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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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Suggested Citation:"OVERVIEW." National Research Council. 1982. Roles of Industry and the University in Computer Research and Development. Washington, DC: The National Academies Press. doi: 10.17226/10453.
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PART I: OVERVIEW

CONCLUSIONS AND RECOMID3NDATIONS CONCLUSIONS Computer science has a much more strongly developmental character than most other sciences, and the developmental consequences of the few inventions most central to it (the stored-program computer, the integrated circuit chip) have not yet been exhausted. As computer technology has matured and become more complex, leadership in carrying out large projects has shifted from universities toward industry. It is thus particularly important that those involved in university research keep in close touch with the industrial and commercial arenas out of which much essential innovation flows. Universities are good at abstracting, codifying, analyzing, disseminating, and inventing those areas where aesthetics or mathematical tradition is a sufficient guide to what needs to be done. However, an insufficiently broad familiarity with rapidly changing technology often limits the applicability of university research. In attempting to overcome this deficiency, university computer science departments have sometimes involved themselves in substantial development projects. This is not to be discouraged, but universities should remember that when they undertake development projects, their aim must be achieving new levels of understanding of principles basic to the management of important real problems; conceptual simplification and clarification of structured approaches; and transmission of organized ideas and new principles to the larger world. ~ should maintain their emphasis on long-range problems rather than shift to short-term development work. m ey must also avoid involvement in "miniproducts" that are unable to compete with more advanced industrial products. Also, their work should concentrate on domains in which they have sufficient experience to discern pragmatically vital issues correctly. Part of the panel's effort was directed at examining the university equipment situation vis-a-vis that of industry. m e panel found that the best equipped universities have computing facilities comparable to those of industry, provided that only raw cycles are measured and availability of floating point capacity and terminals is ignored. However, few universities have access to the large specialized equipment available at strong industrial research laboratories, e.g., 3 Universities

4 computers instrumented as performance test stands, robot manipulators, speech analysis hardware, and state-of-the-art computers for carrying out large numerical calculations. RECOMMENDATIONS 1. The Panel recommends further steps to encourage university- industry interaction. Possible initiatives in this direction are an follows: · increased funding for joint university-industry projects · funding for sabbatical visits to or from industry, emphasizing new courses · direct support of graduate students pursuing doctoral research in industry . and industry organization of special research grants funded jointly by NSF 2. To satisfy the important unfulfilled requirements of the scientific computing community and the growing needs of robotics research, and also to strengthen the ability of university departments to supply industrial researchers trained to a high standard, university access to computing facilities needs to be improved. The aim should be both to bring more schools up to the industrial Standard and to provide specialized equipment. 3. Tb contribute more substantially to manufacturing (as distinct from information-based) industry in general, and to the new field of robotics in particular, university computer science training should include a higher proportion of classical applied science and mathematics than is typical today.

INTRODUCTION Computers and their associated software are a critical factor in the U.S. economy: . The computer and software industry generated revenues of over $45 billion in 1979, and that amount is growing at more than 15 percent per year (Datamation, July 19807. m e U.S. computer industry enjoys a clear technical leadership position worldwide, but the Japanese, among others, have announced their intention of assuming that leadership. . Our national defense is critically dependent on computer and software technology. Technical leadership in this area is crucial to the security of the nation. · Computers have a key role to play in the reindustrialization of American society. Business Week (June 30, 1980) estimates that the decline in U.S. industrial competitiveness in the 1970s alone amounts to some $125 billion in lost production and a loss of at least 2 million industrial jobs. In the use of computers in industry, we are clearly behind Japan and Germany, among others. The impact of computers in the noncomputer segment of U.S. industry is large and growing. Between 10 and 20 percent of computers (in dollar volume) are used for industrial design and control purposes. Over the next decade, the dependence of noncomputer industrial sectors on computer technology will continue to grow. In industry, computers will be required by designers of mechanisms, circuits, control systems, aircraft, ships, and chemical processes. In offices and on the production line, computers will be used for control of inventories, video inspection, instruments, machine tools, and a growing array of robot mechanisms. In support of production, computers will be used by reliability engineers, industrial test designers and test groups, cost estimators, and analysts. These applications will need to be supported by a broad spectrum of computer research activities, ranging from hardware to algorithm design, from systems development to artificial intelligence. The following table shows some ways in which important industrial applications depend on the products of computer research. 5

