Actor Model of Computation: Scalable Robust Information Systems
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QATAR ASW ONLINE SCRIPT V.8.0 Pages
“DEXIGN THE FUTURE: Innovation for Exponential Times” ARNOLD WASSERMAN COMMENCEMENT ADDRESS VIRGINIA COMMONWEALTH UNIVERSITY IN QATAR 2 MAY 2016 مساء الخير .Good afternoon It is an honor to be invited to address you on this important day. Today you artists and designers graduate into the future. Do you think about the future? Of course you do. But how do you think about the future? Is the future tangible - or is it intangible ? Is it a subject for rational deliberation? Or is it an imagined fantasy that forever recedes before us? Is the future simply whatever happens to us next? Or is it something we deliberately create? Can we Dexign the Future? What I would like us to think about for the next few moments is how creative professionals like yourselves might think about the future. What I have to say applies as much to art as it does to design. Both are technologies of creative innovation. I will take the liberty here of calling it all design. My speech is also available online. There you can follow a number of links to what I will talk about here. The future you are graduating into is an exponential future. What that means is that everything is happening faster and at an accelerating rate. For example, human knowledge is doubling every 12 months. And that curve is accelerating. An IBM researcher foresees that within a few years knowledge will be doubling every 12 hours as we build out the internet of things globally. This acceleration of human knowledge implies that the primary skill of a knowledge worker like yourself is research, analysis and synthesis of the not-yet-known. -
A Universal Modular ACTOR Formalism for Artificial
Artificial Intelligence A Universal Modular ACTOR Formalism for Artificial Intelligence Carl Hewitt Peter Bishop Richard Steiger Abstract This paper proposes a modular ACTOR architecture and definitional method for artificial intelligence that is conceptually based on a single kind of object: actors [or, if you will, virtual processors, activation frames, or streams]. The formalism makes no presuppositions about the representation of primitive data structures and control structures. Such structures can be programmed, micro-coded, or hard wired 1n a uniform modular fashion. In fact it is impossible to determine whether a given object is "really" represented as a list, a vector, a hash table, a function, or a process. The architecture will efficiently run the coming generation of PLANNER-like artificial intelligence languages including those requiring a high degree of parallelism. The efficiency is gained without loss of programming generality because it only makes certain actors more efficient; it does not change their behavioral characteristics. The architecture is general with respect to control structure and does not have or need goto, interrupt, or semaphore primitives. The formalism achieves the goals that the disallowed constructs are intended to achieve by other more structured methods. PLANNER Progress "Programs should not only work, but they should appear to work as well." PDP-1X Dogma The PLANNER project is continuing research in natural and effective means for embedding knowledge in procedures. In the course of this work we have succeeded in unifying the formalism around one fundamental concept: the ACTOR. Intuitively, an ACTOR is an active agent which plays a role on cue according to a script" we" use the ACTOR metaphor to emphasize the inseparability of control and data flow in our model. -
The Early History of Smalltalk
The Early History of Smalltalk http://www.accesscom.com/~darius/EarlyHistoryS... The Early History of Smalltalk Alan C. Kay Apple Computer [email protected]# Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. HOPL-II/4/93/MA, USA © 1993 ACM 0-89791-571-2/93/0004/0069...$1.50 Abstract Most ideas come from previous ideas. The sixties, particularly in the ARPA community, gave rise to a host of notions about "human-computer symbiosis" through interactive time-shared computers, graphics screens and pointing devices. Advanced computer languages were invented to simulate complex systems such as oil refineries and semi-intelligent behavior. The soon-to- follow paradigm shift of modern personal computing, overlapping window interfaces, and object-oriented design came from seeing the work of the sixties as something more than a "better old thing." This is, more than a better way: to do mainframe computing; for end-users to invoke functionality; to make data structures more abstract. Instead the promise of exponential growth in computing/$/volume demanded that the sixties be regarded as "almost a new thing" and to find out what the actual "new things" might be. For example, one would compute with a handheld "Dynabook" in a way that would not be possible on a shared mainframe; millions of potential users meant that the user interface would have to become a learning environment along the lines of Montessori and Bruner; and needs for large scope, reduction in complexity, and end-user literacy would require that data and control structures be done away with in favor of a more biological scheme of protected universal cells interacting only through messages that could mimic any desired behavior. -
How Did They Get to the Moon Without Powerpoint?
