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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.¹⁵

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.¹⁶ By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.¹⁷

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.¹⁸ 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.¹⁹ Accurate and efficient reasoning is an unsolved problem.

Knowledge representation

Knowledge representation and knowledge engineering²⁰ 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,²¹ scene interpretation,²² clinical decision support,²³ knowledge discovery (mining "interesting" and actionable inferences from large databases),²⁴ and other areas.²⁵

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.²⁶ Knowledge bases need to represent things such as objects, properties, categories, and relations between objects;²⁷ situations, events, states, and time;²⁸ causes and effects;²⁹ knowledge about knowledge (what we know about what other people know);³⁰ default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);³¹ 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);³² 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).³³ There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.³⁴

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.³⁵³⁶ In automated planning, the agent has a specific goal.³⁷ 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.³⁸

In classical planning, the agent knows exactly what the effect of any action will be.³⁹ 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.⁴⁰

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.⁴¹ Information value theory can be used to weigh the value of exploratory or experimental actions.⁴² 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.⁴³

Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.⁴⁴

Learning

Machine learning is the study of programs that can improve their performance on a given task automatically.⁴⁵ It has been a part of AI from the beginning.⁴⁶

There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance.⁴⁷ 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).⁴⁸

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".⁴⁹ Transfer learning is when the knowledge gained from one problem is applied to a new problem.⁵⁰ Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.⁵¹

Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.⁵²

Natural language processing

Natural language processing (NLP)⁵³ 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.⁵⁴

Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation⁵⁵ unless restricted to small domains called "micro-worlds" (due to the common sense knowledge problem⁵⁶). 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),⁵⁷ transformers (a deep learning architecture using an attention mechanism),⁵⁸ and others.⁵⁹ In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text,⁶⁰⁶¹ 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.⁶²

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.⁶³

The field includes speech recognition,⁶⁴ image classification,⁶⁵ facial recognition, object recognition,⁶⁶ object tracking,⁶⁷ and robotic perception.⁶⁸

Social intelligence

Affective computing is a field that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood.⁶⁹ 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.⁷⁰ 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.⁷¹

General intelligence

A machine with artificial general intelligence would be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.⁷²

Techniques

AI research uses a wide variety of techniques to accomplish the goals above.⁷³

Search and optimization

AI can solve many problems by intelligently searching through many possible solutions.⁷⁴ 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.⁷⁵ 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.⁷⁶

Simple exhaustive searches⁷⁷ 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.⁷⁸ "Heuristics" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.⁷⁹

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.⁸⁰

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.⁸¹

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,⁸² 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.⁸³

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).⁸⁴

Logic

Formal logic is used for reasoning and knowledge representation.⁸⁵ 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")⁸⁶ 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").⁸⁷

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).⁸⁸ 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.⁸⁹ 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.⁹⁰

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.⁹¹

Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.⁹²

Non-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning.⁹³ Other specialized versions of logic have been developed to describe many complex domains.

Probabilistic methods for uncertain reasoning

Many problems in AI (including 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.⁹⁴ Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,⁹⁵ and information value theory.⁹⁶ These tools include models such as Markov decision processes,⁹⁷ dynamic decision networks,⁹⁸ game theory and mechanism design.⁹⁹

Bayesian networks¹⁰⁰ are a tool that can be used for reasoning (using the Bayesian inference algorithm),¹⁰¹¹⁰² learning (using the expectation–maximization algorithm),¹⁰³¹⁰⁴ planning (using decision networks)¹⁰⁵ and perception (using dynamic Bayesian networks).¹⁰⁶

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).¹⁰⁷

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. Classifiers¹⁰⁸ 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.¹⁰⁹

There are many kinds of classifiers in use.¹¹⁰ The decision tree is the simplest and most widely used symbolic machine learning algorithm.¹¹¹ 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.¹¹² The naive Bayes classifier is reportedly the "most widely used learner"¹¹³ at Google, due in part to its scalability.¹¹⁴ Neural networks are also used as classifiers.¹¹⁵

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.¹¹⁶

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.¹¹⁷ 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.¹¹⁸

In feedforward neural networks the signal passes in only one direction.¹¹⁹ 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 architecture for recurrent neural networks.¹²⁰ Perceptrons¹²¹ use only a single layer of neurons; deep learning¹²² 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.¹²³

Deep learning

Deep learning uses several layers of neurons between the network's inputs and outputs.¹²⁴ 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.¹²⁵

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,¹²⁶ and others. The reason that deep learning performs so well in so many applications is not known as of 2021.¹²⁷ 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)¹²⁸ 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.¹²⁹

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 pre-trained 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". These can be reduced with RLHF and quality data, but the problem has been getting worse for reasoning systems.¹³⁰ Such systems are used in chatbots, which allow people to ask a question or request a task in simple text.¹³¹¹³²

Current models and services include Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot, and LLaMA.¹³³ Multimodal GPT models can process different types of data (modalities) such as images, videos, sound, and text.¹³⁴

Hardware and software

Main articles: Programming languages for artificial intelligence and Hardware for artificial intelligence

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.¹³⁵ Specialized programming languages such as Prolog were used in early AI research,¹³⁶ but general-purpose programming languages like Python have become predominant.¹³⁷

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,¹³⁸ a trend sometimes called Huang's law,¹³⁹ named after Nvidia co-founder and CEO Jensen Huang.

Applications

Main article: Applications of artificial intelligence

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 FaceID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used by Facebook, Apple's Photos and TikTok). The deployment of AI may be overseen by a chief automation officer (CAO).

Health and medicine

Main article: Artificial intelligence in healthcare

The application of AI in medicine and medical research has the potential to increase patient care and quality of life.¹⁴⁰ Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.¹⁴¹¹⁴²

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.¹⁴³ It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research.¹⁴⁴¹⁴⁵ 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.¹⁴⁶ 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.¹⁴⁷ 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.¹⁴⁸¹⁴⁹

Games

Main article: Artificial intelligence in video games

Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.¹⁵⁰ Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.¹⁵¹ 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.¹⁵² 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.¹⁵³ Other programs handle imperfect-information games, such as the poker-playing program Pluribus.¹⁵⁴ DeepMind developed increasingly generalistic reinforcement learning models, such as with MuZero, which could be trained to play chess, Go, or Atari games.¹⁵⁵ 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.¹⁵⁶ 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.¹⁵⁷ 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.¹⁵⁸

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-tuning¹⁵⁹ or trained classifiers with human-annotated data to improve answers for new problems and learn from corrections.¹⁶⁰ 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.¹⁶¹ One technique to improve their performance involves training the models to produce correct reasoning steps, rather than just the correct result.¹⁶² The Alibaba Group developed a version of its Qwen models called Qwen2-Math, that achieved state-of-the-art performance on several mathematical benchmarks, including 84% accuracy on the MATH dataset of competition mathematics problems.¹⁶³ 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.¹⁶⁴

Alternatively, dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as AlphaTensor, AlphaGeometry, AlphaProof and AlphaEvolve¹⁶⁵ all from Google DeepMind,¹⁶⁶ Llemma from EleutherAI¹⁶⁷ or Julius.¹⁶⁸

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.¹⁶⁹

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.¹⁷⁰

According to Nicolas Firzli, director of the World Pensions & Investments Forum, it may be too early to see the emergence of highly innovative AI-informed financial products and services. He argues that "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."¹⁷¹

Military

Main article: Military applications of artificial intelligence

Various countries are deploying AI military applications.¹⁷² The main applications enhance command and control, communications, sensors, integration and interoperability.¹⁷³ Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles.¹⁷⁴ 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, both human-operated and autonomous.¹⁷⁵

AI has been used in military operations in Iraq, Syria, Israel and Ukraine.^176177178179

Generative AI

These paragraphs are an excerpt from Generative artificial intelligence.[edit]

Generative artificial intelligence (Generative AI, GenAI,¹⁸⁰ or GAI) is a subfield of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.^181182183 These models learn the underlying patterns and structures of their training data and use them to produce new data¹⁸⁴¹⁸⁵ based on the input, which often comes in the form of natural language prompts.¹⁸⁶¹⁸⁷

Generative AI tools have become more common since the AI boom in the 2020s. This boom was made possible by improvements in transformer-based deep neural networks, particularly large language models (LLMs). Major tools include chatbots such as ChatGPT, Copilot, Gemini, Grok, and DeepSeek; text-to-image models such as Stable Diffusion, Midjourney, and DALL-E; and text-to-video models such as Veo and Sora.^188189190191 Technology companies developing generative AI include OpenAI, Anthropic, Meta AI, Microsoft, Google, DeepSeek, and Baidu.^192193194

