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Preview of the future
Previewof the future by EARL C JOSEPH
B
0th technological knowledge and knowledge about the most probable future, can often have a catalytic impact. That is, a small increase in one’s knowledge about either can have an amplifying payoff. Therefore, it is cogent to ask what new computer systems developments we should expect for the remainder of this decade and on into the 1990s. Can we forecast new breakthroughs or turning points?
Future artificial intelligence Artificial intelligence (AI) type comin the form of ‘inference puters, engines’ and ‘knowledge inference processing’ systems are now being researched. Although AI is a relatively new technology, it is now advancing beyond the research stage into practical use. Even though AI has been investigated for about 30 years, it is only in recent years that it has moved toward the development of practical expert systems. A growth in knowledge about the human mind and its functioning has led to recent breakthroughs in the design and implementation of artificially intelligent computer programs. The primary direction of research is in the area of inference reasoning, leading to the creation of ‘expert’ knowledge-based systems. An expert system is a computer system consisting of a set of AI programs that uses a stored knowledge base and inference procedures
22
0011-684W84/020022-03$03.00
0
1984 Butterworth
& Co (Publishers)
Ltd.
to solve problems. Artificial intelligence research is a subfield of computer science that investigates the imitation of human processes (within computer systems). These AI processes are called heuristics. Heuristics include learning, intuition, symbolic reasoning, logic rules, inductive discovery and reasoning, deductive analysis, problem solving, and other human intelligence processes including machine representation of knowledge for use in inference tasks. AI assumes heuristic knowledge to be of equal importance to ‘factual’ knowledge. In fact, for AI purposes, heuristics is assumed to be the process defined as ‘expertise’. Heuristics goes beyond the use of logical proceduraloriented strings of instructions operating on streams of data, or on databases, that occurs in standard computer program execution. AI heuristics include logical inference procedures which allow semantic access (closely related to natural language) of knowledge bases which use AI processes for making ‘expert’ judgements. AI expert systems require capturing and storage of the known expertise of a field, like medical diagnoses, and the translation of such knowledge, via AI programs and hardware. Thus, an expert system uses AI inference coupled with a knowledge base for assisting as a machine ‘consultant’ in solving problems, planning, making decisions, assessments, diagnosis, and judgements or for creating (discovering or inventing) opportunities. Expert systems allow the
data processing
tackling of problems that are difficult enough to require solutions which go beyond simple arithmetic or logic, and that require heuristics of significant power. The knowledge and AI heuristic processes necessary to perform at such an expert level, plus the AI inference algorithms used, can be viewed in the AI expert system as a model of the expertise of the best expert practitioners in that field. Knowledge, once captured in such a fashion in an AI expert system, could allow a future nonexpert to apply such knowledge and heuristics to nearly match and often exceed the average unaided expert in that field - but to also vastly amplify experts. Further, AI expert systems can be constantly updated as society gains new knowledge. Obviously, such ‘convivial’ and ‘congenial’ intelligent amplifiers will greatly assist the aged to participate as up-to-date and knowledgeable members of modern society.
~0126 no 2
march 1984
In forecasting the future of AI expert systems there are a number of obvious and expanding application areas. Perhaps at the top of the list for the 1980s is AI advice-giving and consulting systems. Included are expert systems for accounting and auditing, medical diagnosis, pharmacy, medical lab analysis, intensive care nursing, chemistry, hospital architectural design, molecular generic design, programming, office management decision making, management and home advice, e.g., financial, medical, repairs. However, perhaps for the shorterterm future, the biggest market could be for home entertainment. Games have been used in AI research from its beginning (dating back to the 195Os), for testing AI features and programs. Therefore, AI games are the natural outcome of expert systems research. Further, intelligent, knowledge-based games can also be used in the education and training of medical/health
care professionals. The moneymaking potential of home entertainment could make it the dominant market for early expert systems. In the long-range future, expert systems would allow home entertainment systems to evolve quickly to include consultation and advice about a broad range of subjects, including medical advice, and later on for working and learning at home. That is, expert knowledge-based systems will go far beyond traditional ‘how-to’ books by allowing real-time expert (interaction) assistance tailored heuristically for the task at hand. Apart from consulting and advice, AI expert systems can also assist in the creative and inventive arts, giving recommendations for the tasks at hand. Such dialogues involve the AI expert system threading itself through its knowledge base via ‘if-then-andelse’ heuristic (logic) rules together with the human that it is advising. They will also help with planning and design. There should be little doubt that as AI expert systems evolve to become ever higher level ‘experts’, we will possess very powerful amplifying tools to assist us in almost any task we tackle. In fact, because a knowledge base arranges knowledge in a somewhat procedural fashion, like a computer program, it must be more complete, correct and comprehensible than the typical text book. Experience with current expert systems shows that when compared with traditional sources of knowledge, books, tapes, classrooms, etc., present and future knowledge-based systems are 10 to 1000 times more complete, correct and comprehensible. Additionally, AI expert systems allow knowledge application in real time decision making. Computer technology silicon chips
