Summary

The Dartmouth summer workshop (regarded as the starting point of artificial intelligence research)

In the summer of 1956, researchers gathered on the top floor of the Dartmouth College mathematics department and discussed thinking machines. It was less a conference with a fixed schedule than a gathering where people came and went, and the daily discussions involved about 3 to 8 people. Only three people stayed for the whole period: John McCarthy, Marvin Minsky and Ray Solomonoff. Allen Newell and Herbert A. Simon brought the results of their program that proves logic theorems (Logic Theorist).

What we still don’t know:An exact list of participants has not survived (McCarthy lost his own list, according to Grace Solomonoff 2016 p.9). Who stayed for how many days can be partly worked out only from notes by Ray Solomonoff and Trenchard More.

R1 (XCON): an AI of “IF–THEN rules” that decides the combination of parts for computer orders, used every day at DEC's factory

John McDermott of Carnegie Mellon University developed R1 (called XCON inside the company), an AI of “IF–THEN rules” that takes orders for the computer company DEC's VAX computers and checks for shortages and combinations of the parts needed (about 90 parts per machine). Development began in December 1978; in October–November 1979 six experts checked it on 50 orders; and from January 1980 it was used routinely at DEC's factory. The number of rules grew from 772 in June 1980 to 2,500 in 1984, and adding knowledge needed about 4 people's worth of work every year. A DEC staff member wrote that maintenance work after development was where “It probably represents the largest cost”, and that specially trained people are essential.

What we still don’t know:How the estimate of “in excess of $40 million per year” was calculated (what was counted as a benefit) is not known, because the original paper could not be opened.

ICOT (the Institute for New Generation Computer Technology), the Fifth Generation research institute, is founded, with Kazuhiro Fuchi as director

To carry out the government's (Ministry of International Trade and Industry's) plan, the foundation “Institute for New Generation Computer Technology (ICOT)” was established in April 1982, and the research institute began its activities in June. The director of the institute was Kazuhiro Fuchi, who came from the Electrotechnical Laboratory. The researchers were people sent for a few years each from the Electrotechnical Laboratory (ETL), the Nippon Telegraph and Telephone Public Corporation (NTT), eight computer companies, and others, and at first there were about 40 of them. All of the research funds were commissioned funds from the Ministry of International Trade and Industry.

In the image-recognition competition ILSVRC 2012, the University of Toronto's deep convolutional neural network (SuperVision) wins by a wide margin. The runner-up is a University of Tokyo team

In a competition to identify 1,000 kinds of objects from photos, the team “SuperVision” of Krizhevsky, Sutskever and Hinton held its error rate, under a rule allowing 5 guesses, to 15.3% (16.4% if only the data supplied by the competition is used). The runner-up, a University of Tokyo team (ISI), scored 26.2%, a gap of about 11 points. They trained an 8-layer neural network with 60 million parameters on 2 gaming GPUs (graphics chips, made by NVIDIA). In a later paper, the competition's organizers wrote that “This was a turning point for large-scale object recognition” for research on recognizing a wide variety of objects.

The Conversational AI “ChatGPT” Is Released as a Free “research preview”

OpenAI released the conversational AI “ChatGPT”. The announcement says it can answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. Inside, it is a sibling of GPT-3.5 whose training finished in early 2022, tuned by having humans score its answers. It was free during the “research preview,” and the stated purpose was to collect users' feedback.

What we still don’t know:As one reason it is hard to fix mistakes, the announcement gives that “during RL training, there’s currently no source of truth” (RL means reinforcement learning). Why and when plausible-sounding mistakes (later called “hallucinations”) happen was something the developers, even at this point, still wrote “Fixing this issue is challenging”.

Japan's Ministry of Internal Affairs and Communications white paper (2024) says that with the spread of generative AI from around 2022, “it is also said that we have now entered the fourth AI boom” (translated)

A national white paper (the Ministry of Internal Affairs and Communications' White Paper on Information and Communications in Japan, 2024 edition) explained that the history of AI “has repeated booms and winters several times” (translated). It then wrote that “with the rapid spread of generative AI from around 2022” (translated) (generative AI is AI that creates text and images), “it is also said that we have now entered the fourth AI boom” (translated). It does not state this flatly, staying with the phrase “it is also said” (translated). In the same section it also writes of the third boom as “from the 2000s” (translated) and “the third AI boom that continues to this day” (translated). In the figure (Figure I-3-1-1), the band for the fourth boom starts in 2022, and the figure says “moving into a worldwide AI boom without passing through a winter era” (translated).

Prediction: “within ten years a digital computer will be the world’s chess champion” (1957 lecture)

Herbert A. Simon and Allen Newell predicted that within ten years a computer would become the world chess champion (unless the rules barred it from competition).

What happened next:As of April 1967, one of the strongest chess programs (MIT's MAC HACK VI) played at the level of a mid-ranked amateur (1450 on the American rating scale). In 1968, International Master David Levy and some researchers made a bet on whether a computer could beat Levy within ten years. In 1978 Levy beat the computer and won the bet. A computer beat a reigning world champion (Kasparov) in a six-game match about 40 years after the prediction, in May 1997 (IBM's Deep Blue, 3.5 to 2.5). Note that a computer has never won the title of human world champion itself (the prediction also says “unless the rules bar it from competition”).

1984: at an AI conference exhibit hall, an ad says “We’ve built a better brain” — a record of the mood that this time it was the real thing

Science writer George Johnson described the exhibit hall at the August 1984 conference of the American Association for Artificial Intelligence (AAAI). Most researchers had come to discuss small steps forward, but some exhibiting companies acted as if the age-old dream of making intelligent companions had already come true. The brochure for TIMM, an AI tool for entering experts' knowledge, boasted “We’ve built a better brain” and said that expert systems never get sick and never resign or retire early. The reporter wrote that the sellers' words sometimes sounded like “prophets proclaiming a new age”.

What we still don’t know:The article does not name the company that sold TIMM.

Yutaka Matsuo's “Will Artificial Intelligence Surpass Humans?” (2015): chapter names use the first, second and third AI booms

This is a book by Yutaka Matsuo of the University of Tokyo. It divides history by boom number, using chapter names such as “The Era of ‘Inference’ and ‘Search’—The First AI Boom”, “Add ‘Knowledge’ and It Gets Smarter—The Second AI Boom”, “The Quiet Spread of ‘Machine Learning’—The Third AI Boom (1)”, and “‘Deep Learning’ Breaks the Silence—The Third AI Boom (2)”. The first section of Chapter 2 is “Booms and the Winter Years”. The 2016 white paper of the Ministry of Internal Affairs and Communications notes in a footnote that its section on the history of AI was written with reference to this book and others.

What we still don’t know:We have not checked which years the book gives for the start and end of each boom (the years in the text and figures), because we have not opened the book.

Yutaka Matsuo (July 2023): “It may be fair to say the fourth AI boom has arrived” (translated), but it is “a phenomenon of a different dimension from the word ‘boom’” (translated)

In an interview with Toyo Keizai, Yutaka Matsuo said, “There have been three AI booms in the past” (translated), and that “the spread of generative AI may be fine to call the arrival of the fourth AI boom” (translated), but that “What is happening now is a phenomenon of a different order from the word ‘boom’” (translated). He also said generative AI would spread through society all at once over the next few years. As with “it is fine to say” (translated) in the March interview, here too he uses the hedged phrasing “may be fine to say” (translated). At the same time, he does not see it as a boom (a fad that eventually ends).