Generative AI has raised many ethical questions. It can be used for cybercrime, or to deceive or manipulate people through fake news or deepfakes.¹⁹⁵ Even if used ethically, it may lead to mass replacement of human jobs.¹⁹⁶ The tools themselves have been criticized as violating intellectual property laws, since they are trained on copyrighted works.¹⁹⁷

Agents

Main article: Agentic AI

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.^198199200

Sexuality

Applications of AI in this domain include AI-enabled menstruation and fertility trackers that analyze user data to offer predictions,²⁰¹ AI-integrated sex toys (e.g., teledildonics),²⁰² AI-generated sexual education content,²⁰³ and AI agents that simulate sexual and romantic partners (e.g., Replika).²⁰⁴ AI is also used for the production of non-consensual deepfake pornography, raising significant ethical and legal concerns.²⁰⁵

AI technologies have also been used to attempt to identify online gender-based violence and online sexual grooming of minors.²⁰⁶²⁰⁷

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.²⁰⁸ 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 patterns in large-scale and small-scale evacuations using historical data from GPS, videos or social media. Furthermore, AI can provide real-time information on the evacuation conditions.^209210211

In agriculture, AI has helped farmers to increase yield and identify areas that need irrigation, fertilization, pesticide treatments. 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 million 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.²¹²

Ethics

Main article: Ethics of artificial intelligence

AI has potential benefits and potential risks.²¹³ 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".²¹⁴ However, as the use of AI has become widespread, several unintended consequences and risks have been identified.²¹⁵ 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.²¹⁶

Risks and harm

Privacy and copyright

Further information: Information privacy and Artificial intelligence 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.²¹⁷ 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.²¹⁸ 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.²¹⁹

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.²²⁰ 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'."²²¹

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".²²²²²³ Website owners who do not wish to have their content scraped can indicate it in a "robots.txt" file.²²⁴ In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI.²²⁵²²⁶ 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.²²⁷

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.^228229230 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.²³¹²³²

Power needs and environmental impacts

See also: Environmental impacts of artificial intelligence

In January 2024, the International Energy Agency (IEA) released Electricity 2024, Analysis and Forecast to 2026, forecasting electric power use.²³³ 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.²³⁴

Prodigious power consumption by AI is responsible for the growth of fossil fuel 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.²³⁵

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.²³⁶ 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.²³⁷

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 US$650 million.²³⁸ Nvidia CEO Jensen Huang said nuclear power is a good option for the data centers.²³⁹

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 US$1.6 billion and is dependent on tax breaks for nuclear power contained in the 2022 US Inflation Reduction Act.²⁴⁰ The US government and the state of Michigan are investing almost US$2 billion 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's spinoff of Constellation.²⁴¹

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.²⁴² Taiwan aims to phase out nuclear power by 2025.²⁴³ 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.²⁴⁴

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.²⁴⁵ Ubitus CEO Wesley Kuo said nuclear power plants are the most efficient, cheap and stable power for AI.²⁴⁶

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.²⁴⁷ 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.²⁴⁸

In 2025, a report prepared by the International Energy Agency estimated the greenhouse gas emissions from the energy consumption of AI at 180 million tons. By 2035, these emissions could rise to 300-500 million tonnes depending on what measures will be taken. This is below 1.5% of the energy sector emissions. The emissions reduction potential of AI was estimated at 5% of the energy sector emissions, but rebound effects (for example if people switch from public transport to autonomous cars) can reduce it.²⁴⁹

Misinformation

See also: YouTube § Moderation and offensive content

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.²⁵⁰ This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government.²⁵¹ 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 some steps to mitigate the problem.²⁵²

In the early 2020s, generative AI began to create images, audio, and text that are virtually indistinguishable from real photographs, recordings, or human writing,²⁵³ while realistic AI-generated videos became feasible in the mid-2020s.^254255256 It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda;²⁵⁷ one such potential malicious use is deepfakes for computational propaganda.²⁵⁸ AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.²⁵⁹

AI researchers at Microsoft, OpenAI, universities and other organisations have suggested using "personhood credentials" as a way to overcome online deception enabled by AI models.²⁶⁰

Algorithmic bias and fairness

Main articles: Algorithmic bias and Fairness (machine learning)

Machine learning applications will be biased²⁶¹ if they learn from biased data.²⁶² The developers may not be aware that the bias exists.²⁶³ Bias can be introduced by the way training data is selected and by the way a model is deployed.²⁶⁴²⁶⁵ 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.²⁶⁶ 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,²⁶⁷ a problem called "sample size disparity".²⁶⁸ 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.²⁶⁹

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.²⁷⁰ In 2017, several researchers²⁷¹ 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.²⁷²

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".²⁷³ Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work."²⁷⁴

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.²⁷⁵ 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.²⁷⁶

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.²⁷⁷

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.²⁷⁸

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]²⁷⁹

Lack of transparency

See also: Explainable AI, Algorithmic transparency, and Right to explanation

Many AI systems are so complex that their designers cannot explain how they reach their decisions.²⁸⁰ Particularly with deep neural networks, in which there are many non-linear relationships between inputs and outputs. But some popular explainability techniques exist.²⁸¹

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.²⁸² 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.²⁸³

People who have been harmed by an algorithm's decision have a right to an explanation.²⁸⁴ 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.²⁸⁵ 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.²⁸⁶

DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems.²⁸⁷

Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output.²⁸⁸ LIME can locally approximate a model's outputs with a simpler, interpretable model.²⁸⁹ 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.²⁹⁰ 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.²⁹¹ For generative pre-trained transformers, Anthropic developed a technique based on dictionary learning that associates patterns of neuron activations with human-understandable concepts.²⁹²

Bad actors and weaponized AI

Main articles: Lethal autonomous weapon, Artificial intelligence arms race, and AI safety

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.²⁹³ 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.²⁹⁴ Even when used in conventional warfare, they currently cannot reliably choose targets and could potentially kill an innocent person.²⁹⁵ 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.²⁹⁶ By 2015, over fifty countries were reported to be researching battlefield robots.²⁹⁷

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.²⁹⁸ All these technologies have been available since 2020 or earlier—AI facial recognition systems are already being used for mass surveillance in China.²⁹⁹³⁰⁰

There are many other ways in which 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.³⁰¹

Technological unemployment

Main articles: Workplace impact of artificial intelligence and 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.³⁰²

In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI.³⁰³ 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.³⁰⁴ 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".³⁰⁵³⁰⁶ 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.³⁰⁷ In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence.³⁰⁸³⁰⁹

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".³¹⁰ 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.³¹¹

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.³¹²

Existential risk

Main article: Existential risk from artificial intelligence

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".³¹³ 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.³¹⁴ 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 maximizer).³¹⁵ 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."³¹⁶ 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".³¹⁷

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.³¹⁸

The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI.³¹⁹ Personalities such as Stephen Hawking, Bill Gates, and Elon Musk,³²⁰ 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".³²¹ He notably mentioned risks of an AI takeover,³²² and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI.³²³

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".³²⁴

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."³²⁵ 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."³²⁶³²⁷ 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."³²⁸ Yann LeCun "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction."³²⁹ 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.³³⁰ However, after 2016, the study of current and future risks and possible solutions became a serious area of research.³³¹

Ethical machines and alignment

Main articles: Machine ethics, AI safety, Friendly artificial intelligence, Artificial moral agents, and Human Compatible

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.³³²

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.³³³ The field of machine ethics is also called computational morality,³³⁴ and was founded at an AAAI symposium in 2005.³³⁵

Other approaches include Wendell Wallach's "artificial moral agents"³³⁶ and Stuart J. Russell's three principles for developing provably beneficial machines.³³⁷

Open source

Active organizations in the AI open-source community include Hugging Face,³³⁸ Google,³³⁹ EleutherAI and Meta.³⁴⁰ Various AI models, such as Llama 2, Mistral or Stable Diffusion, have been made open-weight,³⁴¹³⁴² 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.³⁴³ 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.³⁴⁴

Frameworks

Artificial intelligence projects can be guided by ethical considerations during the design, development, and implementation of an AI system. An AI framework such as the Care and Act Framework, developed by the Alan Turing Institute and based on the SUM values, outlines four main ethical dimensions, defined as follows:³⁴⁵³⁴⁶

• 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;³⁴⁷ however, these principles are not without criticism, especially regarding the people chosen to contribute to these frameworks.³⁴⁸

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.³⁴⁹

The UK AI Safety Institute released in 2024 a testing toolset called 'Inspect' for AI safety evaluations available under an 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.³⁵⁰

Regulation

Main articles: Regulation of artificial intelligence, Regulation of algorithms, and AI safety

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.³⁵¹ The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally.³⁵² 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.³⁵³³⁵⁴ Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI.³⁵⁵ 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.³⁵⁶ 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.³⁵⁷ Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI.³⁵⁸ In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.³⁵⁹ In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, government officials and academics.³⁶⁰ 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.³⁶¹

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".³⁶² A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity.³⁶³ 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".³⁶⁴³⁶⁵

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.³⁶⁶ 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.³⁶⁷³⁶⁸ In May 2024 at the AI Seoul Summit, 16 global AI tech companies agreed to safety commitments on the development of AI.³⁶⁹³⁷⁰

History

Main article: History of artificial intelligence

For a chronological guide, see Timeline of artificial intelligence.

