Computing Machinery and Intelligence
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Reflections from the Developments of School Science Curricula
Imitation game: Reflections from the developments of school science curricula SONG, Jinwoong (宋眞雄) Department of Physics Education, Seoul National University What will fulfill this need can be stated in equally simple terms. It is, ironically enough, that science be taught as science. (Joseph J. Schwab, 1962: 188-9) 1.Introduction The Imitation Game is a 2014 British historical drama film directed by Morten Tyldu m and written by Graham Moore, based on the biography Alan Turing: The Enigma by A ndrew Hodges. It stars Benedict Cumberbatch as British cryptanalyst Alan Turing, who dec rypted German intelligence codes for the British government during the Second World War (https://en.wikipedia.org/wiki/The_Imitation_Game). The ’Imitation Game’ refers to so-calle d ‘Turing Test’ which is a test for artificial intelligence suggested by a famous British ma thematician, Alan Turing (1912-54), who is known as the father of the computer. Through his paper, “Computing machinery and intelligence” (1950), Tuning gave a conceptual fou ndation of AI (Artificial Intelligence), by suggesting the Turing Test, “a test of a machine’ s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a h uman.” (https://en.wikipedia.org/wiki/Turing_test) Science is now considered as one of the core (often three of them) subjects across th e world, together with the national language and mathematics. For example, in PISA studi es, science is included in the three main areas, with literacy and mathematics (e.g. OECD, 2018). And, in TIMSS studies, science and mathematics are the two main subjects for th e international comparison (https://timssandpirls.bc.edu/timss2015/). -
CODEBREAKING Suggested Reading List (Can Also Be Viewed Online at Good Reads)
MARSHALL LEGACY SERIES: CODEBREAKING Suggested Reading List (Can also be viewed online at Good Reads) NON-FICTION • Aldrich, Richard. Intelligence and the War against Japan. Cambridge: Cambridge University Press, 2000. • Allen, Robert. The Cryptogram Challenge: Over 150 Codes to Crack and Ciphers to Break. Philadelphia: Running Press, 2005 • Briggs, Asa. Secret Days Code-breaking in Bletchley Park. Barnsley: Frontline Books, 2011 • Budiansky, Stephen. Battle of Wits: The Complete Story of Codebreaking in World War Two. New York: Free Press, 2000. • Churchhouse, Robert. Codes and Ciphers: Julius Caesar, the Enigma, and the Internet. Cambridge: Cambridge University Press, 2001. • Clark, Ronald W. The Man Who Broke Purple. London: Weidenfeld and Nicholson, 1977. • Drea, Edward J. MacArthur's Ultra: Codebreaking and the War Against Japan, 1942-1945. Kansas: University of Kansas Press, 1992. • Fisher-Alaniz, Karen. Breaking the Code: A Father's Secret, a Daughter's Journey, and the Question That Changed Everything. Naperville, IL: Sourcebooks, 2011. • Friedman, William and Elizebeth Friedman. The Shakespearian Ciphers Examined. Cambridge: Cambridge University Press, 1957. • Gannon, James. Stealing Secrets, Telling Lies: How Spies and Codebreakers Helped Shape the Twentieth century. Washington, D.C.: Potomac Books, 2001. • Garrett, Paul. Making, Breaking Codes: Introduction to Cryptology. London: Pearson, 2000. • Hinsley, F. H. and Alan Stripp. Codebreakers: the inside story of Bletchley Park. Oxford: Oxford University Press, 1993. • Hodges, Andrew. Alan Turing: The Enigma. New York: Walker and Company, 2000. • Kahn, David. Seizing The Enigma: The Race to Break the German U-boat Codes, 1939-1943. New York: Barnes and Noble Books, 2001. • Kahn, David. The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the Internet. -
FUNDAMENTALS of COMPUTING (2019-20) COURSE CODE: 5023 502800CH (Grade 7 for ½ High School Credit) 502900CH (Grade 8 for ½ High School Credit)