beyond
Silicon chip semiconductor technology is expected to continue to grow in
23
usage and expand into a wider range of applications. But what will come after silicon chip technology? Future possibilities include: gallium arsenide, wafers (instead of chips) and biophysical technology. The first is a nonsilicon technology, which portends higher circuit densities and higher performances (in speed and/or reliability), thus allowing continuing evolutionary technological progress. The latter two suggest that for the future, however, step-function revolutionary advances are also possible, and likely. It is now becoming possible to integrate on a silicon chip over a hundred thousand electronic circuits, and soon more than a half million using submicron geometries. Each circuit consists of approximately two and a half transistors, (micro logic switches), resistors and capacitors all made in the silicon by imbedding (doping) other elements (impurities) together with layers of evaporated metal and resistant materials. As evolutionary advances occur we will be able to integrate larger and larger (in capability) machines onto the silicon. Such ‘chip machines’ or ‘component machines’ become building blocks for bigger machines. For example, the advent in late 1971 of the microprocessor calculator chip made possible the modern hand-held calculator. Component machines will be imbedded in wheelchairs, home and factory appliances, office machines, heart pacemakers, and the like, to make them smarter and more capable. It is forecast that before the end of this decade we could be using the complete wafer as the component, instead of breaking it up into little chip pieces (this step is necessary to identify faulty chips). As semiconductor manufacturing technology matures further, allowing purer silicon with fewer processing imperfections, it will become unnecessary to break the wafer into chips. Using the total wafer, we will be able to integrate
24
many millions of circuits - perhaps billions of circuits. That is, we will be able to design, construct and manufacture ‘component institutions’ as basic building blocks to integrate with systems (or to imbed within) for making bigger and more capable and more intelligent machines and/or as people amplifier devices. But what comes after wafer technology? One possible next step in miniaturization, beyond submicron geometries, could be a VSD (very small device) with near nanometer features. That is three orders of magnitude, or 1000 times in each dimension, smaller than current silicon circuit element sizes and interconnection line widths. There are few materials capable of reaching these ultra-small circuit geometries. One candidate now in research is biophysical molecular switch technology. Such biomolecular switches can act as conductors and semiconductors, and, therefore, can be used like computer logic circuits. One way these bioswitches operate is via the use of electron transporting enzymes in electroactive polymers (see article by R Clerman, page 25, for a full discussion of bioelectronics). Thus, one likely long-range future involves ‘growing’ our future computers as living systems - as well as constructing biogenetic parts for repairing people and biogenetic adjuncts for amplifying people.
Conclusion Rapid technological change has always been the norm in the computer field and, recently, in the biogenetic and communications fields. In the past, technology-driven change has forced an increasing diversity; but from the foregoing, we now see that a convergence of technology is occurring. However, most future technology watchers see this convergence as a prelude to new form of diversity. The most likely form that such future splintering will take is along ‘smart’/
‘intelligent’ vs ‘dumb’ technology lines, and according to different application areas. But, whatever direction advances in technology take, there should be little doubt that these developments will allow technological systems to penetrate deeper into society. Thus, providing new opportunities as well as causing considerable change. There is no doubt that future technology will deskill much of the work now performed by human experts, allowing it to be performed by the average person - and be relegated to clerk and technician status. However, expert systems will also amplify human experts allowing them to be more inventive, creative, knowledgeable and innovative. Thus, for the long-range future we should anticipate the need for more, not fewer, human experts and professionals. Of course, there are many other forces operating in society which will alter how people work and the work they will be doing. Some of the key forces are: rapid and accelerating advances in some areas of technology gradual societal transition to the information age aging of the workforce more attention paid to the longrange future - especially for creating opportunities, and concern for technological impacts and consequences vast expenditure on defence - and civilian spin-offs strong international competition office and factory automation, including robots more proactive planning growth of electronic cottage, telecommunications and the information utility. Cl
Anticipatory Sciences Inc, Wesley Building, 123 East Grant Street, Minneapolis, MN 55403, USA. Tel: (612) 872 1841.
data processing
B
0th technological knowledge and knowledge about the most probable future, can often have a catalytic impact. That is, a small increase in one’s knowledge about either can have an amplifying payoff. Therefore, it is cogent to ask what new computer systems developments we should expect for the remainder of this decade and on into the 1990s. Can we forecast new breakthroughs or turning points?