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.³⁷¹³⁷² This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an "electronic brain".³⁷³ They developed several areas of research that would become part of AI,³⁷⁴ such as McCulloch and Pitts design for "artificial neurons" in 1943,³⁷⁵ and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that "machine intelligence" was plausible.³⁷⁶³⁷⁷

The field of AI research was founded at a workshop at Dartmouth College in 1956.³⁷⁸³⁷⁹ The attendees became the leaders of AI research in the 1960s.³⁸⁰ They and their students produced programs that the press described as "astonishing":³⁸¹ computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.³⁸²³⁸³ Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s.³⁸⁴

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.³⁸⁵ In 1965 Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do".³⁸⁶ In 1967 Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".³⁸⁷ They had, however, underestimated the difficulty of the problem.³⁸⁸ In 1974, both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill³⁸⁹ and ongoing pressure from the U.S. Congress to fund more productive projects.³⁹⁰ Minsky 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.³⁹¹ The "AI winter", a period when obtaining funding for AI projects was difficult, followed.³⁹²

In the early 1980s, AI research was revived by the commercial success of expert systems,³⁹³ 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.³⁹⁴ 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.³⁹⁵

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,³⁹⁶ and began to look into "sub-symbolic" approaches.³⁹⁷ Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that move and survive.³⁹⁸ Judea Pearl, Lotfi Zadeh, and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic.³⁹⁹⁴⁰⁰ But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others.⁴⁰¹ In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.⁴⁰²

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).⁴⁰³ 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).⁴⁰⁴ 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.⁴⁰⁵

Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field.⁴⁰⁶ For many specific tasks, other methods were abandoned.⁴⁰⁷ Deep learning's success was based on both hardware improvements (faster computers,⁴⁰⁸ graphics processing units, cloud computing⁴⁰⁹) and access to large amounts of data⁴¹⁰ (including curated datasets,⁴¹¹ such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI.⁴¹² The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.⁴¹³

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.⁴¹⁴

In the late 2010s 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.⁴¹⁵ ChatGPT, launched on November 30, 2022, became the fastest-growing consumer software application in history, gaining over 100 million users in two months.⁴¹⁶ It marked what is widely regarded as AI's breakout year, bringing it into the public consciousness.⁴¹⁷ 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 US$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".⁴¹⁸ About 800,000 "AI"-related U.S. job openings existed in 2022.⁴¹⁹ According to PitchBook research, 22% of newly funded startups in 2024 claimed to be AI companies.⁴²⁰

Philosophy

Main article: Philosophy of artificial intelligence

Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines.⁴²¹ Another major focus has been whether machines can be conscious, and the associated ethical implications.⁴²² Many other topics in philosophy are relevant to AI, such as epistemology and free will.⁴²³ Rapid advancements have intensified public discussions on the philosophy and ethics of AI.⁴²⁴

Defining artificial intelligence

See also: Turing test, Intelligent agent, Dartmouth workshop, and Synthetic intelligence

Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?"⁴²⁵ He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour".⁴²⁶ He devised the Turing test, which measures the ability of a machine to simulate human conversation.⁴²⁷ 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."⁴²⁸

Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure.⁴²⁹ 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.'"⁴³⁰ AI founder John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".⁴³¹

McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world".⁴³² Another AI founder, Marvin Minsky, similarly describes it as "the ability to solve hard problems".⁴³³ 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.⁴³⁴ 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,⁴³⁵ 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,⁴³⁶ 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".⁴³⁷

Evaluating approaches to AI

No established unifying theory or paradigm has guided AI research for most of its history.⁴³⁸ 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")⁴³⁹ 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."⁴⁴⁰

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.⁴⁴¹ 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.⁴⁴² Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.⁴⁴³⁴⁴⁴

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,⁴⁴⁵⁴⁴⁶ 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

Main article: Neats and scruffies

"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,⁴⁴⁷ but eventually was seen as irrelevant. Modern AI has elements of both.

Soft vs. hard computing

Main article: Soft computing

Finding a provably correct or optimal solution is intractable for many important problems.⁴⁴⁸ 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

Main articles: Weak artificial intelligence and Artificial general intelligence

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.⁴⁴⁹⁴⁵⁰ 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

Main articles: Philosophy of artificial intelligence and Artificial consciousness

There is no settled consensus in philosophy of mind on 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."⁴⁵¹ 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

Main articles: Hard problem of consciousness and Theory of mind

David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness.⁴⁵² 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.⁴⁵³

Computationalism and functionalism

Main articles: Computational theory of mind and Functionalism (philosophy of mind)

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.⁴⁵⁴

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."⁴⁵⁵ 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.⁴⁵⁶

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.⁴⁵⁷ 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.⁴⁵⁸⁴⁵⁹ Sapience (a set of capacities related to high intelligence, such as discernment or self-awareness) may provide another moral basis for AI rights.⁴⁶⁰ Robot rights are also sometimes proposed as a practical way to integrate autonomous agents into society.⁴⁶¹

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.⁴⁶² 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 in society on their own.⁴⁶³⁴⁶⁴

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.⁴⁶⁵⁴⁶⁶

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.⁴⁶⁷ 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".⁴⁶⁸

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.⁴⁶⁹

Transhumanism

Main article: 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.⁴⁷⁰

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.⁴⁷¹

In fiction

Main article: Artificial intelligence in fiction

Thought-capable artificial beings have appeared as storytelling devices since antiquity,⁴⁷² and have been a persistent theme in science fiction.⁴⁷³

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.⁴⁷⁴

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;⁴⁷⁵ 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.⁴⁷⁶

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.⁴⁷⁷

See also

• Artificial consciousness – Field in cognitive science
• 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 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
• Intelligent automation – Software process that combines robotic process automation and artificial intelligence
• 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
• The Last Day – 1967 Welsh science fiction novel
• Wetware computer – Computer composed of organic material
• DARWIN EU - A European Union initiative coordinated by the European Medicines Agency (EMA) to generate and utilize real-world evidence (RWE) to support the evaluation and supervision of medicines across the EU.

Explanatory notes

AI textbooks

The two most widely used textbooks in 2023 (see the Open Syllabus):

• Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474.
• Rich, Elaine; Knight, Kevin; Nair, Shivashankar B (2010). Artificial Intelligence (3rd ed.). New Delhi: Tata McGraw Hill India. ISBN 978-0-0700-8770-5.

The four most widely used AI textbooks in 2008:

• Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). Benjamin/Cummings. ISBN 978-0-8053-4780-7. Archived from the original on 26 July 2020. Retrieved 17 December 2019.
• Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-5586-0467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019.
• Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.
• Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-1951-0270-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Later edition: Poole, David; Mackworth, Alan (2017). Artificial Intelligence: Foundations of Computational Agents (2nd ed.). Cambridge University Press. ISBN 978-1-1071-9539-4. Archived from the original on 7 December 2017. Retrieved 6 December 2017.

Other textbooks:

• Ertel, Wolfgang (2017). Introduction to Artificial Intelligence (2nd ed.). Springer. ISBN 978-3-3195-8486-7.
• Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from data analysis to generative AI (1st ed.). Intellisemantic Editions. ISBN 978-8-8947-8760-3.

History of AI

• Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3.
• McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1
• Newquist, H. P. (1994). The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think. New York: Macmillan/SAMS. ISBN 978-0-6723-0412-5.
• Harmon, Paul; Sawyer, Brian (1990). Creating Expert Systems for Business and Industry. New York: John Wiley & Sons. ISBN 0471614963.

