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Artificial Intelligence? 1 V2.0 © J
TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Einführung in die Künstliche Intelligenz Dozenten Prof. Johannes Fürnkranz (Knowledge Engineering) Prof. Ulf Brefeld (Knowledge Mining and Assessment) Homepage http://www.ke.informatik.tu-darmstadt.de/lehre/ki/ Termine: Dienstag 11:40-13:20 S202/C205 Donnerstag 11:40-13:20 S202/C205 3 VO + 1 UE Vorlesungen und Übungen werden in Doppelstunden abgehalten Übungen Terminplan wird auf der Web-Seite aktualisiert Ü voraussichtlich: 30.10., 13.11., 27.11., 11.12., 15.1., 29.1. Tafelübungen What is Artificial Intelligence? 1 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Text Book The course will mostly follow Stuart Russell und Peter Norvig: Artificial Intelligence: A Modern Approach. Prentice Hall, 2nd edition, 2003. Deutsche Ausgabe: Stuart Russell und Peter Norvig: Künstliche Intelligenz: Ein Moderner Ansatz. Pearson- Studium, 2004. ISBN: 978-3-8273-7089-1. 3. Auflage 2012 Home-page for the book: http://aima.cs.berkeley.edu/ Course slides in English (lecture is in German) will be availabe from Home-page What is Artificial Intelligence? 2 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz What is Artificial Intelligence Different definitions due to different criteria Two dimensions: Thought processes/reasoning vs. behavior/action Success according to human standards vs. success according to an ideal concept of intelligence: rationality. Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally What is Artificial Intelligence? 3 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Definitions of Artificial Intelligence What is Artificial Intelligence? 4 V2.0 © J. -
The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
Backpropagation with Callbacks
Backpropagation with Continuation Callbacks: Foundations for Efficient and Expressive Differentiable Programming Fei Wang James Decker Purdue University Purdue University West Lafayette, IN 47906 West Lafayette, IN 47906 [email protected] [email protected] Xilun Wu Grégory Essertel Tiark Rompf Purdue University Purdue University Purdue University West Lafayette, IN 47906 West Lafayette, IN, 47906 West Lafayette, IN, 47906 [email protected] [email protected] [email protected] Abstract Training of deep learning models depends on gradient descent and end-to-end differentiation. Under the slogan of differentiable programming, there is an increas- ing demand for efficient automatic gradient computation for emerging network architectures that incorporate dynamic control flow, especially in NLP. In this paper we propose an implementation of backpropagation using functions with callbacks, where the forward pass is executed as a sequence of function calls, and the backward pass as a corresponding sequence of function returns. A key realization is that this technique of chaining callbacks is well known in the programming languages community as continuation-passing style (CPS). Any program can be converted to this form using standard techniques, and hence, any program can be mechanically converted to compute gradients. Our approach achieves the same flexibility as other reverse-mode automatic differ- entiation (AD) techniques, but it can be implemented without any auxiliary data structures besides the function call stack, and it can easily be combined with graph construction and native code generation techniques through forms of multi-stage programming, leading to a highly efficient implementation that combines the per- formance benefits of define-then-run software frameworks such as TensorFlow with the expressiveness of define-by-run frameworks such as PyTorch. -
Chapter 2 Core Basics
Chapter 2 Core Basics In this chapter we introduce some of the basic concepts that are important for understanding what is to follow. We also introduce a few of the fundamental operations that are involved in the vast majority of useful programs. 