The Age of Spiritual Machines: When Computers Exceed Human Intelligence (19 page)

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Authors: Ray Kurzweil

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BOOK: The Age of Spiritual Machines: When Computers Exceed Human Intelligence
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—Chuang-tzu
 
 
Language is the principal means by which we share our knowledge. And like other human technologies, language is often cited as a salient differentiating characteristic of our species. Although we have limited access to the actual implementation of knowledge in our brains (this will change early in the twenty-first century), we do have ready access to the structures and methods of language. This provides us with a handy laboratory for studying our ability to master knowledge and the thinking process behind it. Work in the laboratory of language shows, not surprisingly, that it is no less complex or subtle a phenomenon than the knowledge it seeks to transmit.
We find that language in both its auditory and written forms is hierarchical with multiple levels. There are ambiguities at each level, so a system that understands language, whether human or machine, needs built-in knowledge at each level. To respond intelligently to human speech, for example, we need to know (although not necessarily consciously) the structure of speech sounds, the way speech is produced by the vocal apparatus, the patterns of sounds that comprise languages and dialects, the rules of word usage, and the subject matter being discussed.
Each level of analysis provides useful constraints that limit the search for the right answer: For example, the basic sounds of speech called phonemes cannot appear in any order (try saying
ptkee).
Only certain sequences of sounds will correspond to words in the language. Although the set of phonemes used is similar (although not identical) from one language to another, factors of context differ dramatically. English, for example, has more than 10,000 possible syllables, whereas Japanese has only 120.
On a higher level, the structure and semantics of a language put further constraints on allowable word sequences. The first area of language to be actively studied was the rules governing the arrangement of words and the roles they play, which we call syntax. On the one hand, computerized sentence-parsing systems can do a good job at analyzing sentences that confuse humans. Minsky cites the example: “This is the cheese that the rat that the cat that the dog chased bit ate,” which confuses humans but which machines parse quite readily Ken Church, then at MIT, cites another sentence with two million syntactically correct interpretations, which his computerized parser dutifully listed.
3
On the other hand, one of the first computer-based sentence-parsing systems, developed in 1963 by Susumu Kuno of Harvard, had difficulty with the simple sentence “Time flies like an arrow.” In what has become a famous response, the computer indicated that it was not quite sure what it meant. It might mean
1. that time passes as quickly as an arrow passes;
2. or maybe it is a command telling us to time the flies the same way that an arrow times flies; that is, “Time flies like an arrow would”;
3. or it could be a command telling us to time only those flies that are similar to arrows; that is, “Time flies that are like an arrow”;
4. or perhaps it means that a type of flies known as time flies have a fondness for arrows: “Time-flies like (that is, cherish) an arrow.”
4
Clearly we need some knowledge here to resolve this ambiguity Armed with the knowledge that flies are not similar to arrows, we can knock out the third interpretation. Knowing that there is no such thing as a time-fly dispatches the fourth explanation. Such tidbits of knowledge as the fact that flies do not show a fondness for arrows (another reason to knock out interpretation four) and that arrows do not have the ability to time events (knocking out interpretation two) leave us with the first interpretation as the only sensible one.
In language, we again find the sequence of human learning and the progression of machine intelligence to be the reverse of each other. A human child starts out listening to and understanding spoken language. Later on he learns to speak. Finally, years later, he starts to master written language. Computers have evolved in the opposite direction, starting out with the ability to generate written language, subsequently learning to understand it, then starting to speak with synthetic voices and only recently mastering the ability to understand continuous human speech. This phenomenon is widely misunderstood. R2D2, for example, the robot character of Star Wars fame, understands many human languages but is unable to speak, which gives the mistaken impression that
generating
human speech is far more difficult than
understanding
it.
I FEEL GOOD WHEN I.LEARN SOMETHING, BUT ACQUIRING KNOWLEDGE SURE IS A TEDIOUS PROCESS. PARTICULARLY WHEN I’VE BEEN UP ALL NIGHT STUDYING FOR AN EXAM. AND I’M NOT SURE HOW MUCH OF THIS STUFF I RETAIN.
 
 
That’s another weakness of the human form of intelligence. Computers can share their knowledge with each other readily and quickly. We humans don’t have a means for sharing knowledge directly, other than the slow process of human communication, of human teaching and learning.
 
DIDN’T YOU SAY THAT COMPUTER NEURAL NETS LEARN THE SAME WAY PEOPLE DO?
 
You mean, slowly?
 
EXACTLY, BY BEING EXPOSED TO PATTERNS THOUSANDS OF TIMES, JUST LIKE US.
 
Yes, that’s the point of neural nets; they’re intended as analogues of human neural nets, at least simplified versions of what we understand them to be. However, we can build our electronic nets in such a way that once the net has painstakingly learned its lessons, the pattern of its synaptic connection strengths can be captured and then quickly downloaded to another machine, or to millions of other machines. Machines can readily share all of their accumulated knowledge, so only one machine has to do the learning. We humans can’t do that. That’s one reason I said that when computers reach the level of human intelligence, they will necessarily roar past it.
 
SO IS TECHNOLOGY GOING TO ENABLE US HUMANS TO DOWNLOAD KNOWLEDGE IN THE FUTURE? I MEAN, I ENJOY LEARNING, DEPENDING ON THE PROFESSOR, OF COURSE, BUT IT CAN BE A DRAG.
 
