Cognitive System on:
[Wikipedia]
[Google]
[Amazon]
Artificial intelligence (AI), in its broadest sense, is
intelligence
Intelligence has been defined in many ways: the capacity for abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. It can be described as the a ...
exhibited by machines, particularly computer systems
A computer is a machine that can be programmed to carry out sequences of arithmetic or logical operations (computation) automatically. Modern digital electronic computers can perform generic sets of operations known as programs. These program ...
. It is a field of research in computer science
Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to practical disciplines (includin ...
that develops and studies methods and software
Software is a set of computer programs and associated software documentation, documentation and data (computing), data. This is in contrast to Computer hardware, hardware, from which the system is built and which actually performs the work.
...
that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.
High-profile applications of AI include advanced web search engine
A search engine is a software system designed to carry out web searches. They search the World Wide Web in a systematic way for particular information specified in a textual web search query. The search results are generally presented in a ...
s (e.g., Google Search
Google Search (also known simply as Google) is a search engine provided by Google. Handling more than 3.5 billion searches per day, it has a 92% share of the global search engine market. It is also the most-visited website in the world.
The ...
); recommendation systems
A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular u ...
(used by YouTube
YouTube is a global online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most ...
, Amazon
Amazon most often refers to:
* Amazons, a tribe of female warriors in Greek mythology
* Amazon rainforest, a rainforest covering most of the Amazon basin
* Amazon River, in South America
* Amazon (company), an American multinational technolog ...
, and Netflix
Netflix, Inc. is an American subscription video on-demand over-the-top streaming service and production company based in Los Gatos, California. Founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California, it offers a ...
); virtual assistants
Virtual may refer to:
* Virtual (horse), a thoroughbred racehorse
* Virtual channel, a channel designation which differs from that of the actual radio channel (or range of frequencies) on which the signal travels
* Virtual function, a programming ...
(e.g., Google Assistant
Google Assistant is a virtual assistant software application developed by Google that is primarily available on mobile and home automation devices. Based on artificial intelligence, Google Assistant can engage in two-way conversations, unlike t ...
, Siri
Siri ( ) is a virtual assistant that is part of Apple Inc.'s iOS, iPadOS, watchOS, macOS, tvOS, and audioOS operating systems. It uses voice queries, gesture based control, focus-tracking and a natural-language user interface to answer ques ...
, and Alexa
Alexa may refer to: Technology
*Amazon Alexa, a virtual assistant developed by Amazon
* Alexa Internet, a defunct website ranking and traffic analysis service
* Arri Alexa, a digital motion picture camera
People
*Alexa (name), a given name and ...
); autonomous vehicles
Vehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent systems to assist the operator of a vehicle (car, aircraft, watercraft, or otherwise).Hu, J.; Bhowmick, P.; Lanzon, A.,Group Coordinated Control ...
(e.g., Waymo
Waymo LLC, formerly known as the Google self-driving car project, is an American autonomous driving technology company headquartered in Mountain View, California. It is a subsidiary of Alphabet Inc, the parent company of Google.
Waymo oper ...
); generative
Generative may refer to:
* Generative actor, a person who instigates social change
* Generative art, art that has been created using an autonomous system that is frequently, but not necessarily, implemented using a computer
* Generative music, ...
and creative
Creative may refer to:
*Creativity, phenomenon whereby something new and valuable is created
* "Creative" (song), a 2008 song by Leon Jackson
* Creative class, a proposed socioeconomic class
* Creative destruction, an economic term
* Creative dir ...
tools (e.g., ChatGPT
ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and is fine-tuned (an approach to transfer learning) with both supervised and ...
and AI art
Artificial intelligence art is any artwork created through the use of artificial intelligence.
Tools and processes Imagery
There are many mechanisms for creating AI art, including procedural 'rule-based' generation of images using mathemat ...
); and superhuman
The term superhuman refers to humans or human-like beings with enhanced qualities and abilities that exceed those naturally found in humans. These qualities may be acquired through natural ability, self-actualization or technological aids. Th ...
play and analysis in strategy game
A strategy game or strategic game is a game (e.g. a board game) in which the players' uncoerced, and often autonomous, decision-making skills have a high significance in determining the outcome. Almost all strategy games require internal decisio ...
s (e.g., chess
Chess is a board game for two players, called White and Black, each controlling an army of chess pieces in their color, with the objective to checkmate the opponent's king. It is sometimes called international chess or Western chess to dist ...
and Go). However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."
Various subfields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning
Reason is the capacity of consciously applying logic by drawing conclusions from new or existing information, with the aim of seeking the truth. It is closely associated with such characteristically human activities as philosophy, science, lang ...
, knowledge representation
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medic ...
, planning
Planning is the process of thinking regarding the activities required to achieve a desired goal. Planning is based on foresight, the fundamental capacity for mental time travel. The evolution of forethought, the capacity to think ahead, is c ...
, learning, natural language processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to proc ...
, perception, and support for robotics
Robotics is an interdisciplinarity, interdisciplinary branch of computer science and engineering. Robotics involves design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist human ...
. General intelligence
The ''g'' factor (also known as general intelligence, general mental ability or general intelligence factor) is a construct developed in psychometric investigations of cognitive abilities and human intelligence. It is a variable that summarizes ...
—the ability to complete any task performed by a human on an at least equal level—is among the field's long-term goals. To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search
Searching or search may refer to:
Computing technology
* Search algorithm, including keyword search
** :Search algorithms
* Search and optimization for problem solving in artificial intelligence
* Search engine technology, software for findi ...
and mathematical optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
, formal logic
Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of logical truths. It is a formal science investigating how conclusions follow from premise ...
, artificial neural network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
An ANN is based on a collection of connected units ...
s, and methods based on statistics, operations research
Operations research ( en-GB, operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialism OR, is a discipline that deals with the development and application of analytical methods to improve dec ...
, and economics
Economics () is the social science that studies the production, distribution, and consumption of goods and services.
Economics focuses on the behaviour and interactions of economic agents and how economies work. Microeconomics analy ...
. AI also draws upon psychology
Psychology is the scientific study of mind and behavior. Psychology includes the study of conscious and unconscious phenomena, including feelings and thoughts. It is an academic discipline of immense scope, crossing the boundaries betwe ...
, linguistics
Linguistics is the scientific study of human language. It is called a scientific study because it entails a comprehensive, systematic, objective, and precise analysis of all aspects of language, particularly its nature and structure. Lingu ...
, philosophy, neuroscience
Neuroscience is the science, scientific study of the nervous system (the brain, spinal cord, and peripheral nervous system), its functions and disorders. It is a Multidisciplinary approach, multidisciplinary science that combines physiology, an ...
, and other fields.
Artificial intelligence was founded as an academic discipline in 1956, and the field went through multiple cycles of optimism, followed by periods of disappointment and loss of funding, known as AI winter
In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. Funding and interest vastly increased after 2012 when deep learning outperformed previous AI techniques. This growth accelerated further after 2017 with the transformer architecture, and by the early 2020s hundreds of billions of dollars were being invested in AI (known as the "
There are several kinds of machine learning.
Proposal for the modern version:
Warnings of overspecialization in AI from leading researchers: , ,
Local search uses
Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from 
An artificial neural network is based on a collection of nodes also known as
Deep learning Deep learning: , , , uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in
In the early 2020s, generative AI gained widespread prominence. GenAI is AI capable of generating text, images, videos, or other data using generative models, often in response to Prompt (natural language), prompts.
In March 2023, 58% of U.S. adults had heard about
51(3) Industrial Law Journal 511–559
. In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are Redistribution of income and wealth, redistributed. Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk". The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies. In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; ''The Economist'' stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement.
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone. Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years. In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics. In 2024, the Council of Europe created the first international legally binding treaty on AI, called the "Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law". It was adopted by the European Union, the United States, the United Kingdom, and other signatories.
In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks". A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".
In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks. 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence. In May 2024 at the AI Seoul Summit, 16 global AI tech companies agreed to safety commitments on the development of AI.
The proposal: The attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving Theorem, logical theorems and speaking English.Successful programs of the 1960s: , , , Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s. Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with artificial general intelligence, general intelligence and considered this the goal of their field. In 1965 Herbert A. Simon, Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". In 1967 Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the Lighthill report, criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to Mansfield Amendment, fund more productive projects. Marvin Minsky, Minsky's and Papert's book ''
AI boom
The AI boom, or AI spring, is the ongoing period of rapid progress in the field of artificial intelligence. Prominent examples include protein folding prediction and generative AI, led by laboratories including Google DeepMind and OpenAI.
...
"). The widespread use of AI in the 21st century exposed several unintended consequences and harms in the present and raised concerns about its risks and long-term effects in the future, prompting discussions about regulatory policies to ensure the safety and benefits of the technology.
Goals
The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.Reasoning and problem-solving
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts fromprobability
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and ...
and economics
Economics () is the social science that studies the production, distribution, and consumption of goods and services.
Economics focuses on the behaviour and interactions of economic agents and how economies work. Microeconomics analy ...
.
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. Intractability and efficiency and the combinatorial explosion
In mathematics, a combinatorial explosion is the rapid growth of the complexity of a problem due to how the combinatorics of the problem is affected by the input, constraints, and bounds of the problem. Combinatorial explosion is sometimes used to ...
: 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.Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: , , , Accurate and efficient reasoning is an unsolved problem.
Knowledge representation
Knowledge representation
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medic ...
and knowledge engineering
Knowledge engineering (KE) refers to all technical, scientific and social aspects involved in building, maintaining and using knowledge-based systems.
Background Expert systems
One of the first examples of an expert system was MYCIN, an appl ...
allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large database
In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases spa ...
s), and other areas.
A knowledge base
A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. ...
is a body of knowledge represented in a form that can be used by a program. An ontology
In metaphysics, ontology is the philosophical study of being, as well as related concepts such as existence, becoming, and reality.
Ontology addresses questions like how entities are grouped into categories and which of these entities ...
is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as objects, properties, categories, and relations between objects; situations, events, states, and time; causes and effects; knowledge about knowledge (what we know about what other people know); default reasoning Default logic is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions.
Default logic can express facts like “by default, something is true”; by contrast, standard logic can only express that somethin ...
(things that humans assume are true until they are told differently and will remain true even when other facts are changing);Default reasoning Default logic is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions.
Default logic can express facts like “by default, something is true”; by contrast, standard logic can only express that somethin ...
