ARCHITECTS of INTELLIGENCE for Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS of INTELLIGENCE
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Backpropagation and Deep Learning in the Brain
Backpropagation and Deep Learning in the Brain Simons Institute -- Computational Theories of the Brain 2018 Timothy Lillicrap DeepMind, UCL With: Sergey Bartunov, Adam Santoro, Jordan Guerguiev, Blake Richards, Luke Marris, Daniel Cownden, Colin Akerman, Douglas Tweed, Geoffrey Hinton The “credit assignment” problem The solution in artificial networks: backprop Credit assignment by backprop works well in practice and shows up in virtually all of the state-of-the-art supervised, unsupervised, and reinforcement learning algorithms. Why Isn’t Backprop “Biologically Plausible”? Why Isn’t Backprop “Biologically Plausible”? Neuroscience Evidence for Backprop in the Brain? A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: How to convince a neuroscientist that the cortex is learning via [something like] backprop - To convince a machine learning researcher, an appeal to variance in gradient estimates might be enough. - But this is rarely enough to convince a neuroscientist. - So what lines of argument help? How to convince a neuroscientist that the cortex is learning via [something like] backprop - What do I mean by “something like backprop”?: - That learning is achieved across multiple layers by sending information from neurons closer to the output back to “earlier” layers to help compute their synaptic updates. How to convince a neuroscientist that the cortex is learning via [something like] backprop 1. Feedback connections in cortex are ubiquitous and modify the -
Painful Barbie in a Global Marketing Perspective Patrick Sim MM* Capital Advisors Partners Asia Pte Ltd, Singapore
ounting cc & A f M Patrick Sim, J Account Mark 2015, 4:3 o a l r a k e n Journal of Accounting & DOI: 10.4172/2168-9601.1000142 t r i n u g o J ISSN: 2168-9601 Marketing Review Article Open Access Painful Barbie in a Global Marketing Perspective Patrick Sim MM* Capital Advisors Partners Asia Pte Ltd, Singapore Abstract The essay discusses the American doll, Barbie within a global marketing perspective. It will summarize the main points of the case and explain how the material in the global marketing review relates to the case. The issues to be discussed will involve Mattel’s global marketing strategy for Barbie and its success. This will be negotiated with its brand strength as well as its icon status and how to create more marketing opportunities and turn the sales around in the US. These issues will also be taken from the perspective of the local Middle East culture when Mattel goes global in search for a befitting cultural nuance from its customers as well as seeking the perfect media response. The essay will also highlight the future suggestions for the product and the company. Keywords: Global marketing; Barbie branding; Sustainable efforts; Branding and key success factors for sustainable efforts Cultural nuance; Iconic marketing Mattel is a big, successful brand, which has good brand strength, Introduction financially and through its strong customer based loyalty. It can be analyzed that Mattel/Barbie must maintain these success factors and Global marketing strategy be sustainable in marketing efforts to be able to increase their future Global marketing as defined by Doyle [1] suggests “marketing on a opportunities:- worldwide scale reconciling or taking commercial advantage of global • Economies of scale in production and distribution; operational differences, similarities and opportunities in order to meet global objectives”. -
TOYS Balls Barbie Clothes Board Books-English and Spanish Books
TOYS Balls Ping Pong Balls Barbie clothes Ping Pong Paddles Board Books-English and Spanish Play Food and Dishes Books-English and Spanish Playskool KickStart Cribgym Busy Boxes Pool Stick Holder Colorful Rainsticks Pool Stick repair kits Crib Mirrors Pool sticks Crib Mobiles-washable (without cloth) Pop-up Toys Etch-A-Sketch Puzzles Fisher Price Medical Kits Rattles Fisher Price people and animals See-n-Say FisherPrice Infant Aquarium Squeeze Toys Infant Boppy Toddler Riding Toys Magna Doodle Toys that Light-up Matchbox Cars Trucks Musical Toys ViewMaster and Slides Nerf Balls-footballs, basketballs GAMES Battleship Life Scattergories Boggle Lotto Scattergories Jr. Boggle Jr. Lucky Ducks Scrabble Checkers Mancala Skipbo Cards Chess Mastermind Sorry Clue Monopoly Taboo Connect Four Monopoly Jr. Trivial Pursuit-90's Cranium Operation Trouble Family Feud Parchesi Uno Attack Guess Who Pictionary Uno Cards-Always Needed Guesstures Pictionary Jr Upwords Jenga Playing Cards Who Wants to Be a Millionaire Lego Game Rack-O Yahtzee ARTS AND CRAFTS Beads & Jewelry Making Kits Crayola Washable Markers Bubbles Disposable Cameras Children’s Scissors Elmer's Glue Coloring Books Fabric Markers Construction Paper-esp. white Fabric Paint Craft Kits Foam shapes and letters Crayola Colored Pencils Glitter Crayola Crayons Glitter Pens Glue Sticks Play-Doh tools Journals Scissor w/Fancy edges Letter Beads Seasonal Crafts Model Magic Sizzex Accessories Paint Brushes Stickers Photo Albums ScrapBooking Materials Plain White T-shirts -all sizes Play-Doh ELECTRONICS -