What we still don’t know:Whether the view that it is “on a different level from the word ‘boom’” (translated) proves right (that is, whether no winter comes) is still unknown. Whether people also said “this time is different” during past booms needs to be checked by comparing with this site's records of each boom.

The worst-case scenario of “The AI Winter” drawn in a 1984 discussion ── checking the answers afterward

At the conference of the American Association for Artificial Intelligence (AAAI), a panel discussion was held titled “The ‘Dark Ages’ of AI – Can We Avoid Them or Survive Them?” The moderator, Drew McDermott, said that behind the bustle in industry, there was deep unease among people who had researched for many years, from a worry that “perhaps expectations about AI are too high ... will eventually result in disaster”. As the worst-case scenario he drew this picture: in five years America's “strategic computing initiative” fails, Japan's Fifth Generation also hits a dead end, start-up companies go under, funding for AI stops, and everybody changes the names of their research to something else. He introduced the fact that some people call this state “The AI Winter.” But he also said he thought it unlikely. In the discussion, another researcher (Roger Schank), when asked whether he thought a catastrophe would come, said that he told them it would. On the other hand, the view “Will there be an AI Winter? I think there may be an AI dusk” was also put forward (the same person went on to say that the bubble might not burst but shrink a little).

What happened next:Afterward, development of the autonomous land vehicle (ALV), one of the plan's showpieces, was cut off in April 1988. The head of the responsible division, who took up the post in 1987, also cut off parts of the AI program. The plan disappeared from DARPA's budget in 1993. Membership in the American Association for Artificial Intelligence peaked at 16,421 in 1987 and settled at 4,000–5,000 by 1996. Many of the AI companies developing expert systems disappeared, were acquired, or changed their business. Japan's Fifth Generation Computer Project ended its main stage in fiscal 1992. Nilsson writes that these events piled up in the mid-to-late 1980s and came to be called “The AI Winter.”

What we still don’t know:Whether one of the scenarios, “Everybody hurriedly changes the names of their research projects to something else”, really happened in the 1990s has not been confirmed in original sources (a candidate dealt with in u08)

Turing’s forecast: “in about fifty years' time ... 70 per cent chance ... after five minutes”

In his 1950 paper, Turing set down his own forecast. In about 50 years, he wrote, computers could be made to play the imitation game well, and an average interrogator would have no more than a 70% chance of identifying correctly after 5 minutes of questioning. In the same paragraph he also wrote that by the end of the century the use of words and people’s ideas would have changed, so that one could “speak of machines thinking” without being contradicted.

What happened next:According to a survey paper from 2000 (Saygin et al.), no program had yet won the grand prize in the contest with prize money that had been held since 1991 (the Loebner Prize), and the records show that the machines were usually spotted quickly. In other words, in the year of the forecast, it had not come true in the form predicted. Some said the results of the 2000 contest “shows Turing’s prediction to have been in error”, but in the same year, 2000, Copeland wrote that “‘About fifty years’ does not mean ‘exactly fifty years’”. Then in March 2025, researchers at a US university (UC San Diego) published in a paper (arXiv) the results of an experiment in which an interrogator chatted for 5 minutes with a human and a machine at the same time. Of two large language AIs told to play the part of a human, one (GPT-4.5) was judged to be the human 73% of the time and the other (LLaMa-3.1-405B) 56% of the time (since there were two choices, the interrogator correctly spotted the first one only in the remaining 27%). But this is the announcement of one research group, and we have not confirmed later evaluations on this site. The part about being able to “speak of machines thinking without expecting to be contradicted” has not been confirmed.

What we still don’t know:Whether a machine being judged human in the 2025 experiment means that it can “think” is not known (Turing himself, regarding the original question as not worth discussing, replaced it with the game).

Simon's prediction “machines will be capable, within twenty years, of doing any work” (1965 book／originally a 1960 paper)

In his 1965 book, Herbert Simon wrote that within 20 years machines would be able to do any work a human can do. In the 1960 paper it was based on, he wrote that in a period much shorter than 25 years machines would have the “technical capability” to replace any human function in organizations. Immediately afterward, however, he went on to say that what is technically possible is separate from how it is actually used. He then assumed that, in the long run, nearly everyone who wants to work can work (full employment), and argued that the division of labor between people and machines is decided by “comparative advantage” (each side takes on what it is relatively better at).

What happened next:No source was found saying that by 1985 machines could do any human work. Nilsson (2010) says that, even as of 2010, Simon's similar statement from 1957 (that the range of problems machines can handle would become as wide as the human mind) was still far from coming true. Simon himself, on the other hand, thought of technical capability and employment separately and expected that, over the long run, unemployment would not rise (in his 1966 reply he wrote that the unemployment rate was below 4% and falling).

The 1981 forecast: it is said that in the early 1990s computers with eyes, ears, and a mouth, that understand speech and can do automatic translation and interpreting, will appear

The report of the survey before the plan (1979–1981; chaired by Tohru Moto-oka of the University of Tokyo) stated that the goal was to complete a prototype in the year 1990. A booklet explaining it for the general public wrote that fifth-generation computers are “said to appear in the early 1990s” (translated) and that research must start early to “realize a prototype in 1990” (translated) (to build a trial machine). As the pictures of what they aimed for, it listed eight functions, including “computer with eyes, ears and a mouth” (translated) that can handle sound, figures, images, and characters as they are; “computer that understands what is said” (translated) that can be spoken to in everyday language (and so can also do automatic translation and interpreting); computers that have common sense and solve unfamiliar problems by reasoning; and computers that can be used without writing programs.

What happened next:The project ran in fiscal years 1982–1992. According to the 1993 final evaluation report, the goal for inference speed (100 million per second) was met and exceeded, at over 150 million per second. On the other hand, the report says that systems that interact with people by voice or images turned out to need a huge amount of background knowledge, so partway through the project narrowed its focus to a base for language research and did not get as far as building concrete systems. In 1992 the director, Kazuhiro Fuchi, said that building application systems such as machine translation had been dropped from the goals when the project began in 1982.

What we still don’t know:The “Statement of R&D Topics” (translated) (1981; 7 groups, 26 tasks), which are said to set numerical goals such as the number of words translated and accuracy, could not be found and have not been checked.

Hinton: “people should stop training radiologists now”, “within 5 years deep learning is going to do better than radiologists” (2016)

In 2016, Geoffrey Hinton spoke at a conference in Toronto about radiologists (doctors who read images such as X-rays and CT scans to find disease). His main points were that training radiologists should stop right now, and that it is obvious that within 5 years (it might be 10) deep learning will do better. An 84-second video recording the occasion was published, and it has been quoted repeatedly in later articles (AuntMinnie 2025, Fortune 2026 and others).

What happened next:In the US residency recruitment (the official NRMP tables), positions in radiology (diagnostic) did not fall. Positions starting in the second year went from 982 in 2016 to 1,057 in 2025, and positions starting in the first year went from 151 in 2016 to 151 in 2025. Almost all the positions in 2025 were filled (1,042 of the 1,057 second-year positions).

What we still don’t know:How AI's ability to read images relates to the whole work of a radiologist (talking with patients and other doctors, deciding on tests, and so on) was not well understood in 2016. Even now, how this job will change with AI is still being studied.

