هوش مصنوعی
هوش مصنوعی چیست ؟
هوش مصنوعی دانش و مهندسی ساخت ماشین های هوشمند و به خصوص برنامه های رایانه ای هوشمند است. هوش مصنوعی با وظیفه مشابه استفاده از کامپیوتر ها برای فهم چگونگی هوش انسان مرتبط است، اما مجبور نیست خودش را به روش هایی محدود کند که بیولوژیکی باشند.
"هوش" چه چیزی است ؟
هوش بخش محاسباتی توانایی است در وجود یک نفر یا شیء برای رسیدن به یک سری اهداف در دنیا. انواع و درجه های مختلفی از هوش در آدم ها، حیوانات و ماشین ها وجود دارد.
آیا تعریف مستقلی از هوش (بدون ارتباط با هوش انسان) وجود دارد ؟
نه هنوز. مشکل این است که ما اهنوز نتوانسته ایم به طور کلی مشخص کنیم که به کدام یک از روش های محاسباتی می خواهیم «هوش» بگوییم. چون از بعضی از مکانیزم های هوش سر در آورده ایم و از بقیه نه.
آیا هوش مصنوعی درباره شبیه سازی هوش انسانی است ؟
گاهی اوقات بله اما نه همیشه. از یک طرف ما با مشاهده آدم های دیگر و یا فقط با مشاهده روش های خودمان، می توانیم چیزهایی درباره حل مسائل توسط ماشین ها یاد بگیریم. از طرف دیگر بیشتر کارها در هوش مصنوعی بیشتر از این که بر اساس مطالعه آدم ها و حیوانات باشد، شامل مطالعه مسایلی است که دنیا به هوش ارائه می کند. محققان هوش مصنوعی برای استفاده از روش هایی که آدم های از آن استفاده نمی کنند و یا استفاده از قدرت محاسباتی بیشتر از توانایی آدم ها آزاد هستند.
تحقیقات هوش مصنوعی از کی شروع شد ؟
بعد از جنگ جهانی دوم، تعدادی از آدم ها به طور مستقل کار روی ماشین های هوشمند را شروع کردند. اولین نفر احتمالا ریاضیدان انگلیسی، آلن تورینگ، است. او در سال 1947 در این باره سخنرانی کرد. او احتمالا اولین نفری هم هست که گفت تحقیقات هوش مصنوعی به جای ساخت ماشین ها بهتر است با برنامه نویسی رایانه ها ادامه پیدا کند. تا اواخر 1950 محققان زیادی در این حوزه فعالیت می کردند و بیشتر آن ها کارشان را بر اساس برنامه نویسی رایانه ها قرار داده بودند.
آیا هدف هوش مصنوعی ایجاد چیزی مثل فکر انسان برای رایانه ها است ؟
بعضی محققان می گویند که آن ها چنین هدفی دارند، اما شاید آن ها دارند از یک اصطلاح مشابه استفاده می کنند. چون فکر انسان ویژگی های عجیب و غریبی دارد و من مطمئن نیستم که کسی به طور جدی بخواهد ساخت همه ویژگی های فکر آدم را عملی کند.
آیا هدف هوش مصنوعی رسیدن به هوشی هم سطح هوش انسان است ؟
بله. نهایت تلاش، ساخت برنامه های رایانه ای است که بتواند به خوبی انسان مسائل را حل کنند و به اهداف مورد نظر برسند. اگر چه سطح آرزو های خیلی از آدم های در گیر در هوش مصنوعی، به خصوص در زمینه های تحقیقاتی، کمتر از این حرف هاست.
هوش مصنوعی چقدر با رسیدن به هوش هم سطح انسان فاصله دارد ؟ این اتفاق کی می افتد ؟
بیشتر محققان هوش مصنوعی عقیده دارند که برای رسیدن به هوش هم سطح انسان، ایده های جدیدی لازم است. برای همین نمی توان پیش بینی کرد چه وقتی می توان به هوش هم سطح انسان رسید.
آیا از بین ماشین ها، رایانه ها انتخاب خوبی برای هوشمند شدن هستند ؟
رایانه های می توانند برای شبیه سازی هر نوع ماشینی برنامه ریزی شوند. خیلی از محققان ماشین های غیر رایانه ها اختراع کردند به این امید که آن ها بتوانند با روش هایی غیر از روش هایی که برنامه های رایانه ای هوشمند می شوند، هوشمند شوند. اگر چه آن ها معمولا ماشین های اختراعی شان را در رایانه ها شبیه سازی می کنند و در شک و تردید می افتند که ماشین جدید ارزش ساخت دارد یا نه. به خاطر میلیارد ها دلاری که صرف سریع تر و سریع تر کردن رایانه ها شده است، ماشین جدید باید خیلی سریع باشد تا بتواند بهتر از برنامه ي رایانه ای، که همان ماشین را شبیه سازی می کند، عمل کند.