Lecture Note on Deep Learning and Quantum Many-Body Computation
Lecture Note on Deep Learning and Quantum Many-Body Computation Jin-Guo Liu, Shuo-Hui Li, and Lei Wang∗ Institute of Physics, Chinese Academy of Sciences Beijing 100190, China November 23, 2018 Abstract This note introduces deep learning from a computa- tional quantum physicist’s perspective. The focus is on deep learning’s impacts to quantum many-body compu- tation, and vice versa. The latest version of the note is at http://wangleiphy.github.io/. Please send comments, suggestions and corrections to the email address in below. ∗ [email protected] CONTENTS 1 introduction2 2 discriminative learning4 2.1 Data Representation 4 2.2 Model: Artificial Neural Networks 6 2.3 Cost Function 9 2.4 Optimization 11 2.4.1 Back Propagation 11 2.4.2 Gradient Descend 13 2.5 Understanding, Visualization and Applications Beyond Classification 15 3 generative modeling 17 3.1 Unsupervised Probabilistic Modeling 17 3.2 Generative Model Zoo 18 3.2.1 Boltzmann Machines 19 3.2.2 Autoregressive Models 22 3.2.3 Normalizing Flow 23 3.2.4 Variational Autoencoders 25 3.2.5 Tensor Networks 28 3.2.6 Generative Adversarial Networks 29 3.3 Summary 32 4 applications to quantum many-body physics and more 33 4.1 Material and Chemistry Discoveries 33 4.2 Density Functional Theory 34 4.3 “Phase” Recognition 34 4.4 Variational Ansatz 34 4.5 Renormalization Group 35 4.6 Monte Carlo Update Proposals 36 4.7 Tensor Networks 37 4.8 Quantum Machine Leanring 38 4.9 Miscellaneous 38 5 hands on session 39 5.1 Computation Graph and Back Propagation 39 5.2 Deep Learning Libraries 41 5.3 Generative Modeling using Normalizing Flows 42 5.4 Restricted Boltzmann Machine for Image Restoration 43 5.5 Neural Network as a Quantum Wave Function Ansatz 43 6 challenges ahead 45 7 resources 46 BIBLIOGRAPHY 47 1 1 INTRODUCTION Deep Learning (DL) ⊂ Machine Learning (ML) ⊂ Artificial Intelli- gence (AI). -
AI Computer Wraps up 4-1 Victory Against Human Champion Nature Reports from Alphago's Victory in Seoul
The Go Files: AI computer wraps up 4-1 victory against human champion Nature reports from AlphaGo's victory in Seoul. Tanguy Chouard 15 March 2016 SEOUL, SOUTH KOREA Google DeepMind Lee Sedol, who has lost 4-1 to AlphaGo. Tanguy Chouard, an editor with Nature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go. This week, he is watching top professional Lee Sedol take on AlphaGo, in Seoul, for a $1 million prize. It’s all over at the Four Seasons Hotel in Seoul, where this morning AlphaGo wrapped up a 4-1 victory over Lee Sedol — incidentally, earning itself and its creators an honorary '9-dan professional' degree from the Korean Baduk Association. After winning the first three games, Google-DeepMind's computer looked impregnable. But the last two games may have revealed some weaknesses in its makeup. Game four totally changed the Go world’s view on AlphaGo’s dominance because it made it clear that the computer can 'bug' — or at least play very poor moves when on the losing side. It was obvious that Lee felt under much less pressure than in game three. And he adopted a different style, one based on taking large amounts of territory early on rather than immediately going for ‘street fighting’ such as making threats to capture stones. This style – called ‘amashi’ – seems to have paid off, because on move 78, Lee produced a play that somehow slipped under AlphaGo’s radar. David Silver, a scientist at DeepMind who's been leading the development of AlphaGo, said the program estimated its probability as 1 in 10,000. -
Accelerators and Obstacles to Emerging ML-Enabled Research Fields
Life at the edge: accelerators and obstacles to emerging ML-enabled research fields Soukayna Mouatadid Steve Easterbrook Department of Computer Science Department of Computer Science University of Toronto University of Toronto [email protected] [email protected] Abstract Machine learning is transforming several scientific disciplines, and has resulted in the emergence of new interdisciplinary data-driven research fields. Surveys of such emerging fields often highlight how machine learning can accelerate scientific discovery. However, a less discussed question is how connections between the parent fields develop and how the emerging interdisciplinary research evolves. This study attempts to answer this question by exploring the interactions between ma- chine learning research and traditional scientific disciplines. We examine different examples of such emerging fields, to identify the obstacles and accelerators that influence interdisciplinary collaborations between the parent fields. 