EXPLORING COMPUTER SCIENCE NEW NAME: FUNDAMENTALS OF COMPUTING (2019-20) COURSE CODE: 5023 502800CH (grade 7 for ½ high school credit) 502900CH (grade 8 for ½ high school credit) COURSE DESCRIPTION: Fundamentals of Computing is designed to introduce students to the field of computer science through an exploration of engaging and accessible topics. Through creativity and innovation, students will use critical thinking and problem solving skills to implement projects that are relevant to students’ lives. They will create a variety of computing artifacts while collaborating in teams. Students will gain a fundamental understanding of the history and operation of computers, programming, and web design. Students will also be introduced to computing careers and will examine societal and ethical issues of computing. OBJECTIVE: Given the necessary equipment, software, supplies, and facilities, the student will be able to successfully complete the following core standards for courses that grant one unit of credit. RECOMMENDED GRADE LEVELS: 9-12 (Preference 9-10) COURSE CREDIT: 1 unit (120 hours) COMPUTER REQUIREMENTS: One computer per student with Internet access RESOURCES: See attached Resource List A. SAFETY Effective professionals know the academic subject matter, including safety as required for proficiency within their area. They will use this knowledge as needed in their role. The following accountability criteria are considered essential for students in any program of study. 1. Review school safety policies and procedures. 2. Review classroom safety rules and procedures. 3. Review safety procedures for using equipment in the classroom. 4. Identify major causes of work-related accidents in office environments. 5. Demonstrate safety skills in an office/work environment. -
Benedict Cumberbatch Talks the Imitation Game
JANUARY 2015 | VOLUME 16 | NUMBER 1 Smart Meets Sexy BENEDICT CUMBERBATCH TALKS THE IMITATION GAME Inside HUGH BONNEVILLE LIAM NEESON PUBLICATIONS MAIL AGREEMENT NO. 41619533 THE 10 INCREDIBLE MOVIES YOU HAVE TO SEE THIS YEAR, PAGE 26 CONTENTS JANUARY 2015 | VOL 16 | Nº1 COVER STORY 40 GENIUS ROLE Benedict Cumberbatch’s fervent fans won’t be disappointed with his latest role. The Imitation Game casts the 38-year-old Brit as Alan Turing, a gay, mathematical genius who helped hasten the end of WWII. Here he talks about bringing Turing to life and his various other talents BY INGRID RANDOJA REGULARS 4 EDITOR’S NOTE 8 SNAPS 10 IN BRIEF 14 SPOTLIGHT: CANADA 16 ALL DRESSED UP 20 IN THEATRES 44 CASTING CALL 47 RETURN ENGAGEMENT 48 AT HOME 50 FINALLY… FEATURES IMAGE HARGRAVE/AUGUST AUSTIN BY PHOTO COVER 26 2015’S BIG PICS! 32 FABLED CAST 34 MAN OF ACTION 38 PAPA BEAR It’s going to be an epic year We break down which famous Taken 3 star Liam Neeson Paddington’s Hugh Bonneville at the movies. We take you actors play which well-known talks about his longtime says playing father figure to a through the 10 films you must fairy tale characters in love of action movies, and mischievous talking bear gave see, starting with the return of the musical extravaganza recent decision to get clean him the chance to revisit his Star Wars! Into the Woods and healthy own childhood BY INGRID RANDOJA BY INGRID RANDOJA BY BOB STRAUSS BY INGRID RANDOJA JANUARY 2015 | CINEPLEX MAGAZINE | 3 EDITOR’S NOTE PUBLISHER SALAH BACHIR EDITOR MARNI WEISZ DEPUTY EDITOR INGRID RANDOJA ART DIRECTOR TREVOR THOMAS STEWART ASSISTANT ART DIRECTOR STEVIE SHIPMAN VICE PRESIDENT, PRODUCTION SHEILA GREGORY CONTRIBUTORS LEO ALEFOUNDER, BOB STRAUSS ADVERTISING SALES FOR CINEPLEX MAGAZINE AND LE MAGAZINE CINEPLEX IS HANDLED BY CINEPLEX MEDIA. -
A Imitation Learning: a Survey of Learning Methods
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by BCU Open Access A Imitation Learning: A Survey of Learning Methods Ahmed Hussein, School of Computing Science and Digital Media, Robert Gordon University Mohamed Medhat Gaber, School of Computing and Digital Technology, Birmingham City University Eyad Elyan , School of Computing Science and Digital Media, Robert Gordon University Chrisina Jayne , School of Computing Science and Digital Media, Robert Gordon University Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years, however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations; without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction and computer games to name a few. -
