able the analysis. Risk computations turn out to be very complex, however. A typical supercomputer in 1995 running at perhaps 1 gigaflop would take about 100 hours to solve a problem involving three risk factors. This solution would be helpful but too slow for the pace of business. In 1999 the same company might have a 25-gigaflop computer that is reasonably affordable and could do the same analysis in two to three hours. This still does not include all the desired parameters, but the company could look at the problem several times a day and start to make some judicious choices that contribute to a competitive edge.

In 2002, when the same company might expect to have a teraflop machine, it could run the same problem in minutes. This speed could begin to change how the company functions and what actions it takes in the stock market in real time. Such computing ability will move into virtually every business. As an example, he cited IBM’s ability to do supply-chain analyses that assess trade offs between inventory and time to market. Such analyses save the company an estimated amount in the hundreds of millions of dollars a year in the personal computer business. Because it is so hard to make a profit in the personal computer business that computing ability probably allows that company to stay in the business.

The Promise of Deep Computing

He closed with the example of what more efficient computing is beginning to do for the pharmaceutical industry. The cost of producing new chemical entities has more than quadrupled since 1990, while the number of new entities has remained essentially flat. The number of new drugs being produced by deep computing, however, is beginning to rise, and this number is estimated to triple by the year 2010. These deep computing processes include rational drug design, genomic data usage, personalized medicine, protein engineering, and molecular assemblies. This projection is speculative, he emphasized, but it shows the extent to which a major industry is counting on radically new uses of information technology.

DISCUSSION

The Wish for Self-Learning Machines

Dr. Myers passed along a wish list from Dan Goldin, administrator of the National Aeronautics and Space Administration, who hopes to develop spacecraft that can cope with unexpected conditions by making autonomous decisions. Remote decision making would eliminate the long time delay involved in communicating with Earth for instructions. True decision making, however, would require computers that are, to some extent, self-actuating, self-learning, and self-programming. This, in turn, may imply a new form of computing architecture.

Mr. Ganek responded that a number of challenges lie ahead in designing such thinking machines. He mentioned the exercise in computer science known



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