Programming Languages and Artificial Intelligence
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Typology of Programming Languages E Early Languages E
Typology of programming languages e Early Languages E Typology of programming languages Early Languages 1 / 71 The Tower of Babel Typology of programming languages Early Languages 2 / 71 Table of Contents 1 Fortran 2 ALGOL 3 COBOL 4 The second wave 5 The finale Typology of programming languages Early Languages 3 / 71 IBM Mathematical Formula Translator system Fortran I, 1954-1956, IBM 704, a team led by John Backus. Typology of programming languages Early Languages 4 / 71 IBM 704 (1956) Typology of programming languages Early Languages 5 / 71 IBM Mathematical Formula Translator system The main goal is user satisfaction (economical interest) rather than academic. Compiled language. a single data structure : arrays comments arithmetics expressions DO loops subprograms and functions I/O machine independence Typology of programming languages Early Languages 6 / 71 FORTRAN’s success Because: programmers productivity easy to learn by IBM the audience was mainly scientific simplifications (e.g., I/O) Typology of programming languages Early Languages 7 / 71 FORTRAN I C FIND THE MEAN OF N NUMBERS AND THE NUMBER OF C VALUES GREATER THAN IT DIMENSION A(99) REAL MEAN READ(1,5)N 5 FORMAT(I2) READ(1,10)(A(I),I=1,N) 10 FORMAT(6F10.5) SUM=0.0 DO 15 I=1,N 15 SUM=SUM+A(I) MEAN=SUM/FLOAT(N) NUMBER=0 DO 20 I=1,N IF (A(I) .LE. MEAN) GOTO 20 NUMBER=NUMBER+1 20 CONTINUE WRITE (2,25) MEAN,NUMBER 25 FORMAT(11H MEAN = ,F10.5,5X,21H NUMBER SUP = ,I5) STOP TypologyEND of programming languages Early Languages 8 / 71 Fortran on Cards Typology of programming languages Early Languages 9 / 71 Fortrans Typology of programming languages Early Languages 10 / 71 Table of Contents 1 Fortran 2 ALGOL 3 COBOL 4 The second wave 5 The finale Typology of programming languages Early Languages 11 / 71 ALGOL, Demon Star, Beta Persei, 26 Persei Typology of programming languages Early Languages 12 / 71 ALGOL 58 Originally, IAL, International Algebraic Language. -
Historical Perspective and Further Reading 162.E1
2.21 Historical Perspective and Further Reading 162.e1 2.21 Historical Perspective and Further Reading Th is section surveys the history of in struction set architectures over time, and we give a short history of programming languages and compilers. ISAs include accumulator architectures, general-purpose register architectures, stack architectures, and a brief history of ARMv7 and the x86. We also review the controversial subjects of high-level-language computer architectures and reduced instruction set computer architectures. Th e history of programming languages includes Fortran, Lisp, Algol, C, Cobol, Pascal, Simula, Smalltalk, C+ + , and Java, and the history of compilers includes the key milestones and the pioneers who achieved them. Accumulator Architectures Hardware was precious in the earliest stored-program computers. Consequently, computer pioneers could not aff ord the number of registers found in today’s architectures. In fact, these architectures had a single register for arithmetic instructions. Since all operations would accumulate in one register, it was called the accumulator , and this style of instruction set is given the same name. For example, accumulator Archaic EDSAC in 1949 had a single accumulator. term for register. On-line Th e three-operand format of RISC-V suggests that a single register is at least two use of it as a synonym for registers shy of our needs. Having the accumulator as both a source operand and “register” is a fairly reliable indication that the user the destination of the operation fi lls part of the shortfall, but it still leaves us one has been around quite a operand short. Th at fi nal operand is found in memory. -
John Mccarthy