, Frame problem
In artificial intelligence, the frame problem describes an issue with using first-order logic (FOL) to express facts about a robot in the world. Representing the state of a robot with traditional FOL requires the use of many axioms that simply impl ...
, default logic, non-monotonic logic
A non-monotonic logic is a formal logic whose conclusion relation is not monotonic. In other words, non-monotonic logics are devised to capture and represent defeasible inferences (cf. defeasible reasoning), i.e., a kind of inference in which re ...
s, circumscription
Circumscription may refer to:
*Circumscribed circle
* Circumscription (logic)
*Circumscription (taxonomy)
*Circumscription theory
The circumscription theory is a theory of the role of warfare in state formation in political anthropology, created ...
, closed world assumption The closed-world assumption (CWA), in a formal system of logic used for knowledge representation, is the presumption that a statement that is true is also known to be true. Therefore, conversely, what is not currently known to be true, is false. Th ...
, abduction
Abduction may refer to:
Media
Film and television
* "Abduction" (''The Outer Limits''), a 2001 television episode
* " Abduction" (''Death Note'') a Japanese animation television series
* " Abductions" (''Totally Spies!''), a 2002 episode of an ...
: , , ,
(Poole ''et al.'' places abduction under "default reasoning". Luger ''et al.'' places this under "uncertain reasoning"). 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);Breadth of commonsense knowledge: , , , (qualification problem
In philosophy
Philosophy (from , ) is the systematized study of general and fundamental questions, such as those about existence, reason, Epistemology, knowledge, Ethics, values, Philosophy of mind, mind, and Philosophy of language, langu ...
) and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as "facts" or "statements" that they could express verbally). There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications.
Planning and decision-making
An "agent" is anything that perceives and takes actions in the world. Arational agent
A rational agent or rational being is a person or entity that always aims to perform optimal actions based on given premises and information. A rational agent can be anything that makes decisions, typically a person, firm, machine, or software.
...
has goals or preferences and takes actions to make them happen. In automated planning
Automation describes a wide range of technologies that reduce human intervention in processes, namely by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines ...
, the agent has a specific goal. In automated decision-making
Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with var ...
, 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
As a topic of economics, utility is used to model worth or value. Its usage has evolved significantly over time. The term was introduced initially as a measure of pleasure or happiness as part of the theory of utilitarianism by moral philosoph ...
") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility The expected utility hypothesis is a popular concept in economics that serves as a reference guide for decisions when the payoff is uncertain. The theory recommends which option rational individuals should choose in a complex situation, based on the ...
": the utility
As a topic of economics, utility is used to model worth or value. Its usage has evolved significantly over time. The term was introduced initially as a measure of pleasure or happiness as part of the theory of utilitarianism by moral philosoph ...
of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.
In classical planning, the agent knows exactly what the effect of any action will be. In most real-world problems, however, the agent may not be certain about the situation they are in (it is "unknown" or "unobservable") and it may not know for certain what will happen after each possible action (it is not "deterministic"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.
In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...
), or the agent can seek information to improve its preferences. Information value theory Value of information (VOI or VoI) is the amount a decision maker would be willing to pay for information prior to making a decision.
Similar terms
VoI is sometimes distinguished into value of perfect information, also called value of clairvoyance ( ...
can be used to weigh the value of exploratory or experimental actions. The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain of what the outcome will be.
A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way and a reward function
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...
that supplies the utility of each state and the cost of each action. A policy
Policy is a deliberate system of guidelines to guide decisions and achieve rational outcomes. A policy is a statement of intent and is implemented as a procedure or protocol. Policies are generally adopted by a governance body within an orga ...
associates a decision with each possible state. The policy could be calculated (e.g., by iteration
Iteration is the repetition of a process in order to generate a (possibly unbounded) sequence of outcomes. Each repetition of the process is a single iteration, and the outcome of each iteration is then the starting point of the next iteration. ...
), be heuristic
A heuristic (; ), or heuristic technique, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediat ...
, or it can be learned.
Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that make decisions that involve other agents.
Learning
Machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning.
There are several kinds of machine learning. Unsupervised learning
Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and t ...
analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning
Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning alg ...
requires labeling the training data with the expected answers, and comes in two main varieties: classification Classification is a process related to categorization, the process in which ideas and objects are recognized, differentiated and understood.
Classification is the grouping of related facts into classes.
It may also refer to:
Business, organizat ...
(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).Supervised learning
Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning alg ...
: (Definition), (Techniques)
In reinforcement learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...
, the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good". Transfer learning
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize ...
is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning.
Computational learning theory
In computer science, computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms.
Overview
Theoretical results in machine learning m ...
can assess learners by computational complexity
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) ...
, by sample complexity
The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function.
More precisely, the sample complexity is the number of training-samples that we need to ...
(how much data is required), or by other notions of optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
.
Natural language processing
Natural language processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to proc ...
(NLP) allows programs to read, write and communicate in human languages such as English
English usually refers to:
* English language
* English people
English may also refer to:
Peoples, culture, and language
* ''English'', an adjective for something of, from, or related to England
** English national id ...
. Specific problems include speech recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the ma ...
, speech synthesis
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal languag ...
, machine translation
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation), is a sub-field of computational linguistics that investigates t ...
, information extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concer ...
, information retrieval and question answering
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural l ...
.
Early work, based on Noam Chomsky
Avram Noam Chomsky (born December 7, 1928) is an American public intellectual: a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. Sometimes called "the father of modern linguistics", Chomsky is ...
's generative grammar
Generative grammar, or generativism , is a linguistic theory that regards linguistics as the study of a hypothesised innate grammatical structure. It is a biological or biologistic modification of earlier structuralist theories of linguistic ...
and semantic network
A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices ...
s, had difficulty with word-sense disambiguation
Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to cons ...
unless restricted to small domains called " micro-worlds" (due to the common sense knowledge problem). Margaret Masterman believed that it was meaning and not grammar that was the key to understanding languages, and that thesauri
A thesaurus (plural ''thesauri'' or ''thesauruses'') or synonym dictionary is a reference work for finding synonyms and sometimes antonyms of words. They are often used by writers to help find the best word to express an idea:
Synonym dictionar ...
and not dictionaries should be the basis of computational language structure.
Modern deep learning techniques for NLP include word embedding
In natural language processing (NLP), word embedding is a term used for the representation of words for text analysis, typically in the form of a real-valued vector that encodes the meaning of the word such that the words that are closer in the v ...
(representing words, typically as vectors encoding their meaning), transformer
A transformer is a passive component that transfers electrical energy from one electrical circuit to another circuit, or multiple circuits. A varying current in any coil of the transformer produces a varying magnetic flux in the transformer' ...
s (a deep learning architecture using an attention
Attention is the behavioral and cognitive process of selectively concentrating on a discrete aspect of information, whether considered subjective or objective, while ignoring other perceivable information. William James (1890) wrote that "Att ...
mechanism), and others. In 2019, generative pre-trained transformer (or "GPT") language models began to generate coherent text, and by 2023, these models were able to get human-level scores on the bar exam
A bar examination is an examination administered by the bar association of a jurisdiction that a lawyer must pass in order to be admitted to the bar of that jurisdiction.
Australia
Administering bar exams is the responsibility of the bar associa ...
, SAT
The SAT ( ) is a standardized test widely used for college admissions in the United States. Since its debut in 1926, its name and scoring have changed several times; originally called the Scholastic Aptitude Test, it was later called the Schol ...
test, GRE test, and many other real-world applications.
Perception
Machine perception
Machine perception is the capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them. The basic method that the computers take in and respond to their environ ...
is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar
Lidar (, also LIDAR, or LiDAR; sometimes LADAR) is a method for determining ranges (variable distance) by targeting an object or a surface with a laser and measuring the time for the reflected light to return to the receiver. It can also be ...
, sonar, radar, and tactile sensor
A tactile sensor is a device that measures information arising from physical interaction with its environment. Tactile sensors are generally modeled after the biological sense of cutaneous touch which is capable of detecting stimuli resultin ...
s) to deduce aspects of the world. Computer vision
Computer vision is an Interdisciplinarity, interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate t ...
is the ability to analyze visual input.
The field includes speech recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the ma ...
, image classification
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human ...
, facial recognition Facial recognition or face recognition may refer to:
* Face detection, often a step done before facial recognition
* Face perception, the process by which the human brain understands and interprets the face
* Pareidolia, which involves, in part, se ...
, object recognition
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the ...
, object tracking, and robotic perception.
Social intelligence
Affective computing
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While so ...
is an interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood. For example, some virtual assistant
An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term " chatbot" is sometimes used to refer to virtua ...
s 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
Human–computer interaction (HCI) is research in the design and the use of computer technology, which focuses on the interfaces between people ( users) and computers. HCI researchers observe the ways humans interact with computers and design ...
.
However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjec ...
and, more recently, multimodal sentiment analysis Multimodal sentiment analysis is a new dimension of the traditional text-based sentiment analysis, which goes beyond the analysis of texts, and includes other modalities such as audio and visual data. It can be bimodal, which includes different com ...
, wherein AI classifies the effects displayed by a videotaped subject.
General intelligence
A machine withartificial general intelligence
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can.
It is a primary goal of some artificial intelligence research and a common topic in science fict ...
should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence
Human intelligence is the intellectual capability of humans, which is marked by complex cognitive feats and high levels of motivation and self-awareness. High intelligence is associated with better outcomes in life.
Through intelligence, humans ...
.
Artificial general intelligence
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can.
It is a primary goal of some artificial intelligence research and a common topic in science fict ...
: Proposal for the modern version:
Warnings of overspecialization in AI from leading researchers: , ,
Techniques
AI research uses a wide variety of techniques to accomplish the goals above.Search and optimization
AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search.State space search
State space search searches through a tree of possible states to try to find a goal state. For example,planning
Planning is the process of thinking regarding the activities required to achieve a desired goal. Planning is based on foresight, the fundamental capacity for mental time travel. The evolution of forethought, the capacity to think ahead, is c ...
algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.
Simple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. "Heuristics
A heuristic (; ), or heuristic technique, is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, ...
" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.
Adversarial search
In computer science, a search algorithm is an algorithm designed to solve a search problem. Search algorithms work to retrieve information stored within particular data structure, or calculated in the search space of a problem domain, with eith ...
is used for game-playing programs, such as chess or Go. It searches through a tree
In botany, a tree is a perennial plant with an elongated stem, or trunk, usually supporting branches and leaves. In some usages, the definition of a tree may be narrower, including only woody plants with secondary growth, plants that are ...
of possible moves and countermoves, looking for a winning position.
