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Stanford University Medical Experimental Computer Resource (SUMEX) Records SC1248
http://oac.cdlib.org/findaid/ark:/13030/c8s46z8g Online items available Guide to the Stanford University Medical Experimental Computer Resource (SUMEX) Records SC1248 Daniel Hartwig & Jenny Johnson Department of Special Collections and University Archives January 2018 Green Library 557 Escondido Mall Stanford 94305-6064 [email protected] URL: http://library.stanford.edu/spc Guide to the Stanford University SC1248 1 Medical Experimental Computer Resource (SUMEX) Records SC... Language of Material: English Contributing Institution: Department of Special Collections and University Archives Title: Stanford University Medical Experimental Computer Resource (SUMEX) records Identifier/Call Number: SC1248 Physical Description: 33 Linear Feet Date (inclusive): 1975-1991 Special Collections and University Archives materials are stored offsite and must be paged 48 hours in advance. For more information on paging collections, see the department's website: http://library.stanford.edu/spc. Conditions Governing Access Materials are open for research use. Audio-visual materials are not available in original format, and must be reformatted to a digital use copy. Conditions Governing Use All requests to reproduce, publish, quote from, or otherwise use collection materials must be submitted in writing to the Head of Special Collections and University Archives, Stanford University Libraries, Stanford, California 94304-6064. Consent is given on behalf of Special Collections as the owner of the physical items and is not intended to include or imply permission from the copyright owner. Such permission must be obtained from the copyright owner, heir(s) or assigns. Restrictions also apply to digital representations of the original materials. Use of digital files is restricted to research and educational purposes. -
The Cedar Programming Environment: a Midterm Report and Examination
The Cedar Programming Environment: A Midterm Report and Examination Warren Teitelman The Cedar Programming Environment: A Midterm Report and Examination Warren Teitelman t CSL-83-11 June 1984 [P83-00012] © Copyright 1984 Xerox Corporation. All rights reserved. CR Categories and Subject Descriptors: D.2_6 [Software Engineering]: Programming environments. Additional Keywords and Phrases: integrated programming environment, experimental programming, display oriented user interface, strongly typed programming language environment, personal computing. t The author's present address is: Sun Microsystems, Inc., 2550 Garcia Avenue, Mountain View, Ca. 94043. The work described here was performed while employed by Xerox Corporation. XEROX Xerox Corporation Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 1 Abstract: This collection of papers comprises a report on Cedar, a state-of-the-art programming system. Cedar combines in a single integrated environment: high-quality graphics, a sophisticated editor and document preparation facility, and a variety of tools for the programmer to use in the construction and debugging of his programs. The Cedar Programming Language is a strongly-typed, compiler-oriented language of the Pascal family. What is especially interesting about the Ce~ar project is that it is one of the few examples where an interactive, experimental programming environment has been built for this kind of language. In the past, such environments have been confined to dynamically typed languages like Lisp and Smalltalk. The first paper, "The Roots of Cedar," describes the conditions in 1978 in the Xerox Palo Alto Research Center's Computer Science Laboratory that led us to embark on the Cedar project and helped to define its objectives and goals. -
Building the Second Mind, 1961-1980: from the Ascendancy of ARPA to the Advent of Commercial Expert Systems Copyright 2013 Rebecca E
Building the Second Mind, 1961-1980: From the Ascendancy of ARPA to the Advent of Commercial Expert Systems copyright 2013 Rebecca E. Skinner ISBN 978 09894543-4-6 Forward Part I. Introduction Preface Chapter 1. Introduction: The Status Quo of AI in 1961 Part II. Twin Bolts of Lightning Chapter 2. The Integrated Circuit Chapter 3. The Advanced Research Projects Agency and the Foundation of the IPTO Chapter 4. Hardware, Systems and Applications in the 1960s Part II. The Belle Epoque of the 1960s Chapter 5. MIT: Work in AI in the Early and Mid-1960s Chapter 6. CMU: From the General Problem Solver to the Physical Symbol System and Production Systems Chapter 7. Stanford University and SRI Part III. The Challenges of 1970 Chapter 8. The Mansfield Amendment, “The Heilmeier Era”, and the Crisis in Research Funding Chapter 9. The AI Culture Wars: the War Inside AI and Academia Chapter 10. The AI Culture Wars: Popular Culture Part IV. Big Ideas and Hardware Improvements in the 1970s invert these and put the hardware chapter first Chapter 11. AI at MIT in the 1970s: The Semantic Fallout of NLR and Vision Chapter 12. Hardware, Software, and Applications in the 1970s Chapter 13. Big Ideas in the 1970s Chapter 14. Conclusion: the Status Quo in 1980 Chapter 15. Acknowledgements Bibliography Endnotes Forward to the Beta Edition This book continues the story initiated in Building the Second Mind: 1956 and the Origins of Artificial Intelligence Computing. Building the Second Mind, 1961-1980: From the Establishment of ARPA to the Advent of Commercial Expert Systems continues this story, through to the fortunate phase of the second decade of AI computing. -