JOHN MCCARTHY: the uncommon logician of common sense Excerpt from Out of their Minds: the lives and discoveries of 15 great computer scientists by Dennis Shasha and Cathy Lazere, Copernicus Press August 23, 2004 If you want the computer to have general intelligence, the outer structure has to be common sense knowledge and reasoning. — John McCarthy When a five-year old receives a plastic toy car, she soon pushes it and beeps the horn. She realizes that she shouldn’t roll it on the dining room table or bounce it on the floor or land it on her little brother’s head. When she returns from school, she expects to find her car in more or less the same place she last put it, because she put it outside her baby brother’s reach. The reasoning is so simple that any five-year old child can understand it, yet most computers can’t. Part of the computer’s problem has to do with its lack of knowledge about day-to-day social conventions that the five-year old has learned from her parents, such as don’t scratch the furniture and don’t injure little brothers. Another part of the problem has to do with a computer’s inability to reason as we do daily, a type of reasoning that’s foreign to conventional logic and therefore to the thinking of the average computer programmer. Conventional logic uses a form of reasoning known as deduction. Deduction permits us to conclude from statements such as “All unemployed actors are waiters, ” and “ Sebastian is an unemployed actor,” the new statement that “Sebastian is a waiter.” The main virtue of deduction is that it is “sound” — if the premises hold, then so will the conclusions. -
John Mccarthy – Father of Artificial Intelligence
Asia Pacific Mathematics Newsletter John McCarthy – Father of Artificial Intelligence V Rajaraman Introduction I first met John McCarthy when he visited IIT, Kanpur, in 1968. During his visit he saw that our computer centre, which I was heading, had two batch processing second generation computers — an IBM 7044/1401 and an IBM 1620, both of them were being used for “production jobs”. IBM 1620 was used primarily to teach programming to all students of IIT and IBM 7044/1401 was used by research students and faculty besides a large number of guest users from several neighbouring universities and research laboratories. There was no interactive computer available for computer science and electrical engineering students to do hardware and software research. McCarthy was a great believer in the power of time-sharing computers. John McCarthy In fact one of his first important contributions was a memo he wrote in 1957 urging the Director of the MIT In this article we summarise the contributions of Computer Centre to modify the IBM 704 into a time- John McCarthy to Computer Science. Among his sharing machine [1]. He later persuaded Digital Equip- contributions are: suggesting that the best method ment Corporation (who made the first mini computers of using computers is in an interactive mode, a mode and the PDP series of computers) to design a mini in which computers become partners of users computer with a time-sharing operating system. enabling them to solve problems. This logically led to the idea of time-sharing of large computers by many users and computing becoming a utility — much like a power utility. -
Oliver Knill: March 2000 Literature: Peter Norvig, Paradigns of Artificial Intelligence Programming Daniel Juravsky and James Martin, Speech and Language Processing
ENTRY ARTIFICIAL INTELLIGENCE [ENTRY ARTIFICIAL INTELLIGENCE] Authors: Oliver Knill: March 2000 Literature: Peter Norvig, Paradigns of Artificial Intelligence Programming Daniel Juravsky and James Martin, Speech and Language Processing Adaptive Simulated Annealing [Adaptive Simulated Annealing] A language interface to a neural net simulator. artificial intelligence [artificial intelligence] (AI) is a field of computer science concerned with the concepts and methods of symbolic knowledge representation. AI attempts to model aspects of human thought on computers. Aspectrs of AI: computer vision • language processing • pattern recognition • expert systems • problem solving • roboting • optical character recognition • artificial life • grammars • game theory • Babelfish [Babelfish] Online translation system from Systran. Chomsky [Chomsky] Noam Chomsky is a pioneer in formal language theory. He is MIT Professor of Linguistics, Linguistic Theory, Syntax, Semantics and Philosophy of Language. Eliza [Eliza] One of the first programs to feature English output as well as input. It was developed by Joseph Weizenbaum at MIT. The paper appears in the January 1966 issue of the "Communications of the Association of Computing Machinery". Google [Google] A search engine emerging at the end of the 20'th century. It has AI features, allows not only to answer questions by pointing to relevant webpages but can also do simple tasks like doing arithmetic computations, convert units, read the news or find pictures with some content. GPS [GPS] General Problem Solver. A program developed in 1957 by Alan Newell and Herbert Simon. The aim was to write a single computer program which could solve any problem. One reason why GPS was destined to fail is now at the core of computer science. -