6 Industrial Applications of Some Products of Computer Research Industrial Application Computer Science Research mechanical design circuit design control system design process engineering airplane design scheduling inspection information networks medical technologists office system designers 2- and 3-D graphics research sparse matrices, numerical techniques very large scale integrated (VLSI) circuit design automation real time operating systems simulation languages numerical methods, computer graphics simulation languages, queuing theory robots, vision distributed processing, systems, data bases clustering, pattern recognition microprocessors, communication algorithms, graphics, data base theory - As an example of this dependence, consider the task of a mechanical designer in industry responsible for conceiving and detailing one of the thousands of special mechanisms required for the functioning of production lines. Over the past decade, this work has come to be heavily dependent on a growing family of current computer-aided-design products, available from major computer manufacturers and smaller specialist companies. In turn, these software packages make use of many research results, including university work on numerical analysis, special programming languages, and graphics. Using the design aids that this research has made available, the mechanical designer can create his product graphically in a simulated environment; he can then exercise his mechanism, checking for problems by looking at it in several three-dimensional views or automatically by using a mathematical model. m e design can be analyzed by SUPERB, NASTRAN, or such computerized stress analysis programs as ANSYS (many of them byproducts of the NASA Apollo program). Production drawings can be prepared and bills of materials transmitted to the manufacturing

7 department for scheduling and purchasing--all as byproducts of a computerized approach. Similar capabilities exist for circuit design, aircraft design, chemical process design, and many other vital industrial activities. Until now, U.S. computer researchers have given U.S. industry a competitive advantage in the international marketplace, but recently doubts have been raised with growing frequency about whether the technological excellence and international competitiveness of the U.S. computer industry will continue. For example, in a May 1980 article in Science entitled "Do the Japanese Make Better IC's?. Richard Anderson - of Hewlett-Packard's Data Systems Division says, "At first glance, the impression is that the Japanese are using low cost and domestic protection as levers to build up a strong base for exports. On close inspection, this premise does not hold up. m e Japanese semiconductor companies are using superior product quality to gain competitive advantages of enormous magnitude. n ~ ~ ~ The same article quotes a recent General accounting thrice report as stating, "ffl e defect ratio in product after product is lower in Japan than in the United States.. Maintaining leadership in the rapidly advancing computer and software field requires a continuing commitment to research and development. In the computer hardware area, this commitment has existed for a number of yearss its results have included a dramatic decrease in size and cost of computer hardware. In the software area, the commitment to research and development has been less clear, but that commitment is equally crucial in meeting the challenges that face the industry. m e papers of Part II of this report give ample testimony to the power of the complementary relationship between basic research and development and between work in universities and that in industry to advance the nation's capability in the computer field. Precis of these papers are given in the following section.

INDUSTRY AND THE UNIVERSITY IN NINE SUBAREAS OF COMPUTER SCIENCE 1. SYSTEMS SOFTWARE Successive waves of development have led to improvements in the reliability of hardware, reductions of costs and economies of scale, development of large-capacity random-access storage, and minicomputers and microcomputers and the distributed computing networks associated with them. Each of these developments has altered the focus and direction of systems software. Though the university contribution to this area was initially quite strong, the increasing functional complexity demanded by the marketplace has progressively transferred dominance in systems software innovation to industry. Universities have played various roles in systems software, including (1) demonstrating new systems concepts, (2) providing analysis, codification, and under- standing of existing systems, (3) developing better algorithms and protocols for improved systems, and (4) building new systems. Universities have been very successful at doing (2) and {3~. A lack of contact with field requirements has limited university successes in (4), but the increasing availability of smaI1 machines may provide an opportunity for university work to have a greater impact in that area. . _ _ . . . , _ ~ , ~ . 2. INTEGRATED CIRCUITS Industry has shown leadership in the development of integrated circuits. In fact, industry has dominated this development-oriented and capital-intensive area of technological innovation. Today, very large scale integrated (VSLI) circuits have become so complex that systems for automating the design of VLSI circuits have become necessary. Universities have made important contributions is this area. The present trend of increased involvement of universities in practical aspects of the VLSI industry shows great promise for providing new research perspectives but also may contain the risk of financial overcommitment. 8