Local search
Local search uses mathematical optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
to find a solution to a problem. It begins with some form of guess and refines it incrementally.
Gradient descent
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of ...
is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "co ...
. Variants of gradient descent
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of ...
are commonly used to train neural networks.
Another type of local search is evolutionary computation
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, ...
, which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation.
Distributed search processes can coordinate via swarm intelligence
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, ...
algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi ...
(inspired by ant trail
Ants are eusocial insects of the family Formicidae and, along with the related wasps and bees, belong to the order Hymenoptera. Ants evolved from vespoid wasp ancestors in the Cretaceous period. More than 13,800 of an estimated total of 22 ...
s).
Logic
Formallogic
Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of logical truths. It is a formal science investigating how conclusions follow from premis ...
is used for reasoning
Reason is the capacity of consciously applying logic by drawing conclusions from new or existing information, with the aim of seeking the truth. It is closely associated with such characteristically human activities as philosophy, science, lang ...
and knowledge representation
Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medic ...
.
Formal logic comes in two main forms: propositional logic
Propositional calculus is a branch of logic. It is also called propositional logic, statement logic, sentential calculus, sentential logic, or sometimes zeroth-order logic. It deals with propositions (which can be true or false) and relations ...
(which operates on statements that are true or false and uses logical connective
In logic, a logical connective (also called a logical operator, sentential connective, or sentential operator) is a logical constant. They can be used to connect logical formulas. For instance in the syntax of propositional logic, the binary ...
s such as "and", "or", "not" and "implies") and predicate logic
First-order logic—also known as predicate logic, quantificational logic, and first-order predicate calculus—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quanti ...
(which also operates on objects, predicates and relations and uses quantifiers such as "''Every'' ''X'' is a ''Y''" and "There are ''some'' ''X''s that are ''Y''s").
Deductive reasoning
Deductive reasoning is the mental process of drawing deductive inferences. An inference is deductively valid if its conclusion follows logically from its premises, i.e. if it is impossible for the premises to be true and the conclusion to be false ...
in logic is the process of proving a new statement ( conclusion) from other statements that are given and assumed to be true (the premise
A premise or premiss is a true or false statement that helps form the body of an argument, which logically leads to a true or false conclusion. A premise makes a declarative statement about its subject matter which enables a reader to either agre ...
s). Proofs can be structured as proof trees
In botany, a tree is a perennial plant with an elongated stem, or trunk, usually supporting branches and leaves. In some usages, the definition of a tree may be narrower, including only woody plants with secondary growth, plants that are ...
, in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rule
In the philosophy of logic, a rule of inference, inference rule or transformation rule is a logical form consisting of a function which takes premises, analyzes their syntax, and returns a conclusion (or conclusions). For example, the rule of ...
s.
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
In computer science, a tree is a widely used abstract data type that represents a hierarchical tree structure with a set of connected nodes. Each node in the tree can be connected to many children (depending on the type of tree), but must be c ...
are labelled by premises or axiom
An axiom, postulate, or assumption is a statement that is taken to be true, to serve as a premise or starting point for further reasoning and arguments. The word comes from the Ancient Greek word (), meaning 'that which is thought worthy o ...
s. In the case of Horn clause In mathematical logic and logic programming, a Horn clause is a logical formula of a particular rule-like form which gives it useful properties for use in logic programming, formal specification, and model theory. Horn clauses are named for the logi ...
s, problem-solving search can be performed by reasoning forwards from the premises or backwards from the problem. In the more general case of the clausal form of first-order logic
First-order logic—also known as predicate logic, quantificational logic, and first-order predicate calculus—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quanti ...
, resolution
Resolution(s) may refer to:
Common meanings
* Resolution (debate), the statement which is debated in policy debate
* Resolution (law), a written motion adopted by a deliberative body
* New Year's resolution, a commitment that an individual ma ...
is a single, axiom-free rule of inference, in which a problem is solved by proving a contradiction from premises that include the negation of the problem to be solved.
Inference in both Horn clause logic and first-order logic is undecidable, and therefore intractable
Intractable may refer to:
* Intractable conflict, a form of complex, severe, and enduring conflict
* Intractable pain, pain which cannot be controlled/cured by any known treatment
* Intractable problem
In theoretical computer science and mathema ...
. However, backward reasoning with Horn clauses, which underpins computation in the logic programming
Logic programming is a programming paradigm which is largely based on formal logic
Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of log ...
language Prolog
Prolog is a logic programming language associated with artificial intelligence and computational linguistics.
Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily a ...
, is Turing complete
Alan Mathison Turing (; 23 June 1912 – 7 June 1954) was an English mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist. Turing was highly influential in the development of theoretical co ...
. Moreover, its efficiency is competitive with computation in other symbolic programming languages.
Fuzzy logic
Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and complet ...
assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true.
Non-monotonic logic
A non-monotonic logic is a formal logic whose conclusion relation is not monotonic. In other words, non-monotonic logics are devised to capture and represent defeasible inferences (cf. defeasible reasoning), i.e., a kind of inference in which re ...
s, including logic programming with negation as failure Negation as failure (NAF, for short) is a non-monotonic inference rule in logic programming, used to derive \mathrm~p (i.e. that ~p is assumed not to hold) from failure to derive ~p. Note that \mathrm ~p can be different from the statement \neg p ...
, are designed to handle default reasoning Default logic is a non-monotonic logic proposed by Raymond Reiter to formalize reasoning with default assumptions.
Default logic can express facts like “by default, something is true”; by contrast, standard logic can only express that somethin ...
. Other specialized versions of logic have been developed to describe many complex domains.
Probabilistic methods for uncertain reasoning
Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and ...
theory and economics.Stochastic methods for uncertain reasoning: , , , Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory
Decision theory (or the theory of choice; not to be confused with choice theory) is a branch of applied probability theory concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical ...
, decision analysis Decision analysis (DA) is the discipline comprising the philosophy, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, ...
, and information value theory Value of information (VOI or VoI) is the amount a decision maker would be willing to pay for information prior to making a decision.
Similar terms
VoI is sometimes distinguished into value of perfect information, also called value of clairvoyance ( ...
. These tools include models such as Markov decision processes, dynamic decision network
Decision may refer to:
Law and politics
* Judgment (law), as the outcome of a legal case
*Landmark decision, the outcome of a case that sets a legal precedent
* ''Per curiam'' decision, by a court with multiple judges
Books
* ''Decision'' (nove ...
s, game theory and mechanism design
Mechanism design is a field in economics and game theory that takes an objectives-first approach to designing economic mechanisms or incentives, toward desired objectives, in strategic settings, where players act rationally. Because it starts a ...
.
Bayesian network
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bay ...
s are a tool that can be used for reasoning
Reason is the capacity of consciously applying logic by drawing conclusions from new or existing information, with the aim of seeking the truth. It is closely associated with such characteristically human activities as philosophy, science, lang ...
(using the Bayesian inference algorithm), learning (using the expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variab ...
), planning
Planning is the process of thinking regarding the activities required to achieve a desired goal. Planning is based on foresight, the fundamental capacity for mental time travel. The evolution of forethought, the capacity to think ahead, is c ...
(using decision network
Decision may refer to:
Law and politics
* Judgment (law), as the outcome of a legal case
*Landmark decision, the outcome of a case that sets a legal precedent
* ''Per curiam'' decision, by a court with multiple judges
Books
* ''Decision'' (nove ...
s) and perception
Perception () is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment. All perception involves signals that go through the nervous system, ...
(using dynamic Bayesian networks).
Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception
Perception () is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment. All perception involves signals that go through the nervous system, ...
systems analyze processes that occur over time (e.g., hidden Markov model
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it X — with unobservable ("''hidden''") states. As part of the definition, HMM requires that there be an ob ...
s or Kalman filter
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estima ...
s).Stochastic temporal models:
Hidden Markov model
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it X — with unobservable ("''hidden''") states. As part of the definition, HMM requires that there be an ob ...
:
Kalman filter
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estima ...
s:
Dynamic Bayesian networks:
Classifiers and statistical learning methods
The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. Classifiers are functions that usepattern matching
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: "either it will or will not be ...
to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning
Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning alg ...
. Each pattern (also called an "observation
Observation is the active acquisition of information from a primary source. In living beings, observation employs the senses. In science, observation can also involve the perception and recording of data via the use of scientific instruments. Th ...
") is labeled with a certain predefined class. All the observations combined with their class labels are known as a data set A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the d ...
. When a new observation is received, that observation is classified based on previous experience.
There are many kinds of classifiers in use. The decision tree
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains co ...
is the simplest and most widely used symbolic machine learning algorithm. K-nearest neighbor
In statistics, the ''k''-nearest neighbors algorithm (''k''-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regre ...
algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (for example c ...
such as the support vector machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories ...
(SVM) displaced k-nearest neighbor in the 1990s.
The naive Bayes classifier
In statistics, naive Bayes classifiers are a family of simple " probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Baye ...
is reportedly the "most widely used learner" at Google, due in part to its scalability.
Neural networks
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological ...
are also used as classifiers.
Artificial neural networks
An artificial neural network is based on a collection of nodes also known as artificial neurons
An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network. The artificial neuron receives one or more inputs (representing ...
, which loosely model the neurons
A neuron, neurone, or nerve cell is an electrically excitable cell that communicates with other cells via specialized connections called synapses. The neuron is the main component of nervous tissue in all animals except sponges and placozoa. ...
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
In science and engineering, the weight of an object is the force acting on the object due to gravity.
Some standard textbooks define weight as a vector quantity, the gravitational force acting on the object. Others define weight as a scalar q ...
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.Neural networks: ,
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
In machine learning, backpropagation (backprop, BP) is a widely used algorithm for training feedforward artificial neural networks. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions gener ...
algorithm. Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In theory, a neural network can learn any function.
In feedforward neural network
A feedforward neural network (FNN) is an artificial neural network wherein connections between the nodes do ''not'' form a cycle. As such, it is different from its descendant: recurrent neural networks.
The feedforward neural network was the ...
s the signal passes in only one direction. Recurrent neural network
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic ...
s feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory
Long short-term memory (LSTM) is an artificial neural network used in the fields of artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) ...
is the most successful network architecture for recurrent networks. Perceptron
In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised classification, supervised learning of binary classification, binary classifiers. A binary classifier is a function which can decide whether or not an ...
s use only a single layer of neurons; deep learning uses multiple layers. Convolutional neural network
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...
s strengthen the connection between neurons that are "close" to each other—this is especially important in image processing
An image is a visual representation of something. It can be two-dimensional, three-dimensional, or somehow otherwise feed into the visual system to convey information. An image can be an artifact, such as a photograph or other two-dimension ...