آیا رایانه های برای هوشمند شدن به اندازه کافی سریع هستند ؟
بعضی ها فکر می کنند هم به رایانه های سریع تر نیاز داریم و هم به ایده های جدید. عقیده شخصی من این است که رایانه های 30 سال پیش هم به اندازه کافی سریع بودند، اگر ما می دانستیم چگونه آن ها را برنامه ریزی کنیم.
آیا امکان ساخت «یک ماشین کودک» وجود دارد که با خواند و یاد گرفتن از تجربه هایش بتواند رشد کند و هوش خود را توسعه دهد ؟
این ایده بارها پیشنهاد شده است. اولین بار هم در دهه 1940 بود. سرانجام هم این کار انجام خواهد شد. به هر حال برنامه های هوش مصنوعی به سطحی نرسیده اند که قادر به یادگیری بیشتر از چیزهایی که بچه ها از تجربیات عملی یاد می گیرند، باشند. هم چنین برنامه های فعلی به اندازه کافی از زبان سر در نمی آورند که بخواهند با خواندن چیزی یاد بگیرند.
آیا ممکن است که یک سیستم هوش مصنوعی قادر باشد با فکر کردن درباره هوش مصنوعی، خودش سطح هوشش را بالا ببرد ؟
من فکر می کنم ممکن است، اما الان در سطحی از هوش مصنوعی نیستیم که این کار بخواهد شروع شود.
شطرنج این طوری نیست ؟
بازی شطرنج به مکانیزم های فکری مشخصی نیاز دارد و به خیلی دیگر از مکانیزم های فکری نیاز ندارد. برنامه های شطرنج الان در سطح جهانی بازی می کنند، اما آن ها با جایگزینی مقادیر زیادی محاسبات به جای ادراک، از مکانیزم های فکری محدودی در مقایسه با مکانیز های استفاده شده توسط یک شطرنج باز استفاده می کنند. زمانی که ما این مکانیزم ها را بهتر بفهمیم می توانیم برنامه های شطرنجی هم سطح انسان بسازیم که محاسبات کمتری را نسبت به برنامه های فعلی انجام دهند. متاسفانه جنبه های رقابتی و تجاری ساخت رایانه های شطرنج باز، بر استفاده از شطرنج به عنوان یک حوزه علمی پیشی گرفته است.
آیا کسانی هستند که بگویند ساخت هوش مصنوعی ایده بدی است ؟
جان سرل که یک فیلسوف است می گوید ایده هوشمند بودن یک ماشین غیربیولوژیک تناقض دارد. یک فیلسوف دیگر هیوبرت دریفوس می گوید که رسیدن به هوش مصنوعی غیر ممکن است. دانشمند رایانه جوزف ویزنبام می گوید این ایده زشت، ضد انسانی و غیراخلاقی است.
آدم های مختلفی گفته اند که چون هوش مصنوعی تا به حال به هوش هم سطح انسان نرسیده است، این کار به طور کلی غیر ممکن است. بعضی دیگر هم نا امید هستند چون شرکت هایی که در این زمینه سرمایه گذاری کردند، ورشکست شدند.
منبع:
http://www.stanford.edu/
WHAT IS ARTIFICIAL INTELLIGENCE?
John McCarthy
Computer Science Department
JanFebMarAprMayJun JulAugSepOctNovDec ,
Stanford University
Revised November 24, 2004:
Abstract:
This article for the layman answers basic questions about artificial intelligence. The opinions expressed here are not all consensus opinion among researchers in AI.
Basic Questions
Q. What is artificial intelligence?
A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Q. Yes, but what is intelligence?
A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.
Q. Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence?
A. Not yet. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We understand some of the mechanisms of intelligence and not others.
Q. Is intelligence a single thing so that one can ask a yes or no question ``Is this machine intelligent or not?''?
A. No. Intelligence involves mechanisms, and AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered ``somewhat intelligent''.
Q. Isn't AI about simulating human intelligence?
A. Sometimes but not always or even usually. On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI researchers are free to use methods that are not observed in people or that involve much more computing than people can do.