9 3. THEORETICAL RESEARCH m eoretical research explores the mathematical limits of the power of computing techniques. Theory provides structures that codify, synthesize, and transmit ideas. It also provides standard problem approaches and a conceptual vocabulary for developing and communicating those approaches. Although industrial contributions to theory have also been substantial, universities have played the leading role in the development of theoretical computer science. Compiler writing, reviewed in more detail in the next section {4), furnishes good examples of the codifying role of theory in an essentially pragmatic area; e.g., formal techniques for syntactic analysis of high-level languages, begun in the 1960s, have become a major influence on language development. m e theory of encryption, which has had a very practical impact on national security, is another example of the practical impact of theory. m ere are many examples of special techniques and algorithms that have made significant contributions to performing computing tasks efficiently. These examples, including linear programming, fast Fourier transform, finite element techniques, data-compression techniques, sorting algorithms, and others, drive home the importance of careful theoretical consideration of the nature of computing tasks and the ultimate limitations of each of there techniques available for dealing with them. Theoreticians have played an important role in making ideas of this kind available, intelligible, and useful to programmers. 4. COMPILER WRITING Compilers are needed for the generation of machine code because of the prohibitively large number of man-hours that would otherwise be required for manual program construction. Although the first efforts took place in industry spurred by pragmatic considerations, theoretical contributions from the university community to the art of compiler development have attained a substantial influence on practice. These contributions include formal syntax analysis, techniques for efficiency improvement through code optimization, and global program analysis using graph-theoretical techniques. Refined algorithms developed by university theoreticians are beginning to have an impact on practical _ compiler design. As an examule. register allocation procedures. which _ , _ are crucial to the production of high-efficiency code, have come from the connection of the register allocation problem with the well-known graph-coloring problem, and thus the extensive mathematical literature on that apparently abstruse topic has had practical applications. The general trend is toward a more extensive use of systematic, theoretically based methods. Ultimately, this development may allow much compiler construction to be handled systematically. .

10 5. ARTIFICIAL INTELLIGENCE When algorithmic methods cannot be found for solving problems, adaptive strategies can be used to seek solutions. The extension and application of these so-called weak methods form part of the substance of the field known as artificial intelligence (AI). Because AI holds forth the possibility of simulating the process of understanding and intelligent response in machines, it has attracted much public interest. Research in AI has focused on such topics as . how to encode knowledge how to make inferences from data how to analyze visual scenes how to understand and generate speech or text how to model problem solving Most of this research has been done at a few university centers with substantial federal funding. But corporate AI research is growing stronger. Although they are byproducts of its central thrusts, some of the most important contributions of AI to computer science and industrial computing have been the invention of new programming techniques. Examples include time sharing and list processing. Domestic industry has been, and to some extent remains, skeptical of the AI field. The Japanese, by contrast, plan to invest $100 million in a fifth-generation computer project having a strong AI focus. 6 . ROBOTIC S In this area, the goals of artificial intelligence and the needs of the marketplace come together. Industry took the lead early on in robotics, but progress was slow until the MIT and Stanford groups began applying the approaches of artificial intelligence to the robotics problem. m e savings in processor cost realized through the development of microprocessors encouraged continued industrial interest in the 1970s. Industrial investment in robotics is now increasing rapidly. A number of university robotics laboratories have been established, with industry now building up large robotics activities of its own and also playing an important role in supporting university developments. To accelerate progress, well-structured university-industry cooperation will be necessary. The research issues that need to be addressed by work in robotics are broad and include geometry, dynamics, image processing, and software. Close collaboration between industry and universities can help give industry the mathematical skills required for work in this field and can help universities to find the capital resources and engineering capacity that they will need.