Artificial Intelligence: Immediate Pronnses and Ultimate Goals
155 Discussion Artificial intelligence: Immediate pronnses and ultimate goals Leonard URR Keywords: Artificial intelligence, real intelligence, YLSI, University of Wisconsin, Computer Science Department, Madi multi-computer networks, ' user friendly systems', son, WI 53706, USA 'smarts', ' human engineering', fads and fallacies, modelling intelligent cognition. Leonard Uhr was born in Philadelphia The great ultimate promise of artificial intelligence (PA) in 1927. He received the B.A. degree from Princeton University in 1949 and the Ph.D. from the Univer Artificial intelligence is, ultimately, the develop sity of Michigan in 1957. From 1957 ment of a theoretical understanding of (intelligent) to 1964 he was an Assistant Research Scientist and Research Scientist at the thinking. Once developed, this understanding can Mental Health Research Institute and then be applied to a myriad of wonderful projects, a Lecturer and Associate Professor in the Psychology Department at the from making computers 'smarter', more ' user University of Michigan, Ann Arbor. friendly' and even 'sympathetic', to building robots From 1964 to the present he has been a Professor in the Computer Sciences that cook, clean up, keep accounts, win at the Department at the University of Wisconsin, Madison. races and beat the stock market, build cars for us His chief research interests center around the long-term goal of developing precise, testable theoretical models of per and drive them, listen to our troubles, tell us jokes, ceptual and cognitive processes, focussing on the following solve our financial, intellectual and personal prob problems: visual perception of ordinary real-world scenes of objects; perception (in real time) of objects in motion; learning lems, and in general lavish us with all the free time by inductive inference and hypothesis-formation; modelling of and friendship we can handle and help us to enjoy simple systems that cycle through the whole perceptual-cogni tive-motor loop; and the design and programming of large it and to use it productively. -
History of the Lisp Language
History of the Lisp Language History of the Lisp Language The following information is derived from the history section of dpANS Common Lisp. Lisp is a family of languages with a long history. Early key ideas in Lisp were developed by John McCarthy during the 1956 Dartmouth Summer Research Project on Artificial Intelligence. McCarthy’s motivation was to develop an algebraic list processing language for artificial intelligence work. Implementation efforts for early dialects of Lisp were undertaken on the IBM 704, the IBM 7090, the Digital Equipment Corporation (DEC) PDP−1, the DEC PDP−6, and the PDP−10. The primary dialect of Lisp between 1960 and 1965 was Lisp 1.5. By the early 1970’s there were two predominant dialects of Lisp, both arising from these early efforts: MacLisp and Interlisp. For further information about very early Lisp dialects, see The Anatomy of Lisp or Lisp 1.5 Programmer’s Manual. MacLisp improved on the Lisp 1.5 notion of special variables and error handling. MacLisp also introduced the concept of functions that could take a variable number of arguments, macros, arrays, non−local dynamic exits, fast arithmetic, the first good Lisp compiler, and an emphasis on execution speed. For further information about Maclisp, see Maclisp Reference Manual, Revision 0 or The Revised Maclisp Manual. Interlisp introduced many ideas into Lisp programming environments and methodology. One of the Interlisp ideas that influenced Common Lisp was an iteration construct implemented by Warren Teitelman that inspired the loop macro used both on the Lisp Machines and in MacLisp, and now in Common Lisp. -
Volume 24: Issue 1
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CLISP - Conversational LISP
