Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100

But the brain more than makes up for this because it is massively parallel, that is, it has 100 billion neurons operating at the same time, each one performing a tiny bit of computation, with each neuron connected to 10,000 other neurons. In a race, a superfast single processor is left in the dust by a superslow parallel processor. (This goes back to the old riddle: if one cat can eat one mouse in one minute, how long does it take a million cats to eat a million mice? Answer: one minute.)
    In addition, the brain is not digital. Transistors are gates that can either be open or closed, represented by a 1 or 0. Neurons, too, are digital (they can fire or not fire), but they can also be analog, transmitting continuous signals as well as discrete ones.
    TWO PROBLEMS WITH ROBOTS
    Given the glaring limitations of computers compared to the human brain, one can appreciate why computers have not been able to accomplish two key tasks that humans perform effortlessly: pattern recognition and common sense. These two problems have defied solution for the past half century. This is the main reason why we do not have robot maids, butlers, and secretaries.
    The first problem is pattern recognition. Robots can see much better than a human, but they don’t understand what they are seeing. When a robot walks into a room, it converts the image into a jumble of dots. By processing these dots, it can recognize a collection of lines, circles, squares, and rectangles. Then a robot tries to match this jumble, one by one, with objects stored in its memory—an extraordinarily tedious task even for a computer. After many hours of calculation, the robot may match these lines with chairs, tables, and people. By contrast, when we walk into a room, within a fraction of a second, we recognize chairs, tables, desks, and people. Indeed, our brains are mainly pattern-recognizing machines.
    Second, robots do not have common sense. Although robots can hear much better than a human, they don’t understand what they are hearing. For example, consider the following statements:
     
    • Children like sweets but not punishment
    • Strings can pull but not push
    • Sticks can push but not pull
    • Animals cannot speak and understand English
    • Spinning makes people feel dizzy
    For us, each of these statements is just common sense. But not to robots. There is no line of logic or programming that proves that strings can pull but not push. We have learned the truth of these “obvious” statements by experience, not because they were programmed into our memories.
    The problem with the top-down approach is that there are simply too many lines of code for common sense necessary to mimic human thought. Hundreds of millions of lines of code, for example, are necessary to describe the laws of common sense that a six-year-old child knows. Hans Moravec, former director of the AI laboratory at Carnegie Mellon, laments, “ To this day, AI programs exhibit no shred of common sense—a medical diagnosis program, for instance, may prescribe an antibiotic when presented a broken bicycle because it lacks a model of people, disease, or bicycles.”
    Some scientists, however, cling to the belief that the only obstacle to mastering common sense is brute force. They feel that a new Manhattan Project, like the program that built the atomic bomb, would surely crackthe common-sense problem. The crash program to create this “encyclopedia of thought” is called CYC, started in 1984. It was to be the crowning achievement of AI, the project to encode all the secrets of common sense into a single program. However, after several decades of hard work, the CYC project has failed to live up to its own goals.
    CYC’s goal is simple: master “ 100 million things, about the number a typical person knows about the world, by 2007.” That deadline, and many previous ones, have slipped by without success. Each of the milestones laid out by CYC engineers has come and gone without scientists being any closer to mastering the essence of intelligence.
    MAN VERSUS MACHINE
    I once had a chance to match wits with a robot in a contest with one built by MIT’s Tomaso Poggio. Although robots cannot recognize simple patterns as we can, Poggio was able to create a computer program that can calculate every bit as fast as a human in one specific area: “immediate recognition.” This is our uncanny ability to instantly recognize an object even before we are aware of it. (Immediate recognition was