Progress in artificial intelligence
Artificial intelligence applications have been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery and toys. 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." "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 1990s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes.
Kaplan and Haenlein structure artificial intelligence along three evolutionary stages: 1) artificial narrow intelligence – applying AI only to specific tasks; 2) artificial general intelligence – applying AI to several areas and able to autonomously solve problems they were never even designed for; and 3) artificial super intelligence – applying AI to any area capable of scientific creativity, social skills, and general wisdom.
To allow comparison with human performance, artificial intelligence can be evaluated on constrained and well-defined problems. Such tests have been termed subject matter expert Turing tests. Also, smaller problems provide more achievable goals and there are an ever-increasing number of positive results.
|Game||Champion year||Legal states (log10)||Game tree complexity (log10)||Game of perfect information?||Ref|
|2p no-limit hold 'em||2017||Imperfect|||
In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. Yet, there are many other useful abilities that can be described as showing some form of intelligence. This gives better insight into the comparative success of artificial intelligence in different areas.
In what has been called the Feigenbaum test, the inventor of expert systems argued for subject specific expert tests. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior.
AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at. Some versions of Moravec's paradox observe that humans are more likely to outperform machines in areas such as physical dexterity that have been the direct target of natural selection. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI."
Games provide a high-profile benchmark for assessing rates of progress; many games have a large professional player base and a well-established competitive rating system. AlphaGo brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory; the most prominent milestone in this area was brought to a close by Libratus' poker victory in 2017. E-sports continue to provide additional benchmarks; Facebook AI, Deepmind, and others have engaged with the popular StarCraft franchise of videogames.
Broad classes of outcome for an AI test may be given as:
- optimal: it is not possible to perform better (note: some of these entries were solved by humans)
- super-human: performs better than all humans
- high-human: performs better than most humans
- par-human: performs similarly to most humans
- sub-human: performs worse than most humans
- Connect Four: 1988
- Checkers (aka 8x8 draughts): Weakly solved (2007)
- Rubik's Cube: Mostly solved (2010)
- Heads-up limit hold'em poker: Statistically optimal in the sense that "a human lifetime of play is not sufficient to establish with statistical significance that the strategy is not an exact solution" (2015)
- Othello (aka reversi): c. 1997
- Scrabble: 2006
- Backgammon: c. 1995-2002
- Chess: Supercomputer (c. 1997); Personal computer (c. 2006); Mobile phone (c. 2009); Computer defeats human + computer (c. 2017) 
- Jeopardy!: Question answering, although the machine did not use speech recognition (2011)
- Shogi: c. 2017
- Arimaa: 2015
- Go: 2017
- Heads-up no-limit hold'em poker: 2017
- Crosswords: c. 2012
- Dota 2: 2018
- Bridge card-playing: According to a 2009 review, "the best programs are attaining expert status as (bridge) card players", excluding bidding.
- StarCraft II: 2019
- Optical character recognition for ISO 1073-1:1976 and similar special characters.
- Classification of images
- Handwriting recognition 
- Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)
- Object recognition[clarification needed]
- Facial recognition: Low to mid human accuracy (as of 2014)
- Visual question answering, such as the VQA 1.0
- Various robotics tasks that may require advances in robot hardware as well as AI, including:
- Speech recognition: "nearly equal to human performance" (2017)
- Explainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.
- Stock market prediction: Financial data collection and processing using Machine Learning algorithms
- Various tasks that are difficult to solve without contextual knowledge, including:
Past and current predictions
An expert poll around 2016, conducted by Katja Grace of the Future of Humanity Institute and associates, gave median estimates of 3 years for championship Angry Birds, 4 years for the World Series of Poker, and 6 years for StarCraft. On more subjective tasks, the poll gave 6 years for folding laundry as well as an average human worker, 7–10 years for expertly answering 'easily Googleable' questions, 8 years for average speech transcription, 9 years for average telephone banking, and 11 years for expert songwriting, but over 30 years for writing a New York Times bestseller or winning the Putnam math competition.
An AI defeated a grandmaster in a regulation tournament game for the first time in 1988; rebranded as Deep Blue, it beat the reigning human world chess champion in 1997 (see Deep Blue versus Garry Kasparov).
|Year prediction made||Predicted year||Number of Years||Predictor||Contemporaneous source|
|1957||1967 or sooner||10 or less||Herbert A. Simon, economist|
|1990||2000 or sooner||10 or less||Ray Kurzweil, futurist||Age of Intelligent Machines|
AlphaGo defeated a European Go champion in October 2015, and Lee Sedol in March 2016, one of the world's top players (see AlphaGo versus Lee Sedol). According to Scientific American and other sources, most observers had expected superhuman Computer Go performance to be at least a decade away.
|Year prediction made||Predicted year||Number of years||Predictor||Affiliation||Contemporaneous source|
|1997||2100 or later||100 or more||Piet Hutt, physicist and Go fan||Institute for Advanced Study||New York Times|
|2007||2017 or sooner||10 or less||Feng-Hsiung Hsu, Deep Blue lead||Microsoft Research Asia||IEEE Spectrum|
|2014||2024||10||Rémi Coulom, Computer Go programmer||CrazyStone||Wired|
Human-level artificial general intelligence (AGI)
AI pioneer and economist Herbert A. Simon inaccurately predicted in 1965: "Machines will be capable, within twenty years, of doing any work a man can do". Similarly, in 1970 Marvin Minsky wrote that "Within a generation... the problem of creating artificial intelligence will substantially be solved."
The Grace poll around 2016 found results varied depending on how the question was framed. Respondents asked to estimate "when unaided machines can accomplish every task better and more cheaply than human workers" gave an aggregated median answer of 45 years and a 10% chance of it occurring within 9 years. Other respondents asked to estimate "when all occupations are fully automatable. That is, when for any occupation, machines could be built to carry out the task better and more cheaply than human workers" estimated a median of 122 years and a 10% probability of 20 years. The median response for when "AI researcher" could be fully automated was around 90 years. No link was found between seniority and optimism, but Asian researchers were much more optimistic than North American researchers on average; Asians predicted 30 years on average for "accomplish every task", compared with the 74 years predicted by North Americans.
|Year prediction made||Predicted year||Number of years||Predictor||Contemporaneous source|
|1965||1985 or sooner||20 or less||Herbert A. Simon||The shape of automation for men and management|
|1993||2023 or sooner||30 or less||Vernor Vinge, science fiction writer||"The Coming Technological Singularity"|
|1995||2040 or sooner||45 or less||Hans Moravec, robotics researcher||Wired|
|2008||Never||Gordon E. Moore, inventor of Moore's Law||IEEE Spectrum|
- Applications of artificial intelligence
- List of artificial intelligence projects
- List of emerging technologies
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