2.1 Literals and Types Section 1.3 introduced the string literal. A string literal is a collection of characters enclosed in quotes. In general, a literal is code that exactly represents a value. In addition to a string literal, there are numeric literals. There are actually a few different types of numeric values in Python. There are integer values which correspond to whole numbers (i.e., the countable numbers that lack a fractional part). In Python, an integer is known as an int. A number that can have a fractional part is called a float (a float corresponds to a real number). For example, 42 is an int while 3.14 is a float. The presence of the decimal point makes a number a float—it doesn’t matter if the fractional part is zero. For example, the literal 3.0, or even just 3., are also floats1 despite the fact that these numbers have fractional parts of zero.2 There are some important differences between how computers work with ints and floats. For example, ints are stored exactly in the computer while, in general, floats are only stored approximately. This is a consequence of the fact that we are entering numbers as decimal numbers, i.e., numbers in the base 10 counting system, but the computer stores these numbers internally as a finite number of binary (base 2) digits. -
Lecture 3: Its Time to See the C
Computer Science 61C Spring 2017 Friedland and Weaver Lecture 3: Its time to see the C... 1 Agenda Computer Science 61C Spring 2017 Friedland and Weaver • Computer Organization • Compile vs. Interpret • C vs Java 2 ENIAC (U.Penn., 1946) First Electronic General-Purpose Computer Computer Science 61C Spring 2017 Friedland and Weaver • Blazingly fast (multiply in 2.8ms!) • 10 decimal digits x 10 decimal digits • But needed 2-3 days to setup new program, as programmed with patch cords and switches • At that time & before, "computer" mostly referred to people who did calculations 3 EDSAC (Cambridge, 1949) First General Stored-Program Computer Computer Science 61C Spring 2017 Friedland and Weaver • Programs held as numbers in memory • This is the revolution: It isn't just programmable, but the program is just the same type of data that the computer computes on • 35-bit binary 2’s complement words 4 Components of a Computer Computer Science 61C Spring 2017 Friedland and Weaver Memory Processor Input Enable? Read/Write Control Program Datapath Address PC Bytes Write Registers Data Arithmetic & Logic Unit Read Data Output (ALU) Data Processor-Memory Interface I/O-Memory Interfaces 5 Great Idea: Levels of Representation/Interpretation Computer Science 61C Spring 2017 Friedland and Weaver temp = v[k]; High Level Language v[k] = v[k+1]; We are here! Program (e.g., C) v[k+1] = temp; lw $t0, 0($2) Compiler Anything can be represented lw $t1, 4($2) Assembly Language as a number, sw $t1, 0($2) Program (e.g., MIPS) i.e., data or instructions sw $t0, 4($2) -
Stuart Russell and Peter Norvig, Artijcial Intelligence: a Modem Approach *
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Artificial Intelligence ELSEVIER Artificial Intelligence 82 ( 1996) 369-380 Book Review Stuart Russell and Peter Norvig, Artijcial Intelligence: A Modem Approach * Nils J. Nilsson Robotics Laboratory, Department of Computer Science, Stanford University, Stanford, CA 94305, USA 1. Introductory remarks I am obliged to begin this review by confessing a conflict of interest: I am a founding director and a stockholder of a publishing company that competes with the publisher of this book, and I am in the process of writing another textbook on AI. What if Russell and Norvig’s book turns out to be outstanding? Well, it did! Its descriptions are extremely clear and readable; its organization is excellent; its examples are motivating; and its coverage is scholarly and thorough! End of review? No; we will go on for some pages-although not for as many as did Russell and Norvig. In their Preface (p. vii), the authors mention five distinguishing features of their book: Unified presentation of the field, Intelligent agent design, Comprehensive and up-to-date coverage, Equal emphasis on theory and practice, and Understanding through implementation. These features do indeed distinguish the book. I begin by making a few brief, summary comments using the authors’ own criteria as a guide. l Unified presentation of the field and Intelligent agent design. I have previously observed that just as Los Angeles has been called “twelve suburbs in search of a city”, AI might be called “twelve topics in search of a subject”. -
Unary Operator
