This article is part of ReadWrite Future Tech, an annual series where we explore how technologies that will shape our lives in the years and decades to come are grounded in the innovation and research of today.
In 1991, the United States Defense Advanced Research Project Agency, the research arm of the Pentagon, reorganized its priorities. Part of that reorganization meant much of the funding the agency had provided for projects like artificial intelligence and deep neural networks was pulled.
It may have been the best and worst thing to ever happen to the field of artificial intelligence.
Peter Lee, the corporate vice president for Microsoft Research, describes the time after DARPA’s 1991 reorganization as the “AI Winter.”
“A lot of hope and optimism went out of the sails of AI then,” Lee said in an interview with ReadWrite. “I was at Carnegie Mellon and I remember in the late 80s and early 90s how AI a lot of the enthusiasm around AI kind of died. It was overhyped, oversold. Expert systems and neural nets and things like that were interesting but people tried to use them in the real world and they didn’t work well. They were really brittle.”
The field of artificial intelligence was set back by more than a decade with DARPA’s decision. Yet, as with many examples of setbacks in technological innovation, opportunity was born. Instead of focusing artificial intelligence on the concept of neural networks, researchers started raising the fields of machine learning and statistical modeling that are now defining artificial intelligence research.
How Researchers Build The Future
Researchers like Lee have a pretty good idea of what the future is going to look like. He was the chair of the Computer Science department at Carnegie Mellon and the head of the Transformational Convergence Technology Office at DARPA before heading to Microsoft.
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