrating big data This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research New AI tools can deepen the understanding of biomedically relevant pathways For example AlphaFold 2 2021 demonstrated the ability to approximate in hours rather than months the 3D structure of a protein In 2023 it was reported that AI guided drug discovery helped find a class of antibiotics capable of killing two different types of drug resistant bacteria In 2024 researchers used machine learning to accelerate the search for Parkinson s disease drug treatments Their aim was to identify compounds that block the clumping or aggregation of alpha synuclein the protein that characterises Parkinson s disease They were able to speed up the initial screening process ten fold and reduce the cost by a thousand fold Games Game playing programs have been used since the 1950s to demonstrate and test AI s most advanced techniques Deep Blue became the first computer chess playing system to beat a reigning world chess champion Garry Kasparov on 11 May 1997 In 2011 in a Jeopardy quiz show exhibition match IBM s question answering system Watson defeated the two greatest Jeopardy champions Brad Rutter and Ken Jennings by a significant margin In March 2016 AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol becoming the first computer Go playing system to beat a professional Go player without handicaps Then in 2017 it defeated Ke Jie who was the best Go player in the world Other programs handle imperfect information games such as the poker playing program Pluribus DeepMind developed increasingly generalistic reinforcement learning models such as with MuZero which could be trained to play chess Go or Atari games In 2019 DeepMind s AlphaStar achieved grandmaster level in StarCraft II a particularly challenging real time strategy game that involves incomplete knowledge of what happens on the map In 2021 an AI agent competed in a PlayStation Gran Turismo competition winning against four of the world s best Gran Turismo drivers using deep reinforcement learning In 2024 Google DeepMind introduced SIMA a type of AI capable of autonomously playing nine previously unseen open world video games by observing screen output as well as executing short specific tasks in response to natural language instructions Mathematics Large language models such as GPT 4 Gemini Claude LLaMa or Mistral are increasingly used in mathematics These probabilistic models are versatile but can also produce wrong answers in the form of hallucinations They sometimes need a large database of mathematical problems to learn from but also methods such as supervised fine tuning or trained classifiers with human annotated data to improve answers for new problems and learn from corrections A February 2024 study showed that the performance of some language models for reasoning capabilities in solving math problems not included in their training data was low even for problems with only minor deviations from trained data One technique to improve their performance involves training the models to produce correct reasoning steps rather than just the correct result In January 2025 Microsoft proposed the technique rStar Math that leverages Monte Carlo tree search and step by step reasoning enabling a relatively small language model like Qwen 7B to solve 53 of the AIME 2024 and 90 of the MATH benchmark problems Alternatively dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as AlphaTensor AlphaGeometry and AlphaProof all from Google DeepMind Llemma from EleutherAI or Julius When natural language is used to describe mathematical problems converters can transform such prompts into a formal language such as Lean to define mathematical tasks Some models have been developed to solve challenging problems and reach good results in benchmark tests others to serve as educational tools in mathematics Topological deep learning integrates various topological approaches Finance Finance is one of the fastest growing sectors where applied AI tools are being deployed from retail online banking to investment advice and insurance where automated robot advisers have been in use for some years World Pensions experts like Nicolas Firzli insist it may be too early to see the emergence of highly innovative AI informed financial products and services the deployment of AI tools will simply further automatise things destroying tens of thousands of jobs in banking financial planning and pension advice in the process but I m not sure it will unleash a new wave of e g sophisticated pension innovation Military Various countries are deploying AI military applications The main applications enhance command and control communications sensors integration and interoperability Research is targeting intelligence collection and analysis logistics cyber operations information operations and semiautonomous and autonomous vehicles AI technologies enable coordination of sensors and effectors threat detection and identification marking of enemy positions target acquisition coordination and deconfliction of distributed Joint Fires between networked combat vehicles involving manned and unmanned teams AI has been used in military operations in Iraq Syria Israel and Ukraine Generative AI Vincent van Gogh in watercolour created by generative AI softwareThese paragraphs are an excerpt from Generative artificial intelligence edit Generative artificial intelligence generative AI GenAI or GAI is a subset of artificial intelligence that uses generative models to produce text images videos or other forms of data These models learn the underlying patterns and structures of their training data and use them to produce new data based on the input which often comes in the form of natural language prompts Improvements in transformer based deep neural networks particularly large language models LLMs enabled an AI boom of generative AI systems in the early 2020s These include chatbots such as ChatGPT Copilot Gemini and LLaMA text to image artificial intelligence image generation systems such as Stable Diffusion Midjourney and DALL E and text to video AI generators such as Sora Companies such as OpenAI Anthropic Microsoft Google and Baidu as well as numerous smaller firms have developed generative AI models Generative AI has uses across a wide range of industries including software development healthcare finance entertainment customer service sales and marketing art writing fashion and product design However concerns have been raised about the potential misuse of generative AI such as cybercrime the use of fake news or deepfakes to deceive or manipulate people and the mass replacement of human jobs Intellectual property law concerns also exist around generative models that are trained on and emulate copyrighted works of art Agents Artificial intelligent AI agents are software entities designed to perceive their environment make decisions and take actions autonomously to achieve specific goals These agents can interact with users their environment or other agents AI agents are used in various applications including virtual assistants chatbots autonomous vehicles game playing systems and industrial robotics AI agents operate within the constraints of their programming available computational resources and hardware limitations This means they are restricted to performing tasks within their defined scope and have finite memory and processing capabilities In real world applications AI agents often face time constraints for decision making and action execution Many AI agents incorporate learning algorithms enabling them to improve their performance over time through experience or training Using machine learning AI agents can adapt to new situations and optimise their behaviour for their designated tasks Sexuality Applications of AI in this domain include AI enabled menstruation and fertility trackers that analyze user data to offer prediction AI integrated sex toys e g teledildonics AI generated sexual education content and AI agents that simulate sexual and romantic partners e g Replika AI is also used for the production of non consensual deepfake pornography raising significant ethical and legal concerns AI technologies have also been used to attempt to identify online gender based violence and online sexual grooming of minors Other industry specific tasks There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions In a 2017 survey one in five companies reported having incorporated AI in some offerings or processes A few examples are energy storage medical diagnosis military logistics applications that predict the result of judicial decisions foreign policy or supply chain management AI applications for evacuation and disaster management are growing AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS videos or social media Further AI can provide real time information on the real time evacuation conditions In agriculture AI has helped farmers identify areas that need irrigation fertilization pesticide treatments or increasing yield Agronomists use AI to conduct research and development AI has been used to predict the ripening time for crops such as tomatoes monitor soil moisture operate agricultural robots conduct predictive analytics classify livestock pig call emotions automate greenhouses detect diseases and pests and save water Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications mainly for classification regression clustering forecasting generation discovery and the development of new scientific insights For example it is used for discovering exoplanets forecasting solar activity and distinguishing between signals and instrumental effects in gravitational wave astronomy Additionally it could be used for activities in space such as space exploration including the analysis of data from space missions real time science decisions of spacecraft space debris avoidance and more autonomous operation During the 2024 Indian elections US 50 millions was spent on authorized AI generated content notably by creating deepfakes of allied including sometimes deceased politicians to better engage with voters and by translating speeches to various local languages EthicsAI has potential benefits and potential risks AI may be able to advance science and find solutions for serious problems Demis Hassabis of DeepMind hopes to solve intelligence and then use that to solve everything else However as the use of AI has become widespread several unintended consequences and risks have been identified In production systems can sometimes not factor ethics and bias into their AI training processes especially when the AI algorithms are inherently unexplainable in deep learning Risks and harm Privacy and copyright Machine learning algorithms require large amounts of data The techniques used to acquire this data have raised concerns about privacy surveillance and copyright AI powered devices and services such as virtual assistants and IoT products continuously collect personal information raising concerns about intrusive data gathering and unauthorized access by third parties The loss of privacy is further exacerbated by AI s ability to process and combine vast amounts of data potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency Sensitive user data collected may include online activity records geolocation data video or audio For example in order to build speech recognition algorithms Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy AI developers argue that this is the only way to deliver valuable applications and have developed several techniques that attempt to preserve privacy while still obtaining the data such as data aggregation de identification and differential privacy Since 2016 some privacy experts such as Cynthia Dwork have begun to view privacy in terms of fairness Brian Christian wrote that experts have pivoted from the question of what they know to the question of what they re doing with it Generative AI is often trained on unlicensed copyrighted works including in domains such as images or computer code the output is then used under the rationale of fair use Experts disagree about how well and under what circumstances this rationale will hold up in courts of law relevant factors may include the purpose and character of the use of the copyrighted work and the effect upon the potential market for the copyrighted work Website owners who do not wish to have their content scraped can indicate it in a robots txt file In 2023 leading authors including John Grisham and Jonathan Franzen sued AI companies for using their work to train