Introduction to Python Operations and Variables 1 Topics 1) Arithmetic Operations 2) Floor Division vs True Division 3) Modulo Operator 4) Operator Precedence 5) String Concatenation 6) Augmented Assignment 2 Arithmetic Operations 3 Mixing Types Any expression that two floats produce a float. x = 17.0 – 10.0 print(x) # 7.0 When an expression’s operands are an int and a float, Python automatically converts the int to a float. x = 17.0 – 10 print(x) # 7.0 y = 17 – 10.0 print(y) # 7.0 4 True Division vs Floor Division The operator / is true division and the operator // returns floor division(round down after true divide). True divide / always gives the answer as a float. print(23 // 7) # 3 print(3 // 9) # 0 print(-4 // 3) # -2 print(6 / 5) # 1.2 print(6 / 3) # 2.0 NOT 2! 5 Remainder with % The % operator computes the remainder after floor division. • 14 % 4 is 2 • 218 % 5 is 3 3 43 4 ) 14 5 ) 218 12 20 2 18 15 3 Applications of % operator: • Obtain last digit of a number: 230857 % 10 is 7 • Obtain last 4 digits: 658236489 % 10000 is 6489 • See whether a number is odd: 7 % 2 is 1, 42 % 2 is 0 6 Modulo Operator The operator % returns the modulus which is the remainder after floor division. print(18 % 5) # 3 print(2 % 9) # 2, if first number is smaller, it's the answer print(125 % 10) # 5 print(0 % 10) # 0 print(10 % 0) # ZeroDivisionError 7 Why floor/modulo division is useful Floor division allows us to extract the integer part of the division while the modulo operator extracts the remainder part of the division. -
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. -
Artificial Intelligence: a Modern Approach (3Rd Edition)
[PDF] Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell, Peter Norvig - pdf download free book Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Ebooks Stuart Russell, Peter Norvig, PDF Artificial Intelligence: A Modern Approach (3rd Edition) Popular Download, Artificial Intelligence: A Modern Approach (3rd Edition) Full Collection, Read Best Book Online Artificial Intelligence: A Modern Approach (3rd Edition), Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Full Popular Stuart Russell, Peter Norvig, I Was So Mad Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell, Peter Norvig Ebook Download, PDF Artificial Intelligence: A Modern Approach (3rd Edition) Full Collection, full book Artificial Intelligence: A Modern Approach (3rd Edition), online pdf Artificial Intelligence: A Modern Approach (3rd Edition), Download Free Artificial Intelligence: A Modern Approach (3rd Edition) Book, pdf Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach (3rd Edition), the book Artificial Intelligence: A Modern Approach (3rd Edition), Download Artificial Intelligence: A Modern Approach (3rd Edition) E-Books, Read Artificial Intelligence: A Modern Approach (3rd Edition) Book Free, Artificial Intelligence: A Modern Approach (3rd Edition) PDF read online, Artificial Intelligence: A Modern Approach (3rd Edition) Ebooks, Artificial Intelligence: A Modern Approach (3rd Edition) Popular Download, Artificial Intelligence: A Modern Approach (3rd Edition) Free Download, Artificial Intelligence: A Modern Approach (3rd Edition) Books Online, Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Books [E-BOOK] Artificial Intelligence: A Modern Approach (3rd Edition) Full eBook, CLICK HERE FOR DOWNLOAD The dorian pencil novels at the end of each chapter featured charts. makes us the meaning of how our trip and unk goes into trouble with others like land. -
Artificial Intelligence
Outline: Introduction to AI Artificial Intelligence N-ways Introduction Introduction to AI - Personal Information and - What is Intelligence? Introduction Background - An Intelligent Entity - The Age of Intelligent Machines Course Outline: - Definitions of AI - Requirements and Expectation - Behaviourist’s View on Intelligent - Module Assessment Machines - Recommended Books - Turing’s Test - Part 1 & 2 - Layout of Course (14 lessons) - History of AI Lecture 1 - Examples of AI systems - Office Hours Course Delivery Methods General Reference for the Course Goals and Objectives of module 2 Course Outline: General Reference for the Course Recommended Books Artificial Intelligence by Patrick Henry Winston AI related information. Whatis.com (Computer Science Dictionary) Logical Foundations of Artificial Intelligence by Michael R. http://whatis.com/search/whatisquery.html Genesereth, Nils J. Nislsson, Nils J. Nilsson General computer-related news sources. Technology Encyclopedia Artificial Intelligence, Luger, Stubblefield http://www.techweb.com/encyclopedia/ General Information Artificial Intelligence : A Modern Approach by Stuart J. Russell, Technology issues. Peter Norvig Computing Dictionary http://wombat.doc.ic.ac.uk/ Artificial Intelligence by Elaine Rich, Kevin Knight (good for All web links in my AI logic, knowledge representation, and search only) website. Webster Dictionary http://work.ucsd.edu:5141/cgi- bin/http_webster 3 4 Goals and Objectives of module Introduction to AI: What is Intelligence? Understand motivation, mechanisms, and potential Intelligence, taken as a whole, consists of of Artificial Intelligence techniques. the following skills:- Balance of breadth of techniques with depth of 1. the ability to reason understanding. 2. the ability to acquire and apply knowledge Conversant with the applicational scope and 3. the ability to manipulate and solution methodologies of AI. -
