Artificial intelligenc (A1) its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.[1] Such machines may be designated as "artificial intelligence" (AI) systems.
Some high-profile applications of AI include advanced web search
engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon,
and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and
Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g.,
ChatGPT, Apple Intelligence, and AI art); and superhuman play and analysis in
strategy games (e.g., chess and Go). However, many AI applications are not
perceived as AI: "A lot of cutting edge AI has filtered into general
applications, often without being called AI because once something becomes
useful enough and common enough it's not labeled AI anymore."[2][3]
Alan Turing was the first person to conduct substantial
research in the field that he called "machine intelligence".[4]
Artificial intelligence was founded as an academic discipline in 1956,[5] by
those now considered the founding fathers of AI: John McCarthy, Marvin Minksy,
Nathaniel Rochester, and Claude Shannon.[6][7] The field went through multiple
cycles of optimism,[8][9] followed by periods of disappointment and loss of
funding, known as AI winter.[10][11] Funding and interest vastly increased
after 2012 when deep learning surpassed all previous AI techniques,[12] and
after 2017 with the transformer architecture.[13] This led to the AI boom of
the early 2020s, with companies, universities, and laboratories overwhelmingly
based in the United States pioneering significant advances in artificial
intelligence.[14]
The growing use of artificial intelligence in the 21st
century is influencing a societal and economic shift towards increased
automation, data-driven decision-making, and the integration of AI systems into
various economic sectors and areas of life, impacting job markets, healthcare,
government, industry, education, propaganda, and disinformation. This raises
questions about the long-term effects, ethical implications, and risks of AI,
prompting discussions about regulatory policies to ensure the safety and
benefits of the technology.
The various subfields of AI research are centered around
particular goals and the use of particular tools. The traditional goals of AI
research include reasoning, knowledge representation, planning, learning,
natural language processing, perception, and support for robotics.[a] General
intelligence—the ability to complete any task performable by a human on an at
least equal level—is among the field's long-term goals.[15]
To reach these goals, AI researchers have adapted and
integrated a wide range of techniques, including search and mathematical
optimization, formal logic, artificial neural networks, and methods based on
statistics, operations research, and economics.[b] AI also draws upon
psychology, linguistics, philosophy, neuroscience, and other fields.[16]
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.[a]
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.[17] By the late 1980s and 1990s, methods were developed for dealing
with uncertain or incomplete information, employing concepts from probability
and economics.[18]
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.[19]
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.[20]
Accurate and efficient reasoning is an unsolved problem.
Some high-profile applications of AI include advanced web search
engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon,
and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and
Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g.,
ChatGPT, Apple Intelligence, and AI art); and superhuman play and analysis in
strategy games (e.g., chess and Go). However, many AI applications are not
perceived as AI: "A lot of cutting edge AI has filtered into general
applications, often without being called AI because once something becomes
useful enough and common enough it's not labeled AI anymore."[2][3]
Alan Turing was the first person to conduct substantial
research in the field that he called "machine intelligence".[4]
Artificial intelligence was founded as an academic discipline in 1956,[5] by
those now considered the founding fathers of AI: John McCarthy, Marvin Minksy,
Nathaniel Rochester, and Claude Shannon.[6][7] The field went through multiple
cycles of optimism,[8][9] followed by periods of disappointment and loss of
funding, known as AI winter.[10][11] Funding and interest vastly increased
after 2012 when deep learning surpassed all previous AI techniques,[12] and
after 2017 with the transformer architecture.[13] This led to the AI boom of
the early 2020s, with companies, universities, and laboratories overwhelmingly
based in the United States pioneering significant advances in artificial
intelligence.[14]
The growing use of artificial intelligence in the 21st
century is influencing a societal and economic shift towards increased
automation, data-driven decision-making, and the integration of AI systems into
various economic sectors and areas of life, impacting job markets, healthcare,
government, industry, education, propaganda, and disinformation. This raises
questions about the long-term effects, ethical implications, and risks of AI,
prompting discussions about regulatory policies to ensure the safety and
benefits of the technology.
The various subfields of AI research are centered around
particular goals and the use of particular tools. The traditional goals of AI
research include reasoning, knowledge representation, planning, learning,
natural language processing, perception, and support for robotics.[a] General
intelligence—the ability to complete any task performable by a human on an at
least equal level—is among the field's long-term goals.[15]
To reach these goals, AI researchers have adapted and
integrated a wide range of techniques, including search and mathematical
optimization, formal logic, artificial neural networks, and methods based on
statistics, operations research, and economics.[b] AI also draws upon
psychology, linguistics, philosophy, neuroscience, and other fields.[16]
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.[a]
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.[17] By the late 1980s and 1990s, methods were developed for dealing
with uncertain or incomplete information, employing concepts from probability
and economics.[18]
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.[19]
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.[20]
Accurate and efficient reasoning is an unsolved problem.