Fpgas As Components in Heterogeneous HPC Systems: Raising the Abstraction Level of Heterogeneous Programming
FPGAs as Components in Heterogeneous HPC Systems: Raising the Abstraction Level of Heterogeneous Programming Wim Vanderbauwhede School of Computing Science University of Glasgow A trip down memory lane 80 Years ago: The Theory Turing, Alan Mathison. "On computable numbers, with an application to the Entscheidungsproblem." J. of Math 58, no. 345-363 (1936): 5. 1936: Universal machine (Alan Turing) 1936: Lambda calculus (Alonzo Church) 1936: Stored-program concept (Konrad Zuse) 1937: Church-Turing thesis 1945: The Von Neumann architecture Church, Alonzo. "A set of postulates for the foundation of logic." Annals of mathematics (1932): 346-366. 60-40 Years ago: The Foundations The first working integrated circuit, 1958. © Texas Instruments. 1957: Fortran, John Backus, IBM 1958: First IC, Jack Kilby, Texas Instruments 1965: Moore’s law 1971: First microprocessor, Texas Instruments 1972: C, Dennis Ritchie, Bell Labs 1977: Fortran-77 1977: von Neumann bottleneck, John Backus 30 Years ago: HDLs and FPGAs Algotronix CAL1024 FPGA, 1989. © Algotronix 1984: Verilog 1984: First reprogrammable logic device, Altera 1985: First FPGA,Xilinx 1987: VHDL Standard IEEE 1076-1987 1989: Algotronix CAL1024, the first FPGA to offer random access to its control memory 20 Years ago: High-level Synthesis Page, Ian. "Closing the gap between hardware and software: hardware-software cosynthesis at Oxford." (1996): 2-2. 1996: Handel-C, Oxford University 2001: Mitrion-C, Mitrionics 2003: Bluespec, MIT 2003: MaxJ, Maxeler Technologies 2003: Impulse-C, Impulse Accelerated -
The Points of Viewing Theory for Engaged Learning with Digital Media
The Points of Viewing Theory for Engaged Learning with Digital Media Ricki Goldman New York University John Black Columbia University John W. Maxwell Simon Fraser University Jan J. L. Plass New York University Mark J. Keitges University of Illinois, Urbana Champaign - 1 - Introduction Theories are dangerous things. All the same we must risk making one this afternoon since we are going to discuss modern tendencies. Directly we speak of tendencies or movements we commit to, the belief that there is some force, influence, outer pressure that is strong enough to stamp itself upon a whole group of different writers so that all their writing has a certain common likeness. — Virginia Woolff, The Leaning Tower, lecture delivered to the Workers' Educational Association, Brighton (May 1940). With full acknowledgement of the warning from the 1940 lecture by Virginia Woolf, this chapter begins by presenting a theory of mind, knowing only too well, that “a whole group of different” learning theorists cannot find adequate coverage under one umbrella. Nor should they. However, there is a movement occurring, a form of social activism created by the affordances of social media, an infrastructure that was built incrementally during two to three decades of hard scholarly research that brought us to this historic time and place. To honor the convergence of theories and technologies, this paper proposes the Points of Viewing Theory to provide researchers, teachers, and the public with an opportunity to discuss and perhaps change the epistemology of education from its formal structures to more do-it-yourself learning environments that dig deeper and better into content knowledge. -
Simulation Higher Order Language Requirements Study
