Six years ago when Dale Addison was speaking to a group of engineers and trying to pitch "artificial intelligence"–meaning neural networks someone in his audience asked him if it was true that a neural network had once mistakenly classified a T-62 tank as a Volkswagen. Although the incident had occurred years before, Addison seems to admit that there was some truth to the story. At the time, he was dismayed to see how little confidence this technologically sophisticated audience had in neural nets because even by that time the technology had made huge progress since computer scientists started tinkering with it.
Addison is on the faculty of the University of Sunderland, and in spite of his audience’s skepticism, he should be feeling fairly smug that many of the applications he foresaw for neural network technology, especially those that involve accurate classification, have been exploited. We have customers who use it on a daily basis for such tricky classification tasks as cancer diagnosis, emergency response systems, exploration and production of gas and oil, and operations research issues in manufacturing.
Nevertheless, Addison still sees resistance to emerging artifical intelligence techniques among engineers and business people, and he is still out there pitching neural nets, especially their use in combination with other new computational analysis methods, such as genetic algorithm optimization and neuro-fuzzy logic. Addison himself is working on some really tough classification problems now in the CASSANDRA project, where the goal is to develop an insider trading and market abuse detection system. He doesn’t worry about the tank and the Volkswagen because he’s confident that sooner rather than later, a neural network will learn to recognize a suspicious transaction when it sees one.