, where a local set of neurons must identify an "edge" before the network can identify an object.
Deep learning
Deep learning Deep learning: , , , uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing
An image is a visual representation of something. It can be two-dimensional, three-dimensional, or somehow otherwise feed into the visual system to convey information. An image can be an artifact, such as a photograph or other two-dimension ...
, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.
Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision
Computer vision is an Interdisciplinarity, interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate t ...
, speech recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the ma ...
, natural language processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to proc ...
, image classification
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human ...
, and others. The reason that deep learning performs so well in so many applications is not known as of 2023. The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation
In machine learning, backpropagation (backprop, BP) is a widely used algorithm for training feedforward artificial neural networks. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions gener ...
had been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPU
A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mob ...
s) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.
GPT
Generative pre-trained transformers (GPT) arelarge language model
A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 an ...
s (LLMs) that generate text based on the semantic relationships between words in sentences. Text-based GPT models are pretrained on a large corpus of text
In linguistics, a corpus (plural ''corpora'') or text corpus is a language resource consisting of a large and structured set of texts (nowadays usually electronically stored and processed). In corpus linguistics, they are used to do statistical ...
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
In machine learning, reinforcement learning from human feedback (RLHF) or reinforcement learning from human preferences is a technique that trains a "reward model" directly from human feedback and uses the model as a reward function to optimize an ...
(RLHF). Current GPT models are prone to generating falsehoods called "hallucinations
A hallucination is a perception in the absence of an external stimulus that has the qualities of a real perception. Hallucinations are vivid, substantial, and are perceived to be located in external objective space. Hallucination is a combinati ...
", although this can be reduced with RLHF and quality data. They are used in chatbot
A chatbot or chatterbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. Designed to convincingly simulate the way a human would behav ...
s, which allow people to ask a question or request a task in simple text.
Current models and services include Gemini
Gemini may refer to:
Space
* Gemini (constellation), one of the constellations of the zodiac
** Gemini in Chinese astronomy
* Project Gemini, the second U.S. crewed spaceflight program
* Gemini Observatory, consisting of telescopes in the Northern ...
(formerly Bard), ChatGPT
ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and is fine-tuned (an approach to transfer learning) with both supervised and ...
, Grok
''Grok'' is a neologism coined by American writer Robert A. Heinlein for his 1961 science fiction novel ''Stranger in a Strange Land''. While the ''Oxford English Dictionary'' summarizes the meaning of ''grok'' as "to understand intuitively or ...
, Claude Claude may refer to:
__NOTOC__ People and fictional characters
* Claude (given name), a list of people and fictional characters
* Claude (surname), a list of people
* Claude Lorrain (c. 1600–1682), French landscape painter, draughtsman and etcher ...
, Copilot
In aviation, the first officer (FO), also called co-pilot, is the pilot who is second-in-command of the aircraft to the captain, who is the legal commander. In the event of incapacitation of the captain, the first officer will assume command of ...
, and LLaMA
The llama (; ) (''Lama glama'') is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the Pre-Columbian era.
Llamas are social animals and live with others as a herd. Their wool is so ...
. Multimodal GPT models can process different types of data ( modalities) such as images, videos, sound, and text.
Hardware and software
In the late 2010s,graphics processing unit
A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mo ...
s (GPUs) that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow
TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. "It is machine learning ...
software had replaced previously used central processing unit
A central processing unit (CPU), also called a central processor, main processor or just processor, is the electronic circuitry that executes instructions comprising a computer program. The CPU performs basic arithmetic, logic, controlling, an ...
(CPUs) as the dominant means for large-scale (commercial and academic) machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
models' training. Specialized programming language
A programming language is a system of notation for writing computer programs. Most programming languages are text-based formal languages, but they may also be graphical. They are a kind of computer language.
The description of a programming l ...
s such as Prolog
Prolog is a logic programming language associated with artificial intelligence and computational linguistics.
Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily a ...
were used in early AI research, but general-purpose programming language
In computer software, a general-purpose programming language (GPL) is a programming language for building software in a wide variety of application domains. Conversely, a domain-specific programming language is used within a specific area. For ex ...
s like Python have become predominant.
The transistor density in integrated circuits has been observed to roughly double every 18 months—a trend known as Moore's law
Moore's law is the observation that the number of transistors in a dense integrated circuit (IC) doubles about every two years. Moore's law is an observation and projection of a historical trend. Rather than a law of physics, it is an empi ...
, named after the Intel
Intel Corporation is an American multinational corporation and technology company headquartered in Santa Clara, California, Santa Clara, California. It is the world's largest semiconductor chip manufacturer by revenue, and is one of the devel ...
co-founder Gordon Moore
Gordon Earle Moore (born January 3, 1929) is an American businessman, engineer, and the co-founder and chairman emeritus of Intel Corporation. He is also the original proponent of Moore's law.
As of March 2021, Moore's net worth is re ...
, who first identified it. Improvements in GPUs
A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mob ...
have been even faster.
Applications
AI and machine learning technology is used in most of the essential applications of the 2020s, including:search engines
A search engine is a software system designed to carry out web searches. They search the World Wide Web in a systematic way for particular information specified in a textual web search query. The search results are generally presented in a l ...
(such as Google Search
Google Search (also known simply as Google) is a search engine provided by Google. Handling more than 3.5 billion searches per day, it has a 92% share of the global search engine market. It is also the most-visited website in the world.
The ...
), targeting online advertisements, recommendation systems
A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular u ...
(offered by Netflix
Netflix, Inc. is an American subscription video on-demand over-the-top streaming service and production company based in Los Gatos, California. Founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California, it offers a ...
, YouTube
YouTube is a global online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most ...
or Amazon
Amazon most often refers to:
* Amazons, a tribe of female warriors in Greek mythology
* Amazon rainforest, a rainforest covering most of the Amazon basin
* Amazon River, in South America
* Amazon (company), an American multinational technolog ...
), driving internet traffic
Internet traffic is the flow of data within the entire Internet, or in certain network links of its constituent networks. Common traffic measurements are total volume, in units of multiples of the byte, or as transmission rates in bytes per cert ...
, targeted advertising
Targeted advertising is a form of advertising, including online advertising, that is directed towards an audience with certain traits, based on the product or person the advertiser is promoting. These traits can either be demographic with a focus ...
(AdSense
Google AdSense is a program run by Google through which website publishers in the Google Network of content sites serve text, images, video, or interactive media advertisements that are targeted to the site content and audience. These adver ...
, Facebook
Facebook is an online social media and social networking service owned by American company Meta Platforms. Founded in 2004 by Mark Zuckerberg with fellow Harvard College students and roommates Eduardo Saverin, Andrew McCollum, Dustin ...
), virtual assistant
An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term " chatbot" is sometimes used to refer to virtua ...
s (such as Siri
Siri ( ) is a virtual assistant that is part of Apple Inc.'s iOS, iPadOS, watchOS, macOS, tvOS, and audioOS operating systems. It uses voice queries, gesture based control, focus-tracking and a natural-language user interface to answer ques ...
or Alexa
Alexa may refer to: Technology
*Amazon Alexa, a virtual assistant developed by Amazon
* Alexa Internet, a defunct website ranking and traffic analysis service
* Arri Alexa, a digital motion picture camera
People
*Alexa (name), a given name and ...
), autonomous vehicles
Vehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent systems to assist the operator of a vehicle (car, aircraft, watercraft, or otherwise).Hu, J.; Bhowmick, P.; Lanzon, A.,Group Coordinated Control ...
(including drones
Drone most commonly refers to:
* Drone (bee), a male bee, from an unfertilized egg
* Unmanned aerial vehicle
* Unmanned surface vehicle, watercraft
* Unmanned underwater vehicle or underwater drone
Drone, drones or The Drones may also refer to:
...
, ADAS and self-driving cars), automatic language translation
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation), is a sub-field of computational linguistics that investigates th ...
(Microsoft Translator
Microsoft Translator is a multilingual machine translation cloud service provided by Microsoft. Microsoft Translator is a part of Microsoft Cognitive Services and integrated across multiple consumer, developer, and enterprise products; including ...
, Google Translate
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, and an A ...
), facial recognition Facial recognition or face recognition may refer to:
* Face detection, often a step done before facial recognition
* Face perception, the process by which the human brain understands and interprets the face
* Pareidolia, which involves, in part, se ...
(Apple
An apple is an edible fruit produced by an apple tree (''Malus domestica''). Apple trees are cultivated worldwide and are the most widely grown species in the genus '' Malus''. The tree originated in Central Asia, where its wild ances ...
's Face ID
Face ID is a facial recognition system designed and developed by Apple Inc. for the iPhone and iPad Pro. The system allows biometric authentication for unlocking a device, making payments, accessing sensitive data, providing detailed facial exp ...
or Microsoft
Microsoft Corporation is an American multinational corporation, multinational technology company, technology corporation producing Software, computer software, consumer electronics, personal computers, and related services headquartered at th ...
's DeepFace DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on ...
and Google
Google LLC () is an American Multinational corporation, multinational technology company focusing on Search Engine, search engine technology, online advertising, cloud computing, software, computer software, quantum computing, e-commerce, ar ...
's FaceNet) and image labeling (used by Facebook
Facebook is an online social media and social networking service owned by American company Meta Platforms. Founded in 2004 by Mark Zuckerberg with fellow Harvard College students and roommates Eduardo Saverin, Andrew McCollum, Dustin ...
, Apple's iPhoto
iPhoto is a discontinued digital photograph manipulation software application developed by Apple Inc. It was included with every Macintosh personal computer from 2002 to 2015, when it was replaced with Apple's Photos application. Originally s ...
and TikTok
TikTok, known in China as Douyin (), is a short-form video hosting service owned by the Chinese company ByteDance. It hosts user-submitted videos, which can range in duration from 15 seconds to 10 minutes.
TikTok is an international version ...
). The deployment of AI may be overseen by a Chief automation officer (CAO).