11 7. SCIENTIFIC COMPUTING Large-scale computing is very important for modeling and understanding problems in climate, aerodynamics, high-energy and solid-state physics, chemistry, and numerous other fields. For example, medical imaging devices such as computerized axial tomographic {CAT) scanners would not be practical without powerful small computers using sophisticated image-reconstruction algorithms. Research in scientific computing is dominated by work in universities, although there are also important efforts in a few industrial computing and government laboratory centers. Improving the precision and fineness of detail with which large physical systems can be modeled often requires large increases in computing power, and this demand for increased scientific computing power has been an important factor in motivating innovations in large machine architecture. But expertise in the theory of the problem to be solved is usually necessary to find practical approaches to large-scale scientific computing problems. Thus, to carry out such computations, expertise in both the relevant science and numerical techniques is required. This makes it important that scientific computing facilities be organized to promote effective interaction among scientists and experts in computer science. But trends in the organization of computing centers over the past decade have not promoted the desirable increased opportunity for experimentation in basic numerical techniques. This has impeded the production of adequate trained manpower in this field. The results of university research in scientific computing are disseminated to only a special group drawn from disciplines other than computer science. This lack of focus also impedes the production of adequately trained manpower in this field. Two steps could alleviate these problems. First, a new model program in large-scale scientific computing at one or two universities could be created with strength in both scientific comDutina and _ _ , ~ _ _ , _ _ · . _ numerical analysis. Second, ways should be sought to promote access for scientific computing groups to the most powerful computer facilities available. 8 . RESEARCH IN DATA PROCESSING The term data processing has historically been associated with business applications, but the usage of the term has now broadened to include more general computer-based information retrieval systems. Data processing accounts for the greatest proportion of institutional computer use measured by computer-hours. As organizations become more complex and requirements for documentation, reporting, and management information become more demanding, the complexity of data processing systems will continue to grow. However, little basic research, especially university research, has been devoted to this area. A list of major developments in the field would include high-level programming languages such as COBOL, data base management systems, decision support systems, and a variety of application development systems that facilitate development of data i

12 processing application programs. All these developments originated in field practice rather than from universities or industrial research centers. For example, COBOL, in wide use for business applications, was developed by a diverse group of hardware vendors. An exception to the general statement that only limited research has been devoted to the problems of data processing is data base management, which is one area where the work of such industrial research centers as the IBM San Jose Laboratory and the Computer Corporation of America has recently attracted university interest and involvement. Aside from this, university-based theoretical work has made its most notable contribution to data base technology in the development of advanced data access structures such as the B*-tree. Interestingly, the B*-tree descends from a collaboration of two researchers, one from industry and one from academe. Continuing interchange between researchers and practitioners must be fostered to promote continued productive development in data processing. m is is the only way in which university researchers can be made aware of the constantly shifting foci of attention in this dynamic field. 9. SOFTWARE DEVELOPMENT The cost of developing software has been widely recognized as a problem for many years, especially in industry, which has taken the lead in developing software to meet certain practical needs. m e productivity of programmers is rising only very slowly, while software efficiency and reliability continue to be subjects of concern in industry. Although significant advances have come over the last 30 years from research in the science of programming, much more must be done to improve the productivity of programmers and the reliability of programs. The concepts that have thus far aided programmer productivity include high-level languages, interactive computing environments, disciplined approaches such as structured programming, and related management methodologies. Continued progress will come from both industrial and university research laboratories. As in the past, developments will come from attempts to solve real problems. Much of the development will need to be done with experimental hardware that may appear uneconomical but that will become practical as costs are reduced through high-volume production and technological breakthroughs. Enhanced progress can come from (1) increased contact between researchers and real-world software development problems, (2) revitalization of experimental computing facilities, and {3) support of experiments with software productivity tools. Such steps promote the conditions that naturally foster progress in software development.

RESEARCH AND DEVELOPMENT IN COMPl1113R SCIENCE Computer science is special among the sciences in that technological developments have tended to govern the overall direction of the field. Two technological achievements--the stored-program computer and the integrated circuit, both of them now old on the timescale typical of such developments--had implications so manifold that they have dominated the activities of practitioners in the field for many years and will continue to do so for years to come. At any given moment within a scientific or technological field, the relative weights of technological development and basic research depend on the richness of the consequences that flow from that field's key discoveries and inventions relative to the significant developmental directions that remain to be explored. As long as exploration remains incomplete, the field will find new application areas, and the background of pragmatic issues that ultimately shapes the pure researcher's mind-set will change rapidly in ways determined by the developmental researcher. This has been the situation of computer science throughout most of its roughly 35-year history. A succession of inventions--magnetic core, disc memory, and others--has triggered successive waves of development in computers. Above all, the invention of the transistor, its subsequent development into a variety of semiconductor devices, and the invention of the integrated circuit have released developments that are still driving the entire computer enterprise. The software side of computer development has also tended to be dominated by developmental research. By rapidly changing the economics of computing, advances in hardware technology have forced software developers to come to grips with a continually widening range of applications. For example, discs have led to data bases, communications technology has led to ARPANET, and VLSI technology is now leading to interest in parallel modes of computation. Moreover, applications pressures have pushed software designers into involvement with systems of ever-growing complexity, making the refinement of tools for the management of this complexity an abiding developmental concern of the software researcher. Computer science education and systems training have also been concentrated in the developmental area, in response to the central concerns of the marketplace. Systems training is what the average · . 13