Q. What about IQ? Do computer programs have IQs?
A. No. IQ is based on the rates at which intelligence develops in children. It is the ratio of the age at which a child normally makes a certain score to the child's age. The scale is extended to adults in a suitable way. IQ correlates well with various measures of success or failure in life, but making computers that can score high on IQ tests would be weakly correlated with their usefulness. For example, the ability of a child to repeat back a long sequence of digits correlates well with other intellectual abilities, perhaps because it measures how much information the child can compute with at once. However, ``digit span'' is trivial for even extremely limited computers.
However, some of the problems on IQ tests are useful challenges for AI.
Q. What about other comparisons between human and computer intelligence?
Arthur R. Jensen [Jen98], a leading researcher in human intelligence, suggests ``as a heuristic hypothesis'' that all normal humans have the same intellectual mechanisms and that differences in intelligence are related to ``quantitative biochemical and physiological conditions''. I see them as speed, short term memory, and the ability to form accurate and retrievable long term memories.
Whether or not Jensen is right about human intelligence, the situation in AI today is the reverse.
Computer programs have plenty of speed and memory but their abilities correspond to the intellectual mechanisms that program designers understand well enough to put in programs. Some abilities that children normally don't develop till they are teenagers may be in, and some abilities possessed by two year olds are still out. The matter is further complicated by the fact that the cognitive sciences still have not succeeded in determining exactly what the human abilities are. Very likely the organization of the intellectual mechanisms for AI can usefully be different from that in people.
Whenever people do better than computers on some task or computers use a lot of computation to do as well as people, this demonstrates that the program designers lack understanding of the intellectual mechanisms required to do the task efficiently.
Q. When did AI research start?
A. After WWII, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on AI, and most of them were basing their work on programming computers.
Q. Does AI aim to put the human mind into the computer?
A. Some researchers say they have that objective, but maybe they are using the phrase metaphorically. The human mind has a lot of peculiarities, and I'm not sure anyone is serious about imitating all of them.
Q. What is the Turing test?
A. Alan Turing's 1950 article Computing Machinery and Intelligence [Tur50] discussed conditions for considering a machine to be intelligent. He argued that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. This test would satisfy most people but not all philosophers. The observer could interact with the machine and a human by teletype (to avoid requiring that the machine imitate the appearance or voice of the person), and the human would try to persuade the observer that it was human and the machine would try to fool the observer.
The Turing test is a one-sided test. A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human.
Daniel Dennett's book Brainchildren [Den98] has an excellent discussion of the Turing test and the various partial Turing tests that have been implemented, i.e. with restrictions on the observer's knowledge of AI and the subject matter of questioning. It turns out that some people are easily led into believing that a rather dumb program is intelligent.
Q. Does AI aim at human-level intelligence?
A. Yes. The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans. However, many people involved in particular research areas are much less ambitious.
Q. How far is AI from reaching human-level intelligence? When will it happen?
A. A few people think that human-level intelligence can be achieved by writing large numbers of programs of the kind people are now writing and assembling vast knowledge bases of facts in the languages now used for expressing knowledge.
However, most AI researchers believe that new fundamental ideas are required, and therefore it cannot be predicted when human level intelligence will be achieved.
Q. Are computers the right kind of machine to be made intelligent?
A. Computers can be programmed to simulate any kind of machine.
Many researchers invented non-computer machines, hoping that they would be intelligent in different ways than the computer programs could be. However, they usually simulate their invented machines on a computer and come to doubt that the new machine is worth building. Because many billions of dollars that have been spent in making computers faster and faster, another kind of machine would have to be very fast to perform better than a program on a computer simulating the machine.
Q. Are computers fast enough to be intelligent?
A. Some people think much faster computers are required as well as new ideas. My own opinion is that the computers of 30 years ago were fast enough if only we knew how to program them. Of course, quite apart from the ambitions of AI researchers, computers will keep getting faster.
Q. What about parallel machines?
A. Machines with many processors are much faster than single processors can be. Parallelism itself presents no advantages, and parallel machines are somewhat awkward to program. When extreme speed is required, it is necessary to face this awkwardness.
Q. What about making a ``child machine'' that could improve by reading and by learning from experience?
A. This idea has been proposed many times, starting in the 1940s. Eventually, it will be made to work. However, AI programs haven't yet reached the level of being able to learn much of what a child learns from physical experience. Nor do present programs understand language well enough to learn much by reading.
