In the February 1989 issue of Popular Science, we dove deep in the reemerging projects developing 'brain-style' computers and their futures in the next two decades.
,” science and medical writer Naomi Freundlich was among the first journalists to anticipate the thaw of that long winter, which lingered into the ‘90s. Even before Geoffrey Hinton, considered one of the founders of modern deep learning techniques, published his seminal, Freundlich’s reporting offered one of the most comprehensive insights into what was about to unfold in AI in the next two decades.
For the military, this means target-recognition systems, self-navigating tanks, and even smart missiles that chase targets. For the business world, neural networks promise handwriting-and face-recognition systems and computer loan officers and bond traders. And for the manufacturing sector, quality-control vision systems and robot control are just two goals.
In 1958 Cornell University psychologist Frank Rosenblatt used hundreds of these artificial “neurons” to develop a two-layer pattern-learning network called the perceptron. The key to Rosenblatt’s system was that it learned. In the brain, learning occurs predominantly by modification of the connections between neurons. Simply put, if two neurons are active at once and they’re connected, then the synapses between them will get stronger.
According to Tom Schwartz, a neural-network consultant in Mountain View, Calif., technology constraints limited the success of perceptrons. “The idea of a multilayer perceptron was proposed by Rosenblatt, but without a good multilayer learning law you were limited in what you could do with neural nets.” Minsky’s book, combined with the perceptron’s failure to achieve developers’ expectations, squelched the neural-network boom.
Generalizing is an important goal for neural networks. To illustrate this, Hopfield described a munition identification problem he worked on two summers ago in Fort Monmouth, N.J. “Let’s say a battalion needs to identify an unexploded munition before it can be disarmed,” he says. “Unfortunately there are 50,000 different kinds of hardware it might be. A traditional computer would make the identification using a treelike decision process,” says Hopfield.
Other companies are providing commercial software simulations of neural networks. One of the most successful is Nestor, Inc., a Providence, Rl.,-based company that developed a software package that allows users to simulate circuits on desk-top computers. So far several job-specific neural networks have been developed.
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