Future artificial intelligence Artificial intelligence (AI) type comin the form of ‘inference puters, engines’ and ‘knowledge inference processing’ systems are now being researched. Although AI is a relatively new technology, it is now advancing beyond the research stage into practical use. Even though AI has been investigated for about 30 years, it is only in recent years that it has moved toward the development of practical expert systems. A growth in knowledge about the human mind and its functioning has led to recent breakthroughs in the design and implementation of artificially intelligent computer programs. The primary direction of research is in the area of inference reasoning, leading to the creation of ‘expert’ knowledge-based systems. An expert system is a computer system consisting of a set of AI programs that uses a stored knowledge base and inference procedures
22
0011-684W84/020022-03$03.00
0
1984 Butterworth
& Co (Publishers)
Ltd.
to solve problems. Artificial intelligence research is a subfield of computer science that investigates the imitation of human processes (within computer systems). These AI processes are called heuristics. Heuristics include learning, intuition, symbolic reasoning, logic rules, inductive discovery and reasoning, deductive analysis, problem solving, and other human intelligence processes including machine representation of knowledge for use in inference tasks. AI assumes heuristic knowledge to be of equal importance to ‘factual’ knowledge. In fact, for AI purposes, heuristics is assumed to be the process defined as ‘expertise’. Heuristics goes beyond the use of logical proceduraloriented strings of instructions operating on streams of data, or on databases, that occurs in standard computer program execution. AI heuristics include logical inference procedures which allow semantic access (closely related to natural language) of knowledge bases which use AI processes for making ‘expert’ judgements. AI expert systems require capturing and storage of the known expertise of a field, like medical diagnoses, and the translation of such knowledge, via AI programs and hardware. Thus, an expert system uses AI inference coupled with a knowledge base for assisting as a machine ‘consultant’ in solving problems, planning, making decisions, assessments, diagnosis, and judgements or for creating (discovering or inventing) opportunities. Expert systems allow the
data processing
tackling of problems that are difficult enough to require solutions which go beyond simple arithmetic or logic, and that require heuristics of significant power. The knowledge and AI heuristic processes necessary to perform at such an expert level, plus the AI inference algorithms used, can be viewed in the AI expert system as a model of the expertise of the best expert practitioners in that field. Knowledge, once captured in such a fashion in an AI expert system, could allow a future nonexpert to apply such knowledge and heuristics to nearly match and often exceed the average unaided expert in that field - but to also vastly amplify experts. Further, AI expert systems can be constantly updated as society gains new knowledge. Obviously, such ‘convivial’ and ‘congenial’ intelligent amplifiers will greatly assist the aged to participate as up-to-date and knowledgeable members of modern society.
~0126 no 2
march 1984
In forecasting the future of AI expert systems there are a number of obvious and expanding application areas. Perhaps at the top of the list for the 1980s is AI advice-giving and consulting systems. Included are expert systems for accounting and auditing, medical diagnosis, pharmacy, medical lab analysis, intensive care nursing, chemistry, hospital architectural design, molecular generic design, programming, office management decision making, management and home advice, e.g., financial, medical, repairs. However, perhaps for the shorterterm future, the biggest market could be for home entertainment. Games have been used in AI research from its beginning (dating back to the 195Os), for testing AI features and programs. Therefore, AI games are the natural outcome of expert systems research. Further, intelligent, knowledge-based games can also be used in the education and training of medical/health
care professionals. The moneymaking potential of home entertainment could make it the dominant market for early expert systems. In the long-range future, expert systems would allow home entertainment systems to evolve quickly to include consultation and advice about a broad range of subjects, including medical advice, and later on for working and learning at home. That is, expert knowledge-based systems will go far beyond traditional ‘how-to’ books by allowing real-time expert (interaction) assistance tailored heuristically for the task at hand. Apart from consulting and advice, AI expert systems can also assist in the creative and inventive arts, giving recommendations for the tasks at hand. Such dialogues involve the AI expert system threading itself through its knowledge base via ‘if-then-andelse’ heuristic (logic) rules together with the human that it is advising. They will also help with planning and design. There should be little doubt that as AI expert systems evolve to become ever higher level ‘experts’, we will possess very powerful amplifying tools to assist us in almost any task we tackle. In fact, because a knowledge base arranges knowledge in a somewhat procedural fashion, like a computer program, it must be more complete, correct and comprehensible than the typical text book. Experience with current expert systems shows that when compared with traditional sources of knowledge, books, tapes, classrooms, etc., present and future knowledge-based systems are 10 to 1000 times more complete, correct and comprehensible. Additionally, AI expert systems allow knowledge application in real time decision making. Computer technology silicon chips
