Claims

In contrast to current times, in the early 1970s when Goertzel first learned about AGI, it was not obvious that superhuman intelligence was feasible due to how primitive computers were; making the inference from primitive 1970s-era computers to superhuman AI required creative intellectual work and was not obviously justified.

When Wolfram showed Marvin Minsky (an AI pioneer) a demo of Wolfram Alpha shortly before release, Minsky was initially dismissive (having seen countless failed question-answering systems), but after seeing it work, became excited and promoted it at the event.

Marvin Minsky, despite originally being interested in neural networks and having written his PhD thesis on them, decided that perceptrons and neural networks were fundamentally trivial and incapable of doing anything interesting, co-authoring a book with Seymour Papert that effectively ended research in neural networks until the 1980s.

In the early 1960s, early AI researchers believed the field would lead humanity to 'go down in the history of the earth as being the thing that created the next species that was AI,' and that species would be more significant than humans.

Expert systems dominated AI research in the 1980s, based on the idea that a computer could be trained with rules from an expert (in geology, medicine, etc.) and then replicate that expert's capabilities through rule-based computation.

Japan launched the Fifth Generation Computer Project in the early 1980s with the goal of leapfrogging ahead of American and European AI research, using Prolog and particular problem-solving methodologies that ultimately did not work out well.

By the end of the 1980s and into the early 1990s, AI was widely considered dead as a field, and anyone claiming to work in AI would no longer describe their work as 'AI' for fear of association with a discredited paradigm.

In 2009, Wolfram released Wolfram Alpha, which takes natural language input, translates it into precise computational language, and returns computed answers; this was viewed as an AI system, but most people in AI at the time believed question-answering systems would never work, having failed for decades despite multiple approaches.

John McCarthy coined the term 'artificial intelligence' at the 1956 Dartmouth Conference and subsequently invented Lisp as the language he believed was necessary for implementing AI, particularly symbolic mathematical computation.

In 1943, Warren McCulloch (a neurophysiologist and psychiatrist) and Walter Pitts (a young mathematician) published a foundational paper on the logical theory of neural nets that laid out an idealized mathematical framework for understanding artificial neural networks, serving as the foundation for all subsequent neural network research.

In the 1950s and 1960s, during the Cold War, there was significant funding and effort devoted to building machine translation systems to automatically translate between Russian and English in diplomatic exchanges, motivated by fear that human interpreters might mislead both sides and trigger world war.

In 2011, Jeff Hinton's group achieved a breakthrough in image recognition by training a deep neural network on a large dataset of images (ImageNet), winning that year's competition by a large margin, which sparked renewed interest in neural networks and led to the deep learning revolution.

When Wolfram showed Wolfram Alpha to Marvin Minsky (the same AI pioneer who had dismissed neural networks in the 1960s) just before its release in 2009, Minsky initially dismissed it as just another failed question-answering system, but when shown a few examples, Minsky became enthusiastic and started telling others 'You've got to see this. It actually works.'

In 2011, a student of Jeff Hinton left a neural network training by accident for a month on GPU hardware, running through millions of images, and the resulting trained network won that year's ImageNet competition, triggering a major resurgence of interest in deep learning and proving that deeper neural networks with multiple layers could solve complex image recognition tasks.

In the 1980s, Japan initiated a Fifth Generation Computer Project aimed at solving AI through intensive research using primarily the Prolog language, which had certain problem-solving approaches that 'doesn't work out so well in the end,' representing another wave of AI hype and funding.

John McCarthy coined the term 'artificial intelligence' in 1956 at the Dartmouth conference and invented LISP, a sophisticated computer language that he believed was the only language suitable for implementing AI, particularly symbolic mathematical computation.

In 1943, Warren McCulloch (a neurophysiologist and psychiatrist) and Walter Pitts (a young mathematician) published a foundational paper laying out a mathematical idealization of neural networks that became the theoretical foundation for all subsequent neural network research.

By the end of the 1980s and early 1990s, despite the neural network renaissance and other AI efforts, the field remained essentially at a low ebb—people stopped identifying themselves as AI researchers, and AI was 'really at a very low ebb for quite a long time.'

In a military trial of early perceptrons, the system performed well at distinguishing pictures containing tanks from those without, but this success was later revealed to be spurious—all tank pictures had been taken during the day while non-tank pictures were taken at night, so the perceptron was merely detecting lighting conditions rather than recognizing tanks.

In the early 1960s, Marvin Minsky—an AI pioneer who had originally been interested in neural networks and even written his PhD thesis on them—decided that perceptrons and neural networks were fundamentally trivial and incapable of doing anything interesting. With Seymour Papert, he published a book arguing this thesis, effectively killing mainstream interest in neural networks.

For many decades, people attempted to build question-answering systems using various AI techniques (statistical, symbolic, etc.) and essentially none of them worked until Wolfram Alpha, which used a different architectural approach that combined natural language understanding with precise computational representation.

During the Cold War, the U.S. government funded machine translation projects because officials feared human interpreters in diplomatic exchanges between the US and Soviet Union might deliberately mislead, so they sought to replace interpreters with automatic translators.

Pre-AlexNet, neural networks were marginalized in AI research because researchers could not prove mathematical theorems about them, and the prevailing academic culture valued theoretical provability as a criterion for good research.