"AI" redirects here. For other uses, see AI (disambiguation), Artificial intelligence (disambiguation), and Intelligent agent.
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Artificial intelligence (AI) is intelligence demonstrated by computers, as opposed to human or animal intelligence. "Intelligence" encompasses the ability to learn and to reason, to generalize, and to infer meaning.[1][2] AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), automated decision-making, and competing at the highest level in strategic game systems (such as chess and Go).[3]
Artificial intelligence was founded as an academic discipline in 1956, and in the years since it has experienced several waves of optimism,[4][5] followed by disappointment and the loss of funding (known as an "AI winter"),[6][7] followed by new approaches, success, and renewed funding.[5][8] AI research has tried and discarded many different approaches, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge, and imitating animal behavior. In the first decades of the 21st century, highly mathematical and statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.[8][9]
The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.[a] General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals.[10][2] To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics. AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".[b] This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction (science fiction), and philosophy since antiquity.[12] Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards goals beneficial to humankind.[c] Economists have frequently highlighted the risks of redundancies from AI, and speculated about unemployment if there is no adequate social policy for full employment.[13] The term artificial intelligence has also been criticized for overhyping AI's true technological capabilities.[14][15][16]
History
Artificial beings with intelligence appeared as storytelling devices in antiquity,[17] and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R.[18] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[19]
The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical 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 act of mathematical deduction. This insight that digital computers can simulate any process of formal reasoning is known as the Church–Turing thesis.[20] This, along with concurrent discoveries in neurobiology, information theory and cybernetics, led researchers to consider the possibility of building an electronic brain.[21] The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".[22]
The field of AI research was born at a workshop at Dartmouth College in 1956.[d][25] The attendees became the founders and leaders of AI research.[e] They and their students produced programs that the press described as "astonishing":[f] computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.[g][27] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[28] and laboratories had been established around the world.[29]
Researchers in the 1960s and the 1970s were convinced that their methods would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.[30] Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do".[31] Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".[32]
They had failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill[33] and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.[6]
In the early 1980s, AI research was revived by the commercial success of expert systems,[34] 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.[5] 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.[7]
Many researchers began to doubt that the current practices would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.[35] Robotics researchers, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move, survive, and learn their environment.[h]
Interest in neural networks and "connectionism" was revived by Geoffrey Hinton, David Rumelhart and others in the middle of the 1980s.[40] Soft computing tools were developed in the 1980s, such as neural networks, fuzzy systems, Grey system theory, evolutionary computation and many tools drawn from statistics or mathematical optimization.
AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This "narrow" and "formal" focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics).[41] By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as "artificial intelligence".[9]
Faster computers[42] and access to large amounts of data[43] enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[44]
According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects.[i] He attributed this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[8] In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes".[45] The amount of research into AI (measured by total publications) increased by 50% in the years 2015–2019.[46] According to AI Impacts at Stanford, around 2022 about $50 billion annually is invested in artificial intelligence in the US, and about 20% of new US Computer Science PhD graduates have specialized in artificial intelligence;[47] about 800,000 AI-related US job openings existed in 2022.[48]
Numerous academic researchers became concerned that AI was no longer pursuing the original goal of creating versatile, fully intelligent machines. Much of current research involves statistical AI, which is overwhelmingly used to solve specific problems, even highly successful techniques such as deep learning. This concern has led to the subfield of artificial general intelligence (or "AGI"),[2] which had several well-funded institutions by the 2010s.[10]
Goals
The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.[a]
Reasoning, problem-solving
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[49] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[50]
Many of these algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.[51] 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.[52]
Knowledge representation
Knowledge representation and knowledge engineering[53] allow AI programs to answer questions intelligently and make deductions about real-world facts.
A representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them.[54] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge and act as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). A truly intelligent program would also need access to commonsense knowledge, the set of facts that an average person knows. The semantics of an ontology is typically represented in description logic, such as the Web Ontology Language.[55]
AI research has developed tools to represent specific domains, such as objects, properties, categories and relations between objects;[55] situations, events, states and time;[56] causes and effects;[57] knowledge about knowledge (what we know about what other people know); and [58] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[59]. Among the most difficult problems in AI are: the breadth of commonsense knowledge (the number of atomic facts that the average person knows is enormous);[60] 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).[52]
Formal knowledge representations are used in content-based indexing and retrieval,[61] scene interpretation,[62] clinical decision support,[63] knowledge discovery (mining "interesting" and actionable inferences from large databases),[64] and other areas.[65]
Learning
Machine learning (ML), a fundamental concept of AI research since the field's inception,[j] is the study of computer algorithms that improve automatically through experience.[k]
Unsupervised learning finds patterns in a stream of input.
Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and numerical regression. Classification is used to determine what category something belongs in – the program sees a number of examples of things from several categories and will learn to classify new inputs. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam".[69]
In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent classifies its responses to form a strategy for operating in its problem space.[70]
Transfer learning is when the knowledge gained from one problem is applied to a new problem.[71]
Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[72]
Natural language processing
Natural language processing (NLP)[73] allows machines to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of NLP include information retrieval, question answering and machine translation.[74]
Symbolic AI used formal syntax to translate the deep structure of sentences into logic. This failed to produce useful applications, due to the intractability of logic[51] and the breadth of commonsense knowledge.[60] Modern statistical techniques include co-occurrence frequencies (how often one word appears near another), "Keyword spotting" (searching for a particular word to retrieve information), transformer-based deep learning (which finds patterns in text), and others.[75] They have achieved acceptable accuracy at the page or paragraph level, and, by 2019, could generate coherent text.[76]
Perception
Machine perception[77] is the ability to use input from sensors (such as cameras, microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[78] facial recognition, and object recognition.[79] Computer vision is the ability to analyze visual input.[80]
Social intelligence
Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood.[82] For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. However, this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.[83] Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject.[84]
General intelligence
A machine with general intelligence can solve a wide variety of problems with breadth and versatility similar to human intelligence. There are several competing ideas about how to develop artificial general intelligence. Hans Moravec and Marvin Minsky argue that work in different individual domains can be incorporated into an advanced multi-agent system or cognitive architecture with general intelligence.[85] Pedro Domingos hopes that there is a conceptually straightforward, but mathematically difficult, "master algorithm" that could lead to AGI.[86] Others believe that anthropomorphic features like an artificial brain[87] or simulated child development[l] will someday reach a critical point where general intelligence emerges.
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