“RoboCup”, started by Japanese researchers: the goal is for a team of humanoid robots to beat the World Cup winners by the middle of the 21st century

“RoboCup”, which makes robot soccer a shared challenge for AI and robotics research, was started by Japanese researchers (Hiroaki Kitano, Minoru Asada, Yasuo Kuniyoshi and others). They conceived it in June 1993 as a Japan-only “Robot J-League”, but within a month people overseas wanted to take part, and it grew into an international project. The first competition was held in Nagoya in 1997, drawing more than 40 teams and more than 5000 spectators. The ultimate goal stated on the official site is that, by the middle of the 21st century, “a team of fully autonomous humanoid robot soccer players shall win a soccer game” against the winner of the most recent World Cup, following the official rules of FIFA.

What happened next:The deadline has not arrived, so there is no result yet. On July 5, 2026, at RoboCup 2026 held in Incheon, South Korea, 11-on-11 matches between humanoid robots were played for the first time (Germany's B-Human beat Germany's HTWK 4–0). But this was a match between robots, not against human champions.

Turing: “at least 100 years” (a radio discussion)

In January 1952 on BBC radio, four people (Turing, the mathematician Newman, the neurosurgeon Jefferson and the philosopher Braithwaite) discussed “Can Automatic Calculating Machines Be Said To Think”. When Newman asked whether it would be “a long time from now ... stand any chance with no questions barred”, Turing answered “Oh yes, at least 100 years, I should say.”

What happened next:The target year has not come yet (awaiting the answer). In 2025 an experiment was published in a paper in which a large language AI was judged to be human at a high rate in the 5-minute conversation form (see → Turing’s forecast: in about fifty years…), but we have not confirmed results for the long form (a game played “with no questions barred”) on this site.

What we still don’t know:We have not been able to view the broadcast script (AMT/B/6) itself on this site. We have not checked whether the recording survives.

List of Predictions of When AI Will Arrive (AI Impacts Revised Version): 65 Entries, Median Prediction 2033–2037

AI Impacts reorganized Armstrong and colleagues' list of 95 entries into 65, removing duplicates and summaries of surveys. The median date the predictions give for human-level AI was 2033 and 2037 (depending on two ways of reading them). Comparing predictions made before 2000 and after, the distribution of how many years after the prediction the arrival is expected is similar (the correlation between the year of the prediction and the number of years is 0.13).

A study finding that AI predictions mostly fall “15 to 25 years in the future” (the paper's first page now carries the note “The findings in this paper are based on a dataset error”)

A paper that collected and analyzed 95 predictions of when AI would arrive. The most common prediction was “15 to 25 years in the future”, and it concluded that predictions by experts and non-experts cannot be told apart. The first page of the publicly available PDF now carries the note “The findings in this paper are based on a dataset error”. The error came from a misreading of the column classifying whether someone is an expert (pointed out by AI Impacts in 2016).

What we still don’t know:Whether predictions cluster around 20 years ahead changes depending on how the data are built. It is not clear.

A study finding that AI predictions mostly fall “15 to 25 years in the future” (the paper's first page now carries the note “The findings in this paper are based on a dataset error”)

A paper that collected and analyzed 95 predictions of when AI would arrive. The most common prediction was “15 to 25 years in the future”, and it concluded that predictions by experts and non-experts cannot be told apart. The first page of the publicly available PDF now carries the note “The findings in this paper are based on a dataset error”. The error came from a misreading of the column classifying whether someone is an expert (pointed out by AI Impacts in 2016).

What we still don’t know:Whether predictions cluster around 20 years ahead changes depending on how the data are built. It is not clear.

List of Predictions of When AI Will Arrive (AI Impacts Revised Version): 65 Entries, Median Prediction 2033–2037

AI Impacts reorganized Armstrong and colleagues' list of 95 entries into 65, removing duplicates and summaries of surveys. The median date the predictions give for human-level AI was 2033 and 2037 (depending on two ways of reading them). Comparing predictions made before 2000 and after, the distribution of how many years after the prediction the arrival is expected is similar (the correlation between the year of the prediction and the number of years is 0.13).

A study finding that AI predictions mostly fall “15 to 25 years in the future” (the paper's first page now carries the note “The findings in this paper are based on a dataset error”)

A paper that collected and analyzed 95 predictions of when AI would arrive. The most common prediction was “15 to 25 years in the future”, and it concluded that predictions by experts and non-experts cannot be told apart. The first page of the publicly available PDF now carries the note “The findings in this paper are based on a dataset error”. The error came from a misreading of the column classifying whether someone is an expert (pointed out by AI Impacts in 2016).

What we still don’t know:Whether predictions cluster around 20 years ahead changes depending on how the data are built. It is not clear.

List of Predictions of When AI Will Arrive (AI Impacts Revised Version): 65 Entries, Median Prediction 2033–2037

AI Impacts reorganized Armstrong and colleagues' list of 95 entries into 65, removing duplicates and summaries of surveys. The median date the predictions give for human-level AI was 2033 and 2037 (depending on two ways of reading them). Comparing predictions made before 2000 and after, the distribution of how many years after the prediction the arrival is expected is similar (the correlation between the year of the prediction and the number of years is 0.13).

Lovelace’s forecast: “the engine might compose elaborate and scientific pieces of music”

In Note A, Lovelace argued that the Analytical Engine should be able to handle not only numbers but anything whose relations can be expressed in symbols. As an example, she wrote that if the relations between pitches of sound could be expressed in the machine’s symbols, the machine might compose music of any degree of complexity and length, elaborate pieces.

What happened next:The Analytical Engine itself was never built. About 110 years later, on August 9, 1956, at the University of Illinois in the United States, students performed the first three movements of a string quartet composed with the computer ILLIAC I (the Illiac Suite) (the university museum’s account; it was completed by the end of 1956, though some sources write 1957).

Prediction: “within ten years a digital computer will be the world’s chess champion” (1957 lecture)

Herbert A. Simon and Allen Newell predicted that within ten years a computer would become the world chess champion (unless the rules barred it from competition).

What happened next:As of April 1967, one of the strongest chess programs (MIT's MAC HACK VI) played at the level of a mid-ranked amateur (1450 on the American rating scale). In 1968, International Master David Levy and some researchers made a bet on whether a computer could beat Levy within ten years. In 1978 Levy beat the computer and won the bet. A computer beat a reigning world champion (Kasparov) in a six-game match about 40 years after the prediction, in May 1997 (IBM's Deep Blue, 3.5 to 2.5). Note that a computer has never won the title of human world champion itself (the prediction also says “unless the rules bar it from competition”).

Turing’s forecast: “in about fifty years' time ... 70 per cent chance ... after five minutes”

In his 1950 paper, Turing set down his own forecast. In about 50 years, he wrote, computers could be made to play the imitation game well, and an average interrogator would have no more than a 70% chance of identifying correctly after 5 minutes of questioning. In the same paragraph he also wrote that by the end of the century the use of words and people’s ideas would have changed, so that one could “speak of machines thinking” without being contradicted.

What happened next:According to a survey paper from 2000 (Saygin et al.), no program had yet won the grand prize in the contest with prize money that had been held since 1991 (the Loebner Prize), and the records show that the machines were usually spotted quickly. In other words, in the year of the forecast, it had not come true in the form predicted. Some said the results of the 2000 contest “shows Turing’s prediction to have been in error”, but in the same year, 2000, Copeland wrote that “‘About fifty years’ does not mean ‘exactly fifty years’”. Then in March 2025, researchers at a US university (UC San Diego) published in a paper (arXiv) the results of an experiment in which an interrogator chatted for 5 minutes with a human and a machine at the same time. Of two large language AIs told to play the part of a human, one (GPT-4.5) was judged to be the human 73% of the time and the other (LLaMa-3.1-405B) 56% of the time (since there were two choices, the interrogator correctly spotted the first one only in the remaining 27%). But this is the announcement of one research group, and we have not confirmed later evaluations on this site. The part about being able to “speak of machines thinking without expecting to be contradicted” has not been confirmed.