How Did They Get to the Moon Without PowerPoint? Mordechai (Moti) Ben-Ari Department of Science Teaching Weizmann Institute of Science [email protected] Keynote speech at the Finnish Computer Science Society, May, 2003. 1 Developing a Technology The invention of writing, however, was the invention of an entirely new Let me start with a description of one of my first technology.[3, p. 9] full-time jobs: There is something to be said for this definition: I developed a technology for data min- do you remember those old movies that show “typ- ing in order to consolidate enterprise- ing pools,” where rows and rows of people, usually customer relations. women, sat pecking away at keyboards all day? Since I held that job in the early 1970s, clearly I would not have described my work in this terminol- ogy! What I actually did was: I wrote a program that read the system log, computed usage of CPU time and printed reports so that the users could be billed. My point in this talk is that hi-tech in general and computer science in particular did not begin in the 1990s, but that we have been doing it for decades. I believe that today’s students are being fed a lot of marketing propaganda to the contrary, and that they Well, things haven’t changed all that much! have completely lost a historical perspective of our discipline. I further believe that we have a respon- sibility as educators to downgrade the hype and to give our students a firm background in the scientific and engineering principles of computer science. -
Software for Numerical Computation
Purdue University Purdue e-Pubs Department of Computer Science Technical Reports Department of Computer Science 1977 Software for Numerical Computation John R. Rice Purdue University, [email protected] Report Number: 77-214 Rice, John R., "Software for Numerical Computation" (1977). Department of Computer Science Technical Reports. Paper 154. https://docs.lib.purdue.edu/cstech/154 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. SOFTWARE FOR NUMERICAL COMPUTATION John R. Rice Department of Computer Sciences Purdue University West Lafayette, IN 47907 CSD TR #214 January 1977 SOFTWARE FOR NUMERICAL COMPUTATION John R. Rice Mathematical Sciences Purdue University CSD-TR 214 January 12, 1977 Article to appear in the book: Research Directions in Software Technology. SOFTWARE FOR NUMERICAL COMPUTATION John R. Rice Mathematical Sciences Purdue University INTRODUCTION AND MOTIVATING PROBLEMS. The purpose of this article is to examine the research developments in software for numerical computation. Research and development of numerical methods is not intended to be discussed for two reasons. First, a reasonable survey of the research in numerical methods would require a book. The COSERS report [Rice et al, 1977] on Numerical Computation does such a survey in about 100 printed pages and even so the discussion of many important fields (never mind topics) is limited to a few paragraphs. Second, the present book is focused on software and thus it is natural to attempt to separate software research from numerical computation research. This, of course, is not easy as the two are intimately intertwined. -
Oral History of Fernando Corbató
Oral History of Fernando Corbató Interviewed by: Steven Webber Recorded: February 1, 2006 West Newton, Massachusetts CHM Reference number: X3438.2006 © 2006 Computer History Museum Oral History of Fernando Corbató Steven Webber: Today the Computer History Museum Oral History Project is going to interview Fernando J. Corbató, known as Corby. Today is February 1 in 2006. We like to start at the very beginning on these interviews. Can you tell us something about your birth, your early days, where you were born, your parents, your family? Fernando Corbató: Okay. That’s going back a long ways of course. I was born in Oakland. My parents were graduate students at Berkeley and then about age 5 we moved down to West Los Angeles, Westwood, where I grew up [and spent] most of my early years. My father was a professor of Spanish literature at UCLA. I went to public schools there in West Los Angeles, [namely,] grammar school, junior high and [the high school called] University High. So I had a straightforward public school education. I guess the most significant thing to get to is that World War II began when I was in high school and that caused several things to happen. I’m meandering here. There’s a little bit of a long story. Because of the wartime pressures on manpower, the high school went into early and late classes and I cleverly saw that I could get a chance to accelerate my progress. I ended up taking both early and late classes and graduating in two years instead of three and [thereby] got a chance to go to UCLA in 1943. -
Simula Mother Tongue for a Generation of Nordic Programmers
Simula! Mother Tongue! for a Generation of! Nordic Programmers! Yngve Sundblad HCI, CSC, KTH! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! Inspired by Ole-Johan Dahl, 1931-2002, and Kristen Nygaard, 1926-2002" “From the cold waters of Norway comes Object-Oriented Programming” " (first line in Bertrand Meyer#s widely used text book Object Oriented Software Construction) ! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! Simula concepts 1967" •# Class of similar Objects (in Simula declaration of CLASS with data and actions)! •# Objects created as Instances of a Class (in Simula NEW object of class)! •# Data attributes of a class (in Simula type declared as parameters or internal)! •# Method attributes are patterns of action (PROCEDURE)! •# Message passing, calls of methods (in Simula dot-notation)! •# Subclasses that inherit from superclasses! •# Polymorphism with several subclasses to a superclass! •# Co-routines (in Simula Detach – Resume)! •# Encapsulation of data supporting abstractions! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! Simula example BEGIN! REF(taxi) t;" CLASS taxi(n); INTEGER n;! BEGIN ! INTEGER pax;" PROCEDURE book;" IF pax<n THEN pax:=pax+1;! pax:=n;" END of taxi;! t:-NEW taxi(5);" t.book; t.book;" print(t.pax)" END! Output: 7 ! ! KTH - CSC (School of Computer Science and Communication) Yngve Sundblad – Simula OctoberYngve 2010Sundblad! -
Quantum Hypercomputation—Hype Or Computation?
