The term AI Winter was established during the American Association of Artificial Intelligence's annual conference in 1984.(now the Association for the Advancement of Artificial Intelligence or AAAI).
Marvin Minsky and Roger Schank, two top academics, used the
phrase to describe the imminent bust in artificial intelligence research and
development at the time.
Daniel Crevier, a Canadian AI researcher, has detailed how
fear of an impending AI Winter caused a domino effect that started with skepticism
in the AI research community, spread to the media, and eventually resulted in
negative funding responses.
As a consequence, real AI research and development came to a
halt.
The initial skepticism may now be ascribed mostly to the
excessively optimistic promises made at the time, with AI's real outcomes being
significantly less than expected.
Other reasons, such as a lack of computer capacity during
the early days of AI research, led to the belief that an AI Winter was
approaching.
This was especially true in the case of neural network
research, which required a large amount of processing power.
Economic reasons, however, limited attention on more
concrete investments, especially during overlapping times of economic crises.
AI Winters have occurred many times during the history of
AI, with two of the most notable eras covering 1974 to 1980 and 1987 to 1993.
Although the dates of AI Winters are debatable and dependent
on the source, times with overlapping patterns are associated with research
abandonment and defunding.
The development of AI systems and technologies has
progressed, similar to the hype and ultimate collapse of breakthrough technologies
such as nanotechnology.
Not only has there been an unprecedented amount of money for
basic research, but there has also been exceptional progress in the development
of machine learning during the present boom time.
The reasons for the investment surge vary depending on the
many stakeholders involved in artificial intelligence research and development.
For example, industry has staked a lot of money on the idea
that discoveries in AI would result in dividends by changing whole market
sectors.
Governmental agencies, such as the military, invest in AI
research to improve the efficiency of both defensive and offensive technology
and to protect troops from imminent damage.
Because AI Winters are triggered by a perceived lack of
trust in what AI can provide, the present buzz around AI and its promises has
sparked fears of another AI Winter.
On the other hand, others argue that current technology
developments in applied AI research have secured future progress in this field.
This argument contrasts sharply with the so-called
"pipeline issue," which claims that a lack of basic AI research will
result in a limited number of applied outcomes.
One of the major elements of prior AI Winters has been the
pipeline issue.
However, if the counterargument is accurate, a feedback loop
between applied breakthroughs and basic research will generate enough pressure
to keep the pipeline moving forward.
~ Jai Krishna Ponnappan
You may also want to read more about Artificial Intelligence here.
See also: Minsky, Marvin.
Further Reading
Crevier, Daniel. 1993. AI: The Tumultuous Search for Artificial Intelligence. New York: Basic Books.
Kurzweil, Ray. 2005. The Singularity Is Near: When Humans Transcend Biology. New York: Viking.
Muehlhauser, Luke. 2016. “What Should We Learn from Past AI Forecasts?” https://www.openphilanthropy.org/focus/global-catastrophic-risks/potential-risks-advanced-artificial-intelligence/what-should-we-learn-past-ai-forecasts.
