In 1949, IBM had high hopes for its new line of digital computers. The company envisioned selling upwards of 10 to 15 machines. Today computers run our homes, offices, factories, hospitals, and airports. They operate our cars, control our appliances, entertain us, and help us make decisions. But the further computers push into our everyday lives, the more obvious it becomes that they are ill-equipped to deal with the real world.

The problem with computers is that they just don't think like we do. In fact, they don't think at all – they calculate. They solve problems and make decisions by plugging numbers into equations. And since much of the world is outside the realm of equations, it is also beyond the reach of computers.

A new breed of software, however, lets computers process information without explicit equations. Free at last, computers are beginning to make sense of the vagaries of the real world, and are learning to solve problems where even the brightest mathematicians fear to tread. Early applications of the new technology include more accurate medical diagnostic systems, smoother transmissions for cars, quieter vacuum cleaners, and cameras that recognize colors and shapes.

Ironically, the new software is based on programming techniques from decades ago, when computers were hulking number crunchers confined to environmentally controlled rooms. At that time, researchers were hoping to build machines that could "think" using artificial intelligence. Although they failed miserably, the researchers came up with several new techniques which, when combined with conventional software, provide a flexible set of tools that allow computers to tackle some of the problems occurring in everyday life.

Does not compute

We are surrounded by computers, yet many of us know little about them. How do they really work? What goes on inside? And why can they solve some problems, but not others?

It's not as if computers are all that complicated. Take away their modems, screens, and disk drives, and what's left is basically a calculator. These calculators, or microprocessors, do essentially one thing: they combine numbers using addition, subtraction, and Boolean logic operations. The fact that the operations can be programmed in software makes computers extremely useful tools.

Over the years, people have programmed computers to automate just about everything, from vehicle ignition systems to aircraft diagnostics. In practically every case, the heart of the software is a mathematical model that represents the decision or process being controlled. While this approach works well in many instances, sometimes it simply won't do.

There are many things that people would like to automate, but for which the math doesn't exist. Programmers cannot open a book and find an equation to predict the onset of cancer, for example. Nor is there an equation to calculate the quality of welds produced by a welding machine. In general, the more variables involved in a decision, the tougher it is to define in terms of math.

Math is also of little help where ambiguity and uncertainty are involved, and when decisions require judgment or instinct. But such is the real world, and the reason computers have yet to master it.

Then there are the practical limits of computers themselves. Even the highly touted Pentiums and Power- PCs have only so many bits and megahertz that they can throw at an equation. Consider the software for a factory robot. Suppose the robot must drill a hole every half second to within 0.001 in. The equations of motion, though complex, can probably be found in an engineering handbook. For argument's sake, say the robot fails its first field test because it takes too long to calculate the position of each hole. To speed up the machine, you reduce the number of bits in the equations from 16 to 12. Now the robot is fast enough, but the holes are out of spec because the machine is less precise.

Tradeoffs between speed and precision are common in automation, and are often related to a computer's clock frequency and the number of bits in its data paths. While faster, more expensive computers may be the answer in some cases, more often it turns out that automation is simply not an alternative.

Beyond the realm of math

Automation engineers are not the first to encounter such walls. Several years ago, scientists ran into similar problems trying to develop software that simulates human thinking. But rather than fall into the math trap, where computers are viewed as calculators, the researchers began to see computers as information processors. So instead of modeling thought using explicit equations, they developed new techniques to represent, process, and store information using the computer's ability to manipulate data.

From the start, research associated with this new field, called artificial intelligence (AI), was highly controversial. It triggered many debates, especially over what constitutes intelligence and whether machines should be allowed to think. Even the researchers disagreed. One group felt strongly that symbolic processing based on Boolean logic was the answer, while another group believed that the way to make computers think was by modeling biological neurons and the functions they perform.

The belief behind symbolic processing is that computers can simulate human intelligence by combining logic rules using Boolean operations. For example, a computer can act as if it knows when someone has a fever by executing two simple rules:

IF temperature is above 99° THEN fever

IF temperature is 99° or less THEN no fever

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