Health and medicine
The application of AI inmedicine
Medicine is the science and Praxis (process), practice of caring for a patient, managing the diagnosis, prognosis, Preventive medicine, prevention, therapy, treatment, Palliative care, palliation of their injury or disease, and Health promotion ...
and medical research
Medical research (or biomedical research), also known as experimental medicine, encompasses a wide array of research, extending from " basic research" (also called ''bench science'' or ''bench research''), – involving fundamental scienti ...
has the potential to increase patient care and quality of life. Through the lens of the Hippocratic Oath
The Hippocratic Oath is an oath of ethics historically taken by physicians. It is one of the most widely known of Greek medical texts. In its original form, it requires a new physician to swear, by a number of healing gods, to uphold specific e ...
, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.
For medical research, AI is an important tool for processing and integrating big data. This is particularly important for organoid
An organoid is a miniaturized and simplified version of an organ produced in vitro in three dimensions that shows realistic micro-anatomy. They are derived from one or a few cells from a tissue, embryonic stem cells or induced pluripotent stem ...
and tissue engineering development which use microscopy imaging as a key technique in fabrication. It has been suggested that AI can overcome discrepancies in funding allocated to different fields of research. New AI tools can deepen the understanding of biomedically relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours rather than months, the 3D Protein structure, structure of a protein. In 2023, it was reported that AI-guided drug discovery helped find a class of antibiotics capable of killing two different types of drug-resistant bacteria. In 2024, researchers used machine learning to accelerate the search for Parkinson's disease drug treatments. Their aim was to identify compounds that block the clumping, or aggregation, of alpha-synuclein (the protein that characterises Parkinson's disease). They were able to speed up the initial screening process ten-fold and reduce the cost by a thousand-fold.
Sexuality
Applications of AI in this domain include AI-enabled menstruation and fertility trackers that analyze user data to offer prediction, AI-integrated sex toys (e.g., teledildonics), AI-generated sexual education content, and AI agents that simulate sexual and romantic partners (e.g., Replika). AI is also used for the production of non-consensual deepfake pornography, raising significant ethical and legal concerns. AI technologies have also been used to attempt to identify online gender-based violence and online sexual grooming of minors.Games
Game AI, Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques. IBM Deep Blue, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a ''Jeopardy!'' quiz show exhibition match, IBM's question answering system, Watson (artificial intelligence software), Watson, defeated the two greatest ''Jeopardy!'' champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without Go handicaps, handicaps. Then, in 2017, it AlphaGo versus Ke Jie, defeated Ke Jie, who was the best Go player in the world. Other programs handle Imperfect information, imperfect-information games, such as the poker-playing program Pluribus (poker bot), Pluribus. DeepMind developed increasingly generalisticreinforcement learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...
models, such as with MuZero, which could be trained to play chess, Go, or Atari games. In 2019, DeepMind's AlphaStar achieved grandmaster level in StarCraft II, a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map. In 2021, an AI agent competed in a PlayStation Gran Turismo (series), Gran Turismo competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning. In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen open-world video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions.
Mathematics
In mathematics, special forms of formal step-by-step Automatic reasoning, reasoning are used. In contrast, LLMs such as ''GPT-4 Turbo'', ''Gemini (chatbot), Gemini Ultra'', ''Claude (language model), Claude Opus'', ''Llama (language model), LLaMa-2'' or ''Mistral AI, Mistral Large'' are working with probabilistic models, which can produce wrong answers in the form ofhallucinations
A hallucination is a perception in the absence of an external stimulus that has the qualities of a real perception. Hallucinations are vivid, substantial, and are perceived to be located in external objective space. Hallucination is a combinati ...
. Therefore, they need not only a large database of mathematical problems to learn from but also methods such as Supervised learning, supervised Fine-tuning (deep learning), fine-tuning or trained Statistical classification, classifiers with human-annotated data to improve answers for new problems and learn from corrections. A 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.
Alternatively, dedicated models for mathematical problem solving with higher precision for the outcome including proof of theorems have been developed such as ''Alpha Tensor'', ''Alpha Geometry'' and ''Alpha Proof'' all from Google DeepMind, ''Llemma'' from eleuther or ''Julius''.
When natural language is used to describe mathematical problems, converters transform such prompts into a formal language such as Lean (proof assistant), 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.
Finance
Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years. World Pensions & Investments Forum, World Pensions experts like Nicolas Firzli insist it may be too early to see the emergence of highly innovative AI-informed financial products and services: "the deployment of AI tools will simply further automatise things: destroying tens of thousands of jobs in banking, financial planning, and pension advice in the process, but I'm not sure it will unleash a new wave of [e.g., sophisticated] pension innovation."Military
Various countries are deploying AI military applications.Template:PD-notice, PD-notice The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and Vehicular automation, autonomous vehicles. AI technologies enable coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Forward observers in the U.S. military, Joint Fires between networked combat vehicles involving manned and unmanned teams. AI has been used in military operations in Iraq, Syria, Israel and Ukraine.Generative AI
In the early 2020s, generative AI gained widespread prominence. GenAI is AI capable of generating text, images, videos, or other data using generative models, often in response to Prompt (natural language), prompts.
In March 2023, 58% of U.S. adults had heard about ChatGPT
ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and is fine-tuned (an approach to transfer learning) with both supervised and ...
and 14% had tried it. The increasing realism and ease-of-use of AI-based text-to-image generators such as Midjourney, DALL-E, and Stable Diffusion sparked a trend of Viral phenomenon, viral AI-generated photos. Widespread attention was gained by a fake photo of Pope Francis wearing a white puffer coat, the fictional arrest of Donald Trump, and a hoax of an attack on the The Pentagon, Pentagon, as well as the usage in professional creative arts.
Agents
Artificial intelligent (AI) agents are software entities designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals. These agents can interact with users, their environment, or other agents. AI agents are used in various applications, includingvirtual assistant
An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term " chatbot" is sometimes used to refer to virtua ...
s, chatbots, autonomous vehicles
Vehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent systems to assist the operator of a vehicle (car, aircraft, watercraft, or otherwise).Hu, J.; Bhowmick, P.; Lanzon, A.,Group Coordinated Control ...
, Video game console, 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.
Other industry-specific tasks
There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported having incorporated "AI" in some offerings or processes. A few examples are energy storage, medical diagnosis, military logistics, applications that predict the result of judicial decisions, foreign policy, or supply chain management. AI applications for evacuation and disaster management are growing. AI has been used to investigate if and how people evacuated in large scale and small scale evacuations using historical data from GPS, videos or social media. Further, AI can provide real time information on the real time evacuation conditions. In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water. Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights." For example, it is used for discovering exoplanets, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. Additionally, it could be used for activities in space, such as space exploration, including the analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation. During the 2024 Indian general election, 2024 Indian elections, US$50 millions was spent on authorized AI-generated content, notably by creating Deepfake, deepfakes of allied (including sometimes deceased) politicians to better engage with voters, and by translating speeches to various local languages.Ethics
AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of DeepMind hopes to "solve intelligence, and then use that to solve everything else". However, as the use of AI has become widespread, several unintended consequences and risks have been identified. In-production systems can sometimes not factor ethics and bias into their AI training processes, especially when the AI algorithms are inherently unexplainable in deep learning.Risks and harm
Privacy and copyright
Machine learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright. AI-powered devices and services, such as virtual assistants and IoT products, continuously collect personal information, raising concerns about intrusive data gathering and unauthorized access by third parties. The loss of privacy is further exacerbated by AI's ability to process and combine vast amounts of data, potentially leading to a surveillance society where individual activities are constantly monitored and analyzed without adequate safeguards or transparency. Sensitive user data collected may include online activity records, geolocation data, video or audio. For example, in order to buildspeech recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the ma ...
algorithms, Amazon
Amazon most often refers to:
* Amazons, a tribe of female warriors in Greek mythology
* Amazon rainforest, a rainforest covering most of the Amazon basin
* Amazon River, in South America
* Amazon (company), an American multinational technolog ...
has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.
AI developers argue that this is the only way to deliver valuable applications. and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy experts, such as Cynthia Dwork, have begun to view privacy in terms of fairness (machine learning), fairness. Brian Christian wrote that experts have pivoted "from the question of 'what they know' to the question of 'what they're doing with it'."
Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "fair use". Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include "the purpose and character of the use of the copyrighted work" and "the effect upon the potential market for the copyrighted work". Website owners who do not wish to have their content scraped can indicate it in a "robots.txt" file. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI. Another discussed approach is to envision a separate ''sui generis'' system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.
Dominance by tech giants
The commercial AI scene is dominated by Big Tech companies such as Alphabet Inc.,Amazon
Amazon most often refers to:
* Amazons, a tribe of female warriors in Greek mythology
* Amazon rainforest, a rainforest covering most of the Amazon basin
* Amazon River, in South America
* Amazon (company), an American multinational technolog ...
, Apple Inc., Meta Platforms, and Microsoft
Microsoft Corporation is an American multinational corporation, multinational technology company, technology corporation producing Software, computer software, consumer electronics, personal computers, and related services headquartered at th ...
. Some of these players already own the vast majority of existing cloud computing, cloud infrastructure and computing power from data centers, allowing them to entrench further in the marketplace.