14 employer wants his new employees to have, it is what the average student wants to acquire, and it constitutes the bread-and-butter activity of nearly all university computer science departments. m e wealth of significant developmental activities at hand has tended to overshadow the work of the pure research community within computer science. Because theory has been so overshadowed, it is well to emphasize its contributions to computer science. First of all, theory gives insight into the capabilities and limitations of computers. Nonexistence results advise practitioners not to waste time in quests for absolutely optimal compilation or general verification of programs. Finer theoretical analysis often reveals how closely or in what cases some of these goals can actually be achieved. Proven bounds define objective standards of performance for algorithms and point out opportunities for improvement. In many cases, these theoretical hints have inspired vigorous searches for algorithms of remarkable efficiency. Finding their way into practice, these algorithms have on occasion solved efficiency problems decisively enough to allow simple uniform approaches to replace less effective systems designs. Theory tells us how to solve problems that would otherwise be out of reach and defines problems that would otherwise be overlooked. Theory also often provides generalizations that simplify computing and extend its reach. m e many industrial applications that fit within the core of well-understood computing can be promptly and reliably implemented. Second-order tools, such as program generators, which suggest themselves in these areas, can be built with confidence. mese truths are particularly apparent in the domain of scientific computing. Witness theoretically based developments such as linear programming, fast Fourier transform, and finite element techniques. There are other examples: complexity theory has given us combinatorial algorithms that help with geometric layout. m e theory of automata has given us fast search techniques applicable to bibliographic retrieval. Formal language theory has given us automatic consistency tests useful to language designers. Algebra has given us new cryptographic techniques. In spite of these important successes of theory, however, the predominant trend in computer science remains that of development. ORIGIN OF IDEAS The distinction between basic and developmental research blurs most completely at those fruitful moments when new fields of investigation and application spring up. In first identifying, defining, naming, and starting to unravel new fields, an investigator performs research that is both basic and developmental: basic because of its novelty and developmental because the practical impact of the new technique is immediately apparent. Only after an initial period of growth will the basic researcher turn his attention from initial discoveries to further challenges. At the same time, the developer, finding competition already in place, will begin to consider quantitative advantages carefully. Only then does the distinction between basic and developmental work arise.

The initial stirrings out of which new fields grow can be and have been felt first in either universities or industry. Its marketing organizations give industry a sensitivity to external applications that universities lack almost totally. Certain crucial, externally generated hints will therefore be felt in industrial development groups well before university researchers become aware of them. Such is the history of the data base concept, which was important in industrial circles years before it became a subject for the university researcher. The importance of object code efficiency was clear to industry from the start, and attempts to optimize it were central to one of the very first major industrial software research efforts--the development of FORTRAN. Important work on code optimization continued in industry for almost a decade before a series of papers by industrial researchers brought this subject to the active attention of the pure university researchers. On the other hand, in initiating certain types of developmental rather than basic lines of work, universities have arguable advantages. Industrial firms orient themselves strongly to established markets and may therefore be reluctant to commit themselves to developments that imply major changes in the way their customers are accustomed to doing things. In contrast, the university developer of an innovative system will often be his own intended customer and can push an innovation because introspection leads him to believe that it will facilitate some of his own, relatively advanced, activities. Forces of this kind played a role in the early history of time sharing, which was developed for local use at MIT at a time when the overwhelming tendency of commercial, and even scientific, computer users was to organize their work quite differently, in batch style. By building an initially expensive special system, and by making visible the increased level of user effectiveness that resulted from this reorganization of work, these MIT experiments (and also the Dartmouth work on BASIC) could whet the appetite of economically more powerful groups of computer users. Another example is the "spacewar. type of video game, which was an after-hours plaything of MIT systems programmers years before it became a billion-dollar amusement industry. MATURATION OF IDEAS AND APPLICATIONS As a new field stabilizes and comes to be guided by established general notions and principles, it begins to mature theoretically. Important problems of the field become formulated abstractly enough for questions of design optimality to be raised. Once perceived, questions about the possibility of fundamental improvements may be answered in the negative, after a clear theoretical understanding has been established that, given appropriate assumptions, certain fragments of established technique are not capable of improvement. On the other hand, such questions may fail of an answer, raising the suspicion that many improved techniques await discovery. The longer a significant question remains unanswered, the greater grows its fame among theorists, and the more attractive a target it appears for research.