Mental States in Animals: Cognitive Ethology Jacques Vauclair
Mental states in animals: cognitive ethology Jacques Vauclair This artHe addresses the quegtion of mentaJ states in animak as viewed in ‘cognitive ethology”. In effect, thk field of research aims at studying naturally occurring behaviours such as food caching, individual recognition, imitation, tool use and communication in wild animals, in order to seek for evidence of mental experiences, self-aw&&reness and intentional@. Cognitive ethologists use some philosophical cencepts (e.g., the ‘intentional stance’) to carry out their programme of the investigation of natural behaviours. A comparison between cognitive ethology and other approaches to the investigation of cognitive processes in animals (e.g., experimental animal psychology) helps to point out the strengths and weaknesses of cognitive ethology. Moreover, laboratory attempts to analyse experimentally Mentional behaviours such as deception, the relationship between seeing and knowing, as well as the ability of animals to monitor their own states of knowing, suggest that cognitive ethology could benefit significantly from the conceptual frameworks and methods of animal cognitive psychology. Both disciplines could, in fact, co&ribute to the understanding of which cognitive abilities are evolutionary adaptations. T he term ‘cognirive ethology’ (CE) was comed by that it advances a purposive or Intentional interpretation Griffin in The Question ofAnimd Au~aarmess’ and later de- for activities which are a mixture of some fixed genetically veloped in other publications’ ‘_ Although Griffin‘s IS’6 transmitred elements with more hexible behaviour?. book was first a strong (and certainly salutary) reacrion (Cognitive ethologists USC conceptual frameworks against the inhibitions imposed by strict behaviourism in provided by philosophers (such as rhe ‘intentional stance’)“. -
Much Has Been Written About the Turing Test in the Last Few Years, Some of It
1 Much has been written about the Turing Test in the last few years, some of it preposterously off the mark. People typically mis-imagine the test by orders of magnitude. This essay is an antidote, a prosthesis for the imagination, showing how huge the task posed by the Turing Test is, and hence how unlikely it is that any computer will ever pass it. It does not go far enough in the imagination-enhancement department, however, and I have updated the essay with a new postscript. Can Machines Think?1 Can machines think? This has been a conundrum for philosophers for years, but in their fascination with the pure conceptual issues they have for the most part overlooked the real social importance of the answer. It is of more than academic importance that we learn to think clearly about the actual cognitive powers of computers, for they are now being introduced into a variety of sensitive social roles, where their powers will be put to the ultimate test: In a wide variety of areas, we are on the verge of making ourselves dependent upon their cognitive powers. The cost of overestimating them could be enormous. One of the principal inventors of the computer was the great 1 Originally appeared in Shafto, M., ed., How We Know (San Francisco: Harper & Row, 1985). 2 British mathematician Alan Turing. It was he who first figured out, in highly abstract terms, how to design a programmable computing device--what we not call a universal Turing machine. All programmable computers in use today are in essence Turing machines. -
Top 10 Reasons to Major in Computing