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Further reading

• Autor, David H., "Why Are There Still So Many Jobs? The History and Future of Workplace Automation" (2015) 29(3) Journal of Economic Perspectives 3.
• Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-1560-1391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020.
• Boyle, James, The Line: AI and the Future of Personhood, MIT Press, 2024.
• Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192–198. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.)
• Evans, Woody (2015). "Posthuman Rights: Dimensions of Transhuman Worlds". Teknokultura. 12 (2). doi:10.5209/rev_TK.2015.v12.n2.49072. S2CID 147612763.
• Frank, Michael (22 September 2023). "US Leadership in Artificial Intelligence Can Shape the 21st Century Global Order". The Diplomat. Archived from the original on 16 September 2024. Retrieved 8 December 2023. Instead, the United States has developed a new area of dominance that the rest of the world views with a mixture of awe, envy, and resentment: artificial intelligence... From AI models and research to cloud computing and venture capital, U.S. companies, universities, and research labs – and their affiliates in allied countries – appear to have an enormous lead in both developing cutting-edge AI and commercializing it. The value of U.S. venture capital investments in AI start-ups exceeds that of the rest of the world combined.
• Gertner, Jon. (2023) "Wikipedia's Moment of Truth: Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process?" New York Times Magazine (July 18, 2023) online Archived 20 July 2023 at the Wayback Machine
• Gleick, James, "The Fate of Free Will" (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27–28, 30. "Agency is what distinguishes us from machines. For biological creatures, reason and purpose come from acting in the world and experiencing the consequences. Artificial intelligences – disembodied, strangers to blood, sweat, and tears – have no occasion for that." (p. 30.)
• Halpern, Sue, "The Coming Tech Autocracy" (review of Verity Harding, AI Needs You: How We Can Change AI's Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind's Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44–46. "'We can't realistically expect that those who hope to get rich from AI are going to have the interests of the rest of us close at heart,' ... writes [Gary Marcus]. 'We can't count on governments driven by campaign finance contributions [from tech companies] to push back.'... Marcus details the demands that citizens should make of their governments and the tech companies. They include transparency on how AI systems work; compensation for individuals if their data [are] used to train LLMs (large language model)s and the right to consent to this use; and the ability to hold tech companies liable for the harms they cause by eliminating Section 230, imposing cash penalties, and passing stricter product liability laws... Marcus also suggests... that a new, AI-specific federal agency, akin to the FDA, the FCC, or the FTC, might provide the most robust oversight.... [T]he Fordham law professor Chinmayi Sharma... suggests... establish[ing] a professional licensing regime for engineers that would function in a similar way to medical licenses, malpractice suits, and the Hippocratic oath in medicine. 'What if, like doctors,' she asks..., 'AI engineers also vowed to do no harm?'" (p. 46.)
• Henderson, Mark (24 April 2007). "Human rights for robots? We're getting carried away". The Times Online. London. Archived from the original on 31 May 2014. Retrieved 31 May 2014.
• Hughes-Castleberry, Kenna, "A Murder Mystery Puzzle: The literary puzzle Cain's Jawbone, which has stumped humans for decades, reveals the limitations of natural-language-processing algorithms", Scientific American, vol. 329, no. 4 (November 2023), pp. 81–82. "This murder mystery competition has revealed that although NLP (natural-language processing) models are capable of incredible feats, their abilities are very much limited by the amount of context they receive. This [...] could cause [difficulties] for researchers who hope to use them to do things such as analyze ancient languages. In some cases, there are few historical records on long-gone civilizations to serve as training data for such a purpose." (p. 82.)
• Immerwahr, Daniel, "Your Lying Eyes: People now use A.I. to generate fake videos indistinguishable from real ones. How much does it matter?", The New Yorker, 20 November 2023, pp. 54–59. "If by 'deepfakes' we mean realistic videos produced using artificial intelligence that actually deceive people, then they barely exist. The fakes aren't deep, and the deeps aren't fake. [...] A.I.-generated videos are not, in general, operating in our media as counterfeited evidence. Their role better resembles that of cartoons, especially smutty ones." (p. 59.)
• Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press.
• Jumper, John; Evans, Richard; Pritzel, Alexander; et al. (26 August 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. S2CID 235959867.
• LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (28 May 2015). "Deep learning". Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442. S2CID 3074096. Archived from the original on 5 June 2023. Retrieved 19 June 2023.
• Leffer, Lauren, "The Risks of Trusting AI: We must avoid humanizing machine-learning models used in scientific research", Scientific American, vol. 330, no. 6 (June 2024), pp. 80–81.
• Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a conversation?", The New Yorker, 7 October 2024, pp. 12–16.
• Maschafilm (2010). "Content: Plug & Pray Film – Artificial Intelligence – Robots". plugandpray-film.de. Archived from the original on 12 February 2016.
• Marcus, Gary, "Artificial Confidence: Even the newest, buzziest systems of artificial general intelligence are stymmied by the same old problems", Scientific American, vol. 327, no. 4 (October 2022), pp. 42–45.
• Mitchell, Melanie (2019). Artificial intelligence: a guide for thinking humans. New York: Farrar, Straus and Giroux. ISBN 978-0-3742-5783-5.
• Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; et al. (26 February 2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740. Archived from the original on 19 June 2023. Retrieved 19 June 2023. Introduced DQN, which produced human-level performance on some Atari games.
• Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?", The New Yorker, 20 November 2023, pp. 20–26.
• "Robots could demand legal rights". BBC News. 21 December 2006. Archived from the original on 15 October 2019. Retrieved 3 February 2011.
• Roivainen, Eka, "AI's IQ: ChatGPT aced a [standard intelligence] test but showed that intelligence cannot be measured by IQ alone", Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT fails at tasks that require real humanlike reasoning or an understanding of the physical and social world.... ChatGPT seemed unable to reason logically and tried to rely on its vast database of... facts derived from online texts."
• Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–144. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.)
• Schulz, Hannes; Behnke, Sven (1 November 2012). "Deep Learning". KI – Künstliche Intelligenz. 26 (4): 357–363. doi:10.1007/s13218-012-0198-z. ISSN 1610-1987. S2CID 220523562.
• Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013.
• Silver, David; Huang, Aja; Maddison, Chris J.; et al. (28 January 2016). "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484–489. Bibcode:2016Natur.529..484S. doi:10.1038/nature16961. PMID 26819042. S2CID 515925. Archived from the original on 18 June 2023. Retrieved 19 June 2023.
• Tarnoff, Ben, "The Labor Theory of AI" (review of Matteo Pasquinelli, The Eye of the Master: A Social History of Artificial Intelligence, Verso, 2024, 264 pp.), The New York Review of Books, vol. LXXII, no. 5 (27 March 2025), pp. 30–32. The reviewer, Ben Tarnoff, writes: "The strangeness at the heart of the generative AI boom is that nobody really knows how the technology works. We know how the large language models within ChatGPT and its counterparts are trained, even if we don't always know which data they're being trained on: they are asked to predict the next string of characters in a sequence. But exactly how they arrive at any given prediction is a mystery. The computations that occur inside the model are simply too intricate for any human to comprehend." (p. 32.)
• Vaswani, Ashish, Noam Shazeer, Niki Parmar et al. "Attention is all you need." Advances in neural information processing systems 30 (2017). Seminal paper on transformers.
• Vincent, James, "Horny Robot Baby Voice: James Vincent on AI chatbots", London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29–32. "[AI chatbot] programs are made possible by new technologies but rely on the timelelss human tendency to anthropomorphise." (p. 29.)
• White Paper: On Artificial Intelligence – A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. Archived (PDF) from the original on 20 February 2020. Retrieved 20 February 2020.

External links

• "Artificial Intelligence". Internet Encyclopedia of Philosophy.