What we still don’t know:Whether a machine being judged human in the 2025 experiment means that it can “think” is not known (Turing himself, regarding the original question as not worth discussing, replaced it with the game).

Will AI Win Gold at the Math Olympiad by 2025? In 2022 Two Researchers Forecast Under 8% and Over 16%; the Result Came in July 2025

The AI researchers Paul Christiano and Eliezer Yudkowsky differed in their views on the progress of AI. So, as a prediction that can be settled one way or the other, they publicly gave their probabilities that, at the International Mathematical Olympiad (a high-school competition), an AI “made before the IMO is able to get a gold … one of 2022-2025” (translated) (the rules, such as time, follow the IMO Grand Challenge). Christiano said under 8%, and Yudkowsky said 16% or more. For solving the hardest problem, Christiano predicted under 4%. In July 2025, Google DeepMind announced that an enhanced version of Gemini had fully solved 5 of the 6 problems and earned 35 points out of 42 (a gold-medal score), formally graded and confirmed by the competition's graders. It wrote its answers in ordinary language within the 4.5-hour time limit.

What happened next:In July 2025, an enhanced version of Google DeepMind's Gemini earned 35 points out of 42 (a gold-medal score). The competition's graders formally graded it, and the president of the International Mathematical Olympiad confirmed it as “a gold medal score” (translated) (however, according to the blog, the confirmation was of the answers, not a check of how the AI works). It was done within the 4.5-hour time limit, and the hardest problem (Problem 6) was not solved (5 of 6 problems). Because both predictions are probabilities, neither can be called right or wrong outright, but the actual result was closer to the side that had put the probability higher (over 16%).

2005, Kurzweil: “I set the date for the Singularity … as 2045”. By his own account, machine intelligence matching humans comes “Within a quarter century” (by 2030), and 2045 is the year of “one billion times more powerful than all human intelligence today”

Ray Kurzweil (introduced in the 2014 Journal of the Japanese Society for Artificial Intelligence as an “American futurist” (translated)), in a Q&A in which he answered questions about his 2005 book “The Singularity Is Near” (Japanese edition: “The Birth of the Post-Human”), wrote that within a quarter century the intelligence of machines would match the breadth and subtlety of human intelligence, and then go on to surpass it. He went on to call the point at which machine intelligence redesigns itself, grows faster and smarter at an ever greater pace, and technology advances too fast for unmodified human intelligence to keep up, “the Singularity”, and wrote “I set the date for the Singularity … as 2045”. The machine intelligence created in that year, he says, would be “one billion times more powerful than all human intelligence today”. As for the amount of computation needed, he also wrote that the work of a whole human brain needs about 10 quadrillion (10^16) calculations per second, that supercomputers will reach this around the end of the 2000s (two Japanese projects are aiming for it), and that by 2020 it will be available for about $1,000.

What happened next:The check on 2045 is still in the future (as of September 2026). Among the milestones along the way, on the forecast that supercomputers would reach 10^16 calculations per second (10 quadrillion), Japan's RIKEN “K computer” scored 10.51 petaflops in the TOP500 list of November 2011. It became the first supercomputer to reach “10 Petaflop/s, or 10 quadrillion calculations per second” (a little later than the forecast of “around the end of this decade”). For the forecast that by 2020 about $1,000 would buy 10^16 calculations per second, this site has not opened any material to check it.

What we still don’t know:The amount of computation needed for the work of a whole human brain differs by a factor of 100 depending on who estimates it (Kurzweil 2005: 10^16 calculations per second; Moravec 1998: 100 million MIPS = 10^14 instructions per second). How much computation the brain actually does is still not known.

Tracing a national teaching material's prediction, “By 2025, performance will reach the point where a single human brain can be fully simulated” (translated), back to its source — it began as a line drawn on a supercomputer-speed figure in Kurzweil's 2005 book

Teacher-training material (2020) from the Ministry of Education, Culture, Sports, Science and Technology for the high-school course “Information II” (Japan's high-school informatics course that follows Information I), based on a document from the Ministry of Internal Affairs and Communications' 2014 White Paper on Information and Communications, says this: if computing speed keeps rising as it is, by 2025 it will reach the performance needed to fully simulate (imitate and run inside a computer) one human brain. Tracing the source, the White Paper carried this statement not in its main text but in Figure 3-1-1-4, “Improvement in Computing Speed”. The figure's source is a document presented at a Ministry of Internal Affairs and Communications meeting on February 13, 2014 by Mr. Kitani (NTT Data Technology Development Headquarters, according to the document's cover). That document says it was made from a figure in Ray Kurzweil's 2005 book The Singularity Is Near. The original figure plots supercomputer speeds (FLOPS: how many decimal-number calculations per second) as dots, with a straight line (Trendline) that doubles every 1.2 years, and at the point where that line reaches 10¹⁹ FLOPS it carries the note “Required for Human Brain Neural Simulation for Uploading (2025)”. In other words, the original was not brain simulation itself but a forecast of computing speed.

What happened next:Checking computing speed: by the same measure as the original figure (the HPL value used in the TOP500 supercomputer performance ranking; the original figure also cites TOP500 as its source), as of November 2025 the leader, El Capitan (USA), is at 1.809 exaflops (about 1.8×10¹⁸ operations per second), only about one fifth (1/5.5) of 10¹⁹ FLOPS. LineShine (China), which took first place in June 2026, is at 2.198 exaflops and has not reached it either. On the other hand, in HPL-MxP (formerly HPL-AI), a different ranking that measures a method mixing in calculations with fewer digits (mixed-precision), three machines passed 10¹⁹ FLOPS in November 2025: El Capitan (16.680), Aurora (11.643) and Frontier (11.390). 10¹⁶ FLOPS (predicted in the figure for 2013) had been reached by Japan's “K computer” in November 2011 at 10.51 petaflops. Brain simulation results: in 2013, using all the compute nodes of the “K computer”, a simulation of 1.73 billion nerve cells and 10.4 trillion synapses (about 1% of the human brain's neural circuitry) took 40 minutes to reproduce 1 second of brain activity (RIKEN and others). In 2018, RIKEN wrote that even the highest-performance supercomputer of the time could not simulate a whole human brain, and announced an approach toward achieving it on next-generation supercomputers from 2020 on. In November 2025 the Allen Institute, the University of Electro-Communications and others announced that they had used “Fugaku” to simulate in detail the whole cerebral cortex of a mouse (about 10 million nerve cells, 26 billion synapses and 86 brain regions). The Allen Institute's Anton Arkhipov said that the long-term goal is to model the whole brain, and eventually a model of the human brain. None of the announcements from the parties concerned that I opened on this site said that a whole human brain had been simulated by 2025.

What we still don’t know:How much computation is really needed to simulate a whole human brain differs greatly depending on who estimates it. A note in another chapter of the same 2014 White Paper gives a forecast, from IBM's Modha in 2008, that simulating a human brain would become possible in 2018 at 38.5 petaflops (about 4×10¹⁶). That is a gap of about 260 times from Kurzweil's figure of 10¹⁹. The speed needed changes with how finely nerve cells are imitated (for example, the 2025 simulation on “Fugaku” was announced as imitating even the flow of electricity inside the branches of nerve cells). This site has not been able to confirm whether there is no example anywhere of a whole human brain being simulated by 2025 (I opened only three announcements from the parties concerned).