beyond
Silicon chip semiconductor technology is expected to continue to grow in
23
usage and expand into a wider range of applications. But what will come after silicon chip technology? Future possibilities include: gallium arsenide, wafers (instead of chips) and biophysical technology. The first is a nonsilicon technology, which portends higher circuit densities and higher performances (in speed and/or reliability), thus allowing continuing evolutionary technological progress. The latter two suggest that for the future, however, step-function revolutionary advances are also possible, and likely. It is now becoming possible to integrate on a silicon chip over a hundred thousand electronic circuits, and soon more than a half million using submicron geometries. Each circuit consists of approximately two and a half transistors, (micro logic switches), resistors and capacitors all made in the silicon by imbedding (doping) other elements (impurities) together with layers of evaporated metal and resistant materials. As evolutionary advances occur we will be able to integrate larger and larger (in capability) machines onto the silicon. Such ‘chip machines’ or ‘component machines’ become building blocks for bigger machines. For example, the advent in late 1971 of the microprocessor calculator chip made possible the modern hand-held calculator. Component machines will be imbedded in wheelchairs, home and factory appliances, office machines, heart pacemakers, and the like, to make them smarter and more capable. It is forecast that before the end of this decade we could be using the complete wafer as the component, instead of breaking it up into little chip pieces (this step is necessary to identify faulty chips). As semiconductor manufacturing technology matures further, allowing purer silicon with fewer processing imperfections, it will become unnecessary to break the wafer into chips. Using the total wafer, we will be able to integrate
24
many millions of circuits - perhaps billions of circuits. That is, we will be able to design, construct and manufacture ‘component institutions’ as basic building blocks to integrate with systems (or to imbed within) for making bigger and more capable and more intelligent machines and/or as people amplifier devices. But what comes after wafer technology? One possible next step in miniaturization, beyond submicron geometries, could be a VSD (very small device) with near nanometer features. That is three orders of magnitude, or 1000 times in each dimension, smaller than current silicon circuit element sizes and interconnection line widths. There are few materials capable of reaching these ultra-small circuit geometries. One candidate now in research is biophysical molecular switch technology. Such biomolecular switches can act as conductors and semiconductors, and, therefore, can be used like computer logic circuits. One way these bioswitches operate is via the use of electron transporting enzymes in electroactive polymers (see article by R Clerman, page 25, for a full discussion of bioelectronics). Thus, one likely long-range future involves ‘growing’ our future computers as living systems - as well as constructing biogenetic parts for repairing people and biogenetic adjuncts for amplifying people.
Conclusion Rapid technological change has always been the norm in the computer field and, recently, in the biogenetic and communications fields. In the past, technology-driven change has forced an increasing diversity; but from the foregoing, we now see that a convergence of technology is occurring. However, most future technology watchers see this convergence as a prelude to new form of diversity. The most likely form that such future splintering will take is along ‘smart’/
‘intelligent’ vs ‘dumb’ technology lines, and according to different application areas. But, whatever direction advances in technology take, there should be little doubt that these developments will allow technological systems to penetrate deeper into society. Thus, providing new opportunities as well as causing considerable change. There is no doubt that future technology will deskill much of the work now performed by human experts, allowing it to be performed by the average person - and be relegated to clerk and technician status. However, expert systems will also amplify human experts allowing them to be more inventive, creative, knowledgeable and innovative. Thus, for the long-range future we should anticipate the need for more, not fewer, human experts and professionals. Of course, there are many other forces operating in society which will alter how people work and the work they will be doing. Some of the key forces are: rapid and accelerating advances in some areas of technology gradual societal transition to the information age aging of the workforce more attention paid to the longrange future - especially for creating opportunities, and concern for technological impacts and consequences vast expenditure on defence - and civilian spin-offs strong international competition office and factory automation, including robots more proactive planning growth of electronic cottage, telecommunications and the information utility. Cl
Anticipatory Sciences Inc, Wesley Building, 123 East Grant Street, Minneapolis, MN 55403, USA. Tel: (612) 872 1841.
data processing