When Will HAL Understand What We Are Saying? Computer Speech Recognition and Understanding, Section 15

01  02  03  04  05  

06  07  08  09  10  

11  12  13  14  15  

16  17

Consider, then, what it would take to build a machine with the capacity of the human brain. We can approach this issue in many ways, one way is just to continue our dogged codification of knowledge and skill on yet-faster machines. Undoubtedly this process will continue. The following scenario, however, is a bit different approach to building a machine with human-level intelligence and knowledge -- that is, building HAL. Note that I've simplified the following analysis in the interest of space; it would take a much longer article to respond to all of the anticipated objections.

Another Paradigm Shift

The human brain uses a radically different computational paradigm than the computers we're used to. A typical computer does one thing at a time, but does it very quickly. The human brain is very slow, but every part of its net of computation works simultaneously. We have about a hundred billion neurons, each of which has an average of a thousand connections to other neurons. Because all these connections can perform their computations at the same time, the brain can perform about a hundred trillion simultaneous computations. So, although human neurons are very slow -- in fact about a million times slower than electronic circuits -- this massive parallelism more than makes up for their slowness. Although each interneuronal connection is capable of performing only about two hundred computations each second, a hundred trillion computations being performed at the same time add up to about twenty million billion calculations per second, give or take a couple of orders of magnitude.

Calculations like these are a little different than conventional computer instructions. At the present time, we can simulate on the order of two billion such neural-connection calculations per second on dedicated machines. That's about ten million times slower than the human brain. A factor of ten million is a big factor and is one reason why present computers are dramatically more brittle and restricted than human intelligence. Some observers looking at this difference conclude that human intelligence is so much more supple and wide-ranging than computer intelligence that the gap can never be bridged.

Yet a factor of ten million, particularly of the kind of massive parallel processing the human brain employs, will be bridged by Moore's law in about two decades. Of course, matching the raw computing speed and memory capacity of the human brain -- even if implemented in massively parallel architectures -- will not automatically result in human level intelligence. The architecture and organization of these resources is even more important than the capacity. There is, however, a source of knowledge we can tap to accelerate greatly our efforts to design machine intelligence. That source is the human brain itself. Probing the brain's circuits will let us, essentially, copy a proven design -- that is, reverse engineer one that took its original designer several billion years to develop. (And it's not even copyrighted, at least not yet.)

This may seem like a daunting effort, but ten years ago so did the Human Genome Project. Nonetheless, the entire human genetic code will soon have been scanned, recorded, and analyzed to accelerate our understanding of the human biogenetic system. A similar effort to scan and record (and perhaps to understand) the neural organization of the human brain could perhaps provide the templates of intelligence. As we approach the computational ability needed to simulate the human brain -- we're not there today, but we will be early in the next century -- I believe researchers will initiate such an effort.

There are already precursors of such a project. For example, a few years ago Carver Mead's company, Synaptics, created an artificial retina chip that is, essentially, a silicon copy of the neural organization of the human retina and its visual-processing layer. The Synaptics chip even uses digitally controlled analog processing, as the human brain does.