Quantum Hypercomputation—Hype or Computation? Amit Hagar,∗ Alex Korolev† February 19, 2007 Abstract A recent attempt to compute a (recursion–theoretic) non–computable func- tion using the quantum adiabatic algorithm is criticized and found wanting. Quantum algorithms may outperform classical algorithms in some cases, but so far they retain the classical (recursion–theoretic) notion of computability. A speculation is then offered as to where the putative power of quantum computers may come from. ∗HPS Department, Indiana University, Bloomington, IN, 47405. [email protected] †Philosophy Department, University of BC, Vancouver, BC. [email protected] 1 1 Introduction Combining physics, mathematics and computer science, quantum computing has developed in the past two decades from a visionary idea (Feynman 1982) to one of the most exciting areas of quantum mechanics (Nielsen and Chuang 2000). The recent excitement in this lively and fashionable domain of research was triggered by Peter Shor (1994) who showed how a quantum computer could exponentially speed–up classical computation and factor numbers much more rapidly (at least in terms of the number of computational steps involved) than any known classical algorithm. Shor’s algorithm was soon followed by several other algorithms that aimed to solve combinatorial and algebraic problems, and in the last few years the- oretical study of quantum systems serving as computational devices has achieved tremendous progress. According to one authority in the field (Aharonov 1998, Abstract), we now have strong theoretical evidence that quantum computers, if built, might be used as powerful computational tool, capable of per- forming tasks which seem intractable for classical computers. -
Topic a Dataflow Model of Computation
Department of Electrical and Computer Engineering Computer Architecture and Parallel Systems Laboratory - CAPSL Topic A Dataflow Model of Computation CPEG 852 - Spring 2014 Advanced Topics in Computing Systems Guang R. Gao ACM Fellow and IEEE Fellow Endowed Distinguished Professor Electrical & Computer Engineering University of Delaware CPEG852-Spring14: Topic A - Dataflow - 1 [email protected] Outline • Parallel Program Execution Models • Dataflow Models of Computation • Dataflow Graphs and Properties • Three Dataflow Models – Static – Recursive Program Graph – Dynamic • Dataflow Architectures CPEG852-Spring14: Topic A - Dataflow - 1 2 Terminology Clarification • Parallel Model of Computation – Parallel Models for Algorithm Designers – Parallel Models for System Designers • Parallel Programming Models • Parallel Execution Models • Parallel Architecture Models CPEG852-Spring14: Topic A - Dataflow - 1 3 What is a Program Execution Model? . Application Code . Software Packages User Code . Program Libraries . Compilers . Utility Applications PXM (API) . Hardware System . Runtime Code . Operating System CPEG852-Spring14: Topic A - Dataflow - 1 4 Features a User Program Depends On . Procedures; call/return Features expressed within .Access to parameters and a Programming language variables .Use of data structures (static and dynamic) But that’s not all !! . File creation, naming and access Features expressed Outside .Object directories a (typical) programming .Communication: networks language and peripherals .Concurrency: coordination; scheduling CPEG852-Spring14: Topic A - Dataflow - 1 5 Developments in the 1960s, 1970s Highlights 1960 Other Events . Burroughs B5000 Project . Project MAC Funded at MIT Started . Rice University Computer . IBM announces System 360 . Vienna Definition Method . Tasking introduced in Algol . Common Base Language, 1970 68 and PL/I Dennis . Burroughs builds Robert . Contour Model, Johnston Barton’s DDM1 . Book on the B6700, . -
Rulemaking: 1999-10 FSOR Emission Standards and Test Procedures
