The Emergence of Artificial Intelligence: Learning to Learn
Total Page：16
File Type：pdf, Size：1020Kb
AI Magazine Volume 6 Number 3 (1985) (© AAAI) The Emergence of Artificial Intelligence: Learning to Learn Peter Bock Department of Electrical Engineering ntd Computer Science The George Washiqton University, Washington, D. C. 20052 All of these capabilities are ones that we associate, in Abstract part, with human intelligence. If we accept the premise The classical approach to the acquisition of knowledge and that the human brain is the seat of these capabilities, and reason in artificial intelligence is to program the facts and rules if we accept the notion that the human brain is a finite into the machine. Unfortunately, the amount of time required machine, albeit extremely complex, then we arrive at the to program the equivalent of human intelligence is prohibitively inescapable conclusion that we ought to be able to repli- large. An alternative approach allows an automaton to learn cate such a machine with a finite number of components to solve problems through iterative trial-and-error interaction in a finite amount of time. with its environment, much as humans do. To solve a problem Even if we insist that the capacity of the brain is in- posed by the environment, the automaton generates a sequence or collection of responses based on its experience. The environ- finite (though somehow contained in a finite volume), and ment evaluates the effectiveness of this collection, and reports that its operation is magical and mystical and, therefore, its evaluation to the automaton. The automaton modifies its unfathomable, then perhaps we may at least accept the strategy accordingly, and then generates a new collection of proposition that we can build a machine that will approach responses. This process is repeated until the automaton con- our cognitive capabilities asymptotically, so that the be- verges to the correct collection of responses. The principles tween difference its intelligence and ours will be indistin- underlying this paradigm, known as collective learning systems guishable. theory, are explained and applied to a simple game, demon- Many insist that even this compromise is not possible- strating robust learning and dynamic adaptivity. that it is arrogant and naive to assume that “we limited humans can duplicate the infinite magic of our brains.” Which is it now? Are our human abilities limited, or are they infinite? Personally, I find it rather more arrogant Foreword to assert that our intelligence capacity is infinite than to accept its finite limitations. As an AI researcher, I am impressed by the many recent This topic is very controversial. When discussing ar- specialized advances in the simulation of intelligence, par- tificial intelligence, people who are otherwise calm often ticularly in the area of expert systems: impressed, but become very emotional, and will go to absurd lengths to unsatisfied. I am still tantalized by the prospect of build- convince me that the endowment of machines with rational ing a machine that exhibits general artificial intelligence- and emotional intelligence as rich and complex as ours is one capable of recognizing and solving a wide and ever- fundamentally impossible. When I was abroad a few years expanding variety of problems in diverse application do- ago, I had just this kind of lively discussion with a profes- mains, capable of rising to the new problem of the mo- sor of philosophy at a prestigious university. I proposed to ment by adapting existing expertise to the solution of a him a classic gedankenexperiment. new problem through analogy, of subordinating long-term goals to sets of shorter term goals that must be achieved Me: Suppose we build a machine that is capable of mak- first in order to tackle the long-term goals successfully- ing an exact copy of an object. Now suppose we put you and even capable of creating something new and beautiful in this machine while you are unconscious, and make a du- just for its own sake. plicate of you. Then we take you and your duplicate and 180 THE AI MAGAZINE Fall, 1985 place you in a room together. Both of you are awakened simultaneously, and presented with the task of determin- ing which of you is the original, which of you is the copy. How would you accomplish this? Him: Oh! That’s easy. I’d know that I was the original! lllE E%%LUTl%N%F HIGI-SPEE% HEM%RY CflPACllY Me: But your counterpart would say the same thing! Him: Yes. But he’d be wrong. MEMORY C%PRClW 6LNE8%n%N PUllO% XEEXN%L%69 l8lT.S PER COMP%?XBl QED. Well, everyone is entitled to his or her view. My 1952 - 1958 UacuMn robe 1% J view is that out intelligence is finite, realizable, and can be replicated in computer hardware and software. 