generative AI Another discussed approach is to envision a separate sui generis system of protection for creations generated by AI to ensure fair attribution and compensation for human authors Dominance by tech giants The commercial AI scene is dominated by Big Tech companies such as Alphabet Inc Amazon Apple Inc Meta Platforms and Microsoft Some of these players already own the vast majority of existing cloud infrastructure and computing power from data centers allowing them to entrench further in the marketplace Power needs and environmental impacts In January 2024 the International Energy Agency IEA released Electricity 2024 Analysis and Forecast to 2026 forecasting electric power use This is the first IEA report to make projections for data centers and power consumption for artificial intelligence and cryptocurrency The report states that power demand for these uses might double by 2026 with additional electric power usage equal to electricity used by the whole Japanese nation Prodigious power consumption by AI is responsible for the growth of fossil fuels use and might delay closings of obsolete carbon emitting coal energy facilities There is a feverish rise in the construction of data centers throughout the US making large technology firms e g Microsoft Meta Google Amazon into voracious consumers of electric power Projected electric consumption is so immense that there is concern that it will be fulfilled no matter the source A ChatGPT search involves the use of 10 times the electrical energy as a Google search The large firms are in haste to find power sources from nuclear energy to geothermal to fusion The tech firms argue that in the long view AI will be eventually kinder to the environment but they need the energy now AI makes the power grid more efficient and intelligent will assist in the growth of nuclear power and track overall carbon emissions according to technology firms A 2024 Goldman Sachs Research Paper AI Data Centers and the Coming US Power Demand Surge found US power demand is likely to experience growth not seen in a generation and forecasts that by 2030 US data centers will consume 8 of US power as opposed to 3 in 2022 presaging growth for the electrical power generation industry by a variety of means Data centers need for more and more electrical power is such that they might max out the electrical grid The Big Tech companies counter that AI can be used to maximize the utilization of the grid by all In 2024 the Wall Street Journal reported that big AI companies have begun negotiations with the US nuclear power providers to provide electricity to the data centers In March 2024 Amazon purchased a Pennsylvania nuclear powered data center for 650 Million US Nvidia CEO Jen Hsun Huang said nuclear power is a good option for the data centers In September 2024 Microsoft announced an agreement with Constellation Energy to re open the Three Mile Island nuclear power plant to provide Microsoft with 100 of all electric power produced by the plant for 20 years Reopening the plant which suffered a partial nuclear meltdown of its Unit 2 reactor in 1979 will require Constellation to get through strict regulatory processes which will include extensive safety scrutiny from the US Nuclear Regulatory Commission If approved this will be the first ever US re commissioning of a nuclear plant over 835 megawatts of power enough for 800 000 homes of energy will be produced The cost for re opening and upgrading is estimated at 1 6 billion US and is dependent on tax breaks for nuclear power contained in the 2022 US Inflation Reduction Act The US government and the state of Michigan are investing almost 2 billion US to reopen the Palisades Nuclear reactor on Lake Michigan Closed since 2022 the plant is planned to be reopened in October 2025 The Three Mile Island facility will be renamed the Crane Clean Energy Center after Chris Crane a nuclear proponent and former CEO of Exelon who was responsible for Exelon spinoff of Constellation After the last approval in September 2023 Taiwan suspended the approval of data centers north of Taoyuan with a capacity of more than 5 MW in 2024 due to power supply shortages Taiwan aims to phase out nuclear power by 2025 On the other hand Singapore imposed a ban on the opening of data centers in 2019 due to electric power but in 2022 lifted this ban Although most nuclear plants in Japan have been shut down after the 2011 Fukushima nuclear accident according to an October 2024 Bloomberg article in Japanese cloud gaming services company Ubitus in which Nvidia has a stake is looking for land in Japan near nuclear power plant for a new data center for generative AI Ubitus CEO Wesley Kuo said nuclear power plants are the most efficient cheap and stable power for AI On 1 November 2024 the Federal Energy Regulatory Commission FERC rejected an application submitted by Talen Energy for approval to supply some electricity from the nuclear power station Susquehanna to Amazon s data center According to the Commission Chairman Willie L Phillips it is a burden on the electricity grid as well as a significant cost shifting concern to households and other business sectors Misinformation YouTube Facebook and others use recommender systems to guide users to more content These AI programs were given the goal of maximizing user engagement that is the only goal was to keep people watching The AI learned that users tended to choose misinformation conspiracy theories and extreme partisan content and to keep them watching the AI recommended more of it Users also tended to watch more content on the same subject so the AI led people into filter bubbles where they received multiple versions of the same misinformation This convinced many users that the misinformation was true and ultimately undermined trust in institutions the media and the government The AI program had correctly learned to maximize its goal but the result was harmful to society After the U S election in 2016 major technology companies took steps to mitigate the problem citation needed In 2022 generative AI began to create images audio video and text that are indistinguishable from real photographs recordings films or human writing It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda AI pioneer Geoffrey Hinton expressed concern about AI enabling authoritarian leaders to manipulate their electorates on a large scale among other risks Algorithmic bias and fairness Machine learning applications will be biased if they learn from biased data The developers may not be aware that the bias exists Bias can be introduced by the way training data is selected and by the way a model is deployed If a biased algorithm is used to make decisions that can seriously harm people as it can in medicine finance recruitment housing or policing then the algorithm may cause discrimination The field of fairness studies how to prevent harms from algorithmic biases On June 28 2015 Google Photos s new image labeling feature mistakenly identified Jacky Alcine and a friend as gorillas because they were black The system was trained on a dataset that contained very few images of black people a problem called sample size disparity Google fixed this problem by preventing the system from labelling anything as a gorilla Eight years later in 2023 Google Photos still could not identify a gorilla and neither could similar products from Apple Facebook Microsoft and Amazon COMPAS is a commercial program widely used by U S courts to assess the likelihood of a defendant becoming a recidivist In 2016 Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias despite the fact that the program was not told the races of the defendants Although the error rate for both whites and blacks was calibrated equal at exactly 61 the errors for each race were different the system consistently overestimated the chance that a black person would re offend and would underestimate the chance that a white person would not re offend In 2017 several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re offense were different for whites and blacks in the data A program can make biased decisions even if the data does not explicitly mention a problematic feature such as race or gender The feature will correlate with other features like address shopping history or first name and the program will make the same decisions based on these features as it would on race or gender Moritz Hardt said the most robust fact in this research area is that fairness through blindness doesn t work Criticism of COMPAS highlighted that machine learning models are designed to make predictions that are only valid if we assume that the future will resemble the past If they are trained on data that includes the results of racist decisions in the past machine learning models must predict that racist decisions will be made in the future If an application then uses these predictions as recommendations some of these recommendations will likely be racist Thus machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past It is descriptive rather than prescriptive Bias and unfairness may go undetected because the developers are overwhelmingly white and male among AI engineers about 4 are black and 20 are women There are various conflicting definitions and mathematical models of fairness These notions depend on ethical assumptions and are influenced by beliefs about society One broad category is distributive fairness which focuses on the outcomes often identifying groups and seeking to compensate for statistical disparities Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible Procedural fairness focuses on the decision process rather than the outcome The most relevant notions of fairness may depend on the context notably the type of AI application and the stakeholders The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases but it may conflict with anti discrimination laws At its 2022 Conference on Fairness Accountability and Transparency ACM FAccT 2022 the Association for Computing Machinery in Seoul South Korea presented and published findings that recommend that until AI and robotics systems are demonstrated to be free of bias mistakes they are unsafe and the use of self learning neural networks trained on vast unregulated sources of flawed internet data should be curtailed dubious discuss Lack of transparency Many AI systems are so complex that their designers cannot explain how they reach their decisions Particularly with deep neural networks in which there are a large amount of non linear relationships between inputs and outputs But some popular explainability techniques exist It is impossible to be certain that a program is operating correctly if no one knows how exactly it works There have been many cases where a machine learning program passed rigorous tests but nevertheless learned something different than what the programmers intended For example a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as cancerous because pictures of malignancies typically include a ruler to show the scale Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at low risk of dying from pneumonia Having asthma is actually a severe risk factor but since the patients having asthma would usually get much more medical care they were relatively unlikely to die according to the training data The correlation between asthma and low risk of dying from pneumonia was real but misleading People who have been harmed by an algorithm s decision have a right to an explanation Doctors for example are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make Early drafts of the European Union s General Data Protection Regulation in 2016 included an explicit statement that this right exists Industry experts noted that this is an unsolved problem with no solution in sight Regulators argued that nevertheless the harm is real if the problem has no solution the tools should not be used DARPA established the XAI Explainable Artificial Intelligence program in 2014 to try to solve these problems Several approaches aim to address the transparency problem SHAP enables to visualise the contribution of each feature to the output LIME can locally approximate a model s outputs with a simpler interpretable model Multitask learning provides a large number of outputs in addition to the target classification These other outputs can help developers deduce what the network has learned Deconvolution DeepDream and other generative methods can allow developers to see what different layers of a deep network for computer vision have learned and produce output that