State of California Environmental Protection Agency AIR RESOURCES BOARD EMISSION STANDARDS AND TEST PROCEDURES FOR NEW 2001 AND LATER MODEL YEAR SPARK-IGNITION MARINE ENGINES FINAL STATEMENT OF REASONS October 1999 State of California AIR RESOURCES BOARD Final Statement of Reasons for Rulemaking, Including Summary of Comments and Agency Response PUBLIC HEARING TO CONSIDER THE ADOPTION OF EMISSION STANDARDS AND TEST PROCEDURES FOR NEW 2001 AND LATER MODEL YEAR SPARK-IGNITION MARINE ENGINES Public Hearing Date: December 10, 1998 Agenda Item No.: 98-14-2 I. INTRODUCTION AND BACKGROUND ........................................................................................ 3 II. SUMMARY OF PUBLIC COMMENTS AND AGENCY RESPONSES – COMMENTS PRIOR TO OR AT THE HEARING .................................................................................................................. 7 A. EMISSION STANDARDS ................................................................................................................... 7 1. Adequacy of National Standards............................................................................................. 7 2. Lead Time ................................................................................................................................. 8 3. Technological Feasibility ........................................................................................................ 13 a. Technological Feasibility of California-specific Standards ..............................................................13 -
CS 2210: Theory of Computation
CS 2210: Theory of Computation Spring, 2019 Administrative Information • Background survey • Textbook: E. Rich, Automata, Computability, and Complexity: Theory and Applications, Prentice-Hall, 2008. • Book website: http://www.cs.utexas.edu/~ear/cs341/automatabook/ • My Office Hours: – Monday, 6:00-8:00pm, Searles 224 – Tuesday, 1:00-2:30pm, Searles 222 • TAs – Anjulee Bhalla: Hours TBA, Searles 224 – Ryan St. Pierre, Hours TBA, Searles 224 What you can expect from the course • How to do proofs • Models of computation • What’s the difference between computability and complexity? • What’s the Halting Problem? • What are P and NP? • Why do we care whether P = NP? • What are NP-complete problems? • Where does this make a difference outside of this class? • How to work the answers to these questions into the conversation at a cocktail party… What I will expect from you • Problem Sets (25%): – Goal: Problems given on Mondays and Wednesdays – Due the next Monday – Graded by following Monday – A learning tool, not a testing tool – Collaboration encouraged; more on this in next slide • Quizzes (15%) • Exams (2 non-cumulative, 30% each): – Closed book, closed notes, but… – Can bring in 8.5 x 11 page with notes on both sides • Class participation: Tiebreaker Other Important Things • Go to the TA hours • Study and work on problem sets in groups • Collaboration Issues: – Level 0 (In-Class Problems) • No restrictions – Level 1 (Homework Problems) • Verbal collaboration • But, individual write-ups – Level 2 (not used in this course) • Discussion with TAs only – Level 3 (Exams) • Professor clarifications only Right now… • What does it mean to study the “theory” of something? • Experience with theory in other disciplines? • Relationship to practice? – “In theory, theory and practice are the same. -
The Problem with Threads
The Problem with Threads Edward A. Lee Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2006-1 http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-1.html January 10, 2006 Copyright © 2006, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Acknowledgement This work was supported in part by the Center for Hybrid and Embedded Software Systems (CHESS) at UC Berkeley, which receives support from the National Science Foundation (NSF award No. CCR-0225610), the State of California Micro Program, and the following companies: Agilent, DGIST, General Motors, Hewlett Packard, Infineon, Microsoft, and Toyota. The Problem with Threads Edward A. Lee Professor, Chair of EE, Associate Chair of EECS EECS Department University of California at Berkeley Berkeley, CA 94720, U.S.A. [email protected] January 10, 2006 Abstract Threads are a seemingly straightforward adaptation of the dominant sequential model of computation to concurrent systems. Languages require little or no syntactic changes to sup- port threads, and operating systems and architectures have evolved to efficiently support them. Many technologists are pushing for increased use of multithreading in software in order to take advantage of the predicted increases in parallelism in computer architectures.