Power needs and environmental impacts
In January 2024, the International Energy Agency (IEA) released ''Electricity 2024, Analysis and Forecast to 2026'', forecasting electric power use. This is the first IEA report to make projections for data centers and power consumption for artificial intelligence and cryptocurrency. The report states that power demand for these uses might double by 2026, with additional electric power usage equal to electricity used by the whole Japanese nation. Prodigious power consumption by AI is responsible for the growth of fossil fuels use, and might delay closings of obsolete, carbon-emitting coal energy facilities. There is a feverish rise in the construction of data centers throughout the US, making large technology firms (e.g., Microsoft, Meta, Google, Amazon) into voracious consumers of electric power. Projected electric consumption is so immense that there is concern that it will be fulfilled no matter the source. A ChatGPT search involves the use of 10 times the electrical energy as a Google search. The large firms are in haste to find power sources – from nuclear energy to geothermal to fusion. The tech firms argue that – in the long view – AI will be eventually kinder to the environment, but they need the energy now. AI makes the power grid more efficient and "intelligent", will assist in the growth of nuclear power, and track overall carbon emissions, according to technology firms. A 2024 Goldman Sachs Research Paper, ''AI Data Centers and the Coming US Power Demand Surge'', found "US power demand (is) likely to experience growth not seen in a generation...." and forecasts that, by 2030, US data centers will consume 8% of US power, as opposed to 3% in 2022, presaging growth for the electrical power generation industry by a variety of means. Data centers' need for more and more electrical power is such that they might max out the electrical grid. The Big Tech companies counter that AI can be used to maximize the utilization of the grid by all. In 2024, the ''Wall Street Journal'' reported that big AI companies have begun negotiations with the US nuclear power providers to provide electricity to the data centers. In March 2024 Amazon purchased a Pennsylvania nuclear-powered data center for $650 Million (US). Nvidia CEO Jen-Hsun Huang said nuclear power is a good option for the data centers. In September 2024,Microsoft
Microsoft Corporation is an American multinational corporation, multinational technology company, technology corporation producing Software, computer software, consumer electronics, personal computers, and related services headquartered at th ...
announced an agreement with Constellation Energy to re-open the Three Mile Island nuclear power plant to provide Microsoft with 100% of all electric power produced by the plant for 20 years. Reopening the plant, which suffered a partial nuclear meltdown of its Unit 2 reactor in 1979, will require Constellation to get through strict regulatory processes which will include extensive safety scrutiny from the US Nuclear Regulatory Commission. If approved (this will be the first ever US re-commissioning of a nuclear plant), over 835 megawatts of power – enough for 800,000 homes – of energy will be produced. The cost for re-opening and upgrading is estimated at $1.6 billion (US) and is dependent on tax breaks for nuclear power contained in the 2022 US Inflation Reduction Act. The US government and the state of Michigan are investing almost $2 billion (US) to reopen the Palisades Nuclear Generating Station, Palisades Nuclear reactor on Lake Michigan. Closed since 2022, the plant is planned to be reopened in October 2025. The Three Mile Island facility will be renamed the Crane Clean Energy Center after Chris Crane, a nuclear proponent and former CEO of Exelon who was responsible for Exelon spinoff of Constellation.
After the last approval in September 2023, Taiwan suspended the approval of data centers north of Taoyuan, Taiwan, Taoyuan with a capacity of more than 5 MW in 2024, due to power supply shortages. Taiwan aims to Nuclear power phase-out, phase out nuclear power by 2025. On the other hand, Singapore imposed a ban on the opening of data centers in 2019 due to electric power, but in 2022, lifted this ban.
Although most nuclear plants in Japan have been shut down after the 2011 Fukushima nuclear accident, according to an October 2024 ''Bloomberg'' article in Japanese, cloud gaming services company Ubitus, in which Nvidia has a stake, is looking for land in Japan near nuclear power plant for a new data center for generative AI. Ubitus CEO Wesley Kuo said nuclear power plants are the most efficient, cheap and stable power for AI.
On 1 November 2024, the Federal Energy Regulatory Commission (FERC) rejected an application submitted by Talen Energy for approval to supply some electricity from the nuclear power station Susquehanna Steam Electric Station, Susquehanna to Amazon's data center.
According to the Commission Chairman Willie L. Phillips, it is a burden on the electricity grid as well as a significant cost shifting concern to households and other business sectors.
Misinformation
YouTube
YouTube is a global online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most ...
, Facebook
Facebook is an online social media and social networking service owned by American company Meta Platforms. Founded in 2004 by Mark Zuckerberg with fellow Harvard College students and roommates Eduardo Saverin, Andrew McCollum, Dustin ...
and others use recommender systems to guide users to more content. These AI programs were given the goal of mathematical optimization, 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 (politics), partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem .
In 2022, generative AI began to create images, audio, video and text that are indistinguishable from real photographs, recordings, films, or human writing. It is possible for bad actors to use this technology to create massive amounts of misinformation or propaganda. AI pioneer Geoffrey Hinton expressed concern about AI enabling "authoritarian leaders to manipulate their electorates" on a large scale, among other risks.
Algorithmic bias and fairness
Machine learning applications will be algorithmic bias, biased if they learn from biased data. The developers may not be aware that the bias exists. Bias can be introduced by the way training data is selected and by the way a model is deployed. If a biased algorithm is used to make decisions that can seriously harm people (as it can in health equity, medicine, credit rating, finance, recruitment, public housing, housing or policing) then the algorithm may cause discrimination. The field of fairness (machine learning), fairness studies how to prevent harms from algorithmic biases. On June 28, 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as "gorillas" because they were black. The system was trained on a dataset that contained very few images of black people, a problem called "sample size disparity". Google "fixed" this problem by preventing the system from labelling ''anything'' as a "gorilla". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon. COMPAS (software), COMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend. In 2017, several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data. A program can make biased decisions even if the data does not explicitly mention a problematic feature (such as "race" or "gender"). The feature will correlate with other features (like "address", "shopping history" or "first name"), and the program will make the same decisions based on these features as it would on "race" or "gender". Moritz Hardt said "the most robust fact in this research area is that fairness through blindness doesn't work." Criticism of COMPAS highlighted that machine learning models are designed to make "predictions" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as ''recommendations'', some of these "recommendations" will likely be racist. Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be ''better'' than the past. It is descriptive rather than prescriptive. Bias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women. There are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is Distributive justice, distributive fairness, which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases, but it may conflict with anti-discrimination laws. At its 2022 ACM Conference on Fairness, Accountability, and Transparency, 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.Lack of transparency
Many AI systems are so complex that their designers cannot explain how they reach their decisions. Particularly with deep neural networks, in which there are a large amount of non-linear relationships between inputs and outputs. But some popular explainability techniques exist. It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as "cancerous", because pictures of malignancies typically include a ruler to show the scale. Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at "low risk" of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading. People who have been harmed by an algorithm's decision have a right to an explanation. Doctors, for example, are expected to clearly and completely explain to their colleagues the reasoning behind any decision they make. Early drafts of the European Union's General Data Protection Regulation in 2016 included an explicit statement that this right exists. Industry experts noted that this is an unsolved problem with no solution in sight. Regulators argued that nevertheless the harm is real: if the problem has no solution, the tools should not be used. DARPA established the Explainable Artificial Intelligence, XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems. Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output. LIME can locally approximate a model's outputs with a simpler, interpretable model. Multitask learning provides a large number of outputs in addition to the target classification. These other outputs can help developers deduce what the network has learned. Deconvolution, DeepDream and other generative AI, generative methods can allow developers to see what different layers of a deep network for computer vision have learned, and produce output that can suggest what the network is learning. For generative pre-trained transformers, Anthropic developed a technique based on dictionary learning that associates patterns of neuron activations with human-understandable concepts.Bad actors and weaponized AI
Artificial intelligence provides a number of tools that are useful to bad actors, such as authoritarian, authoritarian governments, terrorists, criminals or rogue states. A lethal autonomous weapon is a machine that locates, selects and engages human targets without human supervision. Widely available AI tools can be used by bad actors to develop inexpensive autonomous weapons and, if produced at scale, they are potentially weapons of mass destruction. Even when used in conventional warfare, it is unlikely that they will be unable to reliably choose targets and could potentially murder, kill an innocent person. In 2014, 30 nations (including China) supported a ban on autonomous weapons under the United Nations' Convention on Certain Conventional Weapons, however the United States and others disagreed. By 2015, over fifty countries were reported to be researching battlefield robots. AI tools make it easier for Authoritarian, authoritarian governments to efficiently control their citizens in several ways. Facial recognition system, Face and Speaker recognition, voice recognition allow widespread surveillance.Machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
, operating this data, can classifier (machine learning), 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 technocracy, centralized decision making more competitive than liberal and decentralized systems such as market (economics), markets. It lowers the cost and difficulty of digital warfare and spyware, advanced spyware. All these technologies have been available since 2020 or earlier—AI facial recognition systems are already being used for mass surveillance in China.
There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours.
Technological unemployment
Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.E. McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2022)51(3) Industrial Law Journal 511–559
. In the past, technology has tended to increase rather than reduce total employment, but economists acknowledge that "we're in uncharted territory" with AI. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit if productivity gains are Redistribution of income and wealth, redistributed. Risk estimates vary; for example, in the 2010s, Michael Osborne and Carl Benedikt Frey estimated 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classified only 9% of U.S. jobs as "high risk". The methodology of speculating about future employment levels has been criticised as lacking evidential foundation, and for implying that technology, rather than social policy, creates unemployment, as opposed to redundancies. In April 2023, it was reported that 70% of the jobs for Chinese video game illustrators had been eliminated by generative artificial intelligence. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; ''The Economist'' stated in 2015 that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. From the early days of the development of artificial intelligence, there have been arguments, for example, those put forward by Joseph Weizenbaum, about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculation and qualitative, value-based judgement.
Existential risk
It has been argued AI will become so powerful that humanity may irreversibly lose control of it. This could, as physicist Stephen Hawking stated, "Global catastrophic risk, spell the end of the human race". This scenario has been common in science fiction, when a computer or robot suddenly develops a human-like "self-awareness" (or "sentience" or "consciousness") and becomes a malevolent character. These sci-fi scenarios are misleading in several ways. First, AI does not require human-like sentience to be an existential risk. Modern AI programs are given specific goals and use learning and intelligence to achieve them. Philosopher Nick Bostrom argued that if one gives ''almost any'' goal to a sufficiently powerful AI, it may choose to destroy humanity to achieve it (he used the example of a Instrumental convergence#Paperclip maximizer, paperclip factory manager). Stuart J. Russell, Stuart Russell gives the example of household robot that tries to find a way to kill its owner to prevent it from being unplugged, reasoning that "you can't fetch the coffee if you're dead." In order to be safe for humanity, a superintelligence would have to be genuinely AI alignment, aligned with humanity's morality and values so that it is "fundamentally on our side". Second, Yuval Noah Harari argues that AI does not require a robot body or physical control to pose an existential risk. The essential parts of civilization are not physical. Things like ideologies, law, government, money and the economy are built on language; they exist because there are stories that billions of people believe. The current prevalence of misinformation suggests that an AI could use language to convince people to believe anything, even to take actions that are destructive. The opinions amongst experts and industry insiders are mixed, with sizable fractions both concerned and unconcerned by risk from eventual superintelligent AI. Personalities such as Stephen Hawking, Bill Gates, and Elon Musk, as well as AI pioneers such as Yoshua Bengio, Stuart J. Russell, Stuart Russell, Demis Hassabis, and Sam Altman, have expressed concerns about existential risk from AI. In May 2023, Geoffrey Hinton announced his resignation from Google in order to be able to "freely speak out about the risks of AI" without "considering how this impacts Google." He notably mentioned risks of an AI takeover, and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI. In 2023, many leading AI experts endorsed Statement on AI risk of extinction, the joint statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war". Some other researchers were more optimistic. AI pioneer Jürgen Schmidhuber did not sign the joint statement, emphasising that in 95% of all cases, AI research is about making "human lives longer and healthier and easier." While the tools that are now being used to improve lives can also be used by bad actors, "they can also be used against the bad actors." Andrew Ng also argued that "it's a mistake to fall for the doomsday hype on AI—and that regulators who do will only benefit vested interests." Yann LeCun "scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction." In the early 2010s, experts argued that the risks are too distant in the future to warrant research or that humans will be valuable from the perspective of a superintelligent machine. However, after 2016, the study of current and future risks and possible solutions became a serious area of research.Ethical machines and alignment
Friendly AI are machines that have been designed from the beginning to minimize risks and to make choices that benefit humans. Eliezer Yudkowsky, who coined the term, argues that developing friendly AI should be a higher research priority: it may require a large investment and it must be completed before AI becomes an existential risk. Machines with intelligence have the potential to use their intelligence to make ethical decisions. The field of machine ethics provides machines with ethical principles and procedures for resolving ethical dilemmas. The field of machine ethics is also called computational morality, and was founded at an AAAI symposium in 2005. Other approaches include Wendell Wallach's "artificial moral agents" and Stuart J. Russell's Human Compatible#Russell's three principles, three principles for developing provably beneficial machines.Open source
Active organizations in the AI open-source community include Hugging Face,Google
Google LLC () is an American Multinational corporation, multinational technology company focusing on Search Engine, search engine technology, online advertising, cloud computing, software, computer software, quantum computing, e-commerce, ar ...