can suggest what the network is learning For generative pre trained transformers Anthropic developed a technique based on dictionary learning that associates patterns of neuron activations with human understandable concepts Bad actors and weaponized AI Artificial intelligence provides a number of tools that are useful to bad actors such as authoritarian governments terrorists criminals or rogue states A lethal autonomous weapon is a machine that locates selects and engages human targets without human supervision Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and if produced at scale they are potentially weapons of mass destruction Even when used in conventional warfare they currently cannot reliably choose targets and could potentially kill an innocent person In 2014 30 nations including China supported a ban on autonomous weapons under the United Nations Convention on Certain Conventional Weapons however the United States and others disagreed By 2015 over fifty countries were reported to be researching battlefield robots AI tools make it easier for authoritarian governments to efficiently control their citizens in several ways Face and voice recognition allow widespread surveillance Machine learning operating this data can classify potential enemies of the state and prevent them from hiding Recommendation systems can precisely target propaganda and misinformation for maximum effect Deepfakes and generative AI aid in producing misinformation Advanced AI can make authoritarian centralized decision making more competitive than liberal and decentralized systems such as markets It lowers the cost and difficulty of digital warfare and advanced spyware All these technologies have been available since 2020 or earlier AI facial recognition systems are already being used for mass surveillance in China There many other ways that AI is expected to help bad actors some of which can not be foreseen For example machine learning AI is able to design tens of thousands of toxic molecules in a matter of hours Technological unemployment Economists have frequently highlighted the risks of redundancies from AI and speculated about unemployment if there is no adequate social policy for full employment In the past technology has tended to increase rather than reduce total employment but economists acknowledge that we re in uncharted territory with AI A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long term unemployment but they generally agree that it could be a net benefit if productivity gains are redistributed Risk estimates vary for example in the 2010s Michael Osborne and Carl Benedikt Frey estimated 47 of U S jobs are at high risk of potential automation while an OECD report classified only 9 of U S jobs as high risk The methodology of speculating about future employment levels has been criticised as lacking evidential foundation and for implying that technology rather than social policy creates unemployment as opposed to redundancies In April 2023 it was reported that 70 of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence Unlike previous waves of automation many middle class jobs may be eliminated by artificial intelligence The Economist stated in 2015 that the worry that AI could do to white collar jobs what steam power did to blue collar ones during the Industrial Revolution is worth taking seriously Jobs at extreme risk range from paralegals to fast food cooks while job demand is likely to increase for care related professions ranging from personal healthcare to the clergy From the early days of the development of artificial intelligence there have been arguments for example those put forward by Joseph Weizenbaum about whether tasks that can be done by computers actually should be done by them given the difference between computers and humans and between quantitative calculation and qualitative value based judgement Existential risk It has been argued AI will become so powerful that humanity may irreversibly lose control of it This could as physicist Stephen Hawking stated spell the end of the human race This scenario has been common in science fiction when a computer or robot suddenly develops a human like self awareness or sentience or consciousness and becomes a malevolent character These sci fi scenarios are misleading in several ways First AI does not require human like sentience to be an existential risk Modern AI programs are given specific goals and use learning and intelligence to achieve them Philosopher Nick Bostrom argued that if one gives almost any goal to a sufficiently powerful AI it may choose to destroy humanity to achieve it he used the example of a paperclip factory manager Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged reasoning that you can t fetch the coffee if you re dead In order to be safe for humanity a superintelligence would have to be genuinely aligned with humanity s morality and values so that it is fundamentally on our side Second Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk The essential parts of civilization are not physical Things like ideologies law government money and the economy are built on language they exist because there are stories that billions of people believe The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything even to take actions that are destructive The opinions amongst experts and industry insiders are mixed with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI Personalities such as Stephen Hawking Bill Gates and Elon Musk as well as AI pioneers such as Yoshua Bengio Stuart Russell Demis Hassabis and Sam Altman have expressed concerns about existential risk from AI In May 2023 Geoffrey Hinton announced his resignation from Google in order to be able to freely speak out about the risks of AI without considering how this impacts Google He notably mentioned risks of an AI takeover and stressed that in order to avoid the worst outcomes establishing safety guidelines will require cooperation among those competing in use of AI In 2023 many leading AI experts endorsed the joint statement that Mitigating the risk of extinction from AI should be a global priority alongside other societal scale risks such as pandemics and nuclear war Some other researchers were more optimistic AI pioneer Jurgen Schmidhuber did not sign the joint statement emphasising that in 95 of all cases AI research is about making human lives longer and healthier and easier While the tools that are now being used to improve lives can also be used by bad actors they can also be used against the bad actors Andrew Ng also argued that it s a mistake to fall for the doomsday hype on AI and that regulators who do will only benefit vested interests Yann LeCun scoffs at his peers dystopian scenarios of supercharged misinformation and even eventually human extinction In the early 2010s experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine However after 2016 the study of current and future risks and possible solutions became a serious area of research Ethical machines and alignment Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans Eliezer Yudkowsky who coined the term argues that developing friendly AI should be a higher research priority it may require a large investment and it must be completed before AI becomes an existential risk Machines with intelligence have the potential to use their intelligence to make ethical decisions The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas The field of machine ethics is also called computational morality and was founded at an AAAI symposium in 2005 Other approaches include Wendell Wallach s artificial moral agents and Stuart J Russell s three principles for developing provably beneficial machines Open source Active organizations in the AI open source community include Hugging Face Google EleutherAI and Meta Various AI models such as Llama 2 Mistral or Stable Diffusion have been made open weight meaning that their architecture and trained parameters the weights are publicly available Open weight models can be freely fine tuned which allows companies to specialize them with their own data and for their own use case Open weight models are useful for research and innovation but can also be misused Since they can be fine tuned any built in security measure such as objecting to harmful requests can be trained away until it becomes ineffective Some researchers warn that future AI models may develop dangerous capabilities such as the potential to drastically facilitate bioterrorism and that once released on the Internet they cannot be deleted everywhere if needed They recommend pre release audits and cost benefit analyses Frameworks Artificial Intelligence projects can have their ethical permissibility tested while designing developing and implementing an AI system An AI framework such as the Care and Act Framework containing the SUM values developed by the Alan Turing Institute tests projects in four main areas Respect the dignity of individual people Connect with other people sincerely openly and inclusively Care for the wellbeing of everyone Protect social values justice and the public interest Other developments in ethical frameworks include those decided upon during the Asilomar Conference the Montreal Declaration for Responsible AI and the IEEE s Ethics of Autonomous Systems initiative among others however these principles do not go without their criticisms especially regards to the people chosen contributes to these frameworks Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design development and implementation and collaboration between job roles such as data scientists product managers data engineers domain experts and delivery managers The UK AI Safety Institute released in 2024 a testing toolset called Inspect for AI safety evaluations available under a MIT open source licence which is freely available on GitHub and can be improved with third party packages It can be used to evaluate AI models in a range of areas including core knowledge ability to reason and autonomous capabilities Regulation The first global AI Safety Summit was held in 2023 with a declaration calling for international cooperation The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI it is therefore related to the broader regulation of algorithms The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally According to AI Index at Stanford the annual number of AI related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone Between 2016 and 2020 more than 30 countries adopted dedicated strategies for AI Most EU member states had released national AI strategies as had Canada China India Japan Mauritius the Russian Federation Saudi Arabia United Arab Emirates U S and Vietnam Others were in the process of elaborating their own AI strategy including Bangladesh Malaysia and Tunisia The Global Partnership on Artificial Intelligence was launched in June 2020 stating a need for AI to be developed in accordance with human rights and democratic values to ensure public confidence and trust in the technology Henry Kissinger Eric Schmidt and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI In 2023 OpenAI leaders published recommendations for the governance of superintelligence which they believe may happen in less than 10 years In 2023 the United Nations also launched an advisory body to provide recommendations on AI governance the body comprises technology company executives governments officials and academics In 2024 the Council of Europe created the first international legally binding treaty on AI called the Framework Convention on Artificial Intelligence and Human Rights Democracy and the Rule of Law It was adopted by the European Union the United States the United Kingdom and other signatories In a 2022 Ipsos survey attitudes towards AI varied greatly by country 78 of Chinese citizens but only 35 of Americans agreed that products and services using AI have more benefits than drawbacks A 2023 Reuters Ipsos poll found that 61 of Americans agree and 22 disagree that AI poses risks to humanity In a 2023 Fox News poll 35 of Americans thought it very important and an additional 41 thought it somewhat important for the federal government to regulate AI versus 13 responding not very important and 8 responding not at all important In November 2023 the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks 28 countries including the United States China and the European Union issued a declaration at the start of the summit calling for international co operation to manage the challenges and risks of artificial