Arguments for Strong AI
Introduction
The first argument is the argument from scientific progress.
The second argument is the one from technological progress
The third argument views the human brain as a machine
The fourth argument is from progress in Artificial Intelligence
The fifth argument is based on the Church-Turing thesis
The sixth argument for strong AI is based on the nature of learning
Caveats
Introduction
I want to present six arguments for strong AI. Note that I am not saying that we will have intelligent robots in the next few years. I personally believe that we will have them within 30 years, but I am not going to put forward arguments for this belief today. Rather, I am putting forward an "in principle" argument. That is, that at some point in the future, perhaps 20 years, perhaps 300 years, but there will come a time when we have intelligent robots that can do any intelligent thing that we can do. Note the caveat "intelligent" since some of the things that humans get up to, like sleeping, eating and our sex lives, may not necessarily be part of the experience of robots.
The first argument is the argument from scientific progress.
and has been argued by Fredkin from MIT. I believe it is essentially a scientific question to understand the nature of the human mind. Part of this understanding will come from neuroscience, and a great deal of progress has been made in the last 100 years in our understanding of the brain. Recent advances with the use of nuclear magnetic resonance scanners have enabled researchers to study small parts of the brain whilst subjects solve problems. There is no reason to believe that within time we should not have a complete map of the neuroanatomy of the brain. However, we must not underestimate the contribution of 200 million years of evolution in the development of the brain. It is possible that evolution has produced a brain that is so complex that it may take a very long time to understand its structure. But, given time it should be possible to understand the neuroanatomy of the brain.
However, to understand the human mind it will not be sufficient to know the complete map of the brain wiring. Understanding the full circuit diagram of a microcomputer will not help you to understand much of how it runs an application program. But there has also been progress in Cognitive Science in building computational models of human tasks, and in time these models will cover a wider range of human experience. Furthermore, eventually the cognitive science models will relate human behaviour back to our experience and to appropriate circuits in the brain. Clearly, to understand the mind there will have to be progress in philosophy as well as other fields, but again there has been a lot of progress in the last few years, and increasing interest in the philosophy of mind. Once we understand the nature of the mind it should be possible to build artificial minds based on our understanding.
The second argument is the one from technological progress.
This argument was most cogently argued about 10 years ago by Clive Sinclair. In the 1960s the most complex computer used thousands of valves and occupied a large room. Over time the size of computers has decreased and the number of switching elements, transistors, has increased. Now it is possible to put nearly a million transistors on a single integrated circuit. Sincliar has pioneered a technology known as wafer scale integration which uses the whole five inch silicon wafer as a complete electronic component, rather than breaking it up into about a hundred chips. He believes that within about 20 years it will be possible to build a machine with 10 thousand million transistors in a box no larger than the human brain. Provided that such a machine can also have the very high interconnection required, it will be comparable in its complexity to the human brain, and the same size. But, of course, without corresponding scientific progress, we will not know how to program such a machine.
Thus I argue that scientific progress will enable us to understand the mind, and technological progress will allow us to build a mind.
The third argument views the human brain as a machine.
albeit a very complex one, and thus able to be built in an artificial technology such as silicon. Few neuroscientists would doubt the role of neurons in human thought, and we can think of the brain as a very complex network of neurons. This is a simplification because other cells such as glial cells may play some important part, and we need to remember that protein structures play a role in human memory. Nevertheless, most scientists would be happy to view the brain as a vast but complex machine. As such it should then be possible to purely replicate the brain using artificial neurons. This has already been done for very simple life forms such as insects which only have a few thousand neurons in their brains. In principle, it would not be necessary to have a full scientific understanding of how the brain works. One would just build a copy of one using artificial materials and see how it behaves.
The fourth argument is from progress in Artificial Intelligence.
AI programs can do a wider range of intelligent tasks and increasingly complex ones. Programs can show understanding of natural human language, solve problems and learn. It used to be believed that a program can only do what it is programmed to do. But since we have developed programs which can learn, this is no longer the case. In the last five years there has been increasing interest in computational models of creativity and discovery, and whilst some people used to believe that computers could not be creative, there are now machines which discover mathematical hypotheses, paint pictures and compose poems. Attempts by Dreyfus and others to identify things that computers cannot do have only proved to be new challengers for researchers to achieve.
The fifth argument is a technical one from Computer Science known as the Church-Turing thesis.
They separately argued that given an algorithm running on one computer, it could always be rewritten and run on another computer. Thus, in some sense all computers have the same abilities. Now we can apply this argument to humans and existing computers. Given a problem that can be solved by a person, this problem solving can be thought of as an algorithm, and this algorithm can then be run on an ordinary digital computer. Of course the digital computer may run the algorithm much slower than the human brain, and it will need all the knowledge that the person had in executing the algorithm, but at some level of analysis, it is essentially the same algorithm. Incidentally this argument turns on its head an argument by Roger Penrose who argues the opposite position.
The sixth argument for strong AI is based on the nature of learning.
If we could understand the nature of human learning, then we could build a machine with the same learning mechanisms. Such a machine if brought up in a suitably friendly environment would acquire knowledge and experience much in the same way as a human infant. Daniel Dennett argues that there may be as many as forty different learning mechanisms in humans, but given time there is no reason to believe that we should not understand them. Since people do learn, and we can observe what they know before and after a learning task, and even their behaviour whilst learning, this gives us a handle to discover the nature of the learning mechanisms. Of course, there is more to being intelligent than learning, and it may take some time for a very smart learning machine to learn to understand language without already having some special hardware.
Caveats
Of course, there may be some insurmountable barrier to scientific progress. It may be that there is some aspect of human cognition that can never be understood and will always remain a mystery. Or there may be some part of brain function that of necessity needs to use neuroanatomical tissue, so called "wetware", and cannot be achieved with any artificial materials. But I believe we have no grounds for these doubts.
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