Icon Analyst 8
In-Depth Coverage of the Icon Programming Language October 1991 Number 8 subject := OuterEnvir.subject object := OuterEnvir.object s_pos := OuterEnvir.s_pos In this issue … o_pos := OuterEnvir.o_pos fail String Synthesis … 1 An Imaginary Icon Computer … 2 end Augmented Assignment Operations … 7 procedure Eform(OuterEnvir, e2) The Icon Compiler … 8 local InnerEnvir Programming Tips … 12 InnerEnvir := What’s Coming Up … 12 XformEnvir(subject, s_pos, object, o_pos) subject := OuterEnvir.subject String Synthesis object := OuterEnvir.object s_pos := OuterEnvir.s_pos In the last issue of the Analyst, we posed the problem o_pos := OuterEnvir.o_pos of implementing a string-synthesis facility for Icon, using the ideas given earlier about modeling the string-scanning control suspend InnerEnvir.object structure. OuterEnvir.subject := subject Our solution is given below. First we need procedures OuterEnvir.object := object analogous to the procedure used for modeling string scanning. OuterEnvir.s_pos := s_pos In addition to a subject, there’s now an “object”, which is the OuterEnvir.o_pos := o_pos result of string synthesis. There now also are two positions, subject := InnerEnvir.subject one in the subject and one in the object. The global identifiers object := InnerEnvir.object subject, object, s_pos, and o_pos are used for these four s_pos := InnerEnvir.s_pos “state variables” in the procedures that follow. (In a real o_pos := InnerEnvir.o_pos implementation, these would be keywords.) fail The string synthesis control structure is modeled as end expr1 ? expr2 → Eform(Bform(expr1),expr2) Most of the procedures specified in the last issue of the are straightforward. Care must be taken, however, The procedures Bform() and Eform() are very similar Analyst to assure that values assigned to the positions are in range — to Bscan() and Escan() used in the last issue of the Analyst this is done automatically for the keyword &pos, but it must for modeling string scanning. -
View the Index
INDEX Numbers and Symbols __(double underscore), using in 256 objects and 257 objects, 154–155 dunder methods, 322. See also ./, using with Ubuntu, 42 underscore (_) /? command line argument, 25–26 / (forward slash) = assignment operator, 113 purpose of, 18 == comparison operator, 113, 336 using with command line chaining, 103, 105 arguments, 25 using to compare objects, 154 # (hash mark) using with None, 94–95 using with comments, 183 != comparison operator, 336 using with docstrings, 188 * and ** syntax [] (index operator), using, 117 using to create variadic functions, ; (semicolons), using with timeit 167–171 module, 226–227 using to create wrapper functions, ' (single quote), using, 46 171–172 ~ (tilde), using in macOS, 23 using with arguments and - (unary operator), 155–157 functions, 166–167 + (unary operator), 156–157 * character, using as wildcard, 28–29 _ (underscore) ? character, using as wildcard, 28–29 PEP 8’s naming conventions, [:] syntax, using, 104 60–61 < comparison operator, 337 as prefix for methods and <= comparison operator, 337 attributes, 291–292 > comparison operator, 337 private prefix, 81 >= comparison operator, 337 using with _spaces attribute, 290 -> (arrow), using with type hints, 191 using with dunder methods, 120 \ (backslash) using with private attributes and purpose of, 18 methods, 283 using with strings, 95 using with WizCoin class, 279 : (colon), using with lists, 97–98, 104 , (comma), including in single-item A tuples, 150 abcTraceback.py program, saving, 4 - (dash), using with command line __abs__() numeric dunder arguments, 25 method, 328 $ (dollar sign), using in macOS, 23 absolute versus relative paths, 20–21 . (dot), using with commands, 31 __add__() numeric dunder method, 327 -- (double dash), using with command addToTotal() function, creating, line arguments, 25 172–173 algebra for big O, 236 base class, relationship to algorithm analysis.