intelligence In May 2024 at the AI Seoul Summit 16 global AI tech companies agreed to safety commitments on the development of AI HistoryThe study of mechanical or formal reasoning began with philosophers and mathematicians in antiquity The study of logic led directly to Alan Turing s theory of computation which suggested that a machine by shuffling symbols as simple as 0 and 1 could simulate any conceivable form of mathematical reasoning This along with concurrent discoveries in cybernetics information theory and neurobiology led researchers to consider the possibility of building an electronic brain They developed several areas of research that would become part of AI such as McCullouch and Pitts design for artificial neurons in 1943 and Turing s influential 1950 paper Computing Machinery and Intelligence which introduced the Turing test and showed that machine intelligence was plausible The field of AI research was founded at a workshop at Dartmouth College in 1956 The attendees became the leaders of AI research in the 1960s They and their students produced programs that the press described as astonishing computers were learning checkers strategies solving word problems in algebra proving logical theorems and speaking English Artificial intelligence laboratories were set up at a number of British and U S universities in the latter 1950s and early 1960s Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with general intelligence and considered this the goal of their field In 1965 Herbert Simon predicted machines will be capable within twenty years of doing any work a man can do In 1967 Marvin Minsky agreed writing that within a generation the problem of creating artificial intelligence will substantially be solved They had however underestimated the difficulty of the problem In 1974 both the U S and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U S Congress to fund more productive projects Minsky s and Papert s book Perceptrons was understood as proving that artificial neural networks would never be useful for solving real world tasks thus discrediting the approach altogether The AI winter a period when obtaining funding for AI projects was difficult followed In the early 1980s AI research was revived by the commercial success of expert systems a form of AI program that simulated the knowledge and analytical skills of human experts By 1985 the market for AI had reached over a billion dollars At the same time Japan s fifth generation computer project inspired the U S and British governments to restore funding for academic research However beginning with the collapse of the Lisp Machine market in 1987 AI once again fell into disrepute and a second longer lasting winter began Up to this point most of AI s funding had gone to projects that used high level symbols to represent mental objects like plans goals beliefs and known facts In the 1980s some researchers began to doubt that this approach would be able to imitate all the processes of human cognition especially perception robotics learning and pattern recognition and began to look into sub symbolic approaches Rodney Brooks rejected representation in general and focussed directly on engineering machines that move and survive Judea Pearl Lofti Zadeh and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic But the most important development was the revival of connectionism including neural network research by Geoffrey Hinton and others In 1990 Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits the first of many successful applications of neural networks AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems This narrow and formal focus allowed researchers to produce verifiable results and collaborate with other fields such as statistics economics and mathematics By 2000 solutions developed by AI researchers were being widely used although in the 1990s they were rarely described as artificial intelligence a tendency known as the AI effect However several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile fully intelligent machines Beginning around 2002 they founded the subfield of artificial general intelligence or AGI which had several well funded institutions by the 2010s Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field For many specific tasks other methods were abandoned Deep learning s success was based on both hardware improvements faster computers graphics processing units cloud computing and access to large amounts of data including curated datasets such as ImageNet Deep learning s success led to an enormous increase in interest and funding in AI The amount of machine learning research measured by total publications increased by 50 in the years 2015 2019 In 2016 issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences publications vastly increased funding became available and many researchers re focussed their careers on these issues The alignment problem became a serious field of academic study In the late teens and early 2020s AGI companies began to deliver programs that created enormous interest In 2015 AlphaGo developed by DeepMind beat the world champion Go player The program taught only the game s rules and developed a strategy by itself GPT 3 is a large language model that was released in 2020 by OpenAI and is capable of generating high quality human like text ChatGPT launched on November 30 2022 became the fastest growing consumer software application in history gaining over 100 million users in two months It marked what is widely regarded as AI s breakout year bringing it into the public consciousness These programs and others inspired an aggressive AI boom where large companies began investing billions of dollars in AI research According to AI Impacts about 50 billion annually was invested in AI around 2022 in the U S alone and about 20 of the new U S Computer Science PhD graduates have specialized in AI About 800 000 AI related U S job openings existed in 2022 According to PitchBook research 22 of newly funded startups in 2024 claimed to be AI companies PhilosophyPhilosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines Another major focus has been whether machines can be conscious and the associated ethical implications Many other topics in philosophy are relevant to AI such as epistemology and free will Rapid advancements have intensified public discussions on the philosophy and ethics of AI Defining artificial intelligence Alan Turing wrote in 1950 I propose to consider the question can machines think He advised changing the question from whether a machine thinks to whether or not it is possible for machinery to show intelligent behaviour He devised the Turing test which measures the ability of a machine to simulate human conversation Since we can only observe the behavior of the machine it does not matter if it is actually thinking or literally has a mind Turing notes that we can not determine these things about other people but it is usual to have a polite convention that everyone thinks 377 The Turing test can provide some evidence of intelligence but it penalizes non human intelligent behavior Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior not internal structure However they are critical that the test requires the machine to imitate humans Aeronautical engineering texts they wrote do not define the goal of their field as making machines that fly so exactly like pigeons that they can fool other pigeons AI founder John McCarthy agreed writing that Artificial intelligence is not by definition simulation of human intelligence McCarthy defines intelligence as the computational part of the ability to achieve goals in the world Another AI founder Marvin Minsky similarly describes it as the ability to solve hard problems The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals These definitions view intelligence in terms of well defined problems with well defined solutions where both the difficulty of the problem and the performance of the program are direct measures of the intelligence of the machine and no other philosophical discussion is required or may not even be possible Another definition has been adopted by Google a major practitioner in the field of AI This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence similar to the way it is defined in biological intelligence Some authors have suggested in practice that the definition of AI is vague and difficult to define with contention as to whether classical algorithms should be categorised as AI with many companies during the early 2020s AI boom using the term as a marketing buzzword often even if they did not actually use AI in a material way Evaluating approaches to AI No established unifying theory or paradigm has guided AI research for most of its history The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches so much so that some sources especially in the business world use the term artificial intelligence to mean machine learning with neural networks This approach is mostly sub symbolic soft and narrow Critics argue that these questions may have to be revisited by future generations of AI researchers Symbolic AI and its limits Symbolic AI or GOFAI simulated the high level conscious reasoning that people use when they solve puzzles express legal reasoning and do mathematics They were highly successful at intelligent tasks such as algebra or IQ tests In the 1960s Newell and Simon proposed the physical symbol systems hypothesis A physical symbol system has the necessary and sufficient means of general intelligent action However the symbolic approach failed on many tasks that humans solve easily such as learning recognizing an object or commonsense reasoning Moravec s paradox is the discovery that high level intelligent tasks were easy for AI but low level instinctive tasks were extremely difficult Philosopher Hubert Dreyfus had argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a feel for the situation rather than explicit symbolic knowledge Although his arguments had been ridiculed and ignored when they were first presented eventually AI research came to agree with him The issue is not resolved sub symbolic reasoning can make many of the same inscrutable mistakes that human intuition does such as algorithmic bias Critics such as Noam Chomsky argue continuing research into symbolic AI will still be necessary to attain general intelligence in part because sub symbolic AI is a move away from explainable AI it can be difficult or impossible to understand why a modern statistical AI program made a particular decision The emerging field of neuro symbolic artificial intelligence attempts to bridge the two approaches Neat vs scruffy Neats hope that intelligent behavior is described using simple elegant principles such as logic optimization or neural networks Scruffies expect that it necessarily requires solving a large number of unrelated problems Neats defend their programs with theoretical rigor scruffies rely mainly on incremental testing to see if they work This issue was actively discussed in the 1970s and 1980s but eventually was seen as irrelevant Modern AI has elements of both Soft vs hard computing Finding a provably correct or optimal solution is intractable for many important problems Soft computing is a set of techniques including genetic algorithms fuzzy logic and neural networks that are tolerant of imprecision uncertainty partial truth and approximation Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks Narrow vs general AI AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible narrow AI in hopes these solutions will lead indirectly to the field s long term goals General intelligence is difficult to define and difficult to measure and modern AI has had more verifiable successes by focusing on specific problems with specific solutions The sub field of artificial general intelligence studies this area exclusively Machine consciousness sentience and mind The philosophy of mind does not know whether a machine can have a mind consciousness and mental states in the same sense that human beings do This issue considers the internal experiences of the machine rather than its external behavior Mainstream AI research considers this issue irrelevant because it does not affect the goals of the field to build machines that can solve problems using intelligence Russell and Norvig add that t he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on However the question has become central to the philosophy of mind It is also typically the central question at issue in artificial intelligence in fiction