Top 10 Reasons to Major in Computing 1. Computing is part of everything we do! Computing and computer technology are part of just about everything that touches our lives from the cars we drive, to the movies we watch, to the ways businesses and governments deal with us. Understanding different dimensions of computing is part of the necessary skill set for an educated person in the 21st century. Whether you want to be a scientist, develop the latest killer application, or just know what it really means when someone says “the computer made a mistake”, studying computing will provide you with valuable knowledge. 2. Expertise in computing enables you to solve complex, challenging problems. Computing is a discipline that offers rewarding and challenging possibilities for a wide range of people regardless of their range of interests. Computing requires and develops capabilities in solving deep, multidimensional problems requiring imagination and sensitivity to a variety of concerns. 3. Computing enables you to make a positive difference in the world. Computing drives innovation in the sciences (human genome project, AIDS vaccine research, environmental monitoring and protection just to mention a few), and also in engineering, business, entertainment and education. If you want to make a positive difference in the world, study computing. 4. Computing offers many types of lucrative careers. Computing jobs are among the highest paid and have the highest job satisfaction. Computing is very often associated with innovation, and developments in computing tend to drive it. This, in turn, is the key to national competitiveness. The possibilities for future developments are expected to be even greater than they have been in the past. -
Why Python for Chatbots
Schedule: 1. History of chatbots and Artificial Intelligence 2. The growing role of Chatbots in 2020 3. A hands on look at the A.I Chatbot learning sequence 4. Q & A Session Schedule: 1. History of chatbots and Artificial Intelligence 2. The growing role of Chatbots in 2020 3. A hands on look at the A.I Chatbot learning sequence 4. Q & A Session Image credit: Archivio GBB/Contrasto/Redux History •1940 – The Bombe •1948 – Turing Machine •1950 – Touring Test •1980 Zork •1990 – Loebner Prize Conversational Bots •Today – Siri, Alexa Google Assistant Image credit: Photo 172987631 © Pop Nukoonrat - Dreamstime.com 1940 Modern computer history begins with Language Analysis: “The Bombe” Breaking the code of the German Enigma Machine ENIGMA MACHINE THE BOMBE Enigma Machine image: Photographer: Timothy A. Clary/AFP The Bombe image: from movie set for The Imitation Game, The Weinstein Company 1948 – Alan Turing comes up with the concept of Turing Machine Image CC-BY-SA: Wikipedia, wvbailey 1948 – Alan Turing comes up with the concept of Turing Machine youtube.com/watch?v=dNRDvLACg5Q 1950 Imitation Game Image credit: Archivio GBB/Contrasto/Redux Zork 1980 Zork 1980 Text parsing Loebner Prize: Turing Test Competition bit.ly/loebnerP Conversational Chatbots you can try with your students bit.ly/MITsuku bit.ly/CLVbot What modern chatbots do •Convert speech to text •Categorise user input into categories they know •Analyse the emotion emotion in user input •Select from a range of available responses •Synthesize human language responses Image sources: Biglytics -
Nonverbal Imitation Skills in Children with Specific Language Delay
City Research Online City, University of London Institutional Repository Citation: Dohmen, A., Chiat, S. and Roy, P. (2013). Nonverbal imitation skills in children with specific language delay. Research in Developmental Disabilities, 34(10), pp. 3288-3300. doi: 10.1016/j.ridd.2013.06.004 This is the unspecified version of the paper. This version of the publication may differ from the final published version. Permanent repository link: http://openaccess.city.ac.uk/3352/ Link to published version: http://dx.doi.org/10.1016/j.ridd.2013.06.004 Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to. City Research Online: http://openaccess.city.ac.uk/ [email protected] Full title: Nonverbal imitation skills in children with specific language delay Name and affiliations of authors Andrea Dohmen¹˒², Shula Chiat², and Penny Roy² ¹Department of Experimental Psychology, University of Oxford, 9 South Parks Road, Oxford OX1 3UD, UK ²Language and Communication Science Division, City University London, Northampton Square EC1V 0HB London, UK Corresponding author Andrea Dohmen University of Oxford / Department of Experimental Psychology 9 South Parks Road Oxford OX1 3UD, UK Email: [email protected] Telephone: 0044/1865/271334 2 Nonverbal imitation skills in children with specific language delay Abstract Research in children with language problems has focussed on verbal deficits, and we have less understanding of children‟s deficits with nonverbal sociocognitive skills which have been proposed to be important for language acquisition. -