2005, Kurzweil: “I set the date for the Singularity … as 2045”. By his own account, machine intelligence matching humans comes “Within a quarter century” (by 2030), and 2045 is the year of “one billion times more powerful than all human intelligence today”

Ray Kurzweil (introduced in the 2014 Journal of the Japanese Society for Artificial Intelligence as an “American futurist” (translated)), in a Q&A in which he answered questions about his 2005 book “The Singularity Is Near” (Japanese edition: “The Birth of the Post-Human”), wrote that within a quarter century the intelligence of machines would match the breadth and subtlety of human intelligence, and then go on to surpass it. He went on to call the point at which machine intelligence redesigns itself, grows faster and smarter at an ever greater pace, and technology advances too fast for unmodified human intelligence to keep up, “the Singularity”, and wrote “I set the date for the Singularity … as 2045”. The machine intelligence created in that year, he says, would be “one billion times more powerful than all human intelligence today”. As for the amount of computation needed, he also wrote that the work of a whole human brain needs about 10 quadrillion (10^16) calculations per second, that supercomputers will reach this around the end of the 2000s (two Japanese projects are aiming for it), and that by 2020 it will be available for about $1,000.

What happened next:The check on 2045 is still in the future (as of September 2026). Among the milestones along the way, on the forecast that supercomputers would reach 10^16 calculations per second (10 quadrillion), Japan's RIKEN “K computer” scored 10.51 petaflops in the TOP500 list of November 2011. It became the first supercomputer to reach “10 Petaflop/s, or 10 quadrillion calculations per second” (a little later than the forecast of “around the end of this decade”). For the forecast that by 2020 about $1,000 would buy 10^16 calculations per second, this site has not opened any material to check it.

What we still don’t know:The amount of computation needed for the work of a whole human brain differs by a factor of 100 depending on who estimates it (Kurzweil 2005: 10^16 calculations per second; Moravec 1998: 100 million MIPS = 10^14 instructions per second). How much computation the brain actually does is still not known.

1998, Moravec: “When will computer hardware match the human brain?” The answer: “available in cheap machines in the 2020s”. And AI research from 1960 to 1990 lay in “the frozen winter of 1-MIPS computers”

Hans Moravec of the Robotics Institute at Carnegie Mellon University estimated, in a 1998 paper, the computing speed and memory needed to do what a brain does. Replacing the work of the retina with robot image processing comes to 1,000 MIPS (MIPS = millions of instructions per second). Since the brain is about 100,000 times the size of the retina, he put the whole brain at about 100 million MIPS and its memory at about 100 million megabytes. From the trend of computers getting cheaper, he predicted that “the required hardware will be available in cheap machines in the 2020s”, and that this 100 million MIPS “will then arrive in home computers before 2030”. The same paper points out that the performance of computers available for AI research stayed at about 1 MIPS from 1960 to 1990 without growing, so that a researcher's whole career passed in “the frozen winter of 1-MIPS computers”. It also says that performance has doubled every year since 1990, and that now (1998) it is spring.

What happened next:As of September 2026, the deadline of the prediction (the 2020s, before 2030) has not yet arrived. Also, Moravec's yardsticks (MIPS = millions of instructions per second, a home machine of about $1,000) do not match today's measures of AI computing (floating-point operations per second, FLOP/s, and so on). The result changes depending on how one converts between them, so this site does not judge whether the prediction was right or wrong.

What we still don’t know:The estimate that the brain's work equals 100 million MIPS is one way of thinking, based on replacing the retina's work with robot image processing; how much computation the brain does is still not known (the paper itself says that “our estimates will depend on analogy and extrapolation”; extrapolation means extending the line of past trends into the future to estimate).

Tracing a national teaching material's prediction, “By 2025, performance will reach the point where a single human brain can be fully simulated” (translated), back to its source — it began as a line drawn on a supercomputer-speed figure in Kurzweil's 2005 book

Teacher-training material (2020) from the Ministry of Education, Culture, Sports, Science and Technology for the high-school course “Information II” (Japan's high-school informatics course that follows Information I), based on a document from the Ministry of Internal Affairs and Communications' 2014 White Paper on Information and Communications, says this: if computing speed keeps rising as it is, by 2025 it will reach the performance needed to fully simulate (imitate and run inside a computer) one human brain. Tracing the source, the White Paper carried this statement not in its main text but in Figure 3-1-1-4, “Improvement in Computing Speed”. The figure's source is a document presented at a Ministry of Internal Affairs and Communications meeting on February 13, 2014 by Mr. Kitani (NTT Data Technology Development Headquarters, according to the document's cover). That document says it was made from a figure in Ray Kurzweil's 2005 book The Singularity Is Near. The original figure plots supercomputer speeds (FLOPS: how many decimal-number calculations per second) as dots, with a straight line (Trendline) that doubles every 1.2 years, and at the point where that line reaches 10¹⁹ FLOPS it carries the note “Required for Human Brain Neural Simulation for Uploading (2025)”. In other words, the original was not brain simulation itself but a forecast of computing speed.

What happened next:Checking computing speed: by the same measure as the original figure (the HPL value used in the TOP500 supercomputer performance ranking; the original figure also cites TOP500 as its source), as of November 2025 the leader, El Capitan (USA), is at 1.809 exaflops (about 1.8×10¹⁸ operations per second), only about one fifth (1/5.5) of 10¹⁹ FLOPS. LineShine (China), which took first place in June 2026, is at 2.198 exaflops and has not reached it either. On the other hand, in HPL-MxP (formerly HPL-AI), a different ranking that measures a method mixing in calculations with fewer digits (mixed-precision), three machines passed 10¹⁹ FLOPS in November 2025: El Capitan (16.680), Aurora (11.643) and Frontier (11.390). 10¹⁶ FLOPS (predicted in the figure for 2013) had been reached by Japan's “K computer” in November 2011 at 10.51 petaflops. Brain simulation results: in 2013, using all the compute nodes of the “K computer”, a simulation of 1.73 billion nerve cells and 10.4 trillion synapses (about 1% of the human brain's neural circuitry) took 40 minutes to reproduce 1 second of brain activity (RIKEN and others). In 2018, RIKEN wrote that even the highest-performance supercomputer of the time could not simulate a whole human brain, and announced an approach toward achieving it on next-generation supercomputers from 2020 on. In November 2025 the Allen Institute, the University of Electro-Communications and others announced that they had used “Fugaku” to simulate in detail the whole cerebral cortex of a mouse (about 10 million nerve cells, 26 billion synapses and 86 brain regions). The Allen Institute's Anton Arkhipov said that the long-term goal is to model the whole brain, and eventually a model of the human brain. None of the announcements from the parties concerned that I opened on this site said that a whole human brain had been simulated by 2025.

What we still don’t know:How much computation is really needed to simulate a whole human brain differs greatly depending on who estimates it. A note in another chapter of the same 2014 White Paper gives a forecast, from IBM's Modha in 2008, that simulating a human brain would become possible in 2018 at 38.5 petaflops (about 4×10¹⁶). That is a gap of about 260 times from Kurzweil's figure of 10¹⁹. The speed needed changes with how finely nerve cells are imitated (for example, the 2025 simulation on “Fugaku” was announced as imitating even the flow of electricity inside the branches of nerve cells). This site has not been able to confirm whether there is no example anywhere of a whole human brain being simulated by 2025 (I opened only three announcements from the parties concerned).