1 Introduction In recent decades, several scientific research fields have experienced a deluge of data, which is impacting the way science is conducted. Fields such as neuroscience, psychology or social sciences are being reshaped by the advances in machine learning (ML) and the data processing technologies it provides. In some cases, new research fields have emerged at the intersection of machine learning and more traditional research disciplines. This is the case for example, for bioinformatics, born from collaborations between biologists and computer scientists in the mid-80s [1], which is now considered a well-established field with an active community, specialized conferences, university departments and research groups. How do these transformations take place? Do they follow similar paths? If yes, how can the advances in such interdisciplinary fields be accelerated? Researchers in the philosophy of science have long been interested in these questions. -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
Mind-Crafting: Anticipatory Critique of Transhumanist Mind-Uploading in German High Modernist Novels Nathan Jensen Bates a Disse
Mind-Crafting: Anticipatory Critique of Transhumanist Mind-Uploading in German High Modernist Novels Nathan Jensen Bates A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2018 Reading Committee: Richard Block, Chair Sabine Wilke Ellwood Wiggins Program Authorized to Offer Degree: Germanics ©Copyright 2018 Nathan Jensen Bates University of Washington Abstract Mind-Crafting: Anticipatory Critique of Transhumanist Mind-Uploading in German High Modernist Novels Nathan Jensen Bates Chair of the Supervisory Committee: Professor Richard Block Germanics This dissertation explores the question of how German modernist novels anticipate and critique the transhumanist theory of mind-uploading in an attempt to avert binary thinking. German modernist novels simulate the mind and expose the indistinct limits of that simulation. Simulation is understood in this study as defined by Jean Baudrillard in Simulacra and Simulation. The novels discussed in this work include Thomas Mann’s Der Zauberberg; Hermann Broch’s Die Schlafwandler; Alfred Döblin’s Berlin Alexanderplatz: Die Geschichte von Franz Biberkopf; and, in the conclusion, Irmgard Keun’s Das Kunstseidene Mädchen is offered as a field of future inquiry. These primary sources disclose at least three aspects of the mind that are resistant to discrete articulation; that is, the uploading or extraction of the mind into a foreign context. A fourth is proposed, but only provisionally, in the conclusion of this work. The aspects resistant to uploading are defined and discussed as situatedness, plurality, and adaptability to ambiguity. Each of these aspects relates to one of the three steps of mind- uploading summarized in Nick Bostrom’s treatment of the subject. -
Sustainable Development, Technological Singularity and Ethics
European Research Studies Journal Volume XXI, Issue 4, 2018 pp. 714- 725 Sustainable Development, Technological Singularity and Ethics Vyacheslav Mantatov1, Vitaly Tutubalin2 Abstract: The development of modern convergent technologies opens the prospect of a new technological order. Its image as a “technological singularity”, i.e. such “transhuman” stage of scientific and technical progress, on which the superintelligence will be practically implemented, seems to be quite realistic. The determination of the basic philosophical coordinates of this future reality in the movement along the path of sustainable development of mankind is the most important task of modern science. The article is devoted to the study of the basic ontological, epistemological and moral aspects in the reception of the coming technological singularity. The method of this study is integrating dialectical and system approach. The authors come to the conclusion: the technological singularity in the form of a “computronium” (superintelligence) opens up broad prospects for the sustainable development of mankind in the cosmic dimension. This superintelligence will become an ally of man in the process of cosmic evolution. Keywords: Technological Singularity, Superintelligence, Convergent Technologies, Cosmocentrism, Human and Universe JEL code: Q01, Q56. 1East Siberia State University of Technology and Management, Ulan-Ude, Russia [email protected] 2East Siberia State University of Technology and Management, Ulan-Ude, Russia, [email protected] V. Mantatov, V. Tutubalin 715 1. Introduction Intelligence organizes the world by organizing itself. J. Piaget Technological singularity is defined as a certain moment or stage in the development of mankind, when scientific and technological progress will become so fast and complex that it will be unpredictable. -
Dynamic Behavior of Constained Back-Propagation Networks