Open Dissertation Draft Revised Final.Pdf
The Pennsylvania State University The Graduate School ICT AND STEM EDUCATION AT THE COLONIAL BORDER: A POSTCOLONIAL COMPUTING PERSPECTIVE OF INDIGENOUS CULTURAL INTEGRATION INTO ICT AND STEM OUTREACH IN BRITISH COLUMBIA A Dissertation in Information Sciences and Technology by Richard Canevez © 2020 Richard Canevez Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy December 2020 ii The dissertation of Richard Canevez was reviewed and approved by the following: Carleen Maitland Associate Professor of Information Sciences and Technology Dissertation Advisor Chair of Committee Daniel Susser Assistant Professor of Information Sciences and Technology and Philosophy Lynette (Kvasny) Yarger Associate Professor of Information Sciences and Technology Craig Campbell Assistant Teaching Professor of Education (Lifelong Learning and Adult Education) Mary Beth Rosson Professor of Information Sciences and Technology Director of Graduate Programs iii ABSTRACT Information and communication technologies (ICTs) have achieved a global reach, particularly in social groups within the ‘Global North,’ such as those within the province of British Columbia (BC), Canada. It has produced the need for a computing workforce, and increasingly, diversity is becoming an integral aspect of that workforce. Today, educational outreach programs with ICT components that are extending education to Indigenous communities in BC are charting a new direction in crossing the cultural barrier in education by tailoring their curricula to distinct Indigenous cultures, commonly within broader science, technology, engineering, and mathematics (STEM) initiatives. These efforts require examination, as they integrate Indigenous cultural material and guidance into what has been a largely Euro-Western-centric domain of education. Postcolonial computing theory provides a lens through which this integration can be investigated, connecting technological development and education disciplines within the parallel goals of cross-cultural, cross-colonial humanitarian development. -
Deep Reinforcement Learning Via Imitation Learning
Deep Reinforcement Learning via Imitation Learning Sergey Levine perception Action (run away) action sensorimotor loop Action (run away) End-to-end vision standard features mid-level features classifier computer (e.g. HOG) (e.g. DPM) (e.g. SVM) vision Felzenszwalb ‘08 deep learning Krizhevsky ‘12 End-to-end control standard state low-level modeling & motion motor observations estimation controller robotic prediction planning torques control (e.g. vision) (e.g. PD) deep motor sensorimotor observations torques learning indirect supervision actions have consequences Contents Imitation learning Imitation without a human Research frontiers Terminology & notation 1. run away 2. ignore 3. pet Terminology & notation 1. run away 2. ignore 3. pet Terminology & notation 1. run away 2. ignore 3. pet a bit of history… управление Lev Pontryagin Richard Bellman Contents Imitation learning Imitation without a human Research frontiers Imitation Learning training supervised data learning Images: Bojarski et al. ‘16, NVIDIA Does it work? No! Does it work? Yes! Video: Bojarski et al. ‘16, NVIDIA Why did that work? Bojarski et al. ‘16, NVIDIA Can we make it work more often? cost stability Learning from a stabilizing controller (more on this later) Can we make it work more often? Can we make it work more often? DAgger: Dataset Aggregation Ross et al. ‘11 DAgger Example Ross et al. ‘11 What’s the problem? Ross et al. ‘11 Imitation learning: recap training supervised data learning • Usually (but not always) insufficient by itself – Distribution mismatch problem • Sometimes works well – Hacks (e.g. left/right images) – Samples from a stable trajectory distribution – Add more on-policy data, e.g.