Consciousness David Chalmers identified two problems in understanding the mind which he named the hard and easy problems of consciousness The easy problem is understanding how the brain processes signals makes plans and controls behavior The hard problem is explaining how this feels or why it should feel like anything at all assuming we are right in thinking that it truly does feel like something Dennett s consciousness illusionism says this is an illusion While human information processing is easy to explain human subjective experience is difficult to explain For example it is easy to imagine a color blind person who has learned to identify which objects in their field of view are red but it is not clear what would be required for the person to know what red looks like Computationalism and functionalism Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind body problem This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam Philosopher John Searle characterized this position as strong AI The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds Searle challenges this claim with his Chinese room argument which attempts to show that even a computer capable of perfectly simulating human behavior would not have a mind AI welfare and rights It is difficult or impossible to reliably evaluate whether an advanced AI is sentient has the ability to feel and if so to what degree But if there is a significant chance that a given machine can feel and suffer then it may be entitled to certain rights or welfare protection measures similarly to animals Sapience a set of capacities related to high intelligence such as discernment or self awareness may provide another moral basis for AI rights Robot rights are also sometimes proposed as a practical way to integrate autonomous agents into society In 2017 the European Union considered granting electronic personhood to some of the most capable AI systems Similarly to the legal status of companies it would have conferred rights but also responsibilities Critics argued in 2018 that granting rights to AI systems would downplay the importance of human rights and that legislation should focus on user needs rather than speculative futuristic scenarios They also noted that robots lacked the autonomy to take part to society on their own Progress in AI increased interest in the topic Proponents of AI welfare and rights often argue that AI sentience if it emerges would be particularly easy to deny They warn that this may be a moral blind spot analogous to slavery or factory farming which could lead to large scale suffering if sentient AI is created and carelessly exploited FutureSuperintelligence and the singularity A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind If research into artificial general intelligence produced sufficiently intelligent software it might be able to reprogram and improve itself The improved software would be even better at improving itself leading to what I J Good called an intelligence explosion and Vernor Vinge called a singularity However technologies cannot improve exponentially indefinitely and typically follow an S shaped curve slowing when they reach the physical limits of what the technology can do Transhumanism Robot designer Hans Moravec cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines may merge in the future into cyborgs that are more capable and powerful than either This idea called transhumanism has roots in the writings of Aldous Huxley and Robert Ettinger Edward Fredkin argues that artificial intelligence is the next step in evolution an idea first proposed by Samuel Butler s Darwin among the Machines as far back as 1863 and expanded upon by George Dyson in his 1998 book Darwin Among the Machines The Evolution of Global Intelligence Decomputing Arguments for decomputing have been raised by Dan McQuillan Resisting AI An Anti fascist Approach to Artificial Intelligence 2022 meaning an opposition to the sweeping application and expansion of artificial intelligence Similar to degrowth the approach criticizes AI as an outgrowth of the systemic issues and capitalist world we live in Arguing that a different future is possible in which distance between people is reduced and not increased to AI intermediaries In fictionThe word robot itself was coined by Karel Capek in his 1921 play R U R the title standing for Rossum s Universal Robots Thought capable artificial beings have appeared as storytelling devices since antiquity and have been a persistent theme in science fiction A common trope in these works began with Mary Shelley s Frankenstein where a human creation becomes a threat to its masters This includes such works as Arthur C Clarke s and Stanley Kubrick s 2001 A Space Odyssey both 1968 with HAL 9000 the murderous computer in charge of the Discovery One spaceship as well as The Terminator 1984 and The Matrix 1999 In contrast the rare loyal robots such as Gort from The Day the Earth Stood Still 1951 and Bishop from Aliens 1986 are less prominent in popular culture Isaac Asimov introduced the Three Laws of Robotics in many stories most notably with the Multivac super intelligent computer Asimov s laws are often brought up during lay discussions of machine ethics while almost all artificial intelligence researchers are familiar with Asimov s laws through popular culture they generally consider the laws useless for many reasons one of which is their ambiguity Several works use AI to force us to confront the fundamental question of what makes us human showing us artificial beings that have the ability to feel and thus to suffer This appears in Karel Capek s R U R the films A I Artificial Intelligence and Ex Machina as well as the novel Do Androids Dream of Electric Sheep by Philip K Dick Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence See alsoArtificial intelligence and elections Use and impact of AI on political elections Artificial intelligence content detection Software to detect AI generated content Behavior selection algorithm Algorithm that selects actions for intelligent agents Business process automation Automation of business processes Case based reasoning Process of solving new problems based on the solutions of similar past problems Computational intelligence Ability of a computer to learn a specific task from data or experimental observation Digital immortality Hypothetical concept of storing a personality in digital form Emergent algorithm Algorithm exhibiting emergent behavior Female gendering of AI technologies Gender biases in digital technologyPages displaying short descriptions of redirect targets Glossary of artificial intelligence List of definitions of terms and concepts commonly used in the study of artificial intelligence Intelligence amplification Use of information technology to augment human intelligence Intelligent agent Software agent which acts autonomously Mind uploading Hypothetical process of digitally emulating a brain Organoid intelligence Use of brain cells and brain organoids for intelligent computing Robotic process automation Form of business process automation technology Wetware computer Computer composed of organic materialExplanatory notesThis list of intelligent traits is based on the topics covered by the major AI textbooks including Russell amp Norvig 2021 Luger amp Stubblefield 2004 Poole Mackworth amp Goebel 1998 and Nilsson 1998 This list of tools is based on the topics covered by the major AI textbooks including Russell amp Norvig 2021 Luger amp Stubblefield 2004 Poole Mackworth amp Goebel 1998 and Nilsson 1998 It is among the reasons that expert systems proved to be inefficient for capturing knowledge Rational agent is general term used in economics philosophy and theoretical artificial intelligence It can refer to anything that directs its behavior to accomplish goals such as a person an animal a corporation a nation or in the case of AI a computer program Alan Turing discussed the centrality of learning as early as 1950 in his classic paper Computing Machinery and Intelligence In 1956 at the original Dartmouth AI summer conference Ray Solomonoff wrote a report on unsupervised probabilistic machine learning An Inductive Inference Machine See AI winter Machine translation and the ALPAC report of 1966 Compared with symbolic logic formal Bayesian inference is computationally expensive For inference to be tractable most observations must be conditionally independent of one another AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve Expectation maximization one of the most popular algorithms in machine learning allows clustering in the presence of unknown latent variables Some form of deep neural networks without a specific learning algorithm were described by Warren S McCulloch and Walter Pitts 1943 Alan Turing 1948 Karl Steinbuch and 1961 Deep or recurrent networks that learned or used gradient descent were developed by Frank Rosenblatt 1957 Oliver Selfridge 1959 Alexey Ivakhnenko and 1965 1971 Shun Ichi Amari 1972 John Joseph Hopfield 1982 Precursors to backpropagation were developed by Henry J Kelley 1960 Arthur E Bryson 1962 Stuart Dreyfus 1962 Arthur E Bryson and Yu Chi Ho 1969 Backpropagation was independently developed by Seppo Linnainmaa 1970 Paul Werbos 1974 Geoffrey Hinton said of his work on neural networks in the 1990s our labeled datasets were thousands of times too small And our computers were millions of times too slow In statistics a bias is a systematic error or deviation from the correct value But in the context of fairness it refers to a tendency in favor or against a certain group or individual characteristic usually in a way that is considered unfair or harmful A statistically unbiased AI system that produces disparate outcomes for different demographic groups may thus be viewed as biased in the ethical sense Including Jon Kleinberg Cornell University Sendhil Mullainathan University of Chicago Cynthia Chouldechova Carnegie Mellon and Sam Corbett Davis Stanford Moritz Hardt a director at the Max Planck Institute for Intelligent Systems argues that machine learning is fundamentally the wrong tool for a lot of domains where you re trying to design interventions and mechanisms that change the world When the law was passed in 2018 it still contained a form of this provision This is the United Nations definition and includes things like land mines as well See table 4 9 is both the OECD average and the U S average Sometimes called a robopocalypse Electronic brain was the term used by the press around this time Daniel Crevier wrote the conference is generally recognized as the official birthdate of the new science Russell and Norvig called the conference the inception of artificial intelligence Russell and Norvig wrote for the next 20 years the field would be dominated by these people and their students Russell and Norvig wrote it was astonishing whenever a computer did anything kind of smartish The programs described are Arthur Samuel s checkers program for the IBM 701 Daniel Bobrow s STUDENT Newell and Simon s Logic Theorist and Terry Winograd s SHRDLU Russell and Norvig write in almost all cases these early systems failed on more difficult problems Embodied approaches to AI were championed by Hans Moravec and Rodney Brooks and went by many names Nouvelle AI Developmental robotics Matteo Wong wrote in The Atlantic Whereas for decades computer science fields such as natural language processing computer vision and robotics used extremely different methods now they all use a programming method called deep learning As a result their code and approaches have become more similar and their models are easier to integrate into one another Jack Clark wrote in Bloomberg After a half decade of quiet breakthroughs in artificial intelligence 2015 has been a landmark year Computers are smarter and learning faster than ever and noted that the number of software projects that use machine learning at Google increased from a sporadic usage in 2012 to more than 2 700 projects in 2015 Nils Nilsson wrote in 1983 Simply put there is wide disagreement in the field about what AI is all about Daniel Crevier wrote that time has proven the accuracy and perceptiveness of some of Dreyfus s comments Had he formulated them less aggressively constructive actions they suggested might have been taken much earlier Searle presented this definition of Strong AI in 1999 Searle s original formulation was The appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states Strong AI is defined similarly by Russell and Norvig Stong AI the assertion that machines that do so