, EleutherAI and Meta Platforms, Meta. Various AI models, such as LLaMA, Llama 2, Mistral AI, Mistral or Stable Diffusion, have been made open-weight, meaning that their architecture and trained parameters (the "weights") are publicly available. Open-weight models can be freely Fine-tuning (deep learning), fine-tuned, which allows companies to specialize them with their own data and for their own use-case. Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitate bioterrorism) and that once released on the Internet, they cannot be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.
Frameworks
Artificial Intelligence projects can have their ethical permissibility tested while designing, developing, and implementing an AI system. An AI framework such as the Care and Act Framework containing the SUM values—developed by the Alan Turing Institute tests projects in four main areas: * Respect the dignity of individual people * Connect with other people sincerely, openly, and inclusively * Care for the wellbeing of everyone * Protect social values, justice, and the public interest Other developments in ethical frameworks include those decided upon during the Asilomar Conference on Beneficial AI, Asilomar Conference, the Montreal Declaration for Responsible AI, and the IEEE's Ethics of Autonomous Systems initiative, among others; however, these principles do not go without their criticisms, especially regards to the people chosen contributes to these frameworks. Promotion of the wellbeing of the people and communities that these technologies affect requires consideration of the social and ethical implications at all stages of AI system design, development and implementation, and collaboration between job roles such as data scientists, product managers, data engineers, domain experts, and delivery managers. The AI Safety Institute (United Kingdom), UK AI Safety Institute released in 2024 a testing toolset called 'Inspect' for AI safety evaluations available under a MIT open-source licence which is freely available on GitHub and can be improved with third-party packages. It can be used to evaluate AI models in a range of areas including core knowledge, ability to reason, and autonomous capabilities.Regulation
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. According to AI Index at Stanford, the annual number of AI-related laws passed in the 127 survey countries jumped from one passed in 2016 to 37 passed in 2022 alone. Between 2016 and 2020, more than 30 countries adopted dedicated strategies for AI. Most EU member states had released national AI strategies, as had Canada, China, India, Japan, Mauritius, the Russian Federation, Saudi Arabia, United Arab Emirates, U.S., and Vietnam. Others were in the process of elaborating their own AI strategy, including Bangladesh, Malaysia and Tunisia. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology. Henry Kissinger, Eric Schmidt, and Daniel Huttenlocher published a joint statement in November 2021 calling for a government commission to regulate AI. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years. In 2023, the United Nations also launched an advisory body to provide recommendations on AI governance; the body comprises technology company executives, governments officials and academics. In 2024, the Council of Europe created the first international legally binding treaty on AI, called the "Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law". It was adopted by the European Union, the United States, the United Kingdom, and other signatories.
In a 2022 Ipsos survey, attitudes towards AI varied greatly by country; 78% of Chinese citizens, but only 35% of Americans, agreed that "products and services using AI have more benefits than drawbacks". A 2023 Reuters/Ipsos poll found that 61% of Americans agree, and 22% disagree, that AI poses risks to humanity. In a 2023 Fox News poll, 35% of Americans thought it "very important", and an additional 41% thought it "somewhat important", for the federal government to regulate AI, versus 13% responding "not very important" and 8% responding "not at all important".
In November 2023, the first global AI Safety Summit was held in Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary regulatory frameworks. 28 countries including the United States, China, and the European Union issued a declaration at the start of the summit, calling for international co-operation to manage the challenges and risks of artificial intelligence. In May 2024 at the AI Seoul Summit, 16 global AI tech companies agreed to safety commitments on the development of AI.
History
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable form of mathematical reasoning. This, along with concurrent discoveries in cybernetics, information theory and neurobiology, led researchers to consider the possibility of building an "electronic brain". They developed several areas of research that would become part of AI, such as Warren McCullouch, McCullouch and Walter Pitts, Pitts design for "artificial neurons" in 1943, and Turing's influential 1950 paper 'Computing Machinery and Intelligence', which introduced the Turing test and showed that "machine intelligence" was plausible. The field of AI research was founded at Dartmouth workshop, a workshop at Dartmouth College in 1956.Dartmouth workshop: , ,The proposal: The attendees became the leaders of AI research in the 1960s. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving Theorem, logical theorems and speaking English.Successful programs of the 1960s: , , , Artificial intelligence laboratories were set up at a number of British and U.S. universities in the latter 1950s and early 1960s. Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with artificial general intelligence, general intelligence and considered this the goal of their field. In 1965 Herbert A. Simon, Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". In 1967 Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". They had, however, underestimated the difficulty of the problem. In 1974, both the U.S. and British governments cut off exploratory research in response to the Lighthill report, criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to Mansfield Amendment, fund more productive projects. Marvin Minsky, Minsky's and Papert's book ''
Perceptron
In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised classification, supervised learning of binary classification, binary classifiers. A binary classifier is a function which can decide whether or not an ...
s'' was understood as proving that artificial neural networks would never be useful for solving real-world tasks, thus discrediting the approach altogether. The "AI winter
In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research.First AI Winter, Lighthill report, Mansfield Amendment: , , , ,
In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research.Funding initiatives in the early 1980s: Fifth Generation Project (Japan), Alvey (UK), Microelectronics and Computer Technology Corporation (US), Strategic Computing Initiative (US): , , , , 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.Second AI Winter: , , , ,
Up to this point, most of AI's funding had gone to projects that used high-level symbolic AI, 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 machine perception, perception,
Stuart J. Russell, Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure. However, they are critical that the test requires the machine to imitate humans. "Aeronautics, Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons. AI founder John McCarthy (computer scientist), John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".
McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world". Another AI founder, Marvin Minsky similarly describes it as "the ability to solve hard problems". The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals. These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible.
Another definition has been adopted by Google, a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.
Some authors have suggested in practice, that the definition of AI is vague and difficult to define, with contention as to whether classical algorithms should be categorised as AI, with many companies during the early 2020s AI boom using the term as a marketing buzzword, often even if they did "not actually use AI in a material way".
Thought-capable artificial beings have appeared as storytelling devices since antiquity,AI in myth: and have been a persistent theme in science fiction.
A common Trope (literature), 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 2001: A Space Odyssey (novel), Arthur C. Clarke's and 2001: A Space Odyssey, 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 (film), Aliens'' (1986) are less prominent in popular culture.
Isaac Asimov introduced the Three Laws of Robotics in many stories, most notably with the "Multivac" super-intelligent computer. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have sentience, 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 (film), 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.
Open Syllabus
: * * The four most widely used AI textbooks in 2008: * * * . * Later edition: Other textbooks: * *
AI & ML in Fusion
AI & ML in Fusion, video lecture
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Presidential Address to the Association for the Advancement of Artificial Intelligence. * * * * * * * * * * * * * * * * * * * * * * * * * Later published as
* * * * * * * * * * * * * * * * * * * * * * * * *
online
* 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 (philosophy), Agency is what distinguishes us from machines. For biological creatures, reason and motivation, 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 company, tech companies. They include Transparency (behavior), transparency on how AI systems work; compensation for individuals if their data [are] used to train LLMs (
robotics
Robotics is an interdisciplinarity, interdisciplinary branch of computer science and engineering. Robotics involves design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist human ...
, learning and pattern recognition, and began to look into "sub-symbolic" approaches. Rodney Brooks rejected "representation" in general and focussed directly on engineering machines that move and survive. Judea Pearl, Lofti Zadeh and others developed methods that handled incomplete and uncertain information by making reasonable guesses rather than precise logic. But the most important development was the revival of "connectionism", including neural network research, by Geoffrey Hinton and others. In 1990, Yann LeCun successfully showed that convolutional neural networks can recognize handwritten digits, the first of many successful applications of neural networks.
AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow AI, narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics
Economics () is the social science that studies the production, distribution, and consumption of goods and services.
Economics focuses on the behaviour and interactions of economic agents and how economies work. Microeconomics analy ...
and mathematical optimization, mathematics).#Neat vs. scruffy, Formal and #Narrow vs. general AI, narrow methods adopted in the 1990s: , By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence" (a tendency known as the AI effect).
However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can.
It is a primary goal of some artificial intelligence research and a common topic in science fict ...
(or "AGI"), which had several well-funded institutions by the 2010s.
Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field. Deep learning revolution, AlexNet: , ,
For many specific tasks, other methods were abandoned.
Deep learning's success was based on both hardware improvements (Moore's law, faster computers, graphics processing unit
A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mo ...
s, cloud computing) and access to big data, large amounts of data (including curated datasets, such as ImageNet). Deep learning's success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.
In 2016, issues of algorithmic fairness, 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 AI alignment, alignment problem became a serious field of academic study.
In the late teens and early 2020s, artificial general intelligence, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model
A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 an ...
that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom
The AI boom, or AI spring, is the ongoing period of rapid progress in the field of artificial intelligence. Prominent examples include protein folding prediction and generative AI, led by laboratories including Google DeepMind and OpenAI.
...
, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI". About 800,000 "AI"-related U.S. job openings existed in 2022.
Philosophy
Philosophical debates have historically sought to determine the nature of intelligence and how to make intelligent machines. Another major focus has been whether machines can be conscious, and the associated ethical implications. Many other topics in philosophy are relevant to AI, such as epistemology and free will. Rapid advancements have intensified public discussions on the philosophy and ethics of AI.Defining artificial intelligence
Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?" He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation.Turing's original publication of the Turing test in "Computing machinery and intelligence": Historical influence and philosophical implications: , , , 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 Problem of other minds, we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks."
Stuart J. Russell, Russell and Norvig agree with Turing that intelligence must be defined in terms of external behavior, not internal structure. However, they are critical that the test requires the machine to imitate humans. "Aeronautics, Aeronautical engineering texts," they wrote, "do not define the goal of their field as making 'machines that fly so exactly like pigeons that they can fool other pigeons. AI founder John McCarthy (computer scientist), John McCarthy agreed, writing that "Artificial intelligence is not, by definition, simulation of human intelligence".
McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world". Another AI founder, Marvin Minsky similarly describes it as "the ability to solve hard problems". The leading AI textbook defines it as the study of agents that perceive their environment and take actions that maximize their chances of achieving defined goals. These definitions view intelligence in terms of well-defined problems with well-defined solutions, where both the difficulty of the problem and the performance of the program are direct measures of the "intelligence" of the machine—and no other philosophical discussion is required, or may not even be possible.
Another definition has been adopted by Google, a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence.
Some authors have suggested in practice, that the definition of AI is vague and difficult to define, with contention as to whether classical algorithms should be categorised as AI, with many companies during the early 2020s AI boom using the term as a marketing buzzword, often even if they did "not actually use AI in a material way".
Evaluating approaches to AI
No established unifying theory or paradigm has guided AI research for most of its history. The unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much so that some sources, especially in the business world, use the term "artificial intelligence" to mean "machine learning with neural networks"). This approach is mostly sub-symbolic, soft computing, soft and artificial general intelligence, narrow. Critics argue that these questions may have to be revisited by future generations of AI researchers.Symbolic AI and its limits
Symbolic AI (or "GOFAI") simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the physical symbol systems hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action." However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or commonsense reasoning. Moravec's paradox is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult. Philosopher Hubert Dreyfus had Dreyfus' critique of AI, argued since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge. Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him. The issue is not resolved: sub-symbolic reasoning can make many of the same inscrutable mistakes that human intuition does, such as algorithmic bias. Critics such asNoam Chomsky
Avram Noam Chomsky (born December 7, 1928) is an American public intellectual: a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. Sometimes called "the father of modern linguistics", Chomsky is ...
argue continuing research into symbolic AI will still be necessary to attain general intelligence, in part because sub-symbolic AI is a move away from explainable AI: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of Neuro-symbolic AI, neuro-symbolic artificial intelligence attempts to bridge the two approaches.
Neat vs. scruffy
"Neats" hope that intelligent behavior is described using simple, elegant principles (such aslogic
Logic is the study of correct reasoning. It includes both formal and informal logic. Formal logic is the science of deductively valid inferences or of logical truths. It is a formal science investigating how conclusions follow from premis ...
, optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
, or Artificial neural network, neural networks). "Scruffies" expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s, but eventually was seen as irrelevant. Modern AI has elements of both.
Soft vs. hard computing
Finding a provably correct or optimal solution is Intractability (complexity), intractable for many important problems. Soft computing is a set of techniques, including genetic algorithms, fuzzy logic and neural networks, that are tolerant of imprecision, uncertainty, partial truth and approximation. Soft computing was introduced in the late 1980s and most successful AI programs in the 21st century are examples of soft computing with neural networks.Narrow vs. general AI
AI researchers are divided as to whether to pursue the goals of artificial general intelligence and superintelligence directly or to solve as many specific problems as possible (narrow AI) in hopes these solutions will lead indirectly to the field's long-term goals. General intelligence is difficult to define and difficult to measure, and modern AI has had more verifiable successes by focusing on specific problems with specific solutions. The sub-field of artificial general intelligence studies this area exclusively.Machine consciousness, sentience, and mind
The philosophy of mind does not know whether a machine can have a mind, consciousness and philosophy of mind, 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. Stuart J. Russell, Russell and Norvig add that "[t]he additional project of making a machine conscious in exactly the way humans are is not one that we are equipped to take on." However, the question has become central to the philosophy of mind. It is also typically the central question at issue in artificial intelligence in fiction.Consciousness
David Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this ''feels'' or why it should feel like anything at all, assuming we are right in thinking that it truly does feel like something (Dennett's consciousness illusionism says this is an illusion). While human Information processing (psychology), information processing is easy to explain, human subjective experience is difficult to explain. For example, it is easy to imagine a color-blind person who has learned to identify which objects in their field of view are red, but it is not clear what would be required for the person to ''know what red looks like''.Computationalism and functionalism
Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind–body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam. Philosopher John Searle characterized this position as "Strong AI hypothesis, strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle challenges this claim with his Chinese room argument, which attempts to show that even a computer capable of perfectly simulating human behavior would not have a mind.AI welfare and rights
It is difficult or impossible to reliably evaluate whether an advanced Sentient AI, AI is sentient (has the ability to feel), and if so, to what degree. But if there is a significant chance that a given machine can feel and suffer, then it may be entitled to certain rights or welfare protection measures, similarly to animals. Sapience (a set of capacities related to high intelligence, such as discernment or self-awareness) may provide another moral basis for AI rights. Robot rights are also sometimes proposed as a practical way to integrate autonomous agents into society. In 2017, the European Union considered granting "electronic personhood" to some of the most capable AI systems. Similarly to the legal status of companies, it would have conferred rights but also responsibilities. Critics argued in 2018 that granting rights to AI systems would downplay the importance of human rights, and that legislation should focus on user needs rather than speculative futuristic scenarios. They also noted that robots lacked the autonomy to take part to society on their own. Progress in AI increased interest in the topic. Proponents of AI welfare and rights often argue that AI sentience, if it emerges, would be particularly easy to deny. They warn that this may be a Moral blindness, moral blind spot analogous to slavery or factory farming, which could lead to Suffering risks, large-scale suffering if sentient AI is created and carelessly exploited.Future
Superintelligence and the singularity
A superintelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. If research intoartificial general intelligence
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can.
It is a primary goal of some artificial intelligence research and a common topic in science fict ...
produced sufficiently intelligent software, it might be able to Recursive self-improvement, 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 "Technological singularity, singularity".
However, technologies cannot improve exponentially indefinitely, and typically follow an S-shaped curve, slowing when they reach the physical limits of what the technology can do.
Transhumanism
Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines may merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in the writings of Aldous Huxley and Robert Ettinger. Edward Fredkin argues that "artificial intelligence is the next step in evolution", an idea first proposed by Samuel Butler (novelist), Samuel Butler's "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson (science historian), George Dyson in his 1998 book ''Darwin Among the Machines#Evolution of Global Intelligence, Darwin Among the Machines: The Evolution of Global Intelligence''.AI as evolution: Edward Fredkin is quoted in , ,In fiction
Thought-capable artificial beings have appeared as storytelling devices since antiquity,AI in myth: and have been a persistent theme in science fiction.
A common Trope (literature), 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 2001: A Space Odyssey (novel), Arthur C. Clarke's and 2001: A Space Odyssey, 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 (film), Aliens'' (1986) are less prominent in popular culture.
Isaac Asimov introduced the Three Laws of Robotics in many stories, most notably with the "Multivac" super-intelligent computer. Asimov's laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have sentience, 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 (film), Ex Machina'', as well as the novel ''Do Androids Dream of Electric Sheep?'', by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.
See also
* * * * * * * * * * * * * Organoid intelligence – Use of brain cells and brain organoids for intelligent computing * *Explanatory notes
References
AI textbooks
The two most widely used textbooks in 2023 (see thOpen Syllabus
: * * The four most widely used AI textbooks in 2008: * * * . * Later edition: Other textbooks: * *
History of AI
* * * *Other sources
AI & ML in Fusion
AI & ML in Fusion, video lecture
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Presidential Address to the Association for the Advancement of Artificial Intelligence. * * * * * * * * * * * * * * * * * * * * * * * * * Later published as
* * * * * * * * * * * * * * * * * * * * * * * * *
Further reading
* David Autor, 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. * * Margaret Boden, Boden, Margaret, ''Mind As Machine'', Oxford University Press, 2006. * Kenneth Cukier, 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 (science historian), 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 machine learning, AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.) * * * 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, 2023online
* 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 (philosophy), Agency is what distinguishes us from machines. For biological creatures, reason and motivation, 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 company, tech companies. They include Transparency (behavior), transparency on how AI systems work; compensation for individuals if their data [are] used to train LLMs (
large language model
A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 an ...
)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 Federal Trade Commission, FTC, might provide the most robust oversight.... [T]he Fordham University, 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 Primum non nocere, do no harm?'" (p. 46.)
*
* 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 (linguistics), 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.
*
*
* 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.
* Jill Lepore, Lepore, Jill, "The Chit-Chatbot: Is talking with a machine a conversation?", ''The New Yorker'', 7 October 2024, pp. 12–16.
*
* 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.
*
* Introduced Deep Q-learning, DQN, which produced human-level performance on some Atari games.
* Eyal Press, 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.
*
* Roivainen, Eka, "AI's IQ: ChatGPT
ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and is fine-tuned (an approach to transfer learning) with both supervised and ...
aced a [standard intelligence] test but showed that intelligence
Intelligence has been defined in many ways: the capacity for abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. It can be described as the a ...
cannot be measured by IQ alone", ''Scientific American'', vol. 329, no. 1 (July/August 2023), p. 7. "Despite its high IQ, ChatGPT
ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and is fine-tuned (an approach to transfer learning) with both supervised and ...
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.)
*
*
*
* Ashish Vaswani, Vaswani, Ashish, Noam Shazeer, Niki Parmar et al. "Attention is all you need." Advances in neural information processing systems 30 (2017). Seminal paper on transformer
A transformer is a passive component that transfers electrical energy from one electrical circuit to another circuit, or multiple circuits. A varying current in any coil of the transformer produces a varying magnetic flux in the transformer' ...
s.
* 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.)
*
External links
* {{Authority control Artificial intelligence, Artificial intelligence Computational fields of study Computational neuroscience Cybernetics Data science Formal sciences Intelligence by type