The technology to communicate between the electronic world and the human neural world is already taking shape. So we will be able to directly feed streams of data to our neural pathways. Unfortunately, that doesn’t mean we can directly download knowledge, at least not to the human neural circuits we now use. As we’ve talked about, human learning is distributed throughout a region of our brain. Knowledge involves millions of connections, so our knowledge structures are not localized. Nature didn’t provide a direct pathway to adjust all those connections, other than the slow conventional way. While we will be able to create certain specific pathways to our neural connections, and indeed we’re already doing that, I don’t see how it would be practical to directly communicate to the many millions of interneuronal connections necessary to quickly download knowledge.
 
I GUESS I’LL JUST HAVE TO KEEP HITTING THE BOOKS. SOME OF MY PROFESSORS ARE KIND OF COOL, THOUGH, THE WAY THEY SEEM TO KNOW EVERYTHING.
 
As I said, humans are good at faking it when we go outside of our area of expertise. However, there is a way that downloading knowledge will be feasible by the middle of the twenty-first century.
 
I’M LISTENING.
 
Downloading knowledge will be one of the benefits of the neural-implant technology. We’ll have implants that extend our capacity for retaining knowledge, for enhancing memory. Unlike nature, we won’t leave out a quick knowledge downloading port in the electronic version of our synapses. So it will be feasible to quickly download knowledge to these electronic extensions of our brains. Of course, when we fully port our minds to a new computational medium, downloading knowledge will become even easier.
 
SO I’LL BE ABLE TO BUY MEMORY IMPLANTS PRELOADED WITH A KNOWLEDGE OF, SAY, MY FRENCH LIT COURSE.
 
Sure, or you can mentally click on a French literature web site and download the knowledge directly from the site.
 
KIND OF DEFEATS THE PURPOSE OF LITERATURE, DOESN’T IT? I MEAN SOME OF THIS STUFF IS NEAT TO READ.
 
I would prefer to think that intensifying knowledge will enhance the appreciation of literature, or any art form. After all, we need knowledge to appreciate an artistic expression. Otherwise, we don’t understand the vocabulary and the allusions.
 
Anyway, you’ll still be able to read, just a lot faster. In the second half of the twenty-first century, you’ll be able to read a book in a few seconds.
 
I DON’T THINK I COULD TURN THE PAGES THAT FAST.
 
Oh come on, the pages will be—
 
VIRTUAL PAGES, OF COURSE.
PART TWO
 
PREPARING THE PRESENT
 
CHAPTER SIX
 
BUILDING NEW BRAINS ...
 
THE HARDWARE OF INTELLIGENCE
 
You can only make a certain amount with your hands, but with your mind, it’s unlimited.
—Kal Seinfeld’s advice to his son, Jerry
 
 
Let’s review what we need to build an intelligent machine. One resource required is the right set of formulas. We examined three quintessential formulas in chapter 4. There are dozens of others in use, and a more complete understanding of the brain will undoubtedly introduce hundreds more. But all of these appear to be variations on the three basic themes: recursive search, self-organizing networks of elements, and evolutionary improvement through repeated struggle among competing designs.
A second resource needed is knowledge. Some pieces of knowledge are needed as seeds for a process to converge on a meaningful result. Much of the rest can be automatically learned by adaptive methods when neural nets or evolutionary algorithms are exposed to the right learning environment.
The third resource required is computation itself. In this regard, the human brain is eminently capable in some ways, and remarkably weak in others. Its strength is reflected in its massive parallelism, an approach that our computers can also benefit from. The brain’s weakness is the extraordinarily slow speed of its computing medium, a limitation that computers do not share with us. For this reason, DNA-based evolution will eventually have to be abandoned. DNA-based evolution is good at tinkering with and extending its designs, but it is unable to scrap an entire design and start over. Organisms created through DNA-based evolution are stuck with an extremely plodding type of circuitry.
But the Law of Accelerating Returns tells us that evolution will not remain stuck at a dead end for very long. And indeed, evolution has found a way around the computational limitations of neural circuitry. Cleverly, it has created organisms that in turn invented a computational technology a million times faster than carbon-based neurons (which are continuing to get yet faster). Ultimately, the computing conducted on extremely slow mammalian neural circuits will be ported to a far more versatile and speedier electronic (and photonic) equivalent.
When will this happen? Let’s take another look at the Law of Accelerating Returns as applied to computation.
Achieving the Hardware Capacity of the Human Brain
 
In the chapter 1 chart, “The Exponential Growth of Computing, 1900-1998,” we saw that the slope of the curve representing exponential growth was itself gradually increasing. Computer speed (as measured in calculations per second per thousand dollars) doubled every three years between 1910 and 1950, doubled every two years between 1950 and 1966, and is now doubling every year. This suggests possible exponential growth in the rate of exponential growth.
1
This apparent acceleration in the acceleration may result, however, from the confounding of the two strands of the Law of Accelerating Returns, which for the past forty years has expressed itself using the Moore’s Law paradigm of shrinking transistor sizes on an integrated circuit. As transistor die sizes decrease, the electrons streaming through the transistor have less distance to travel, hence the switching speed of the transistor increases. So exponentially improving speed is the first strand. Reduced transistor die sizes also enable chip manufacturers to squeeze a greater number of transistors onto an integrated circuit, so exponentially improving densities of computation is the second strand.
In the early years of the computer age, it was primarily the first strand—increasing circuit speeds—that improved the overall computation rate of computers. During the 1990s, however, advanced microprocessors began using a form of parallel processing called pipelining, in which multiple calculations were performed at the same time (some mainframes going back to the 1970s used this technique). Thus the speed of computer processors as measured in instructions per second now also reflects the second strand: greater densities of computation resulting from the use of parallel processing.

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