Alphago Vs Alphago Game 1: “Fighting”
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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Booxter Export Page 1
Cover Title Authors Edition Volume Genre Format ISBN Keywords The Museum of Found Mirjam, LINSCHOOTEN Exhibition Soft cover 9780968546819 Objects: Toronto (ed.), Sameer, FAROOQ Catalogue (Maharaja and - ) (ed.), Haema, SIVANESAN (Da bao)(Takeout) Anik, GLAUDE (ed.), Meg, Exhibition Soft cover 9780973589689 Chinese, TAYLOR (ed.), Ruth, Catalogue Canadian art, GASKILL (ed.), Jing Yuan, multimedia, 21st HUANG (trans.), Xiao, century, Ontario, OUYANG (trans.), Mark, Markham TIMMINGS Piercing Brightness Shezad, DAWOOD. (ill.), Exhibition Hard 9783863351465 film Gerrie, van NOORD. (ed.), Catalogue cover Malenie, POCOCK (ed.), Abake 52nd International Art Ming-Liang, TSAI (ill.), Exhibition Soft cover film, mixed Exhibition - La Biennale Huang-Chen, TANG (ill.), Catalogue media, print, di Venezia - Atopia Kuo Min, LEE (ill.), Shih performance art Chieh, HUANG (ill.), VIVA (ill.), Hongjohn, LIN (ed.) Passage Osvaldo, YERO (ill.), Exhibition Soft cover 9780978241995 Sculpture, mixed Charo, NEVILLE (ed.), Catalogue media, ceramic, Scott, WATSON (ed.) Installaion China International Arata, ISOZAKI (ill.), Exhibition Soft cover architecture, Practical Exhibition of Jiakun, LIU (ill.), Jiang, XU Catalogue design, China Architecture (ill.), Xiaoshan, LI (ill.), Steven, HOLL (ill.), Kai, ZHOU (ill.), Mathias, KLOTZ (ill.), Qingyun, MA (ill.), Hrvoje, NJIRIC (ill.), Kazuyo, SEJIMA (ill.), Ryue, NISHIZAWA (ill.), David, ADJAYE (ill.), Ettore, SOTTSASS (ill.), Lei, ZHANG (ill.), Luis M. MANSILLA (ill.), Sean, GODSELL (ill.), Gabor, BACHMAN (ill.), Yung -
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. -
Weiqi in Australia
Weiqi in Australia Neville Smythe, Australian Go Association australiango.asn.au Weiqi in Australia • Earliest game played in Australia? • 1960 – Go played at Sydney Chess Club • by 1970, Sydney Go Club formed • 1972 Canberra club • 1977 Brisbane club • 1980 Melbourne club An early Sydney player… John Power (right) with Richard Bozulich and friend (WAGC 2008) Weiqi in Australia • 1978 – Sydney Brisbane and Canberra Clubs meet to form Australian Go Association • 1978 –1st National Championship • 1979 – played in first WAGC • 1982 – foundation member IGF • National, State and other tournaments since then Tournaments National Championship 2007 (Sydney Go Club) Tournaments NEC Cup 2009 Australian Go Congress 2nd Australian Go Congress 2016 [4 colour Go – madness!] World Collegiate Championship Sydney University, July 2019 International participation 1st WAGC 1979: Dae Hahn (Australia) v Shin-Auk Kang (USA) International participation Team Australia WMSG Beijing 2008 (we claim JiaJia as Australian!) 2019 status • Over 500 members, 15 clubs • Strong participation and support by the expatriate Chinese and Korean communities • Not enough female players The future Internet – blessing or curse? • Beginners now progress very quickly and can play anyone anywhere any time • But • We have no idea how many people in Australia only play on-line • Viability of physical clubs is under threat • But it can be an opportunity… Internet – blessing or curse? http://club.artofgo.org/ National Treasures Wu Soong Shen at 2004 Australian National Championship Canberra National Treasures An Younggil, National Coach Visiting Professionals Kobayashi Chizu meeting emus (Canberra, 1981) Visiting Professionals • Chizu was one of the first pro’s to visit • (Before Chizu 3 players from the Nihon Kiin in 1975) • Since then many professionals from Japan, Korea and China have visited • .. -
In-Datacenter Performance Analysis of a Tensor Processing Unit
In-Datacenter Performance Analysis of a Tensor Processing Unit Presented by Josh Fried Background: Machine Learning Neural Networks: ● Multi Layer Perceptrons ● Recurrent Neural Networks (mostly LSTMs) ● Convolutional Neural Networks Synapse - each edge, has a weight Neuron - each node, sums weights and uses non-linear activation function over sum Propagating inputs through a layer of the NN is a matrix multiplication followed by an activation Background: Machine Learning Two phases: ● Training (offline) ○ relaxed deadlines ○ large batches to amortize costs of loading weights from DRAM ○ well suited to GPUs ○ Usually uses floating points ● Inference (online) ○ strict deadlines: 7-10ms at Google for some workloads ■ limited possibility for batching because of deadlines ○ Facebook uses CPUs for inference (last class) ○ Can use lower precision integers (faster/smaller/more efficient) ML Workloads @ Google 90% of ML workload time at Google spent on MLPs and LSTMs, despite broader focus on CNNs RankBrain (search) Inception (image classification), Google Translate