References

1. Russell & Norvig (2021), pp. 1–4. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

2. AI set to exceed human brain power Archived 2008-02-19 at the Wayback Machine CNN.com (July 26, 2006) http://www.cnn.com/2006/TECH/science/07/24/ai.bostrom/ ↩

3. 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. /wiki/Doi_(identifier) ↩

4. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

5. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

6. Russell & Norvig (2021, §1.2). - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

7. "Tech companies want to build artificial general intelligence. But who decides when AGI is attained?". AP News. 4 April 2024. Retrieved 20 May 2025. https://apnews.com/article/agi-artificial-general-intelligence-existential-risk-meta-openai-deepmind-science-ff5662a056d3cf3c5889a73e929e5a34 ↩

8. Dartmouth workshop: Russell & Norvig (2021, p. 18), McCorduck (2004, pp. 111–136), NRC (1999, pp. 200–201)The proposal: McCarthy et al. (1955) /wiki/Dartmouth_workshop ↩

9. 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) - McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1 ↩

10. 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) /wiki/Fifth_Generation_Project ↩

11. 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) /wiki/AI_Winter ↩

12. 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) /wiki/AI_Winter ↩

13. Deep learning revolution, AlexNet: Goldman (2022), Russell & Norvig (2021, p. 26), McKinsey (2018) /wiki/Deep_learning ↩

14. Toews (2023). - Toews, Rob (3 September 2023). "Transformers Revolutionized AI. What Will Replace Them?". Forbes. Archived from the original on 8 December 2023. Retrieved 8 December 2023. https://www.forbes.com/sites/robtoews/2023/09/03/transformers-revolutionized-ai-what-will-replace-them ↩

15. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

16. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

17. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

18. Intractability and efficiency and the combinatorial explosion: Russell & Norvig (2021, p. 21) /wiki/Intractably ↩

19. Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: Kahneman (2011), Dreyfus & Dreyfus (1986), Wason & Shapiro (1966), Kahneman, Slovic & Tversky (1982) - Kahneman, Daniel (2011). Thinking, Fast and Slow. Macmillan. ISBN 978-1-4299-6935-2. Archived from the original on 15 March 2023. Retrieved 8 April 2012. https://books.google.com/books?id=ZuKTvERuPG8C ↩

20. 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) /wiki/Knowledge_representation ↩

21. Smoliar & Zhang (1994). - Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE MultiMedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. https://doi.org/10.1109%2F93.311653 ↩

22. Neumann & Möller (2008). - Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. S2CID 10767011. https://doi.org/10.1016%2Fj.imavis.2007.08.013 ↩

23. Kuperman, Reichley & Bailey (2006). - Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1513681 ↩

24. McGarry (2005). - McGarry, Ken (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. https://doi.org/10.1017%2FS0269888905000408 ↩

25. Bertini, Del Bimbo & Torniai (2006). - Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. ↩

26. Russell & Norvig (2021), pp. 272. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

27. 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) /wiki/Semantic_network ↩

28. 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) /wiki/Situation_calculus ↩

29. Causal calculus: Poole, Mackworth & Goebel (1998, pp. 335–337) /wiki/Causality#Causal_calculus ↩

30. Representing knowledge about knowledge: Belief calculus, modal logics: Russell & Norvig (2021, §10.4), Poole, Mackworth & Goebel (1998, pp. 275–277) /wiki/Modal_logic ↩

31. 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"). /wiki/Default_reasoning ↩

32. 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) - Lenat, Douglas; Guha, R. V. (1989). Building Large Knowledge-Based Systems. Addison-Wesley. ISBN 978-0-2015-1752-1. ↩

33. Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: Kahneman (2011), Dreyfus & Dreyfus (1986), Wason & Shapiro (1966), Kahneman, Slovic & Tversky (1982) - Kahneman, Daniel (2011). Thinking, Fast and Slow. Macmillan. ISBN 978-1-4299-6935-2. Archived from the original on 15 March 2023. Retrieved 8 April 2012. https://books.google.com/books?id=ZuKTvERuPG8C ↩

34. It is among the reasons that expert systems proved to be inefficient for capturing knowledge.[30][31] /wiki/Expert_system ↩

35. "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. /wiki/Economics ↩

36. Russell & Norvig (2021), p. 528. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

37. Automated planning: Russell & Norvig (2021, chpt. 11). /wiki/Automated_planning ↩

38. Automated decision making, Decision theory: Russell & Norvig (2021, chpt. 16–18). /wiki/Automated_decision_making ↩

39. Classical planning: Russell & Norvig (2021, Section 11.2). /wiki/Automated_planning_and_scheduling#classical_planning ↩

40. Sensorless or "conformant" planning, contingent planning, replanning (a.k.a. online planning): Russell & Norvig (2021, Section 11.5). - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

41. Uncertain preferences: Russell & Norvig (2021, Section 16.7) Inverse reinforcement learning: Russell & Norvig (2021, Section 22.6) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

42. Information value theory: Russell & Norvig (2021, Section 16.6). /wiki/Information_value_theory ↩

43. Markov decision process: Russell & Norvig (2021, chpt. 17). /wiki/Markov_decision_process ↩

44. Game theory and multi-agent decision theory: Russell & Norvig (2021, chpt. 18). /wiki/Game_theory ↩

45. 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) /wiki/Machine_learning ↩

46. Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".[42] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[43] /wiki/Alan_Turing ↩

47. Unsupervised learning: Russell & Norvig (2021, pp. 653) (definition), Russell & Norvig (2021, pp. 738–740) (cluster analysis), Russell & Norvig (2021, pp. 846–860) (word embedding) /wiki/Unsupervised_learning ↩

48. Supervised learning: Russell & Norvig (2021, §19.2) (Definition), Russell & Norvig (2021, Chpt. 19–20) (Techniques) /wiki/Supervised_learning ↩

49. Reinforcement learning: Russell & Norvig (2021, chpt. 22), Luger & Stubblefield (2004, pp. 442–449) /wiki/Reinforcement_learning ↩

50. Transfer learning: Russell & Norvig (2021, pp. 281), The Economist (2016) /wiki/Transfer_learning ↩

51. "Artificial Intelligence (AI): What Is AI and How Does It Work? | Built In". builtin.com. Retrieved 30 October 2023. https://builtin.com/artificial-intelligence ↩

52. Computational learning theory: Russell & Norvig (2021, pp. 672–674), Jordan & Mitchell (2015) /wiki/Computational_learning_theory ↩

53. Natural language processing (NLP): Russell & Norvig (2021, chpt. 23–24), Poole, Mackworth & Goebel (1998, pp. 91–104), Luger & Stubblefield (2004, pp. 591–632) /wiki/Natural_language_processing ↩

54. Subproblems of NLP: Russell & Norvig (2021, pp. 849–850) /wiki/Natural_language_processing ↩

55. See AI winter § Machine translation and the ALPAC report of 1966 /wiki/AI_winter#Machine_translation_and_the_ALPAC_report_of_1966 ↩

56. 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) - Lenat, Douglas; Guha, R. V. (1989). Building Large Knowledge-Based Systems. Addison-Wesley. ISBN 978-0-2015-1752-1. ↩

57. Russell & Norvig (2021), pp. 856–858. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

58. Dickson (2022). - Dickson, Ben (2 May 2022). "Machine learning: What is the transformer architecture?". TechTalks. Archived from the original on 22 November 2023. Retrieved 22 November 2023. https://bdtechtalks.com/2022/05/02/what-is-the-transformer ↩

59. Modern statistical and deep learning approaches to NLP: Russell & Norvig (2021, chpt. 24), Cambria & White (2014) /wiki/Natural_language_processing ↩

60. Vincent (2019). - Vincent, James (7 November 2019). "OpenAI has published the text-generating AI it said was too dangerous to share". The Verge. Archived from the original on 11 June 2020. Retrieved 11 June 2020. https://www.theverge.com/2019/11/7/20953040/openai-text-generation-ai-gpt-2-full-model-release-1-5b-parameters ↩

61. Russell & Norvig (2021), pp. 875–878. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

62. Bushwick (2023). - Bushwick, Sophie (16 March 2023), "What the New GPT-4 AI Can Do", Scientific American, archived from the original on 22 August 2023, retrieved 5 October 2024 https://www.scientificamerican.com/article/what-the-new-gpt-4-ai-can-do/ ↩

63. Computer vision: Russell & Norvig (2021, chpt. 25), Nilsson (1998, chpt. 6) /wiki/Computer_vision ↩

64. Russell & Norvig (2021), pp. 849–850. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

65. Russell & Norvig (2021), pp. 895–899. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

66. Russell & Norvig (2021), pp. 899–901. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

67. Challa et al. (2011). - Challa, Subhash; Moreland, Mark R.; Mušicki, Darko; Evans, Robin J. (2011). Fundamentals of Object Tracking. Cambridge University Press. doi:10.1017/CBO9780511975837. ISBN 978-0-5218-7628-5. https://doi.org/10.1017%2FCBO9780511975837 ↩

68. Russell & Norvig (2021), pp. 931–938. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

69. Affective computing: Thro (1993), Edelson (1991), Tao & Tan (2005), Scassellati (2002) /wiki/Affective_computing ↩

70. Waddell (2018). - Waddell, Kaveh (2018). "Chatbots Have Entered the Uncanny Valley". The Atlantic. Archived from the original on 24 April 2018. Retrieved 24 April 2018. https://www.theatlantic.com/technology/archive/2017/04/uncanny-valley-digital-assistants/523806 ↩

71. Poria et al. (2017). - Poria, Soujanya; Cambria, Erik; Bajpai, Rajiv; Hussain, Amir (September 2017). "A review of affective computing: From unimodal analysis to multimodal fusion". Information Fusion. 37: 98–125. doi:10.1016/j.inffus.2017.02.003. hdl:1893/25490. S2CID 205433041. Archived from the original on 23 March 2023. Retrieved 27 April 2021. http://researchrepository.napier.ac.uk/Output/1792429 ↩