are actually thinking as opposed to simulating thinking ReferencesRussell amp Norvig 2021 pp 1 4 AI set to exceed human brain power Archived 2008 02 19 at the Wayback Machine CNN com July 26 2006 Kaplan Andreas Haenlein Michael 2019 Siri Siri in my hand Who s the fairest in the land On the interpretations illustrations and implications of artificial intelligence Business Horizons 62 15 25 doi 10 1016 j bushor 2018 08 004 ISSN 0007 6813 S2CID 158433736 Artificial general intelligence Russell amp Norvig 2021 pp 32 33 1020 1021 Proposal for the modern version Pennachin amp Goertzel 2007 Warnings of overspecialization in AI from leading researchers Nilsson 1995 McCarthy 2007 Beal amp Winston 2009 Russell amp Norvig 2021 1 2 Dartmouth workshop Russell amp Norvig 2021 p 18 McCorduck 2004 pp 111 136 NRC 1999 pp 200 201 The proposal McCarthy et al 1955 Successful programs of the 1960s McCorduck 2004 pp 243 252 Crevier 1993 pp 52 107 Moravec 1988 p 9 Russell amp Norvig 2021 pp 19 21 Funding initiatives in the early 1980s Fifth Generation Project Japan Alvey UK Microelectronics and Computer Technology Corporation US Strategic Computing Initiative US McCorduck 2004 pp 426 441 Crevier 1993 pp 161 162 197 203 211 240 Russell amp Norvig 2021 p 23 NRC 1999 pp 210 211 Newquist 1994 pp 235 248 First AI Winter Lighthill report Mansfield Amendment Crevier 1993 pp 115 117 Russell amp Norvig 2021 pp 21 22 NRC 1999 pp 212 213 Howe 1994 Newquist 1994 pp 189 201 Second AI Winter Russell amp Norvig 2021 p 24 McCorduck 2004 pp 430 435 Crevier 1993 pp 209 210 NRC 1999 pp 214 216 Newquist 1994 pp 301 318 Deep learning revolution AlexNet Goldman 2022 Russell amp Norvig 2021 p 26 McKinsey 2018 Toews 2023 Problem solving puzzle solving game playing and deduction Russell amp Norvig 2021 chpt 3 5 Russell amp Norvig 2021 chpt 6 constraint satisfaction Poole Mackworth amp Goebel 1998 chpt 2 3 7 9 Luger amp Stubblefield 2004 chpt 3 4 6 8 Nilsson 1998 chpt 7 12 Uncertain reasoning Russell amp Norvig 2021 chpt 12 18 Poole Mackworth amp Goebel 1998 pp 345 395 Luger amp Stubblefield 2004 pp 333 381 Nilsson 1998 chpt 7 12 Intractability and efficiency and the combinatorial explosion Russell amp Norvig 2021 p 21 Psychological evidence of the prevalence of sub symbolic reasoning and knowledge Kahneman 2011 Dreyfus amp Dreyfus 1986 Wason amp Shapiro 1966 Kahneman Slovic amp Tversky 1982 Knowledge representation and knowledge engineering Russell amp Norvig 2021 chpt 10 Poole Mackworth amp Goebel 1998 pp 23 46 69 81 169 233 235 277 281 298 319 345 Luger amp Stubblefield 2004 pp 227 243 Nilsson 1998 chpt 17 1 17 4 18 Smoliar amp Zhang 1994 Neumann amp Moller 2008 Kuperman Reichley amp Bailey 2006 McGarry 2005 Bertini Del Bimbo amp Torniai 2006 Russell amp Norvig 2021 pp 272 Representing categories and relations Semantic networks description logics inheritance including frames and scripts Russell amp Norvig 2021 10 2 amp 10 5 Poole Mackworth amp Goebel 1998 pp 174 177 Luger amp Stubblefield 2004 pp 248 258 Nilsson 1998 chpt 18 3 Representing events and time Situation calculus event calculus fluent calculus including solving the frame problem Russell amp Norvig 2021 10 3 Poole Mackworth amp Goebel 1998 pp 281 298 Nilsson 1998 chpt 18 2 Causal calculus Poole Mackworth amp Goebel 1998 pp 335 337 Representing knowledge about knowledge Belief calculus modal logics Russell amp Norvig 2021 10 4 Poole Mackworth amp Goebel 1998 pp 275 277 Default reasoning Frame problem default logic non monotonic logics circumscription closed world assumption abduction Russell amp Norvig 2021 10 6 Poole Mackworth amp Goebel 1998 pp 248 256 323 335 Luger amp Stubblefield 2004 pp 335 363 Nilsson 1998 18 3 3 Poole et al places abduction under default reasoning Luger et al places this under uncertain reasoning Breadth of commonsense knowledge Lenat amp Guha 1989 Introduction Crevier 1993 pp 113 114 Moravec 1988 p 13 Russell amp Norvig 2021 pp 241 385 982 qualification problem Newquist 1994 p 296 Crevier 1993 pp 204 208 Russell amp Norvig 2021 p 528 Automated planning Russell amp Norvig 2021 chpt 11 Automated decision making Decision theory Russell amp Norvig 2021 chpt 16 18 Classical planning Russell amp Norvig 2021 Section 11 2 Sensorless or conformant planning contingent planning replanning a k a online planning Russell amp Norvig 2021 Section 11 5 Uncertain preferences Russell amp Norvig 2021 Section 16 7 Inverse reinforcement learning Russell amp Norvig 2021 Section 22 6 Information value theory Russell amp Norvig 2021 Section 16 6 Markov decision process Russell amp Norvig 2021 chpt 17 Game theory and multi agent decision theory Russell amp Norvig 2021 chpt 18 Learning Russell amp Norvig 2021 chpt 19 22 Poole Mackworth amp Goebel 1998 pp 397 438 Luger amp Stubblefield 2004 pp 385 542 Nilsson 1998 chpt 3 3 10 3 17 5 20 Turing 1950 Solomonoff 1956 Unsupervised learning Russell amp Norvig 2021 pp 653 definition Russell amp Norvig 2021 pp 738 740 cluster analysis Russell amp Norvig 2021 pp 846 860 word embedding Supervised learning Russell amp Norvig 2021 19 2 Definition Russell amp Norvig 2021 Chpt 19 20 Techniques Reinforcement learning Russell amp Norvig 2021 chpt 22 Luger amp Stubblefield 2004 pp 442 449 Transfer learning Russell amp Norvig 2021 pp 281 The Economist 2016 Artificial Intelligence AI What Is AI and How Does It Work Built In builtin com Retrieved 30 October 2023 Computational learning theory Russell amp Norvig 2021 pp 672 674 Jordan amp Mitchell 2015 Natural language processing NLP Russell amp Norvig 2021 chpt 23 24 Poole Mackworth amp Goebel 1998 pp 91 104 Luger amp Stubblefield 2004 pp 591 632 Subproblems of NLP Russell amp Norvig 2021 pp 849 850 Russell amp Norvig 2021 pp 856 858 Dickson 2022 Modern statistical and deep learning approaches to NLP Russell amp Norvig 2021 chpt 24 Cambria amp White 2014 Vincent 2019 Russell amp Norvig 2021 pp 875 878 Bushwick 2023 Computer vision Russell amp Norvig 2021 chpt 25 Nilsson 1998 chpt 6 Russell amp Norvig 2021 pp 849 850 Russell amp Norvig 2021 pp 895 899 Russell amp Norvig 2021 pp 899 901 Challa et al 2011 Russell amp Norvig 2021 pp 931 938 MIT AIL 2014 Affective computing Thro 1993 Edelson 1991 Tao amp Tan 2005 Scassellati 2002 Waddell 2018 Poria et al 2017 Search algorithms Russell amp Norvig 2021 chpts 3 5 Poole Mackworth amp Goebel 1998 pp 113 163 Luger amp Stubblefield 2004 pp 79 164 193 219 Nilsson 1998 chpts 7 12 State space search Russell amp Norvig 2021 chpt 3 Russell amp Norvig 2021 sect 11 2 Uninformed searches breadth first search depth first search and general state space search Russell amp Norvig 2021 sect 3 4 Poole Mackworth amp Goebel 1998 pp 113 132 Luger amp Stubblefield 2004 pp 79 121 Nilsson 1998 chpt 8 Heuristic or informed searches e g greedy best first and A Russell amp Norvig 2021 sect 3 5 Poole Mackworth amp Goebel 1998 pp 132 147 Poole amp Mackworth 2017 sect 3 6 Luger amp Stubblefield 2004 pp 133 150 Adversarial search Russell amp Norvig 2021 chpt 5 Local or optimization search Russell amp Norvig 2021 chpt 4 Singh Chauhan Nagesh 18 December 2020 Optimization Algorithms in Neural Networks KDnuggets Retrieved 13 January 2024 Evolutionary computation Russell amp Norvig 2021 sect 4 1 2 Merkle amp Middendorf 2013 Logic Russell amp Norvig 2021 chpts 6 9 Luger amp Stubblefield 2004 pp 35 77 Nilsson 1998 chpt 13 16 Propositional logic Russell amp Norvig 2021 chpt 6 Luger amp Stubblefield 2004 pp 45 50 Nilsson 1998 chpt 13 First order logic and features such as equality Russell amp Norvig 2021 chpt 7 Poole Mackworth amp Goebel 1998 pp 268 275 Luger amp Stubblefield 2004 pp 50 62 Nilsson 1998 chpt 15 Logical inference Russell amp Norvig 2021 chpt 10 logical deduction as search Russell amp Norvig 2021 sects 9 3 9 4 Poole Mackworth amp Goebel 1998 pp 46 52 Luger amp Stubblefield 2004 pp 62 73 Nilsson 1998 chpt 4 2 7 2 Resolution and unification Russell amp Norvig 2021 sections 7 5 2 9 2 9 5 Warren D H Pereira L M Pereira F 1977 Prolog the language and its implementation compared with Lisp ACM SIGPLAN Notices 12 8 109 115 doi 10 1145 872734 806939 Fuzzy logic Russell amp Norvig 2021 pp 214 255 459 Scientific American 1999 Stochastic methods for uncertain reasoning Russell amp Norvig 2021 chpt 12 18 20 Poole Mackworth amp Goebel 1998 pp 345 395 Luger amp Stubblefield 2004 pp 165 191 333 381 Nilsson 1998 chpt 19 decision theory and decision analysis Russell amp Norvig 2021 chpt 16 18 Poole Mackworth amp Goebel 1998 pp 381 394 Information value theory Russell amp Norvig 2021 sect 16 6 Markov decision processes and dynamic decision networks Russell amp Norvig 2021 chpt 17 Stochastic temporal models Russell amp Norvig 2021 chpt 14 Hidden Markov model Russell amp Norvig 2021 sect 14 3 Kalman filters Russell amp Norvig 2021 sect 14 4 Dynamic Bayesian networks Russell amp Norvig 2021 sect 14 5 Game theory and mechanism design Russell amp Norvig 2021 chpt 18 Bayesian networks Russell amp Norvig 2021 sects 12 5 12 6 13 4 13 5 14 3 14 5 16 5 20 2 20 3 Poole Mackworth amp Goebel 1998 pp 361 381 Luger amp Stubblefield 2004 pp 182 190 363 379 Nilsson 1998 chpt 19 3 19 4 Domingos 2015 chpt 6 Bayesian inference algorithm Russell amp Norvig 2021 sect 13 3 13 5 Poole Mackworth amp Goebel 1998 pp 361 381 Luger amp Stubblefield 2004 pp 363 379 Nilsson 1998 chpt 19 4 amp 7 Domingos 2015 p 210 Bayesian learning and the expectation maximization algorithm Russell amp Norvig 2021 chpt 20 Poole Mackworth amp Goebel 1998 pp 424 433 Nilsson 1998 chpt 20 Domingos 2015 p 210 Bayesian decision theory and Bayesian decision networks Russell amp Norvig 2021 sect 16 5 Statistical learning methods and classifiers Russell amp Norvig 2021 chpt 20 Ciaramella Alberto Ciaramella Marco 2024 Introduction to Artificial Intelligence from data analysis to generative AI Intellisemantic Editions ISBN 978 8 8947 8760 3 Decision trees Russell amp Norvig 2021 sect 19 3 Domingos 2015 p 88 Non parameteric learning models such as K nearest neighbor and support vector machines Russell amp Norvig 2021 sect 19 7 Domingos 2015 p 187 k nearest neighbor Domingos 2015 p 88 kernel methods Domingos 2015 p 152 Naive Bayes classifier Russell amp Norvig 2021 sect 12 6 Domingos 2015 p 152 Neural networks Russell amp Norvig 2021 chpt 21 Domingos 2015 Chapter 4 Gradient calculation in computational graphs backpropagation automatic differentiation Russell amp Norvig 2021 sect 21 2 Luger amp Stubblefield 2004 pp 467 474 Nilsson 1998 chpt 3 3 Universal approximation theorem Russell amp Norvig 2021 p 752 The theorem Cybenko 1988 Hornik Stinchcombe amp White 1989 Feedforward neural networks Russell amp Norvig 2021 sect 21 1 Recurrent neural networks Russell amp Norvig 2021 sect 21 6 Perceptrons Russell amp Norvig 2021 pp 21 22 683 22 Deep learning Russell amp Norvig 2021 chpt 21 Goodfellow Bengio amp Courville 2016 Hinton et al 2016 Schmidhuber 2015 Convolutional neural networks Russell amp Norvig 2021 sect 21 3 Deng amp Yu 2014 pp 199 200 Ciresan Meier amp Schmidhuber 2012 Russell amp Norvig 2021 p 751 Russell amp Norvig 2021 p 17 Russell amp Norvig 2021 p 785 Schmidhuber 2022 sect 5 Schmidhuber 2022 sect 6 Schmidhuber 2022 sect 7 Schmidhuber 2022 sect 8 Quoted in Christian 2020 p 22 Smith 2023 Explained Generative AI 9 November 2023 AI Writing and Content Creation Tools MIT Sloan Teaching amp Learning Technologies Archived from the original on 25 December 2023 Retrieved 25 December 2023 Marmouyet 2023 Kobielus 2019 Thomason James 21 May 2024 Mojo Rising The resurgence of AI first programming languages VentureBeat Archived from the original on 27 June 2024 Retrieved 26 May 2024 Wodecki Ben 5 May 2023 7 AI Programming Languages You Need to Know AI Business Archived from the original on 25 July 2024 Retrieved 5 October 2024 Plumb Taryn 18 September 2024 Why Jensen Huang and Marc Benioff see gigantic opportunity for agentic AI VentureBeat Archived from the original on 5 October 2024 Retrieved 4 October 2024 Mims Christopher 19 September 2020 Huang s Law Is the New Moore s Law and Explains Why Nvidia Wants Arm Wall Street Journal ISSN 0099 9660 Archived from the original on 2 October 2023 Retrieved 19 January 2025 Davenport T Kalakota R June 2019 The potential for artificial intelligence in healthcare Future Healthc J 6 2 94 98 doi 10 7861 futurehosp 6 2 94 PMC 6616181 PMID 31363513 Lyakhova U A Lyakhov P A 2024 Systematic review of approaches to detection and classification of skin cancer using artificial intelligence Development and prospects Computers in Biology and Medicine 178 108742 doi 10 1016 j compbiomed 2024 108742 PMID 38875908 Archived from the original