The pattern “expectations rise to a peak, pass through a valley of disappointment, and settle” did not fit exactly when checked by research

A study checked, using Gartner's own records, whether the research firm Gartner's chart of an expectations peak and valley (the Hype Cycle) really happens. It reports that there were mismatches between the actual records and the shape of the chart.

What we still don’t know:“The same flow repeats each time” is a shape that appears in history, and cannot be called a fixed law. We do not know whether the fourth boom will follow the same shape

In 2019, a Japanese Research Firm Places Artificial Intelligence in the “Trough of Disillusionment”

In “Hype Cycle for Technologies in Japan: 2019”, published by Gartner Japan, artificial intelligence was placed in the “Trough of Disillusionment” (the period when disappointment spreads after expectations have grown too high). It explains that this was because trial uses had led people to face reality instead of expectations.

What we still don’t know:The shape of the Hype Cycle curve is the research firm's own view and has not been confirmed as a law.

2025: in the same research firm's view for Japan, generative AI is at the “Peak of Inflated Expectations” (on the boundary with the trough of disillusionment in the figure)

The main text of Gartner Japan's “Hype Cycle for Future-Oriented Infrastructure Technologies in Japan, 2025” wrote that AI agents and generative AI are “Peak of Inflated Expectations” (translated). Meanwhile, the generative AI dot in the figure sits on the line between the peak and the trough of disillusionment, and the headline of one news article reported “Generative AI is in the trough of disillusionment” (translated).

What we still don’t know:Where the fourth boom is on the curve right now differs depending on who looks, and is not known.

[Japan] In the Diet and in book titles, the second peak is in 1990 (about 3 years later than in English) and the third in 2019; rising again from 2023

From the Diet’s records of speeches and from Japanese book titles, we counted how many times “artificial intelligence” and “AI” (recounted with parts of other words removed, so that it means AI) appear. In book titles, “artificial intelligence” is highest in 1990 (37 books). In the Diet, “artificial intelligence” peaks in 1985 (7 mentions) and 1990 (9), and was almost 0 from 1993 to 2014. In the Diet, “AI” meaning AI went 11 in 2015 → 593 in 2019 → down to 336 in 2021 → 761 in 2023 → 2,424 in 2025 (the year the AI law was made). “Generative AI” was 0 until 2022 and reached 172 in 2023.

What we still don’t know:The reason the counts fell in 2020–2021 cannot be pinned down to one cause (it overlaps with the period of the infectious disease epidemic).

From which year to which year was the “First AI Winter”? The answer differs by source. Some historians say “There Was No ‘First AI Winter’”

Japan's white paper from the Ministry of Internal Affairs and Communications (2016) calls the 1970s “the winter years” (translated), and Wikipedia gives 1974 to 1980. On the other hand, the researcher Nilsson (2010), who wrote a history of AI, calls about 1975 to 1985 the “boomtimes” of AI and says the “AI winter” came in the late 1980s. And the historian Haigh (2023) argued “There Was No ‘First AI Winter’,” pointing out among other things that the membership of SIGART, the AI special interest group of the computing society ACM, nearly tripled from 1973 to 1978. In fact, an early example of the term “AI Winter” is its use in 1984 as a word expressing worry about a winter that might be coming.

What we still don’t know:We have not found figures that line up total AI research funding in the 1970s by country and by year. Whether it was an “AI winter” changes with what you use as the yardstick (the budgets of large labs／the membership of the society／how the words were used)

1984: “AI Winter” was spoken of as the name of the worst thing that might come

At a panel discussion at AAAI-84, the moderator, Drew McDermott, said that researchers were worried that expectations for AI were too high and that it would someday end in trouble. As the worst-case scenario he gave this picture: in five years the strategic computing initiative fails miserably, Japan's Fifth Generation also does not work out, the Japanese government quickly pulls out of computers, the start-up companies all go bankrupt, and no money goes to anything related to AI. He described this as “This condition, called the ‘AI Winter’ by some”. He urged them to warn themselves so that it would not come to that, and to tell the world. In other words, “The AI Winter” was a phrase that appeared in the middle of the second boom as a warning about the future. Calling the decline in research funding in the 1970s the “first AI winter” is a label added afterward.

What we still don’t know:Who first said “AI Winter” is not known (even in the discussion it was “by some”). What book the small value for 1983 in the Ngram comes from has not been confirmed.

Japanese Society for Artificial Intelligence roundtable (2023): asked whether this is the fourth boom, “Nobody has authorized it, right?” (translated), “It may be about a 3.1st boom” (translated), and AI may have become “a field that doesn’t cool down” (translated)

In a roundtable by the people who made the history picture scroll, Fujio Toriumi asked, “So is it now treated as the fourth AI boom after all?” (translated). Hitoshi Matsubara answered, “Some professors say so” (translated), and Tadahiro Taniguchi said, “Whoever says it first wins, in a way (laughs)” (translated). When Toriumi asked again whether anyone has actually decided it, Matsubara answered, “Nobody has authorized it, have they?” (translated) (authorize means to approve formally), and “But I also feel it has stayed up since the third boom” (translated). Mai Sugimoto of Kansai University's Faculty of Sociology said, “To begin with, globally, how to treat the second AI boom and the second winter after it is delicate” (translated) and “In Japan, I think the influence of the Fifth Generation Computer project was big” (translated), and added that “unless funding is cut off, it does not leave a picture (in the public mind) of having gone down” (translated); since funding never ran out from the third AI boom onward and it turned into the generative AI boom, she said “I don't think it counts as the boom having ended once” (translated). Toriumi said, “It may be about a 3.1st boom” (translated), and Sugimoto continued: “it has become a field that grows even without relying on a boom, a field that doesn’t cool down” (translated). The exchange ends with Toriumi's “Another winter might come (laughs)” (translated) and Sugimoto's reply, “We may have just planted a flag (laughs)” (translated) (“planting a flag” is an expression meaning an omen that something is about to happen).

What we still don’t know:If you mark the boundary between booms by whether funding was cut off, there is no boundary between the third boom and now. If you mark it by the size of public attention, there is a boundary in 2022–2023. The answer changes depending on which standard you use.

The UK's central bank warns: “On a number of measures, equity market valuations appear stretched, particularly for technology companies” focused on AI

The committee that watches financial stability at the UK's central bank (the Bank of England) wrote in its October 2025 record, “On a number of measures, equity market valuations appear stretched, particularly for technology companies” that are focused on AI. Because stock indexes are concentrated in a small number of companies, it said markets could be hit hard “should expectations around the impact of AI become less optimistic”. As examples of what could trigger a fall, it lists AI performance or adoption disappointing, and shortages of power, data and materials. It also gave as an example a major breakthrough in ideas that makes large AI facilities unnecessary. In its July 2026 report it also wrote that “Equity prices have increased especially for AI-related stocks”, that “on some metrics, valuations have also become more stretched”, and that “The risk of a sharp correction in equity markets remains high”.

What we still don’t know:Whether a winter will come after the current boom is not known. As of July 2026, the central bank was writing that “The risk of a sharp correction in equity markets remains high.” Meanwhile, share prices kept rising. The answer has not yet come.