DOCUMENT RESDEE ED .162 661 IB 066 666 AUTHOR Goodenough, John B.; Eraun, Christine I. TITLE Simulation Higher Order ,Language Reguiresents Study. INSTITUTION' SofTech, Inc., Waltham, Mass. SPONS'AGENCY Air Force Human Resources Lab., Broca_APE,' Texas. REPORT NO AFHPI-TE-78-34 PUB DATE ,Aug 78 NOTE 229.; Not available in hard' copy due to iarginal legibility of parts of document AVAILABLE FROM'Superintendent of Docusents, U.S. Government Printing' Office, Washington, L.C. 20402 (1978-771-122/53) EDRS PRICE MF-$0.83 Plus Postage. EC Not Availatle from EDRS. DESCRIPTORS *Comparative Analysis; Computer Assisted Instruction; *Computer Programs; Evaluation Criteria; Military Training; *Needs Assessment; *Prograaing Iangbaqes; *Simulation IDENTIFIERS FORTRAN; IRCNMAN; JOVIAL; PASCAL ABSTRACT The definitions provided for high order language (HOL) requirements for programming flight/ training simulators are based on the analysis of programs written fcr.a variety of simulators. Examples drawn from these programs are used to justify' the need for certain HOL capabilities. A description of the general 'K..structure and organization of the TRONEAN requirements for the DOI) Common Language effort IS followed-tyditailed specifications of simulator HOL requirements., pvi, FORTRAN, JCVIAI J3E, JCVIAL.J73I, and PASCAL are analyzed to see how well" each language satisfies the simulator HOL requirements. Results indicate that PL/I and JOVIAL J3D are the best suited for simulator programming, chile TOBIRANis clearly the least suitable language. All the larguages failed to satisfy some simulator requirements and improvement modificationsare specified Analysis of recommended modifications shows:that PL/I is the m st easily modified language. (CMV) *********************************************************************** Reproductions suppliid by EDRS are the best that can be made from the original dccumett. -
The Computer Scientist As Toolsmith—Studies in Interactive Computer Graphics
Frederick P. Brooks, Jr. Fred Brooks is the first recipient of the ACM Allen Newell Award—an honor to be presented annually to an individual whose career contributions have bridged computer science and other disciplines. Brooks was honored for a breadth of career contributions within computer science and engineering and his interdisciplinary contributions to visualization methods for biochemistry. Here, we present his acceptance lecture delivered at SIGGRAPH 94. The Computer Scientist Toolsmithas II t is a special honor to receive an award computer science. Another view of computer science named for Allen Newell. Allen was one of sees it as a discipline focused on problem-solving sys- the fathers of computer science. He was tems, and in this view computer graphics is very near especially important as a visionary and a the center of the discipline. leader in developing artificial intelligence (AI) as a subdiscipline, and in enunciating A Discipline Misnamed a vision for it. When our discipline was newborn, there was the What a man is is more important than what he usual perplexity as to its proper name. We at Chapel Idoes professionally, however, and it is Allen’s hum- Hill, following, I believe, Allen Newell and Herb ble, honorable, and self-giving character that makes it Simon, settled on “computer science” as our depart- a double honor to be a Newell awardee. I am pro- ment’s name. Now, with the benefit of three decades’ foundly grateful to the awards committee. hindsight, I believe that to have been a mistake. If we Rather than talking about one particular research understand why, we will better understand our craft. -
The Dartmouth College Artificial Intelligence Conference: the Next
AI Magazine Volume 27 Number 4 (2006) (© AAAI) Reports continued this earlier work because he became convinced that advances The Dartmouth College could be made with other approaches using computers. Minsky expressed the concern that too many in AI today Artificial Intelligence try to do what is popular and publish only successes. He argued that AI can never be a science until it publishes what fails as well as what succeeds. Conference: Oliver Selfridge highlighted the im- portance of many related areas of re- search before and after the 1956 sum- The Next mer project that helped to propel AI as a field. The development of improved languages and machines was essential. Fifty Years He offered tribute to many early pio- neering activities such as J. C. R. Lick- leiter developing time-sharing, Nat Rochester designing IBM computers, and Frank Rosenblatt working with James Moor perceptrons. Trenchard More was sent to the summer project for two separate weeks by the