Session 25 Hardware and Software for Artificial Intelligence CLISP - Conversational LISP Warren Teitelman Xerox Palo Alto Research center Palo Alto, California 94304 Abstract CLISP is an attempt to make LISP programs The syntax for a conditional expression is easier to read and write by extending the correspondingly simple:* syntax of LISP to include infix operators, IF-THEN statements, FOR-DO-WHILE statements, Figure 2 and similar ALGOL-like constructs, without Syntax of a conditional Expression changing the structure or representation of the language. CLISP is implemented through <conditional expression> ::x LISP's error handling machinery, rather than (COND <clause> ... <clause>) by modifying the interpreter: when an <clause> :;= (<form> <form>) expression is encountered whose evaluation causes an error, the expression is scanned Note that there are no IF's, THEN's, for possible CLISP constructs, which are BEGIN's, END'S, or semi-colons. LISP avoids then converted to the equivalent LISP the problem of parsing the conditional expressions. Thus, users can freely expression, i.e., delimiting the individual intermix LISP and CLISP without having to clauses, and delimiting the predicates and distinguish which is which. Emphasis in the consequents within the clauses, by requiring design and development of CLISP has been on that each clause be a separate element in the system aspects of such a facility, with the conditional expression, namely a the goal in mind of producing a useful tool, sublist, of which the predicate is always not just another language. To this end, the first element, and the consequent the CLISP includes interactive error correction second element- As an example, let us and many 'Do-what-I-Mean* features. -
Predicting Developers' IDE Commands with Machine Learning
Predicting Developers’ IDE Commands with Machine Learning Tyson Bulmer, Lloyd Montgomery, Daniela Damian University of Victoria Victoria, Canada {tysonbul,lloydrm,danielad}@uvic.ca ABSTRACT research in bug prediction has touched on the topic of developer When a developer is writing code they are usually focused and “focus” as a contributor to bug proneness. Di Nucci et al. propose in a state-of-mind which some refer to as flow. Breaking out of that the higher a developer’s focus on their activities, the less likely this flow can cause the developer to lose their train of thought and they are to introduce bugs to the code [3]. Zhang et al. proposed have to start their thought process from the beginning. This loss of that certain file editing patterns by developers writing code could thought can be caused by interruptions and sometimes slow IDE contribute to bug proneness as well. Two of their patterns which interactions. Predictive functionality has been harnessed in user highlight the “focus” of a developer are the “Interrupted” and “Ex- applications to speed up load times, such as in Google Chrome’s tended” editing patterns [21], which they found introduced 2.1 and browser which has a feature called “Predicting Network Actions". 2.28 (respectively) times more future bugs than without. This will pre-load web-pages that the user is most likely to click The bigger picture of developer activity within an IDE, as char- through. This mitigates the interruption that load times can intro- acterized by our own investigation of this research area, can be duce. -
Tryouts Toward the Production of Thought
------LEONARD UHR------ Tryouts toward the Production of Thought This paper explores what we might mean by terms like "sense," "perceive," "know,'' ''consciousness,'' ''self,'' ''short-term memory," "analog," "schema" and "cognitive model," by trying to find what they refer to in actual computer programs. The different types of systems for perception are briefly surveyed, to indicate structures that they must have in common. Then more wholistic cognitive systems are described. One system (Uhr, 1975a, b, 1976a) called a SEER1 attempts to handle simultaneously all the cognitive processes-including per ception, remembering, deductive problem-solving, language process ing, acting, and learning-that are usually studied separately by psychologists and computer scientists. SEERs are first attempts to develop well-integrated wholistic cognitive systems that are designed to do a variety of things, albeit drably (much as most human beings go about their everyday tasks of thinking), rather than one particu lar difficult thing well (like chess or proving theorems). They are therefore required to decide what type of thing to do, and when, and to choose, organize, integrate, and orchestrate the sets of cog nitive transforms needed to carry out the chosen actions, cutting across the separate cognitive systems. RATIONALE Our problem is the development of a science of information processing entities, a science coordinate with physics-the science of matter/energy. Psychology and the building of "artificial intel- Note: The author's research described in this paper was partially supported by the Na tional Science Foundation and the University of Wisconsin Graduate School. 327 328 Leonard Uhr ligences" will someday be viewed as the natural and artifactual ap plications of our science, just as geology and engineering are today viewed as the natural and artifactual applications of physics. -
Interlisp Is a Programming Environment Based on the Energy Fighting a System That at Times Seems Bent on Lisp Programming Language