72. 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) /wiki/Artificial_general_intelligence ↩

73. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

74. 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) /wiki/Search_algorithm ↩

75. State space search: Russell & Norvig (2021, chpt. 3) /wiki/State_space_search ↩

76. Russell & Norvig (2021), sect. 11.2. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

77. 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) /wiki/Uninformed_search ↩

78. Intractability and efficiency and the combinatorial explosion: Russell & Norvig (2021, p. 21) /wiki/Intractably ↩

79. 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) /wiki/Heuristic ↩

80. Adversarial search: Russell & Norvig (2021, chpt. 5) /wiki/Adversarial_search ↩

81. Local or "optimization" search: Russell & Norvig (2021, chpt. 4) /wiki/Local_search_(optimization) ↩

82. Singh Chauhan, Nagesh (18 December 2020). "Optimization Algorithms in Neural Networks". KDnuggets. Retrieved 13 January 2024. https://www.kdnuggets.com/optimization-algorithms-in-neural-networks ↩

83. Evolutionary computation: Russell & Norvig (2021, sect. 4.1.2) /wiki/Evolutionary_computation ↩

84. Merkle & Middendorf (2013). - Merkle, Daniel; Middendorf, Martin (2013). "Swarm Intelligence". In Burke, Edmund K.; Kendall, Graham (eds.). Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer Science & Business Media. ISBN 978-1-4614-6940-7. ↩

85. Logic: Russell & Norvig (2021, chpts. 6–9), Luger & Stubblefield (2004, pp. 35–77), Nilsson (1998, chpt. 13–16) /wiki/Logic ↩

86. Propositional logic: Russell & Norvig (2021, chpt. 6), Luger & Stubblefield (2004, pp. 45–50), Nilsson (1998, chpt. 13) /wiki/Propositional_logic ↩

87. 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) /wiki/First-order_logic ↩

88. Logical inference: Russell & Norvig (2021, chpt. 10) /wiki/Logical_inference ↩

89. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

90. Resolution and unification: Russell & Norvig (2021, sections 7.5.2, 9.2, 9.5) /wiki/Resolution_(logic) ↩

91. 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. /wiki/ACM_SIGPLAN_Notices ↩

92. Fuzzy logic: Russell & Norvig (2021, pp. 214, 255, 459), Scientific American (1999) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

93. 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"). /wiki/Default_reasoning ↩

94. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

95. decision theory and decision analysis: Russell & Norvig (2021, chpt. 16–18), Poole, Mackworth & Goebel (1998, pp. 381–394) /wiki/Decision_theory ↩

96. Information value theory: Russell & Norvig (2021, sect. 16.6) /wiki/Information_value_theory ↩

97. Markov decision processes and dynamic decision networks: Russell & Norvig (2021, chpt. 17) /wiki/Markov_decision_process ↩

98. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

99. Game theory and mechanism design: Russell & Norvig (2021, chpt. 18) /wiki/Game_theory ↩

100. 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) /wiki/Bayesian_network ↩

101. 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.[94] /wiki/Conditionally_independent ↩

102. 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) /wiki/Bayesian_inference ↩

103. Expectation–maximization, one of the most popular algorithms in machine learning, allows clustering in the presence of unknown latent variables.[96] /wiki/Latent_variables ↩

104. 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) /wiki/Bayesian_learning ↩

105. Bayesian decision theory and Bayesian decision networks: Russell & Norvig (2021, sect. 16.5) /wiki/Bayesian_decision_theory ↩

106. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

107. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

108. Statistical learning methods and classifiers: Russell & Norvig (2021, chpt. 20), /wiki/Classifier_(mathematics) ↩

109. Supervised learning: Russell & Norvig (2021, §19.2) (Definition), Russell & Norvig (2021, Chpt. 19–20) (Techniques) /wiki/Supervised_learning ↩

110. Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from data analysis to generative AI. Intellisemantic Editions. ISBN 978-8-8947-8760-3. 978-8-8947-8760-3 ↩

111. Decision trees: Russell & Norvig (2021, sect. 19.3), Domingos (2015, p. 88) /wiki/Alternating_decision_tree ↩

112. 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) /wiki/Nonparametric_statistics ↩

113. Domingos (2015), p. 152. - Domingos, Pedro (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0-4650-6570-7. ↩

114. Naive Bayes classifier: Russell & Norvig (2021, sect. 12.6), Domingos (2015, p. 152) /wiki/Naive_Bayes_classifier ↩

115. Neural networks: Russell & Norvig (2021, chpt. 21), Domingos (2015, Chapter 4) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

116. Neural networks: Russell & Norvig (2021, chpt. 21), Domingos (2015, Chapter 4) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

117. 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) /wiki/Backpropagation ↩

118. Universal approximation theorem: Russell & Norvig (2021, p. 752) The theorem: Cybenko (1988), Hornik, Stinchcombe & White (1989) /wiki/Universal_approximation_theorem ↩

119. Feedforward neural networks: Russell & Norvig (2021, sect. 21.1) /wiki/Feedforward_neural_network ↩

120. Recurrent neural networks: Russell & Norvig (2021, sect. 21.6) /wiki/Recurrent_neural_network ↩

121. Perceptrons: Russell & Norvig (2021, pp. 21, 22, 683, 22) /wiki/Perceptron ↩

122. Deep learning: Russell & Norvig (2021, chpt. 21), Goodfellow, Bengio & Courville (2016), Hinton et al. (2016), Schmidhuber (2015) /wiki/Deep_learning ↩

123. Convolutional neural networks: Russell & Norvig (2021, sect. 21.3) /wiki/Convolutional_neural_networks ↩

124. Deep learning: Russell & Norvig (2021, chpt. 21), Goodfellow, Bengio & Courville (2016), Hinton et al. (2016), Schmidhuber (2015) /wiki/Deep_learning ↩

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365. Fox News (2023). - Fox News (2023). "Fox News Poll" (PDF). Fox News. Archived (PDF) from the original on 12 May 2023. Retrieved 19 June 2023. https://static.foxnews.com/foxnews.com/content/uploads/2023/05/Fox_April-21-24-2023_Complete_National_Topline_May-1-Release.pdf ↩

366. Milmo, Dan (3 November 2023). "Hope or Horror? The great AI debate dividing its pioneers". The Guardian Weekly. pp. 10–12. /wiki/The_Guardian_Weekly ↩

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371. Russell & Norvig 2021, p. 9. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

372. Copeland, J., ed. (2004). The Essential Turing: the ideas that gave birth to the computer age. Oxford, England: Clarendon Press. ISBN 0-1982-5079-7. 0-1982-5079-7 ↩

373. "Electronic brain" was the term used by the press around this time.[346][348] ↩

374. AI's immediate precursors: McCorduck (2004, pp. 51–107), Crevier (1993, pp. 27–32), Russell & Norvig (2021, pp. 8–17), Moravec (1988, p. 3) - McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1 ↩

375. Russell & Norvig (2021), p. 17. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

376. Turing's original publication of the Turing test in "Computing machinery and intelligence": Turing (1950) Historical influence and philosophical implications: Haugeland (1985, pp. 6–9), Crevier (1993, p. 24), McCorduck (2004, pp. 70–71), Russell & Norvig (2021, pp. 2, 984) /wiki/Turing_test ↩

377. Copeland, J., ed. (2004). The Essential Turing: the ideas that gave birth to the computer age. Oxford, England: Clarendon Press. ISBN 0-1982-5079-7. 0-1982-5079-7 ↩

378. Daniel Crevier wrote, "the conference is generally recognized as the official birthdate of the new science."[351] Russell and Norvig called the conference "the inception of artificial intelligence."[116] ↩

379. Dartmouth workshop: Russell & Norvig (2021, p. 18), McCorduck (2004, pp. 111–136), NRC (1999, pp. 200–201)The proposal: McCarthy et al. (1955) /wiki/Dartmouth_workshop ↩

380. Russell and Norvig wrote "for the next 20 years the field would be dominated by these people and their students."[352] /wiki/Stuart_J._Russell ↩

381. Russell and Norvig wrote, "it was astonishing whenever a computer did anything kind of smartish".[353] /wiki/Stuart_J._Russell ↩

382. 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. /wiki/Arthur_Samuel_(computer_scientist) ↩

383. 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) - McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1 ↩

384. Copeland, J., ed. (2004). The Essential Turing: the ideas that gave birth to the computer age. Oxford, England: Clarendon Press. ISBN 0-1982-5079-7. 0-1982-5079-7 ↩