2 1958 - 1964 rranslstoa 1% 4 My efforts to simulate human intelligence begin with 3 ?%4 - 197% SSI l%s a careful examination of the size and nature of our intel- 4 1970 - 1976 MSI 186 ligence capacity and processes. Research in neurophysiol- s 1916 - 1982 LSI 1% -I ogy has revealed that the brain and central nervous system 6 $982 - 1988 IlLSI lo a consist of about 1011 individual parallel processors, called . neurons. Each neuron is capable of storing about lo4 bits * . of information. The information capacity of the brain is . 9 . thus about 1015 bits (Sagan, 1977). Much of this infor- 13 2024 - 2038 ???????? 10 lS mation is probably redundant, but let us use this high value as a conservative estimate of the memory require- ments for a computer that can store all this information. Furthermore, for the sake of rapid response (which is cer- Figure 1. tainly part of intelligence) we will require that high-speed random-access memory be used to store this vast amount of information. When can we expect to have high-speed still come up with impossible times. We’ll never get any- memories of 1Or5 bits? where by trying to program human intelligence into our Examination of historical data reveals that the amount machine. of high-speed random-access memory that may be conve- What other options are available to us? One is di- niently accessed by a large computer has increased by an rect transfer. It goes something like this: I put clcctrodes order of magnitude every six years. This growth is de- all over your head, read out the contents of your brain picted in Figure 1, in which I have dubbed each six-year (nondestructively, I hope), and transfer these data over a period a “generation.” high-speed bus to our machine. Without belaboring the If we can trust this simple extrapolation, in generation calculations, this task would require about twelve days. thirteen, (AD 2024-2030), the average high-speed memory Only one problem remains: I haven’t the faintest, foggiest capacity of a large computer will reach the 1015 bit infor- notion of how to build such a device, let alone what prin- mation capacity of the human brain. Note that an order of ciples it would employ, or even how to propose a research magnitude error in the estimate of brain capacity results project to investigate its feasibility. If someone does, more in an error of just six years in my prediction. power to him. So much for the hardware. Now we have to fill this What’s left? There must be another alternative, be- capacious memory with software. Of course, even adult cause intelligence is acquired every day. Every day babies brains are not filled to capacity. So we will assume that are born, grow up, and in the process acquire a full spec- 10% of the total capacity, or 1014 bits, is the extent of the trum of intelligence. How do they do it? The answer, of intelligence base of an adult human brain. How long will course, is that they learn. it take to write the programs to fill 1014 bits (production If we assume that the eyes, our major source of sensory rules, knowledge bases, etc.)? The currently accepted rate input, receive information at the rate of about 250,000 for production of software, from conception to installation, bits per second, we can fill the 1014 bits of our machine’s is about one line of code per hour. Assuming, generously, memory capacity in about twenty years. Now we’re getting that an average line of code contains approximately 60 somewhere. characters, or 500 bits, we discover that the project will Maybe what we must do is connect our machine brain require 100 million person-years!!! (One of my students to a large number of high-data-rate sensors, endow it with suggested that we put ten programmers on the project.) a comparatively simple algorithm for self-organization, pro- Now we can fiddle with these numbers all we want, cut the vide it with a continuous and varied stream of stimuli and brain capacity estimate drastically, increase programmer evaluations for its responses, and let it learn. productivity, use huge programming teams, etc., and we Of course the task is not that simple, but I believe THE AI MAGAZINE Fall, 1985 181 that the suggestion is on the right track-and perhaps the only track. S[t+&] =T S[t] Introduction where S[t] is the state of the automaton at time t, S[t + St] is the state of the automaton at the time t, and Consistent with my professed mechanistic view of human T is the transformation that, when applied to S[t], yields intelligence, and my profound admiration for the ingenious S[t+&]. In a deductive process, the transform T is known. and efficient design and function of the brain, I offer the Thus, if S[t] is known (subject to limits of uncertainty, of following definition of artificial intelligence.