2025: in the same research firm's view for Japan, generative AI is at the “Peak of Inflated Expectations” (on the boundary with the trough of disillusionment in the figure)

The main text of Gartner Japan's “Hype Cycle for Future-Oriented Infrastructure Technologies in Japan, 2025” wrote that AI agents and generative AI are “Peak of Inflated Expectations” (translated). Meanwhile, the generative AI dot in the figure sits on the line between the peak and the trough of disillusionment, and the headline of one news article reported “Generative AI is in the trough of disillusionment” (translated).

What we still don’t know:Where the fourth boom is on the curve right now differs depending on who looks, and is not known.

Japanese Society for Artificial Intelligence roundtable (2023): asked whether this is the fourth boom, “Nobody has authorized it, right?” (translated), “It may be about a 3.1st boom” (translated), and AI may have become “a field that doesn’t cool down” (translated)

In a roundtable by the people who made the history picture scroll, Fujio Toriumi asked, “So is it now treated as the fourth AI boom after all?” (translated). Hitoshi Matsubara answered, “Some professors say so” (translated), and Tadahiro Taniguchi said, “Whoever says it first wins, in a way (laughs)” (translated). When Toriumi asked again whether anyone has actually decided it, Matsubara answered, “Nobody has authorized it, have they?” (translated) (authorize means to approve formally), and “But I also feel it has stayed up since the third boom” (translated). Mai Sugimoto of Kansai University's Faculty of Sociology said, “To begin with, globally, how to treat the second AI boom and the second winter after it is delicate” (translated) and “In Japan, I think the influence of the Fifth Generation Computer project was big” (translated), and added that “unless funding is cut off, it does not leave a picture (in the public mind) of having gone down” (translated); since funding never ran out from the third AI boom onward and it turned into the generative AI boom, she said “I don't think it counts as the boom having ended once” (translated). Toriumi said, “It may be about a 3.1st boom” (translated), and Sugimoto continued: “it has become a field that grows even without relying on a boom, a field that doesn’t cool down” (translated). The exchange ends with Toriumi's “Another winter might come (laughs)” (translated) and Sugimoto's reply, “We may have just planted a flag (laughs)” (translated) (“planting a flag” is an expression meaning an omen that something is about to happen).

What we still don’t know:If you mark the boundary between booms by whether funding was cut off, there is no boundary between the third boom and now. If you mark it by the size of public attention, there is a boundary in 2022–2023. The answer changes depending on which standard you use.

Japan's Ministry of Internal Affairs and Communications white paper (2024) says that with the spread of generative AI from around 2022, “it is also said that we have now entered the fourth AI boom” (translated)

A national white paper (the Ministry of Internal Affairs and Communications' White Paper on Information and Communications in Japan, 2024 edition) explained that the history of AI “has repeated booms and winters several times” (translated). It then wrote that “with the rapid spread of generative AI from around 2022” (translated) (generative AI is AI that creates text and images), “it is also said that we have now entered the fourth AI boom” (translated). It does not state this flatly, staying with the phrase “it is also said” (translated). In the same section it also writes of the third boom as “from the 2000s” (translated) and “the third AI boom that continues to this day” (translated). In the figure (Figure I-3-1-1), the band for the fourth boom starts in 2022, and the figure says “moving into a worldwide AI boom without passing through a winter era” (translated).

International report by 96 experts (including an advisory panel chosen by 30 countries and others): “The inner workings of these models are largely inscrutable, including to the model developers” and “experts disagree on what to expect even in the coming months and years”

Following the Bletchley meeting (2023), 96 experts chaired by Yoshua Bengio (including an advisory panel chosen by 30 countries and the UN, EU and OECD) compiled an international report on AI safety (Hiroaki Kitano took part from Japan). The report explains that general-purpose AI models are “not programmed in the traditional sense” but are built through learning, and wrote that “The inner workings of these models are largely inscrutable, including to the model developers” and that “interpretability techniques for explaining why … any given output remain severely limited”. On future progress, it said the capabilities of general-purpose AI “could advance slowly, rapidly, or extremely rapidly”, and that experts “disagree about the pace … different views on the promise of further ‘scaling’”. The second report of February 2026 also points out that “researchers cannot reliably predict when specific capabilities will emerge” and that “experts disagree about whether exponential increases in inputs will continue” (inputs: computation, algorithm improvements and data).

What we still don’t know:What we still don't know (points made by experts chosen by the countries): (1) The inner workings of modern large AI (the inner mechanisms are largely unreadable even to the developers, and explanation techniques are severely limited). (2) The pace of future progress (anything from slow to extremely rapid is possible; experts disagree about how far scaling will keep working). The 2026 report shows the same uncertainty continuing.

Researchers who peer inside AI sum up for themselves that “there are many open problems in the field that require solutions” (29 people from universities, institutes and companies)

29 researchers who study how each part inside a large AI computes (understanding its mechanisms, called mechanistic interpretability) compiled the “open problems” of the field. The authors' affiliations cut across companies and universities, including Apollo Research, Anthropic, Google DeepMind, MIT, Harvard University, Tel Aviv University and the University of Melbourne. While acknowledging recent progress, the paper wrote that “many open problems … require solutions before many scientific and practical benefits can be realized”. The points it lists include the following. What “features”, the main clue used for investigation, actually is remains unclear. At present AI's abilities are mainly checked with “black box” tests that look at the answers from outside. And it is not known whether methods that worked on small experimental AI can be extended to the largest frontier AI.

What we still don’t know:What we still don't know (points made by experts who study the inner structure themselves): The methods for reading what is computed inside large AI are still unclear even at the basic idea (what a “feature” is). Today's AI abilities are mainly checked by testing from the outside.

Turing’s forecast: “in about fifty years' time ... 70 per cent chance ... after five minutes”

In his 1950 paper, Turing set down his own forecast. In about 50 years, he wrote, computers could be made to play the imitation game well, and an average interrogator would have no more than a 70% chance of identifying correctly after 5 minutes of questioning. In the same paragraph he also wrote that by the end of the century the use of words and people’s ideas would have changed, so that one could “speak of machines thinking” without being contradicted.

What happened next:According to a survey paper from 2000 (Saygin et al.), no program had yet won the grand prize in the contest with prize money that had been held since 1991 (the Loebner Prize), and the records show that the machines were usually spotted quickly. In other words, in the year of the forecast, it had not come true in the form predicted. Some said the results of the 2000 contest “shows Turing’s prediction to have been in error”, but in the same year, 2000, Copeland wrote that “‘About fifty years’ does not mean ‘exactly fifty years’”. Then in March 2025, researchers at a US university (UC San Diego) published in a paper (arXiv) the results of an experiment in which an interrogator chatted for 5 minutes with a human and a machine at the same time. Of two large language AIs told to play the part of a human, one (GPT-4.5) was judged to be the human 73% of the time and the other (LLaMa-3.1-405B) 56% of the time (since there were two choices, the interrogator correctly spotted the first one only in the remaining 27%). But this is the announcement of one research group, and we have not confirmed later evaluations on this site. The part about being able to “speak of machines thinking without expecting to be contradicted” has not been confirmed.

What we still don’t know:Whether a machine being judged human in the 2025 experiment means that it can “think” is not known (Turing himself, regarding the original question as not worth discussing, replaced it with the game).