385. Newquist (1994), pp. 86–86. - Newquist, H. P. (1994). The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think. New York: Macmillan/SAMS. ISBN 978-0-6723-0412-5. ↩

386. Simon (1965, p. 96) quoted in Crevier (1993, p. 109) - Simon, H. A. (1965), The Shape of Automation for Men and Management, New York: Harper & Row ↩

387. Minsky (1967, p. 2) quoted in Crevier (1993, p. 109) - Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall ↩

388. Russell and Norvig write: "in almost all cases, these early systems failed on more difficult problems"[357] /wiki/Stuart_J._Russell ↩

389. Lighthill (1973). - Lighthill, James (1973). "Artificial Intelligence: A General Survey". Artificial Intelligence: a paper symposium. Science Research Council. ↩

390. NRC 1999, pp. 212–213. - NRC (United States National Research Council) (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press. ↩

391. Russell & Norvig (2021), p. 22. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

392. 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) /wiki/AI_Winter ↩

393. Expert systems: Russell & Norvig (2021, pp. 23, 292), Luger & Stubblefield (2004, pp. 227–331), Nilsson (1998, chpt. 17.4), McCorduck (2004, pp. 327–335, 434–435), Crevier (1993, pp. 145–162, 197–203), Newquist (1994, pp. 155–183) /wiki/Expert_systems ↩

394. 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) /wiki/Fifth_Generation_Project ↩

395. 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) /wiki/AI_Winter ↩

396. Russell & Norvig (2021), p. 24. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

397. Nilsson (1998), p. 7. - Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-5586-0467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019. https://archive.org/details/artificialintell0000nils ↩

398. Embodied approaches to AI[364] were championed by Hans Moravec[365] and Rodney Brooks[366] and went by many names: Nouvelle AI.[366] Developmental robotics.[367] /wiki/Embodied_mind ↩

399. 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) - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

400. Russell & Norvig (2021), p. 25. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

401. Crevier (1993, pp. 214–215), Russell & Norvig (2021, pp. 24, 26) - Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3. ↩

402. Russell & Norvig (2021), p. 26. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

403. Formal and narrow methods adopted in the 1990s: Russell & Norvig (2021, pp. 24–26), McCorduck (2004, pp. 486–487) ↩

404. AI widely used in the late 1990s: Kurzweil (2005, p. 265), NRC (1999, pp. 216–222), Newquist (1994, pp. 189–201) - Kurzweil, Ray (2005). The Singularity is Near. Penguin Books. ISBN 978-0-6700-3384-3. ↩

405. 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) /wiki/Artificial_general_intelligence ↩

406. Deep learning revolution, AlexNet: Goldman (2022), Russell & Norvig (2021, p. 26), McKinsey (2018) /wiki/Deep_learning ↩

407. 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."[373] /wiki/The_Atlantic ↩

408. Moore's Law and AI: Russell & Norvig (2021, pp. 14, 27) /wiki/Moore%27s_Law ↩

409. Clark (2015b). - Clark, Jack (2015b). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg.com. Archived from the original on 23 November 2016. Retrieved 23 November 2016. https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence ↩

410. Big data: Russell & Norvig (2021, p. 26) /wiki/Big_data ↩

411. Clark (2015b). - Clark, Jack (2015b). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg.com. Archived from the original on 23 November 2016. Retrieved 23 November 2016. https://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence ↩

412. 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.[375] /wiki/Bloomberg_News ↩

413. UNESCO (2021). - UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 2021. ISBN 978-9-2310-0450-6. Archived from the original on 18 June 2022. Retrieved 18 September 2021. https://unesdoc.unesco.org/ark:/48223/pf0000377433/PDF/377433eng.pdf.multi ↩

414. Christian (2020), pp. 67, 73. - Christian, Brian (2020). The Alignment Problem: Machine learning and human values. W. W. Norton & Company. ISBN 978-0-3938-6833-3. OCLC 1233266753. https://search.worldcat.org/oclc/1233266753 ↩

415. Sagar, Ram (3 June 2020). "OpenAI Releases GPT-3, The Largest Model So Far". Analytics India Magazine. Archived from the original on 4 August 2020. Retrieved 15 March 2023. https://analyticsindiamag.com/open-ai-gpt-3-language-model ↩

416. Milmo, Dan (2 February 2023). "ChatGPT reaches 100 million users two months after launch". The Guardian. ISSN 0261-3077. Archived from the original on 3 February 2023. Retrieved 31 December 2024. https://www.theguardian.com/technology/2023/feb/02/chatgpt-100-million-users-open-ai-fastest-growing-app ↩

417. Gorichanaz, Tim (29 November 2023). "ChatGPT turns 1: AI chatbot's success says as much about humans as technology". The Conversation. Archived from the original on 31 December 2024. Retrieved 31 December 2024. https://theconversation.com/chatgpt-turns-1-ai-chatbots-success-says-as-much-about-humans-as-technology-218704 ↩

418. DiFeliciantonio (2023). - DiFeliciantonio, Chase (3 April 2023). "AI has already changed the world. This report shows how". San Francisco Chronicle. Archived from the original on 19 June 2023. Retrieved 19 June 2023. https://www.sfchronicle.com/tech/article/ai-artificial-intelligence-report-stanford-17869558.php ↩

419. Goswami (2023). - Goswami, Rohan (5 April 2023). "Here's where the A.I. jobs are". CNBC. Archived from the original on 19 June 2023. Retrieved 19 June 2023. https://www.cnbc.com/2023/04/05/ai-jobs-see-the-state-by-state-data-from-a-stanford-study.html ↩

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421. Grayling, Anthony; Ball, Brian (1 August 2024). "Philosophy is crucial in the age of AI". The Conversation. Archived from the original on 5 October 2024. Retrieved 4 October 2024. https://theconversation.com/philosophy-is-crucial-in-the-age-of-ai-235907 ↩

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423. McCarthy, John. "The Philosophy of AI and the AI of Philosophy". jmc.stanford.edu. Archived from the original on 23 October 2018. Retrieved 3 October 2024. https://web.archive.org/web/20181023181725/http://jmc.stanford.edu/articles/aiphil2.html ↩

424. Jarow, Oshan (15 June 2024). "Will AI ever become conscious? It depends on how you think about biology". Vox. Archived from the original on 21 September 2024. Retrieved 4 October 2024. https://www.vox.com/future-perfect/351893/consciousness-ai-machines-neuroscience-mind ↩

425. Turing (1950), p. 1. - Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236): 433–460. doi:10.1093/mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783. https://academic.oup.com/mind/article/LIX/236/433/986238 ↩

426. Turing (1950), p. 1. - Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236): 433–460. doi:10.1093/mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783. https://academic.oup.com/mind/article/LIX/236/433/986238 ↩

427. Turing's original publication of the Turing test in "Computing machinery and intelligence": Turing (1950) Historical influence and philosophical implications: Haugeland (1985, pp. 6–9), Crevier (1993, p. 24), McCorduck (2004, pp. 70–71), Russell & Norvig (2021, pp. 2, 984) /wiki/Turing_test ↩

428. Turing (1950), Under "The Argument from Consciousness". - Turing, Alan (October 1950). "Computing Machinery and Intelligence". Mind. 59 (236): 433–460. doi:10.1093/mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783. https://academic.oup.com/mind/article/LIX/236/433/986238 ↩

429. Russell & Norvig (2021), pp. 1–4. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

430. Russell & Norvig (2021), p. 3. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

431. Maker (2006). - Maker, Meg Houston (2006), AI@50: AI Past, Present, Future, Dartmouth College, archived from the original on 8 October 2008, retrieved 16 October 2008 https://web.archive.org/web/20081008120238/http://www.engagingexperience.com/2006/07/ai50_ai_past_pr.html ↩

432. McCarthy (1999). - McCarthy, John (1999), What is AI?, archived from the original on 4 December 2022, retrieved 4 December 2022 http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html ↩

433. Minsky (1986). - Minsky, Marvin (1986), The Society of Mind, Simon and Schuster ↩

434. Russell & Norvig (2021), pp. 1–4. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

435. "What Is Artificial Intelligence (AI)?". Google Cloud Platform. Archived from the original on 31 July 2023. Retrieved 16 October 2023. https://cloud.google.com/learn/what-is-artificial-intelligence ↩

436. "One of the Biggest Problems in Regulating AI Is Agreeing on a Definition". Carnegie Endowment for International Peace. Retrieved 31 July 2024. https://carnegieendowment.org/posts/2022/10/one-of-the-biggest-problems-in-regulating-ai-is-agreeing-on-a-definition?lang=en ↩