Integration, extensibility, and ease of modification made Interlisp unique and powerful. Its adaptations will enhance the power of the coming world of personal computing and advanced displays. The InterIisp Programmg l:nvionment Warren Teitelman and Larry Masinter Xerox Palo Alto Research Center Interlisp is a programming environment based on the energy fighting a system that at times seems bent on Lisp programming language. 1.2 In widespread use in the frustrating his best efforts. "6 artificial intelligence community, Interlisp has an ex- The environmental considerations were greatly influ- tensive set of user facilities, including syntax extension, enced by the perceived user community and the style of uniform error handling, automatic error correction, an programming in that community: first, typical Lisp users integrated structure-based editor, a sophisticated debug- were engaged in experimental rather than production pro- ger, a compiler, and a fiing system. Its most popular im- gramming; second, they were willing to expend computer plementation is Interlisp-IO, which runs under both the resources to improve human productivity; third, we be- Tenex and Tops-20 operating systems for the DEe lieved users would prefer sophisticated tools, even at the PDP-IO family. Interlisp-IO now has approximately 300 expense of simplicity. users at 20 different sites (mostly universities) in the US and abroad. It is an extremely well documented and well Experimental programming and structured growth. maintained system. The original architects of the Interlisp system were in- Interlisp has been used to develop and implement a terested in large artificial intelligence applications pro- wide variety of large application systems. -
Artificial Intelligence
Artificial Intelligence: An Overview ∗ Vasant Honavar Artificial Intelligence Research Laboratory Center for Computational Intelligence, Learning, and Discovery Department of Computer Science 226 Atanasoff Hall Iowa State University Ames, Iowa 50011 Last revised: August 21, 2006 Abstract This chapter reviews common-sense definitions of intelligence; motivates the research in artificial in- telligence (AI) that is aimed at design and analysis of programs and computers that model minds/brains; lays out the fundamental guiding hypothesis of AI; reviews the historical development of AI as a sci- entific and engineering discipline; explores the relationship of AI to other disciplines; and presents an overview of the scope, problems, and major areas of AI. Hopefully this discussion will not only provide a useful context for the technical material that follows but also convey a sense of what scientists, engineers, mathematicians, and philosophers who have been drawn to the field find exciting about AI. The views presented here are necessarily biased by the author's own research. The readers are encouraged to explore alternative views and perspectives on this subject. c Vasant Honavar, 1992-2006. 1 What is Intelligence? Try precisely defining intelligence. It is next to impossible. Despite the wide use (and misuse) of terms such as intelligent systems, there is no widely agreed-upon scientific definition of intelligence. It is therefore useful to think of intelligence in terms of an open collection of attributes. What follows is a wish-list of general characteristics -
Tim Box __I/2 Fol )Ll STANFORD UNIVERSITY CALIFORNIA 94305
Ctyrf. <^ S^«c^ Co*ecfron6 no* Tim Box __i/2 Fol )lL STANFORD UNIVERSITY CALIFORNIA 94305 COMPUTER SCIENCE DEPARTMENT Telephone 415-321-2300 November 16, 197 7 Professor Leonard Uhr University of Wisconsin -Madison Computer Science Department 1210 West Dayton Street Madison, Wisconsin 53706 Dear Len: Please excuse the delay in answering your letter of October 19th The outline of your new book looks generally good to me and I would like to proceed toward a consideration of it for the new Series in Artificial Intelligence that has been initiated under my editorship at McGraw Hill. The cognizant editor at McGraw Hill would be either Rose Ciofolo or Charles Stewart. I am sending each a copy of your letter. I would appreciate receiving the existing portions of the manuscript for a preliminary look. I would like to thank you for thinking about us in connection with this book and I intend getting back to you soon after receiving your material. Best wishes, / C-f^. Edward A. Feigenbaum Professor and Chairman EAF/lsl cc: Rose Ciofolo Charles Stex>;art STANFORD, UNIVERSITY OF WISCONSIN-MADISON COMPUTER SCIENCES DEPARTMENT 210 WEST DAYTON STREET MADISON, WISCONSIN 53706 (608)262-1204 October 19, 1977 Professor Edward Feigenbaum Computer Science Department Stanford University Palo Alto, California Dear Ed: I have just completed the almost-final draft of a book, entitled "Toward the Production of Thought," that attempts to give a coherent picture of research in cognitive model- ling and artificial intelligence, and to examine this re- search from the point of view of philosophy, psychology, and the neu rose i ences .