642 Chauvin Dynamic Behavior of Constrained Back-Propagation Networks Yves Chauvin! Thomson-CSF, Inc. 630 Hansen Way, Suite 250 Palo Alto, CA. 94304 ABSTRACT The learning dynamics of the back-propagation algorithm are in vestigated when complexity constraints are added to the standard Least Mean Square (LMS) cost function. It is shown that loss of generalization performance due to overtraining can be avoided when using such complexity constraints. Furthermore, "energy," hidden representations and weight distributions are observed and compared during learning. An attempt is made at explaining the results in terms of linear and non-linear effects in relation to the gradient descent learning algorithm. 1 INTRODUCTION It is generally admitted that generalization performance of back-propagation net works (Rumelhart, Hinton & Williams, 1986) will depend on the relative size ofthe training data and of the trained network. By analogy to curve-fitting and for theo retical considerations, the generalization performance of the network should de crease as the size of the network and the associated number of degrees of freedom increase (Rumelhart, 1987; Denker et al., 1987; Hanson & Pratt, 1989). This paper examines the dynamics of the standard back-propagation algorithm (BP) and of a constrained back-propagation variation (CBP), designed to adapt the size of the network to the training data base. The performance, learning dynamics and the representations resulting from the two algorithms are compared. 1. Also in the Psychology Department, Stanford University, Stanford, CA. 94305 Dynamic Behavior of Constrained Back-Propagation Networks 643 2 GENERALIZATION PERFORM:ANCE 2.1 STANDARD BACK-PROPAGATION In Chauvin (In Press). the generalization performance of a back-propagation net work was observed for a classification task from spectrograms into phonemic cate gories (single speaker. -
Generative Models, Structural Similarity, and Mental Representation
The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation Daniel George Williams Trinity Hall College University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy August 2018 The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation Daniel Williams Abstract I outline and defend a theory of mental representation based on three ideas that I extract from the work of the mid-twentieth century philosopher, psychologist, and cybernetician Kenneth Craik: first, an account of mental representation in terms of idealised models that capitalize on structural similarity to their targets; second, an appreciation of prediction as the core function of such models; and third, a regulatory understanding of brain function. I clarify and elaborate on each of these ideas, relate them to contemporary advances in neuroscience and machine learning, and favourably contrast a predictive model-based theory of mental representation with other prominent accounts of the nature, importance, and functions of mental representations in cognitive science and philosophy. For Marcella Montagnese Preface Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. -
Cogsci Little Red Book.Indd
OUTSTANDING QUESTIONS IN COGNITIVE SCIENCE A SYMPOSIUM HONORING TEN YEARS OF THE DAVID E. RUMELHART PRIZE IN COGNITIVE SCIENCE August 14, 2010 Portland Oregon, USA OUTSTANDING QUESTIONS IN COGNITIVE SCIENCE A SYMPOSIUM HONORING TEN YEARS OF THE DAVID E. RUMELHART PRIZE IN COGNITIVE SCIENCE August 14, 2010 Portland Oregon, USA David E. Rumelhart David E. Rumelhart ABOUT THE RUMELHART PRIZE AND THE 10 YEAR SYMPOSIUM At the August 2000 meeting of the Cognitive Science Society, Drs. James L. McClelland and Robert J. Glushko presented the initial plan to honor the intellectual contributions of David E. Rumelhart to Cognitive Science. Rumelhart had retired from Stanford University in 1998, suffering from Pick’s disease, a degenerative neurological illness. The plan involved honoring Rumelhart through an annual prize of $100,000, funded by the Robert J. Glushko and Pamela Samuelson Foundation. McClelland was a close collaborator of Rumelhart, and, together, they had written numerous articles and books on parallel distributed processing. Glushko, who had been Rumelhart’s Ph.D student in the late 1970s and a Silicon Valley entrepreneur in the 1990s, is currently an adjunct professor at the University of California – Berkeley. The David E. Rumelhart prize was conceived to honor outstanding research in formal approaches to human cognition. Rumelhart’s own seminal contributions to cognitive science included both connectionist and symbolic models, employing both computational and mathematical tools. These contributions progressed from his early work on analogies and story grammars to the development of backpropagation and the use of parallel distributed processing to model various cognitive abilities. Critically, Rumelhart believed that future progress in cognitive science would depend upon researchers being able to develop rigorous, formal theories of mental structures and processes.