437. "AI or BS? How to tell if a marketing tool really uses artificial intelligence". The Drum. Retrieved 31 July 2024. https://www.thedrum.com/opinion/2023/03/30/ai-or-bs-how-tell-if-marketing-tool-really-uses-artificial-intelligence ↩

438. Nils Nilsson wrote in 1983: "Simply put, there is wide disagreement in the field about what AI is all about."[396] /wiki/Nils_Nilsson_(researcher) ↩

439. Haugeland (1985), pp. 112–117. - Haugeland, John (1985). Artificial Intelligence: The Very Idea. Cambridge, Mass.: MIT Press. ISBN 978-0-2620-8153-5. ↩

440. Physical symbol system hypothesis: Newell & Simon (1976, p. 116) Historical significance: McCorduck (2004, p. 153), Russell & Norvig (2021, p. 19) - Newell, Allen; Simon, H. A. (1976). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3): 113–126. doi:10.1145/360018.360022. https://doi.org/10.1145%2F360018.360022 ↩

441. Moravec's paradox: Moravec (1988, pp. 15–16), Minsky (1986, p. 29), Pinker (2007, pp. 190–191) /wiki/Moravec%27s_paradox ↩

442. Dreyfus' critique of AI: Dreyfus (1972), Dreyfus & Dreyfus (1986) Historical significance and philosophical implications: Crevier (1993, pp. 120–132), McCorduck (2004, pp. 211–239), Russell & Norvig (2021, pp. 981–982), Fearn (2007, chpt. 3) /wiki/Dreyfus%27_critique_of_AI ↩

443. 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."[401] ↩

444. Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: Kahneman (2011), Dreyfus & Dreyfus (1986), Wason & Shapiro (1966), Kahneman, Slovic & Tversky (1982) - Kahneman, Daniel (2011). Thinking, Fast and Slow. Macmillan. ISBN 978-1-4299-6935-2. Archived from the original on 15 March 2023. Retrieved 8 April 2012. https://books.google.com/books?id=ZuKTvERuPG8C ↩

445. Langley (2011). - Langley, Pat (2011). "The changing science of machine learning". Machine Learning. 82 (3): 275–279. doi:10.1007/s10994-011-5242-y. https://doi.org/10.1007%2Fs10994-011-5242-y ↩

446. Katz (2012). - Katz, Yarden (1 November 2012). "Noam Chomsky on Where Artificial Intelligence Went Wrong". The Atlantic. Archived from the original on 28 February 2019. Retrieved 26 October 2014. https://www.theatlantic.com/technology/archive/2012/11/noam-chomsky-on-where-artificial-intelligence-went-wrong/261637/?single_page=true ↩

447. Neats vs. scruffies, the historic debate: McCorduck (2004, pp. 421–424, 486–489), Crevier (1993, p. 168), Nilsson (1983, pp. 10–11), Russell & Norvig (2021, p. 24) A classic example of the "scruffy" approach to intelligence: Minsky (1986) A modern example of neat AI and its aspirations in the 21st century: Domingos (2015) /wiki/Neats_vs._scruffies ↩

448. Intractability and efficiency and the combinatorial explosion: Russell & Norvig (2021, p. 21) /wiki/Intractably ↩

449. Pennachin & Goertzel (2007). - Pennachin, C.; Goertzel, B. (2007). "Contemporary Approaches to Artificial General Intelligence". Artificial General Intelligence. Cognitive Technologies. Berlin, Heidelberg: Springer. pp. 1–30. doi:10.1007/978-3-540-68677-4_1. ISBN 978-3-5402-3733-4. https://doi.org/10.1007%2F978-3-540-68677-4_1 ↩

450. Roberts (2016). - Roberts, Jacob (2016). "Thinking Machines: The Search for Artificial Intelligence". Distillations. Vol. 2, no. 2. pp. 14–23. Archived from the original on 19 August 2018. Retrieved 20 March 2018. https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence ↩

451. Russell & Norvig (2021), p. 986. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

452. Chalmers (1995). - Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. CiteSeerX 10.1.1.103.8362. Archived from the original on 8 March 2005. Retrieved 11 October 2018. http://www.imprint.co.uk/chalmers.html ↩

453. Dennett (1991). - Dennett, Daniel (1991). Consciousness Explained. The Penguin Press. ISBN 978-0-7139-9037-9. ↩

454. Horst (2005). - Horst, Steven (2005). "The Computational Theory of Mind". The Stanford Encyclopedia of Philosophy. Archived from the original on 6 March 2016. Retrieved 7 March 2016. http://plato.stanford.edu/entries/computational-mind ↩

455. Searle presented this definition of "Strong AI" in 1999.[411] 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."[412] 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)."[413] ↩

456. Searle's Chinese room argument: Searle (1980). Searle's original presentation of the thought experiment., Searle (1999). Discussion: Russell & Norvig (2021, pp. 985), McCorduck (2004, pp. 443–445), Crevier (1993, pp. 269–271) /wiki/Chinese_room ↩

457. Leith, Sam (7 July 2022). "Nick Bostrom: How can we be certain a machine isn't conscious?". The Spectator. Archived from the original on 26 September 2024. Retrieved 23 February 2024. https://www.spectator.co.uk/article/nick-bostrom-how-can-we-be-certain-a-machine-isnt-conscious ↩

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460. Thomson, Jonny (31 October 2022). "Why don't robots have rights?". Big Think. Archived from the original on 13 September 2024. Retrieved 23 February 2024. https://bigthink.com/thinking/why-dont-robots-have-rights ↩

461. Wong, Jeff (10 July 2023). "What leaders need to know about robot rights". Fast Company. https://www.fastcompany.com/90920769/what-leaders-need-to-know-about-robot-rights ↩

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464. Cuddy, Alice (13 April 2018). "Robot rights violate human rights, experts warn EU". euronews. Archived from the original on 19 September 2024. Retrieved 23 February 2024. https://www.euronews.com/2018/04/13/robot-rights-violate-human-rights-experts-warn-eu ↩

465. Kateman, Brian (24 July 2023). "AI Should Be Terrified of Humans". Time. Archived from the original on 25 September 2024. Retrieved 23 February 2024. https://time.com/6296234/ai-should-be-terrified-of-humans ↩

466. Thomson, Jonny (31 October 2022). "Why don't robots have rights?". Big Think. Archived from the original on 13 September 2024. Retrieved 23 February 2024. https://bigthink.com/thinking/why-dont-robots-have-rights ↩

467. Roberts (2016). - Roberts, Jacob (2016). "Thinking Machines: The Search for Artificial Intelligence". Distillations. Vol. 2, no. 2. pp. 14–23. Archived from the original on 19 August 2018. Retrieved 20 March 2018. https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence ↩

468. The Intelligence explosion and technological singularity: Russell & Norvig (2021, pp. 1004–1005), Omohundro (2008), Kurzweil (2005) I. J. Good's "intelligence explosion": Good (1965) Vernor Vinge's "singularity": Vinge (1993) /wiki/Intelligence_explosion ↩

469. Russell & Norvig (2021), p. 1005. - Russell, Stuart J.; Norvig, Peter (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0-1346-1099-3. LCCN 20190474. https://lccn.loc.gov/20190474 ↩

470. Transhumanism: Moravec (1988), Kurzweil (2005), Russell & Norvig (2021, p. 1005) /wiki/Transhumanism ↩

471. AI as evolution: Edward Fredkin is quoted in McCorduck (2004, p. 401), Butler (1863), Dyson (1998) /wiki/Edward_Fredkin ↩

472. AI in myth: McCorduck (2004, pp. 4–5) - McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1 ↩

473. McCorduck (2004), pp. 340–400. - McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1 ↩

474. Buttazzo (2001). - Buttazzo, G. (July 2001). "Artificial consciousness: Utopia or real possibility?". Computer. 34 (7): 24–30. doi:10.1109/2.933500. https://doi.org/10.1109%2F2.933500 ↩

475. Anderson (2008). - Anderson, Susan Leigh (2008). "Asimov's "three laws of robotics" and machine metaethics". AI & Society. 22 (4): 477–493. doi:10.1007/s00146-007-0094-5. S2CID 1809459. https://doi.org/10.1007%2Fs00146-007-0094-5 ↩

476. McCauley (2007). - McCauley, Lee (2007). "AI armageddon and the three laws of robotics". Ethics and Information Technology. 9 (2): 153–164. CiteSeerX 10.1.1.85.8904. doi:10.1007/s10676-007-9138-2. S2CID 37272949. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.8904 ↩

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