Andrew Maykuth Online
Lotus Magazine
July, 1991
Add a few points to your PC's IQ
Neural-network programs simulate human deductive reasoning.

By Andrew Maykuth

Two years ago, Connecticut currency trader James O'Sullivan bought some new software, one of a new class of brainy programs known as neural networks. He stuffed the software full of historical data about inflation, money supply, interest rates, and currency prices. Then he entered the current economic indicators. The system rewarded him with straightforward advice: Buy yen. Sell marks. Hold dollars.
"The results are superior," says British-born O'Sullivan, who religiously applies the neural network's recommendations to one portfolio. "I'm not saying what I've got is perfect, but it does seem to work."

What O'Sullivan's got is Brainmaker, from California Scientific Software in Grass Valley, Calif. It was one of the first of a growing number of neural-network packages available for PCs.

Originally, neural networks were envisioned as a computer imitation of the human brain's web of interconnected neurons, using thousands of processors operating simultaneously in a massively parallel structure. Such hardware has proven difficult and expensive to design, however, and in recent years, software developers have devised simulated neural networks using a newly discovered algorithm called back-propagation. This algorithm compares the relation between various inputs and outputs through a process of trial and error.

Make your own rules.

Neural networks are good at the things that have traditionally tripped up computers: making associations and generalizations, drawing inferences, recognizing patterns, and disregarding errors. Unlike expert systems and other conventional rule-based programs, neural-net software does not need to be preprogrammed to consider every conceivable aspect of a problem. Instead, users "train" neural networks by filling them with massive amounts of data. The programs make exhaustive comparisons of the relationships among the data and arrive at their own rules.

The result is applications that are eerily similar to human behavior. Gerald Tesauro, a staff member at the IBM Research Center in Yorktown, N.Y., trained a neural network to play backgammon by showing it thousands of random moves. Unlike chess, which is rule-based, "backgammon relies more heavily on judgment than it does on search," says Tesauro, who constructed his game on a Unix-based workstation. "Chess programs rely on massive, brute-force search programs that project various options into the future. But they don't have to be very smart." When the training was over, Tesauro's computer could respond to moves it had never before encountered. In fact, it blew away all commercial backgammon programs.

Not everyone has had as much success. William H. Harder, a Los Angeles sales representative for Quotron Systems, says he achieved only mixed results predicting stock market indexes with Braincel, a neural-net add-in for Microsoft Excel 3.0 from Promised Land Technology (New Haven, Conn.). On the other hand, Harder may simply be keeping a good thing to himself. "If it does work, I guarantee you won't know about it," he says. "I'll be using it myself."

Many companies have had significant success with neural networks. Chase Manhattan Bank, for example, has begun to train neural nets to make initial assessments of loan applicants. "Banks and insurance companies are getting 3%, 4%, and 5% improvements," says Steven J. Weaver, a senior scientist at Computer Sciences Corp. in Laurel, Md. "That's literally money in the bank when you're lending out hundreds of millions."

Using Neural Works from NeuralWare Inc. of Pittsburgh, Weaver recently devised a neural network for a more astronomical task: diagnosing problems in the trajectory of the Gamma Ray Observatory, which was launched in April from the space shuttle Atlantis.

From soup to nuts

The possible applications are virtually limitless. The Defense Department has poured millions of dollars into classified systems that identify targets for fighter aircraft or submarines. On a simpler scale, Sumitomo Heavy Industries in Japan has created a system-based on ExploreNet 3000 from HNC Inc. in San Diego-that can distinguish subtle differences in apples streaming down a conveyor belt. At a rate of 40 apples per second, the machine counts and evaluates defects before deciding how to grade each piece of fruit.

The Ford Motor Co. is working on a neural-net application that "listens" to a stream of data from a car's microprocessors the way an expert mechanic might listen to an engine's purr. "Long term, it looks like a ripe area for on-board diagnostics," says Lee Feldkamp, a Ford researcher. "Near term, we're probably talking about diagnostics at the end of the production line or at the dealer."

 Jumping headlong into this market, a small stampede of software vendors have sold about 40,000 neural networks in the last two years, according to Tom Schwartz, head of The Schwartz Associates in Mountain View, Calif.

NeuralWare, which calls itself the industry sales leader, sells several versions of Neural Works at prices starting at $ 1,895. California Scientific Software sells Brainmaker for $ 195 and a professional version for $ 795. Prices for HNC's ExploreNet 3000 range from $ 600 to $ 1,500. Science Applications International Corp. (San Diego) sells a neural-net program called Ansim for $ 495. Neurix (Boston) sells a $ 1,495 Macintosh package called MacBrain. And Murray Ruggiero, Promised Land Technology's vice president, says his company may adapt Braincel, the $ 250 Excel add-in, to run with other applications.

Most of the vendors say their products can import 1-2-3 files. The cheaper versions can process only smaller files and lack some of the features of more advanced versions, such as C compilers, which let users embed the neural network in another application. Some of the packages come with proprietary accelerator boards or math coprocessors to speed up the training process.

But even with a math coprocessor, nearly everyone who has used a neural net cautions against expecting instant results. The programs take time to set up, and training neural-network software on a complex data set can tie up even the most powerful desktop machines for the weekend.

And after that, it may take time to learn to trust the results. "Neural networks are not for the dilettante," confirms O'Sullivan, the currency trader. "It took me a good year to develop enough comfort and security to commit my capital." home page   
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