Blog: History of the Second AI Winter
Events Leading to the Second AI Winter
After the effects of the first AI winter had begun to decline, a new era of AI began to start. This time a lot more effort was focused on creating commercial products. Additionally, large conferences, like AAAI started in the early 1980s and experienced a rapid increase in tickets sold. The general industry and government officials alike started showing a renewed interest in AI technology.
At the heart of the commercialization of AI were expert systems. These systems were handcrafted by surveying experts and creating “if-then” rule sets accordingly. This method is called the “top-down” approach to AI with many believing that expert knowledge was the best way to create AI. These systems were implemented in fields like financial planning, medical diagnosis, geological exploration, and microelectronic circuit design.
The magazine Business Week joined the hype and published the headline “AI: It’s Here” in 1984. Similarly, many companies made extraordinary claims like: “We’ve built a better brain” and declared that “[I]t is now possible to program human knowledge and experience into a computer … Artificial intelligence has finally come of age.”
Fears of an upcoming Winter
As the hype regarding AI increased, researchers feared that the field might not deliver the expected results. At a panel called “The Dark Ages of AI — Can we avoid or survive them?” at the 1984 AAAI conference scientists discussed if an upcoming AI winter could be prevented.
“This unease is due to the worry that perhaps expectations about AI are too high, and that this will eventually result in disaster. I think it is important that we take steps to make sure the AI winter doesn’t happen […].”
The fear was that funding would once again dry up, when unrealistic expectations could not be fulfilled. This fear proved to be correct.
The second AI Winter
In the following years, the claims of what AI systems were capable of slowly had to face reality. The expert systems at the center of the revolution faced many issues. In 1984, John McCarthy criticized expert systems because they lacked common sense and knowledge about their own limitations.
He described the expert system MYCIN built to assist physicians. He then laid out a situation where a patient has Cholerae Vibrio in his intestines. When asked, the systems prescribed two weeks of tetracycline. This would most likely kill off all the bacteria, but by then the patient would already be dead. Additionally, many tasks were too complicated for engineers to design rules around them manually. Systems for vision and speech contained too many edge cases.
Schwarz, Director of DARPA ISTO (Defense Advanced Research Projects Agency/Information Science and Technology Office) from 1987 to 1989 concluded that AI research has always had
“… very limited success in particular areas, followed immediately by failure to reach the broader goal at which these initial successes seem at first to hint…”.
This led to a decrease in funding in AI research. The general interest in AI declined as the expectations could not be met. At this time, many AI companies closed their doors. The AAAI conference that attracted over 6000 visitors in 1986 quickly decreased to just 2000 by 1991. Similarly, a decrease in AI-related articles starting in 1987 and reaching its lowest point in 1995 can be observed in The New York Times.