University of Rochester. Some of the best notes describing the AI project were taken by More, al- though ironically he admitted that he ■ The Dartmouth College Artificial Intelli- Marvin Minsky, Claude Shannon, and never liked the use of “artificial” or gence Conference: The Next 50 Years Nathaniel Rochester for the 1956 “intelligence” as terms for the field. (AI@50) took place July 13–15, 2006. The event, McCarthy wanted, as he ex- Ray Solomonoff said he went to the conference had three objectives: to cele- plained at AI@50, “to nail the flag to brate the Dartmouth Summer Research summer project hoping to convince the mast.” McCarthy is credited for Project, which occurred in 1956; to as- everyone of the importance of ma- coining the phrase “artificial intelli- sess how far AI has progressed; and to chine learning. -
The Computational Attitude in Music Theory
The Computational Attitude in Music Theory Eamonn Bell Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 © 2019 Eamonn Bell All rights reserved ABSTRACT The Computational Attitude in Music Theory Eamonn Bell Music studies’s turn to computation during the twentieth century has engendered particular habits of thought about music, habits that remain in operation long after the music scholar has stepped away from the computer. The computational attitude is a way of thinking about music that is learned at the computer but can be applied away from it. It may be manifest in actual computer use, or in invocations of computationalism, a theory of mind whose influence on twentieth-century music theory is palpable. It may also be manifest in more informal discussions about music, which make liberal use of computational metaphors. In Chapter 1, I describe this attitude, the stakes for considering the computer as one of its instruments, and the kinds of historical sources and methodologies we might draw on to chart its ascendance. The remainder of this dissertation considers distinct and varied cases from the mid-twentieth century in which computers or computationalist musical ideas were used to pursue new musical objects, to quantify and classify musical scores as data, and to instantiate a generally music-structuralist mode of analysis. I present an account of the decades-long effort to prepare an exhaustive and accurate catalog of the all-interval twelve-tone series (Chapter 2). This problem was first posed in the 1920s but was not solved until 1959, when the composer Hanns Jelinek collaborated with the computer engineer Heinz Zemanek to jointly develop and run a computer program. -
An Overview of Artificial Intelligence and Robotics
AN OVERVIEW OF ARTIFICIAL INTELLIGENCE AND ROBOTICS VOLUME1—ARTIFICIAL INTELLIGENCE PART A—THE CORE INGREDIENTS January 1984 Prepared for National Aeronautics and Space Administration Headquarters Washington, D.C. 20546 NATIONAL E'j • OF STA. IDALL L LIBRARY qc ~iy?f NBSIR 83-2687 AN OVERVIEW OF ARTIFICIAL INTELLIGENCE AND ROBOTICS VOLUME1 —ARTIFICIAL INTELLIGENCE PART A—THE CORE INGREDIENTS William B. Gevarter* U.S. DEPARTMENT OF COMMERCE National Bureau of Standards National Engineering Laboratory Center for Manufacturing Engineering Industrial Systems Division Metrology Building, Room A127 Washington, DC 20234 January 1984 Prepared for: National Aeronautics and Space Administration Headquarters Washington, DC 20546 U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary NATIONAL BUREAU OF STANDARDS. Ernest Ambler, Director •Research Associate at the National Bureau of Standards Sponsored by NASA Headquarters PREFACE Artificial Intelligence (AI) is a field with over a quarter of a century of history. However, it wasn’t until the dawn of the decade of the 80’s that AI really burst forth—finding economic and popular acclaim. Intelligent computer programs are now emerging from the laboratory into prac- tical applications. This report endeavors to indicate what AI is, the foundations on which it rests, the techniques utilized, the applications, the participants and finally its state-of-the-art and future trends. Due to the scope of AI, this volume is issued in three parts: Part A: The Core Ingredients I. Artificial Intelligence—What It Is II. The Rise, Fall and Rebirth of AI III. Basic Elements of AI IV. Applications V. The Principal Participants VI. State-of-the-Art VII.