Neural networks, artificial intelligence and computational reality
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Jbzsef Hatvany Memorial 145
AI Tools for Manufacturing Automation
Neural Networks, Artificial Intelligence and Computational Reality Jerome A. Feldman International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1105, U.S.A.
Artificial intelligence and, more recently, neural networks have been claimed to yield revolutionary advances in manufactur- ing. J6/,sef Hatvany had the clearest view of the advantages and problems with AI in manufacturing. The situation with neural networks is similar and can benefit from an extension of Hatvany's analysis.
Keywords: Artificial intelligence, Neural network, Con- nectionist, Automated manufacturing.
Elsevier Computers in Industry 14 (1990) 145-148
Computers and computer science have made remarkable contributions to engineering practice, but have also left a trail of unfulfilled promises. One of the most difficult tasks in engineering research is to assess which of the half-baked ideas of the computer scientists might be promising for industrial use. In recent years, various manifesta-
Jerome A. Feldman received his Bachelor in Physics from the Univer- sity of Rochester in 1960 and a Mas- ters in Mathematics from the Univer- sity of Pittsburgh in 1961. He received his PhD from Carnegie-Mellon Uni- versity in Mathematics and Computer Science in 1964.
Feldman's professional experience includes various positions. In 1964-66, he became a staff member at Mas- sachusetts Institute of Technology in Cambridge, Massachusetts. From
1966-74, he was an associate professor of Computer Science at Stanford University, California. From 1974-81, Feldman re- turned to the University of Rochester as professor and chair- man of the Computer Science Department and helped build the department's artificial intelligence program into one of the best in the nation. In 1977-79, he served for two years as vice provost for computing. In 1981, Feldman was named the first Dessauer professor which honors John H. Dessauer (retired vice president of Xerox Corporation) who supervised the devel- opment of the Xerox copier.
Presently, he is both the Director of the International Com- puter Science Institute (Berkeley, California) and Professor of Electronic Engineering and Computer Science at the Univer- sity of California (Berkely, California). He is an internationally recognized expert in programming languages, artificial intelli- gence, and massively parallel systems. According to BYTE magazine (January, 1989 issue), Dr. Feldman has been popu- larly called the "father of connectionism".
As director of the International Computer Science Institute, Feldman heads a staff of thirty scientists, a number of students and post-doctoral fellows. The core of the Institute's program is the intramural research effort which strives to maintain ongoing basic research projects of the highest standard in selected areas of computer science and engineering. ICSI is currently addressing issues in four key areas: theory of parallel computeration, realization of massively parallel systems, appli- cations of such systems, and very large distributed networks.
In addition to its intramural research programs, ICSI main- tains a number of other programs in support of international cooperation in advanced computer science and engineering such as a post-doctoral program, exchange visits, summer jobs for graduate students, and partial support of selected working conferences.
0166-3615/90/$3.50 1990 - Elsevier Science Publishers B.V.
146 Jrzsef Hatvany Memorial: A 1 Tools for Manufacturing Automation Computers in Industry
tions of artificial intelligence (AI) and now neural networks (NN) have been touted as providing revolutionary capabilities in a variety of domains, including manufacturing.
An outstanding analysis of the potential of AI systems for automated manufacturing was pro- vided in a series of papers by Hatvany [3,10,13]. These papers correctly suggested that the knowl- edge-based AI techniques could be of enormous importance in maintaining general plans and in reflecting the consequences of anticipated and un- anticipated actions on the plan structure. This has not yet been realized, but there is a growing un- derstanding that static data bases must be re- placed by active knowledge structures that evaluate changes for consistency and other consequences. Such systems will have a profound effect on the feasibility of large integrated manufacturing and control tasks.
But some of the other claims for AI techniques have inherent limitations, as Hatvany pointed out. One major AI claim, which is recurring in neural net promotions, is that programming can be eliminated.
"The new technology offers us the chance of describing in the terms of mathematical symbolisms the general condi- tions of a bounded set of possible environments and of rules for a system's behaviour amid these circumstances. An AI system will then evaluate the given particular situa- tion and, using the set of criteria communicated to it, will choose a course of action that complies maximally with the rules prescribed for this environment." 
This has not worked out well in complex manufac- turing situations for reasons which Hatvany un- derstood clearly. The main problem is situation assessment--combining the vast amount of possi- bly conflicting information.
"Although computers can analyze and discriminate very complex pictures, they are not able to do what the experi- enced factory foreman does, who in the classical workshop environment combined his auditory and visual inputs with rule-bassed and empirically acquired knowledge to form a set of heuristic judgments." .
It was also recognized that the distribution and quantity of information available makes the analy- sis of large manufacturing systems super-human.
" In the case of an automated manufacturing facility the task becomes too complex for a human and on the other hand the necessary sensors and computing facilities for an automated solution are mostly already at hand." 