on 3 December 2024 Retrieved 10 October 2024 Alqudaihi Kawther S Aslam Nida Khan Irfan Ullah Almuhaideb Abdullah M Alsunaidi Shikah J Ibrahim Nehad M Abdel Rahman Alhaidari Fahd A Shaikh Fatema S Alsenbel Yasmine M Alalharith Dima M Alharthi Hajar M Alghamdi Wejdan M Alshahrani Mohammed S 2021 Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques Challenges and Opportunities IEEE Access 9 102327 102344 Bibcode 2021IEEEA 9j2327A doi 10 1109 ACCESS 2021 3097559 ISSN 2169 3536 PMC 8545201 PMID 34786317 Bax Monique Thorpe Jordan Romanov Valentin December 2023 The future of personalized cardiovascular medicine demands 3D and 4D printing stem cells and artificial intelligence Frontiers in Sensors 4 doi 10 3389 fsens 2023 1294721 ISSN 2673 5067 Jumper J Evans R Pritzel A 2021 Highly accurate protein structure prediction with AlphaFold Nature 596 7873 583 589 Bibcode 2021Natur 596 583J doi 10 1038 s41586 021 03819 2 PMC 8371605 PMID 34265844 AI discovers new class of antibiotics to kill drug resistant bacteria 20 December 2023 Archived from the original on 16 September 2024 Retrieved 5 October 2024 AI speeds up drug design for Parkinson s ten fold Cambridge University 17 April 2024 Archived from the original on 5 October 2024 Retrieved 5 October 2024 Horne Robert I Andrzejewska Ewa A Alam Parvez Brotzakis Z Faidon Srivastava Ankit Aubert Alice Nowinska Magdalena Gregory Rebecca C Staats Roxine Possenti Andrea Chia Sean Sormanni Pietro Ghetti Bernardino Caughey Byron Knowles Tuomas P J Vendruscolo Michele 17 April 2024 Discovery of potent inhibitors of a synuclein aggregation using structure based iterative learning Nature Chemical Biology 20 5 Nature 634 645 doi 10 1038 s41589 024 01580 x PMC 11062903 PMID 38632492 Grant Eugene F Lardner Rex 25 July 1952 The Talk of the Town It The New Yorker ISSN 0028 792X Archived from the original on 16 February 2020 Retrieved 28 January 2024 Anderson Mark Robert 11 May 2017 Twenty years on from Deep Blue vs Kasparov how a chess match started the big data revolution The Conversation Archived from the original on 17 September 2024 Retrieved 28 January 2024 Markoff John 16 February 2011 Computer Wins on Jeopardy Trivial It s Not The New York Times ISSN 0362 4331 Archived from the original on 22 October 2014 Retrieved 28 January 2024 Byford Sam 27 May 2017 AlphaGo retires from competitive Go after defeating world number one 3 0 The Verge Archived from the original on 7 June 2017 Retrieved 28 January 2024 Brown Noam Sandholm Tuomas 30 August 2019 Superhuman AI for multiplayer poker Science 365 6456 885 890 Bibcode 2019Sci 365 885B doi 10 1126 science aay2400 ISSN 0036 8075 PMID 31296650 MuZero Mastering Go chess shogi and Atari without rules Google DeepMind 23 December 2020 Retrieved 28 January 2024 Sample Ian 30 October 2019 AI becomes grandmaster in fiendishly complex StarCraft II The Guardian ISSN 0261 3077 Archived from the original on 29 December 2020 Retrieved 28 January 2024 Wurman P R Barrett S Kawamoto K 2022 Outracing champion Gran Turismo drivers with deep reinforcement learning PDF Nature 602 7896 223 228 Bibcode 2022Natur 602 223W doi 10 1038 s41586 021 04357 7 PMID 35140384 Wilkins Alex 13 March 2024 Google AI learns to play open world video games by watching them New Scientist Archived from the original on 26 July 2024 Retrieved 21 July 2024 Improving mathematical reasoning with process supervision OpenAI 31 May 2023 Retrieved 26 January 2025 Srivastava Saurabh 29 February 2024 Functional Benchmarks for Robust Evaluation of Reasoning Performance and the Reasoning Gap arXiv 2402 19450 cs AI Lightman Hunter Kosaraju Vineet Burda Yura Edwards Harri Baker Bowen Lee Teddy Leike Jan Schulman John Sutskever Ilya Cobbe Karl 2023 Let s Verify Step by Step arXiv 2305 20050v1 cs LG Franzen Carl 9 January 2025 Microsoft s new rStar Math technique upgrades small models to outperform OpenAI s o1 preview at math problems VentureBeat Retrieved 26 January 2025 Roberts Siobhan 25 July 2024 AI achieves silver medal standard solving International Mathematical Olympiad problems The New York Times Archived from the original on 26 September 2024 Retrieved 7 August 2024 Azerbayev Zhangir Schoelkopf Hailey Paster Keiran Santos Marco Dos McAleer Stephen Jiang Albert Q Deng Jia Biderman Stella Welleck Sean 16 October 2023 Llemma An Open Language Model For Mathematics EleutherAI Blog Retrieved 26 January 2025 Julius AI julius ai McFarland Alex 12 July 2024 8 Best AI for Math Tools January 2025 Unite AI Retrieved 26 January 2025 Matthew Finio amp Amanda Downie IBM Think 2024 Primer What is Artificial Intelligence AI in Finance 8 Dec 2023 M Nicolas J Firzli Pensions Age European Pensions magazine Artificial Intelligence Ask the Industry May June 2024 https videovoice org ai in finance innovation entrepreneurship vs over regulation with the eus artificial intelligence act wont work as intended Archived 11 September 2024 at the Wayback Machine Congressional Research Service 2019 Artificial Intelligence and National Security PDF Washington DC Congressional Research Service Archived PDF from the original on 8 May 2020 Retrieved 25 February 2024 PD notice Slyusar Vadym 2019 Artificial intelligence as the basis of future control networks Preprint doi 10 13140 RG 2 2 30247 50087 Iraqi Amjad 3 April 2024 Lavender The AI machine directing Israel s bombing spree in Gaza 972 Magazine Archived from the original on 10 October 2024 Retrieved 6 April 2024 Davies Harry McKernan Bethan Sabbagh Dan 1 December 2023 The Gospel how Israel uses AI to select bombing targets in Gaza The Guardian Archived from the original on 6 December 2023 Retrieved 4 December 2023 Marti J Werner 10 August 2024 Drohnen haben den Krieg in der Ukraine revolutioniert doch sie sind empfindlich auf Storsender deshalb sollen sie jetzt autonom operieren Neue Zurcher Zeitung in German Archived from the original on 10 August 2024 Retrieved 10 August 2024 Newsom Gavin Weber Shirley N 5 September 2023 Executive Order N 12 23 PDF Executive Department State of California Archived PDF from the original on 21 February 2024 Retrieved 7 September 2023 Pinaya Walter H L Graham Mark S Kerfoot Eric Tudosiu Petru Daniel Dafflon Jessica Fernandez Virginia Sanchez Pedro Wolleb Julia da Costa Pedro F Patel Ashay 2023 Generative AI for Medical Imaging extending the MONAI Framework arXiv 2307 15208 eess IV What is ChatGPT DALL E and generative AI McKinsey Retrieved 14 December 2024 What is generative AI IBM 22 March 2024 Pasick Adam 27 March 2023 Artificial Intelligence Glossary Neural Networks and Other Terms Explained The New York Times ISSN 0362 4331 Archived from the original on 1 September 2023 Retrieved 22 April 2023 Karpathy Andrej Abbeel Pieter Brockman Greg Chen Peter Cheung Vicki Duan Yan Goodfellow Ian Kingma Durk Ho Jonathan Rein Houthooft Tim Salimans John Schulman Ilya Sutskever Wojciech Zaremba 16 June 2016 Generative models OpenAI Archived from the original on 17 November 2023 Retrieved 15 March 2023 Griffith Erin Metz Cade 27 January 2023 Anthropic Said to Be Closing In on 300 Million in New A I Funding The New York Times Archived from the original on 9 December 2023 Retrieved 14 March 2023 Lanxon Nate Bass Dina Davalos Jackie 10 March 2023 A Cheat Sheet to AI Buzzwords and Their Meanings Bloomberg News Archived from the original on 17 November 2023 Retrieved 14 March 2023 Metz Cade 14 March 2023 OpenAI Plans to Up the Ante in Tech s A I Race The New York Times ISSN 0362 4331 Archived from the original on 31 March 2023 Retrieved 31 March 2023 Thoppilan Romal De Freitas Daniel Hall Jamie Shazeer Noam Kulshreshtha Apoorv 20 January 2022 LaMDA Language Models for Dialog Applications arXiv 2201 08239 cs CL Roose Kevin 21 October 2022 A Coming Out Party for Generative A I Silicon Valley s New Craze The New York Times Archived from the original on 15 February 2023 Retrieved 14 March 2023 Metz Cade 15 February 2024 OpenAI Unveils A I That Instantly Generates Eye Popping Videos The New York Times ISSN 0362 4331 Archived from the original on 15 February 2024 Retrieved 16 February 2024 The race of the AI labs heats up The Economist 30 January 2023 Archived from the original on 17 November 2023 Retrieved 14 March 2023 Yang June Gokturk Burak 14 March 2023 Google Cloud brings generative AI to developers businesses and governments Archived from the original on 17 November 2023 Retrieved 15 March 2023 Brynjolfsson Erik Li Danielle Raymond Lindsey R April 2023 Generative AI at Work Working Paper Working Paper Series doi 10 3386 w31161 archived from the original on 28 March 2024 retrieved 21 January 2024 Don t fear an AI induced jobs apocalypse just yet The Economist 6 March 2023 Archived from the original on 17 November 2023 Retrieved 14 March 2023 Coyle Jake 27 September 2023 In Hollywood writers battle against AI humans win for now AP News Associated Press Archived from the original on 3 April 2024 Retrieved 26 January 2024 Harreis H Koullias T Roberts Roger Generative AI Unlocking the future of fashion Archived from the original on 17 November 2023 Retrieved 14 March 2023 How Generative AI Can Augment Human Creativity Harvard Business Review 16 June 2023 ISSN 0017 8012 Archived from the original on 20 June 2023 Retrieved 20 June 2023 Hendrix Justin 16 May 2023 Transcript Senate Judiciary Subcommittee Hearing on Oversight of AI techpolicy press Archived from the original on 17 November 2023 Retrieved 19 May 2023 Simon Felix M Altay Sacha Mercier Hugo 18 October 2023 Misinformation reloaded Fears about the impact of generative AI on misinformation are overblown Harvard Kennedy School Misinformation Review doi 10 37016 mr 2020 127 S2CID 264113883 Archived from the original on 17 November 2023 Retrieved 16 November 2023 New AI systems collide with copyright law BBC News 1 August 2023 Retrieved 28 September 2024 Poole David Mackworth Alan 2023 Artificial Intelligence Foundations of Computational Agents 3rd ed Cambridge University Press doi 10 1017 9781009258227 ISBN 978 1 0092 5819 7 Archived from the original on 5 October 2024 Retrieved 5 October 2024 Russell Stuart Norvig Peter 2020 Artificial Intelligence A Modern Approach 4th ed Pearson ISBN 978 0 1346 1099 3 Why agents are the next frontier of generative AI McKinsey Digital 24 July 2024 Archived from the original on 3 October 2024 Retrieved 10 August 2024 Figueiredo Mayara Costa Ankrah Elizabeth Powell Jacquelyn E Epstein Daniel A Chen Yunan 12 January 2024 Powered by AI Examining How AI Descriptions Influence Perceptions of Fertility Tracking Applications Proc ACM Interact Mob Wearable Ubiquitous Technol 7 4 154 1 154 24 doi 10 1145 3631414 Power Jennifer Pym Tinonee James Alexandra Waling Andrea 5 July 2024 Smart Sex Toys A Narrative Review of Recent Research on Cultural Health and Safety Considerations Current Sexual Health Reports 16 3 199 215 doi 10 1007 s11930 024 00392 3 ISSN 1548 3592 Marcantonio Tiffany L Avery Gracie Thrash Anna Leone Ruschelle M 10 September 2024 Large Language Models in an App Conducting a Qualitative Synthetic Data Analysis of How Snapchat s My AI Responds to Questions About Sexual Consent Sexual Refusals Sexual Assault and Sexting The Journal of Sex Research 1 15 doi 10 1080 00224499 2024 2396457 ISSN 0022 4499 PMID 39254628 Archived from the original on 9 December 2024 Retrieved 9 December 2024 Hanson Kenneth R Bolthouse Hannah 2024 Replika Removing Erotic Role Play Is Like Grand Theft Auto Removing Guns or Cars Reddit Discourse on Artificial Intelligence Chatbots and Sexual Technologies Socius Sociological Research for a Dynamic World 10 doi 10 1177 23780231241259627 ISSN 2378 0231 Mania Karolina 1 January 2024 Legal Protection of Revenge and Deepfake Porn Victims in the European Union Findings From a Comparative Legal Study Trauma Violence amp Abuse 25 1 117 129 doi 10 1177 15248380221143772 ISSN 1524 8380 PMID 36565267 Singh Suyesha Nambiar Vaishnavi 2024 Role of Artificial Intelligence in the Prevention of Online Child Sexual Abuse A Systematic Review of Literature Journal of Applied Security Research 19 4 586 627 doi 10 1080 19361610 2024 2331885 ISSN 1936 1610 Archived from the original on 9 December 2024 Retrieved 9 December 2024 Razi Afsaneh Kim Seunghyun Alsoubai Ashwaq Stringhini Gianluca Solorio Thamar De Choudhury Munmun Wisniewski Pamela J 13 October 2021 A Human Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection Proceedings of the ACM on Human Computer Interaction 5 CSCW2 1 38 doi 10 1145 3479609 ISSN 2573 0142 Archived from the original on 9 December 2024 Retrieved 9 December 2024 Ransbotham Sam Kiron David Gerbert Philipp