Q. Might an AI system be able to bootstrap itself to higher and higher level intelligence by thinking about AI?
A. I think yes, but we aren't yet at a level of AI at which this process can begin.
Q. What about chess?
A. Alexander Kronrod, a Russian AI researcher, said ``Chess is the Drosophila of AI.'' He was making an analogy with geneticists' use of that fruit fly to study inheritance. Playing chess requires certain intellectual mechanisms and not others. Chess programs now play at grandmaster level, but they do it with limited intellectual mechanisms compared to those used by a human chess player, substituting large amounts of computation for understanding. Once we understand these mechanisms better, we can build human-level chess programs that do far less computation than do present programs.
Unfortunately, the competitive and commercial aspects of making computers play chess have taken precedence over using chess as a scientific domain. It is as if the geneticists after 1910 had organized fruit fly races and concentrated their efforts on breeding fruit flies that could win these races.
Q. What about Go?
A. The Chinese and Japanese game of Go is also a board game in which the players take turns moving. Go exposes the weakness of our present understanding of the intellectual mechanisms involved in human game playing. Go programs are very bad players, in spite of considerable effort (not as much as for chess). The problem seems to be that a position in Go has to be divided mentally into a collection of subpositions which are first analyzed separately followed by an analysis of their interaction. Humans use this in chess also, but chess programs consider the position as a whole. Chess programs compensate for the lack of this intellectual mechanism by doing thousands or, in the case of Deep Blue, many millions of times as much computation.
Sooner or later, AI research will overcome this scandalous weakness.
Q. Don't some people say that AI is a bad idea?
A. The philosopher John Searle says that the idea of a non-biological machine being intelligent is incoherent. He proposes the Chinese room argument www-formal.stanford.edu/jmc/chinese.html The philosopher Hubert Dreyfus says that AI is impossible. The computer scientist Joseph Weizenbaum says the idea is obscene, anti-human and immoral. Various people have said that since artificial intelligence hasn't reached human level by now, it must be impossible. Still other people are disappointed that companies they invested in went bankrupt.
Q. Aren't computability theory and computational complexity the keys to AI? [Note to the layman and beginners in computer science: These are quite technical branches of mathematical logic and computer science, and the answer to the question has to be somewhat technical.]
A. No. These theories are relevant but don't address the fundamental problems of AI.
In the 1930s mathematical logicians, especially Kurt Gödel and Alan Turing, established that there did not exist algorithms that were guaranteed to solve all problems in certain important mathematical domains. Whether a sentence of first order logic is a theorem is one example, and whether a polynomial equations in several variables has integer solutions is another. Humans solve problems in these domains all the time, and this has been offered as an argument (usually with some decorations) that computers are intrinsically incapable of doing what people do. Roger Penrose claims this. However, people can't guarantee to solve arbitrary problems in these domains either. See my Review of The Emperor's New Mind by Roger Penrose. More essays and reviews defending AI research are in [McC96a].
In the 1960s computer scientists, especially Steve Cook and Richard Karp developed the theory of NP-complete problem domains. Problems in these domains are solvable, but seem to take time exponential in the size of the problem. Which sentences of propositional calculus are satisfiable is a basic example of an NP-complete problem domain. Humans often solve problems in NP-complete domains in times much shorter than is guaranteed by the general algorithms, but can't solve them quickly in general.
What is important for AI is to have algorithms as capable as people at solving problems. The identification of subdomains for which good algorithms exist is important, but a lot of AI problem solvers are not associated with readily identified subdomains.
The theory of the difficulty of general classes of problems is called computational complexity. So far this theory hasn't interacted with AI as much as might have been hoped. Success in problem solving by humans and by AI programs seems to rely on properties of problems and problem solving methods that the neither the complexity researchers nor the AI community have been able to identify precisely.
Algorithmic complexity theory as developed by Solomonoff, Kolmogorov and Chaitin (independently of one another) is also relevant. It defines the complexity of a symbolic object as the length of the shortest program that will generate it. Proving that a candidate program is the shortest or close to the shortest is an unsolvable problem, but representing objects by short programs that generate them should sometimes be illuminating even when you can't prove that the program is the shortest.
Applications of AI
Q. What are the applications of AI?
A. Here are some.
game playing
You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.
speech recognition
In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
understanding natural language
Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains.
computer vision
The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
expert systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Namely, its ontology included bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery, and events occurring in time. Its interactions depended on a single patient being considered. Since the experts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that the knowledge engineers forced what the experts told them into a predetermined framework. In the present state of AI, this has to be true. The usefulness of current expert systems depends on their users having common sense.
heuristic classification
One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).