From which year to which year was the “First AI Winter”? The answer differs by source. Some historians say “There Was No ‘First AI Winter’”

Japan's white paper from the Ministry of Internal Affairs and Communications (2016) calls the 1970s “the winter years” (translated), and Wikipedia gives 1974 to 1980. On the other hand, the researcher Nilsson (2010), who wrote a history of AI, calls about 1975 to 1985 the “boomtimes” of AI and says the “AI winter” came in the late 1980s. And the historian Haigh (2023) argued “There Was No ‘First AI Winter’,” pointing out among other things that the membership of SIGART, the AI special interest group of the computing society ACM, nearly tripled from 1973 to 1978. In fact, an early example of the term “AI Winter” is its use in 1984 as a word expressing worry about a winter that might be coming.

What we still don’t know:We have not found figures that line up total AI research funding in the 1970s by country and by year. Whether it was an “AI winter” changes with what you use as the yardstick (the budgets of large labs／the membership of the society／how the words were used)

Japanese Wikipedia has the heading “[2020s and later] Fourth AI Boom”; English Wikipedia uses the unnumbered “AI boom” (the 2020s) and “AI spring”

The Japanese Wikipedia article “History of Artificial Intelligence” has headings that put the first AI boom at 1956–1974, the first AI winter at 1974–1980, the second boom at 1980–1987, the second AI winter at 1987–1993, the third boom at 2006–2019, and the fourth boom at “the 2020s and later” (translated). The Japanese article “The AI Winter” has a section called “The present AI spring (2022–present)” (translated). The English article “AI boom” dates the latest boom to the 2020s without a number, and says it is also called the “AI spring.” The English article “AI winter” treats the big winters as two: 1974–1980 and 1987–2000.

What we still don’t know:Wikipedia is updated all the time. What is written here is the description in the versions as of September 2026.

Kurzweil's prediction that AI will reach human-level intelligence by 2029: in 2002 he bet $20,000 against Kapor, who took the opposite side, and he revisited it in a 2024 book

Inventor Ray Kurzweil, known for his predictions of the future, predicted in 1999 that AI would reach human-level intelligence by 2029 (according to the publisher's description). In 2002 he bet $20,000 with Mitch Kapor on the prediction (Kapor's side) that “By 2029 no computer … will have passed the Turing Test”. The rules were set in detail: three judges would each exchange text with four parties, a computer and three humans, for two hours each (24 hours in all), and the computer would pass if it fooled at least two of the three judges. In his 2024 book “The Singularity Is Nearer”, Kurzweil revisits this 2029 prediction.

What happened next:The result will be known in 2029. In 2025 there was a study in which GPT-4.5 was judged to be human 73% of the time in a test of 5-minute conversations. But that is a different test from the conditions of the bet (a test totaling 24 hours), and no ruling on the bet itself has been made (we found no record of a ruling on the bet).

Will AI Win Gold at the Math Olympiad by 2025? In 2022 Two Researchers Forecast Under 8% and Over 16%; the Result Came in July 2025

The AI researchers Paul Christiano and Eliezer Yudkowsky differed in their views on the progress of AI. So, as a prediction that can be settled one way or the other, they publicly gave their probabilities that, at the International Mathematical Olympiad (a high-school competition), an AI “made before the IMO is able to get a gold … one of 2022-2025” (translated) (the rules, such as time, follow the IMO Grand Challenge). Christiano said under 8%, and Yudkowsky said 16% or more. For solving the hardest problem, Christiano predicted under 4%. In July 2025, Google DeepMind announced that an enhanced version of Gemini had fully solved 5 of the 6 problems and earned 35 points out of 42 (a gold-medal score), formally graded and confirmed by the competition's graders. It wrote its answers in ordinary language within the 4.5-hour time limit.

What happened next:In July 2025, an enhanced version of Google DeepMind's Gemini earned 35 points out of 42 (a gold-medal score). The competition's graders formally graded it, and the president of the International Mathematical Olympiad confirmed it as “a gold medal score” (translated) (however, according to the blog, the confirmation was of the answers, not a check of how the AI works). It was done within the 4.5-hour time limit, and the hardest problem (Problem 6) was not solved (5 of 6 problems). Because both predictions are probabilities, neither can be called right or wrong outright, but the actual result was closer to the side that had put the probability higher (over 16%).

Kurzweil's prediction that AI will reach human-level intelligence by 2029: in 2002 he bet $20,000 against Kapor, who took the opposite side, and he revisited it in a 2024 book

Inventor Ray Kurzweil, known for his predictions of the future, predicted in 1999 that AI would reach human-level intelligence by 2029 (according to the publisher's description). In 2002 he bet $20,000 with Mitch Kapor on the prediction (Kapor's side) that “By 2029 no computer … will have passed the Turing Test”. The rules were set in detail: three judges would each exchange text with four parties, a computer and three humans, for two hours each (24 hours in all), and the computer would pass if it fooled at least two of the three judges. In his 2024 book “The Singularity Is Nearer”, Kurzweil revisits this 2029 prediction.

What happened next:The result will be known in 2029. In 2025 there was a study in which GPT-4.5 was judged to be human 73% of the time in a test of 5-minute conversations. But that is a different test from the conditions of the bet (a test totaling 24 hours), and no ruling on the bet itself has been made (we found no record of a ruling on the bet).

In 2011, Shane Legg, who later became a Google DeepMind researcher, predicts “human level AGI … a mean of 2028 and a mode of 2025”

The AI researcher Shane Legg published on New Year's Eve 2011 the prediction he posted every year on his blog. He expressed the year in which human-level artificial general intelligence (AGI) will be built as a probability distribution, with a mean of 2028 and a most likely year of 2025 (assuming no major disaster such as nuclear war). He also wrote, “I expect to see an impressive proto-AGI within the next 8 years”. He explains a proto-AGI as a system that has acquired the basic abilities of vision, hearing, bodily control and language by learning rather than having them built in beforehand. In the comments, he himself wrote, “One complication with this prediction is that it might be impossible to falsify” (he goes on to say, for example, that if a large military built such a system it would have a strong reason to keep it secret), and “this might be my last annual prediction … kind of a lose-lose proposition” (a situation in which he loses whether the prediction comes true or not).

What happened next:The most likely year, 2025, has passed. This site has found no source in which a third party judged that human-level AGI had been achieved. The mean, 2028, is waiting to be checked. For the “proto-AGI” (translated) by 2019, he himself gave a standard for judging (something that a knowledgeable person, after a 1–2 hour briefing and some hands-on use, would think is within 10 years of human level), but this site has not found material judging whether anything met it.

Prediction: AGI (human-level general-purpose AI) is “In the next five to ten years, I think.” (2025, Google DeepMind's Hassabis, in a TV interview)

In April 2025, on the U.S. CBS program “60 Minutes”, Demis Hassabis, head of Google DeepMind (2024 Nobel Prize in Chemistry), answered an interviewer. When asked “On track for AGI in--” (AGI is AI that can do any intellectual task like a human), Hassabis said, “In the next five to ten years, I think.” To the question “And in 2030 you will have what?” he answered with “everything around you ... nuanced and deep ways ... kind of embedded in your everyday life” (a system that understands everything around you in fine detail and deeply, and is more or less built into daily life). In the same interview, he pointed out that today's AI cannot yet come up with new questions or hypotheses on its own, has no curiosity, and probably still lacks a little imagination and intuition.

What happened next:The years the prediction refers to have not yet arrived (as of September 2026).

What we still don’t know:People and companies differ on what must be achieved to call something “AGI”, so the yardstick for judging whether it has been achieved is itself not settled (this site's estimate; the transcript introduces it as “with the versatility of a human mind”).