Given that the instrumentation, communication and computation is available, it was first thought that overall central control would be straightfor- ward, but:
"The problem of a generalized situation recognition has proved a good deal more intractable than was anticipated.'" [101
In fact, this is essentially the same problem faced in other real-world control systems such as air-traffic control or military situations. A ques- tion of current interest is the extent to which neural networks might help provide a solution to the problem. I would like to suggest that the answer is "yes" and "no". The negative answer is appropriate if the question is one of automatic learning by unstructured neural networks.
The current explosion of interests in neural networks (connectionist models, etc). is based on a number of scientific and economic expectations, some of which are unreasonable. We can be quite sure that neural networks will not replace conven- tional computers, eliminate programming, or un- ravel the mysteries of the mind. We can expect better understanding of massively parallel compu- tation to have an important role in practical tasks and in the behavioral and brain sciences, but only through interaction with other approaches to these problems. As always, specific structure of prob- lems, disciplines, and computational systems are the cornerstone of success. The main hope of massively parallel (neural network) research is that it will provide a better basis for such efforts.
One particularly simplistic view of neural net- works is that they will support intelligence by implementing a holographic-style memory. The basic problems with any holographic representa- tional scheme are cross-talk, communication, in- variance, and the inability to capture structure. Essentially the same problems have prevented the development of holographic computer memories or recognition systems despite considerable effort. Consider the problem of representing the concept grandmother as a pattern of activity of all the units in some memory network. Notice what hap- pens if two (or more) concepts are presented at the same time - - for example, grandmother at the White House. Obviously, if every single unit must have a specified value for the network to capture grandmother, then no other concept can be active
Computers in Industry J.A. Feldman / Neural Networks, AI and Reality 147
at the same time. If each concept is spread over some fraction of the units, the chances are high that the encodings will overlap and cause confu- sion.
A related problem with diffuse representations is that only one concept at a time can be trans- mitted between sub-systems, if each concept is a pattern on the whole bus. The sequential nature of diffuse representations is particularly troublesome when we consider how information about a com- plex scene can be transferred from vision to other systems such as language and motor control. There appears no alternative to assuming that, at least for simultaneous communication, representation of concepts must be largely disjoint and thus compact.
A more sophisticated version of the universal neural-net hope is that new learning techniques will induce the required structure in an initially uniform or random network. There has been sig- nificant recent progress in connectionist learning  but any nontrivial neural model also requires a great deal of prior structure. For example, the primate visual system has at least a dozen subsys- tems, each with elaborate internal and external connection structure. Any notion that a general learning scheme will obviate the need for neurosci- ence, psychophysics, perceptual psychology, and computer vision research disappears as soon as one takes the vision problem seriously, and there is no reason to believe that language, problem solving etc., are simpler or less structured.
Some of the naive and overly optimistic claims for neural network style computation have come in the area of robotics. Several groups have desig- ned networks that learn from examples the map- ping from desired end-effector position to joint angles (inverse kinematics). The natural interpola- tion properties of neural networks allow these systems to do more than just table-look-up, but they fall far short of adequacy for even simple manipulation tasks. Real robot arms must deal with approaches, gripping positions, forces, ob- stacle avoidance and a number of other problems that have not been captured in existing neural models and are unlikely to be feasible without elaborate structures capturing our knowledge of robotics.
For general computation, Blum and Riverst  show that even the problem of learning weights to memorize a lookup table for a 3-unit network is
NP-complete and thus intractable. This implies that our formalisms, simulators, and neurocom- puters must support both complex structure specification and dynamic weight change. Pro- gramming such systems, understanding their be- havior, and controlling how they adapt are major continuing concerns.
Once one abandons the notion that neural net- works will just learn to solve our problems in robotics and elsewhere, the way is open to realistic engineering considerations. Several of the proper- ties of neural style computation seem particularly well suited to the problems of automated manu- facturing. The basic computation step is to com- bine weighted inputs (evidence) from a large num- ber of predecessors. This operation is inherently noise-tolerant and is naturally context sensitive. Neural style networks are very good at computing "best match" descriptions of complex patterns. This is the essence of the situation assessment problem noted by Hatvany as the key to in- tegrated control of manufacturing. It will take a great deal of analysis and design to produce situa- tion assessment and response networks, but the neural substrate seems much more promising than symbolic rules for this task.
Similar arguments extend to the more complex vision and other sensory integration tasks. The evidence-combining aspects of neural style net- works should prove important in inspection and recognition. The other major feature of neural style computing is adaptation. While learning will not replace design of networks it can play two important roles. It should be possible to design systems that adjust internal weights to improve their performance. Even more importantly, adap- tation provides a mechanism that could allow such systems to adapt to changing situations. All of this presents technical challenges and opportunities of the highest order. Joe Hatvany, in personal com- munication, had the same excitement and concern with the new Neural Network ideas as he ex- hibited so effectively with symbolic artificial intel- ligence.
 A. Blum and R. Rivest, "Training a 3-node neural net- work is NP-complete", COLT "88 Proceedings, MIT, 1988.
148 Jbzsef Hatvany Memorial." AI Tools for Manufacturing Automation Computers in Industry
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