Reeves Martin 6 September 2017 Reshaping Business With Artificial Intelligence MIT Sloan Management Review Archived from the original on 13 February 2024 Sun Yuran Zhao Xilei Lovreglio Ruggiero Kuligowski Erica 1 January 2024 Naser M Z ed 8 AI for large scale evacuation modeling promises and challenges Interpretable Machine Learning for the Analysis Design Assessment and Informed Decision Making for Civil Infrastructure Woodhead Publishing Series in Civil and Structural Engineering Woodhead Publishing pp 185 204 ISBN 978 0 1282 4073 1 archived from the original on 19 May 2024 retrieved 28 June 2024 Gomaa Islam Adelzadeh Masoud Gwynne Steven Spencer Bruce Ko Yoon Benichou Noureddine Ma Chunyun Elsagan Nour Duong Dana Zalok Ehab Kinateder Max 1 November 2021 A Framework for Intelligent Fire Detection and Evacuation System Fire Technology 57 6 3179 3185 doi 10 1007 s10694 021 01157 3 ISSN 1572 8099 Archived from the original on 5 October 2024 Retrieved 5 October 2024 Zhao Xilei Lovreglio Ruggiero Nilsson Daniel 1 May 2020 Modelling and interpreting pre evacuation decision making using machine learning Automation in Construction 113 103140 doi 10 1016 j autcon 2020 103140 hdl 10179 17315 ISSN 0926 5805 Archived from the original on 19 May 2024 Retrieved 5 October 2024 India s latest election embraced AI technology Here are some ways it was used constructively PBS News 12 June 2024 Archived from the original on 17 September 2024 Retrieved 28 October 2024 Muller Vincent C 30 April 2020 Ethics of Artificial Intelligence and Robotics Stanford Encyclopedia of Philosophy Archive Archived from the original on 5 October 2024 Retrieved 5 October 2024 Simonite 2016 Russell amp Norvig 2021 p 987 Laskowski 2023 GAO 2022 Valinsky 2019 Russell amp Norvig 2021 p 991 Russell amp Norvig 2021 pp 991 992 Christian 2020 p 63 Vincent 2022 Kopel Matthew Copyright Services Fair Use Cornell University Library Archived from the original on 26 September 2024 Retrieved 26 April 2024 Burgess Matt How to Stop Your Data From Being Used to Train AI Wired ISSN 1059 1028 Archived from the original on 3 October 2024 Retrieved 26 April 2024 Reisner 2023 Alter amp Harris 2023 Getting the Innovation Ecosystem Ready for AI An IP policy toolkit PDF WIPO Hammond George 27 December 2023 Big Tech is spending more than VC firms on AI startups Ars Technica Archived from the original on 10 January 2024 Wong Matteo 24 October 2023 The Future of AI Is GOMA The Atlantic Archived from the original on 5 January 2024 Big tech and the pursuit of AI dominance The Economist 26 March 2023 Archived from the original on 29 December 2023 Fung Brian 19 December 2023 Where the battle to dominate AI may be won CNN Business Archived from the original on 13 January 2024 Metz Cade 5 July 2023 In the Age of A I Tech s Little Guys Need Big Friends The New York Times Archived from the original on 8 July 2024 Retrieved 5 October 2024 Electricity 2024 Analysis IEA 24 January 2024 Retrieved 13 July 2024 Calvert Brian 28 March 2024 AI already uses as much energy as a small country It s only the beginning Vox New York New York Archived from the original on 3 July 2024 Retrieved 5 October 2024 Halper Evan O Donovan Caroline 21 June 2024 AI is exhausting the power grid Tech firms are seeking a miracle solution Washington Post Davenport Carly AI Data Centers and the Coming YS Power Demand Surge PDF Goldman Sachs Archived from the original PDF on 26 July 2024 Retrieved 5 October 2024 Ryan Carol 12 April 2024 Energy Guzzling AI Is Also the Future of Energy Savings Wall Street Journal Dow Jones Hiller Jennifer 1 July 2024 Tech Industry Wants to Lock Up Nuclear Power for AI Wall Street Journal Dow Jones Archived from the original on 5 October 2024 Retrieved 5 October 2024 Kendall Tyler 28 September 2024 Nvidia s Huang Says Nuclear Power an Option to Feed Data Centers Bloomberg Halper Evan 20 September 2024 Microsoft deal would reopen Three Mile Island nuclear plant to power AI Washington Post Hiller Jennifer 20 September 2024 Three Mile Island s Nuclear Plant to Reopen Help Power Microsoft s AI Centers Wall Street Journal Dow Jones Archived from the original on 5 October 2024 Retrieved 5 October 2024 Niva Yadav 19 August 2024 Taiwan to stop large data centers in the North cites insufficient power DatacenterDynamics Archived from the original on 8 November 2024 Retrieved 7 November 2024 Mochizuki Takashi Oda Shoko 18 October 2024 エヌビディア出資の日本企業 原発近くでＡＩデータセンター新設検討 Bloomberg in Japanese Archived from the original on 8 November 2024 Retrieved 7 November 2024 Naureen S Malik and Will Wade 5 November 2024 Nuclear Hungry AI Campuses Need New Plan to Find Power Fast Bloomberg Nicas 2018 Rainie Lee Keeter Scott Perrin Andrew 22 July 2019 Trust and Distrust in America Pew Research Center Archived from the original on 22 February 2024 Williams 2023 Taylor amp Hern 2023 Samuel Sigal 19 April 2022 Why it s so damn hard to make AI fair and unbiased Vox Archived from the original on 5 October 2024 Retrieved 24 July 2024 Rose 2023 CNA 2019 Goffrey 2008 p 17 Berdahl et al 2023 Goffrey 2008 p 17 Rose 2023 Russell amp Norvig 2021 p 995 Christian 2020 p 25 Russell amp Norvig 2021 p 995 Grant amp Hill 2023 Larson amp Angwin 2016 Christian 2020 p 67 70 Christian 2020 pp 67 70 Russell amp Norvig 2021 pp 993 994 Russell amp Norvig 2021 p 995 Lipartito 2011 p 36 Goodman amp Flaxman 2017 p 6 Christian 2020 pp 39 40 65 Quoted in Christian 2020 p 65 Russell amp Norvig 2021 p 994 Christian 2020 pp 40 80 81 Quoted in Christian 2020 p 80 Dockrill 2022 Sample 2017 Black Box AI 16 June 2023 Archived from the original on 15 June 2024 Retrieved 5 October 2024 Christian 2020 p 110 Christian 2020 pp 88 91 Christian 2020 p 83 Russell amp Norvig 2021 p 997 Christian 2020 p 91 Christian 2020 p 83 Verma 2021 Rothman 2020 Christian 2020 pp 105 108 Christian 2020 pp 108 112 Ropek Lucas 21 May 2024 New Anthropic Research Sheds Light on AI s Black Box Gizmodo Archived from the original on 5 October 2024 Retrieved 23 May 2024 Russell amp Norvig 2021 p 989 Russell amp Norvig 2021 pp 987 990 Russell amp Norvig 2021 p 988 Robitzski 2018 Sainato 2015 Harari 2018 Buckley Chris Mozur Paul 22 May 2019 How China Uses High Tech Surveillance to Subdue Minorities The New York Times Archived from the original on 25 November 2019 Retrieved 2 July 2019 Security lapse exposed a Chinese smart city surveillance system 3 May 2019 Archived from the original on 7 March 2021 Retrieved 14 September 2020 Urbina et al 2022 E McGaughey Will Robots Automate Your Job Away Full Employment Basic Income and Economic Democracy 2022 51 3 Industrial Law Journal 511 559 Archived 27 May 2023 at the Wayback Machine Ford amp Colvin 2015 McGaughey 2022 IGM Chicago 2017 Arntz Gregory amp Zierahn 2016 p 33 Lohr 2017 Frey amp Osborne 2017 Arntz Gregory amp Zierahn 2016 p 33 Zhou Viola 11 April 2023 AI is already taking video game illustrators jobs in China Rest of World Archived from the original on 21 February 2024 Retrieved 17 August 2023 Carter Justin 11 April 2023 China s game art industry reportedly decimated by growing AI use Game Developer Archived from the original on 17 August 2023 Retrieved 17 August 2023 Morgenstern 2015 Mahdawi 2017 Thompson 2014 Tarnoff Ben 4 August 2023 Lessons from Eliza The Guardian Weekly pp 34 39 Cellan Jones 2014 Russell amp Norvig 2021 p 1001 Bostrom 2014 Russell 2019 Bostrom 2014 Muller amp Bostrom 2014 Bostrom 2015 Harari 2023 Muller amp Bostrom 2014 Leaders concerns about the existential risks of AI around 2015 Rawlinson 2015 Holley 2015 Gibbs 2014 Sainato 2015 Godfather of artificial intelligence talks impact and potential of new AI CBS News 25 March 2023 Archived from the original on 28 March 2023 Retrieved 28 March 2023 Pittis Don 4 May 2023 Canadian artificial intelligence leader Geoffrey Hinton piles on fears of computer takeover CBC Archived from the original on 7 July 2024 Retrieved 5 October 2024 50 50 chance that AI outsmarts humanity Geoffrey Hinton says Bloomberg BNN 14 June 2024 Archived from the original on 14 June 2024 Retrieved 6 July 2024 Valance 2023 Taylor Josh 7 May 2023 Rise of artificial intelligence is inevitable but should not be feared father of AI says The Guardian Archived from the original on 23 October 2023 Retrieved 26 May 2023 Colton Emma 7 May 2023 Father of AI says tech fears misplaced You cannot stop it Fox News Archived from the original on 26 May 2023 Retrieved 26 May 2023 Jones Hessie 23 May 2023 Juergen Schmidhuber Renowned Father Of Modern AI Says His Life s Work Won t Lead To Dystopia Forbes Archived from the original on 26 May 2023 Retrieved 26 May 2023 McMorrow Ryan 19 December 2023 Andrew Ng Do we think the world is better off with more or less intelligence Financial Times Archived from the original on 25 January 2024 Retrieved 30 December 2023 Levy Steven 22 December 2023 How Not to Be Stupid About AI With Yann LeCun Wired Archived from the original on 28 December 2023 Retrieved 30 December 2023 Arguments that AI is not an imminent risk Brooks 2014 Geist 2015 Madrigal 2015 Lee 2014 Christian 2020 pp 67 73 Yudkowsky 2008 Anderson amp Anderson 2011 AAAI 2014 Wallach 2010 Russell 2019 p 173 Stewart Ashley Melton Monica Hugging Face CEO says he s focused on building a sustainable model for the 4 5 billion open source AI startup Business Insider Archived from the original on 25 September 2024 Retrieved 14 April 2024 Wiggers Kyle 9 April 2024 Google open sources tools to support AI model development TechCrunch Archived from the original on 10 September 2024 Retrieved 14 April 2024 Heaven Will Douglas 12 May 2023 The open source AI boom is built on Big Tech s handouts How long will it last MIT Technology Review Retrieved 14 April 2024 Brodsky Sascha 19 December 2023 Mistral AI s New Language Model Aims for Open Source Supremacy AI Business Archived from the original on 5 September 2024 Retrieved 5 October 2024 Edwards Benj 22 February 2024 Stability announces Stable Diffusion 3 a next gen AI image generator Ars Technica Archived from the original on 5 October 2024 Retrieved 14 April 2024 Marshall Matt 29 January 2024 How enterprises are using open source LLMs 16 examples VentureBeat Archived from the original on 26 September 2024 Retrieved 5 October 2024 Piper Kelsey 2 February 2024 Should we make our most powerful AI models open source to all Vox Archived from the original on 5 October 2024 Retrieved 14 April 2024 Alan Turing Institute 2019 Understanding artificial intelligence ethics and safety PDF Archived PDF from the original on 11 September 2024 Retrieved 5 October 2024 Alan Turing Institute 2023 AI Ethics and Governance in Practice PDF Archived PDF from the original on 11 September 2024 Retrieved 5 October 2024 Floridi Luciano Cowls Josh 23 June 2019 A Unified Framework of Five Principles for AI in Society Harvard Data Science Review 1 1 doi 10 1162 99608f92 8cd550d1 S2CID 198775713 Archived from the original on 7 August 2019 Retrieved 5 December 2023 Buruk Banu Ekmekci Perihan Elif Arda Berna 1 September 2020 A critical perspective on guidelines for responsible and trustworthy artificial intelligence Medicine Health Care and Philosophy 23 3 387 399 doi 10 1007 s11019 020 09948 1 ISSN 1572 8633 PMID 32236794 S2CID 214766800 Archived from the original on 5 October 2024 Retrieved 5 October 2024 Kamila Manoj Kumar Jasrotia Sahil Singh 1 January 2023 Ethical issues in the development of artificial intelligence recognizing the risks International Journal of Ethics and Systems ahead of print ahead of print doi 10 1108 IJOES 05 2023 0107 ISSN 2514 9369 S2CID 259614124 Archived from the original on 5 October 2024 Retrieved 5 October 2024 AI Safety Institute releases new AI safety evaluations platform UK Government 10 May 2024 Archived from the original on 5 October 2024 Retrieved 14 May 2024 Regulation of AI to mitigate risks Berryhill et al 2019 Barfield amp Pagallo 2018 Iphofen amp Kritikos 2019 Wirtz Weyerer amp Geyer 2018 Buiten 2019 Law Library of Congress U S Global Legal Research Directorate 2019 Vincent 2023 Stanford University 2023 UNESCO 2021 Kissinger 2021 Altman Brockman amp Sutskever 2023 VOA News 25 October 2023 UN Announces Advisory Body on Artificial Intelligence Archived from the original on 18 September 2024 Retrieved 5 October 2024 Council of Europe opens first ever global treaty on AI for signature Council of Europe 5 September 2024 Archived from the original on 17 September 2024 Retrieved 17 September 2024 Edwards 2023 Kasperowicz 2023 Fox News 2023 Milmo Dan 3 November 2023 Hope or Horror The great AI debate dividing its pioneers The Guardian Weekly pp 10 12
