14.3 AI Evolution
AI was reignited in the 1980’s by two sources: an expansion of the algorithmic toolkit, and a boost of funds. John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn using experience. On the other hand Edward Feigenbaum introduced expert systems which mimicked the decision making process of a human expert. The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program.
Expert systems were widely used in industries. The Japanese government heavily funded expert systems and other AI related endeavours as part of their Fifth Generation Computer Project (FGCP). From 1982-1990, they invested $400 million dollars with the goals of revolutionizing computer processing, implementing logic programming, and improving artificial intelligence. Unfortunately, most of the ambitious goals were not met. However, it could be argued that the indirect effects of the FGCP inspired a talented young generation of engineers and scientists. Regardless, funding of the FGCP ceased, and AI fell out of the limelight.
During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program. This highly publicized match was the first time a reigning world chess champion lost to a computer and served as a huge step towards an artificially intelligent decision making program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavour. It seemed that there wasn’t a problem machines couldn’t handle. Even human emotion was fair game as evidenced by Kismet, a robot developed by Cynthia Breazeal that could recognize and display emotions. in 2017, Google’s Alpha Go was able to defeat Chinese Go champion Ke Jie.
The Future: A Cautionary Approach?
The application of artificial intelligence in the age of Big Data has been successful in several industries such as technology, banking, marketing, and entertainment. We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. The advancement in this technology also raises questions with respect to governance and regulations to its development. Concerns over ethical and privacy issues along with military development and deployment are among some issues to consider.
Information Systems for Business and Beyond: 13.3 by Shauna Roch; James Fowler; Barbara Smith; and David Bourgeois is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.