16 Whereas only the largest industrial firms can devote research to difficult theoretical problems, such research is the central role of universities. On the other hand, universities do not have at their disposal developmental resources nearly comparable to those of industry. Since polish counts in the marketplace, it is often necessary to expend large amounts of resources on aspects of the system that are -entirely ancillary to a technological innovation: documentation, testing, distribution, response to problem reports, and support in a wide range of computer and device environments. An industrial organization will see such expenditure as a well understood kind of overhead, recoverable from the wide sale of a successful product. University researchers will have neither access to such levels of resources nor interest in these vital secondary areas. For sustained, multifaceted development demanding the careful establishment of supporting structures, as opposed to the exhilarating exploration of a single new idea, industry has decisive advantages over universities. Sufficient funding to sustain powerful technical support staffs for extended periods is generally not consistently available from granting agencies. Technological development that needs continuing support and can predictably yield a continuing return will have a better chance to succeed in industry. TRANSMISSION OF IDEAS Good theory codifies, synthesizes, and transmits ideas. It lives most comfortably in universities and plays a dominant role in the advanced curriculum of computer science. meory provides a common body of understanding that practitioners automatically employ in attacking technical tasks. In universities, researchers are encouraged to publish the results of their works and professors are accustomed to writing textbooks and monographs that marshal the major techniques of their field. In industry, however, documentation and publication tend to be regarded as secondary. The ambitious industrial researcher, striving for a competitive advantage in a fast-moving field, will generally not be motivated to invest the time necessary for this archiving function. If proprietary aspects of research become involved, the legal department of his firm may be much more concerned with preventing rather than facilitating publication. His factor has a considerable, and in some cases decisive, effect on the dissemination of technical information developed within industry, a point emphasized in a series of interviews with industrial research managers conducted in connection with the preparation of this report (see the Appendix). Many of these managers felt that the currently available means of protecting intellectual property--patents, copyrights, and trade secrets--were poorly matched to the needs of the computer industry. m ey believed that patent law gave the strongest protection, but it was seen to apply only in narrowly defined areas. Copyright protection is used, especially for software, but it is not a strong form of protection because there are almost no legal precedents to draw on for its successful use in the

17 computer area. Even conceptually, it is not clear how well copyrights can protect software. Copyright does not protect against the use of ideas, but only against the use of particular expressions of ideas; and the point of most software is to be used, not to be read. Thus most companies are forced to rely on secrecy as one of the primary means for protecting their ideas. But this tactic works against the need for maintaining an open research environment that is perceived at some of the larger corporations, where greater emphasis is placed on publishing. Even in those segments of the industry in which secrecy is practical, its effects can be quite detrimental. Developments in VLSI circuitry illustrate this point. Because many of the important ideas in the development of VLSI technology are process techniques that are not easily discovered from inspection of the product, and because legal protection was perceived to be inappropriate for this technology, the details of technical progress have been kept secret. Isolated from these independently developing bodies of knowledge, universities have not developed significant expertise in these areas. A shortage of adequately trained people is the result. In the long run, this state of affairs must prove harmful to innovation in America. When information is needed from universities, industry is more likely to hire university graduates or consultants than to encourage its employees to read academic publications systematically. When information needs to be transmitted internally, industry will tend to organize task forces or to transfer personnel rather than to provide textbooks or monographs. Over the long run, this failure to codify and to archive its technical knowledge probably works to the detriment of industry, or at least accentuates the dependence of industry on the university researchers who perform this function. If neither failures nor successes are adequately analyzed and archived, knowledge of what they have revealed will not be retained when the relevant technical groups are broken up and their key members moved to other positions. Once technical insights dissipate, nothing guarantees that complex future technical projects will be even as successful as past projects. Depending on reinvention from scratch, they may be worse. By and large, the work of academic codifiers prevents this from happening and pushes the generally available level of technology steadily forward. However, many useful technical ideas, especially in the systems area, may fail to reach publication and require rediscovery. Publication may justify support of university projects that fall technically below corresponding industrial efforts because such projects can give academics firsthand familiarity with the problems and methods, which they then document.

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