AlphaGo (and others) Background: Hardware Trends End of Moore’s Law & Dennard Scaling ● Moore - transistor density is doubling every two years ● Dennard - power stays proportional to chip area as transistors shrink Machine Learning causing a huge growth in demand for compute ● 2006: Excess CPU capacity in datacenters is enough ● 2013: Projected 3 minutes per-day per-user of speech recognition ○ will require doubling datacenter compute capacity! Google’s Answer: Custom ASIC Goal: Build a chip that improves cost-performance for NN inference What are the main costs? Capital Costs Operational Costs (power bill!) TPU (V1) Design Goals Short design-deployment cycle: ~15 months! Plugs in to PCIe slot on existing servers Accelerates matrix multiplication operations Uses 8-bit integer operations instead of floating point How does the TPU work? CISC instructions, issued by host. -
Sydney Go Journal Issue Date – February 2007
Author – David Mitchell on behalf of The Sydney Go Club Sydney Go Journal Issue Date – February 2007 Dr. Geoffrey Gray’s antique Go Ban (picture courtesy of Dr Gray) Up coming events Queensland Go Championship Saturday 17th and Sunday 18th February in Brisbane. Venue: Brisbane Bridge Centre Registration and other details on page 33 For the latest details visit www.uq.net.au/~zzjhardy/brisgo.html Contributions, comments and suggestions for the SGJ to: [email protected] Special thanks to Devon Bailey and Geoffrey Gray for proof reading this edition and correcting my mistakes. © Copyright 2007 – David Mitchell Page 1 February 2007 Author – David Mitchell on behalf of The Sydney Go Club Sydney Lightning Tournament report 3 Changqi Cup 4 3rd Changqi Cup – 1st Qualifier 6 3rd Changqi Cup – 2nd Qualifier 10 Problems 14 Handicap Strategy 15 Four Corners 29 Two page Joseki lesson 35 Answers 37 Korean Go Terms 39 The Sydney Go Club Meets Friday nights at :- At Philas House 17 Brisbane St Surry Hills From 5.00pm Entrance fee - $5 per head; Concession $3; Children free - includes tea and coffee. For further information from Robert [email protected] © Copyright 2007 – David Mitchell Page 2 February 2007 Lightning Tournament The lightning tournament was held on the January 12th and a good time was had by all, thanks to Robert Vadas organising skills. The final was between Max Latey and David Mitchell, the latter managing another lucky win. The following pictures tell the story David Mitchell (foreground); Max more eloquently than words. Latey (background); the two finalists Robert giving some sage advice. -
Chinese Health App Arrives Access to a Large Population Used to Sharing Data Could Give Icarbonx an Edge Over Rivals
NEWS IN FOCUS ASTROPHYSICS Legendary CHEMISTRY Deceptive spice POLITICS Scientists spy ECOLOGY New Zealand Arecibo telescope faces molecule offers cautionary chance to green UK plans to kill off all uncertain future p.143 tale p.144 after Brexit p.145 invasive predators p.148 ICARBONX Jun Wang, founder of digital biotechnology firm iCarbonX, showcases the Meum app that will use reams of health data to provide customized medical advice. BIOTECHNOLOGY Chinese health app arrives Access to a large population used to sharing data could give iCarbonX an edge over rivals. BY DAVID CYRANOSKI, SHENZHEN medical advice directly to consumers through another $400 million had been invested in the an app. alliance members, but he declined to name the ne of China’s most intriguing biotech- The announcement was a long-anticipated source. Wang also demonstrated the smart- nology companies has fleshed out an debut for iCarbonX, which Wang founded phone app, called Meum after the Latin for earlier quixotic promise to use artificial in October 2015 shortly after he left his lead- ‘my’, that customers would use to enter data Ointelligence (AI) to revolutionize health care. ership position at China’s genomics pow- and receive advice. The Shenzhen firm iCarbonX has formed erhouse, BGI, also in Shenzhen. The firm As well as Google, IBM and various smaller an ambitious alliance with seven technology has now raised more than US$600 million in companies, such as Arivale of Seattle, Wash- companies from around the world that special- investment — this contrasts with the tens of ington, are working on similar technology. But ize in gathering different types of health-care millions that most of its rivals are thought Wang says that the iCarbonX alliance will be data, said the company’s founder, Jun Wang, to have invested (although several big play- able to collect data more cheaply and quickly. -
Understanding & Generalizing Alphago Zero
Under review as a conference paper at ICLR 2019 UNDERSTANDING &GENERALIZING ALPHAGO ZERO Anonymous authors Paper under double-blind review ABSTRACT AlphaGo Zero (AGZ) (Silver et al., 2017b) introduced a new tabula rasa rein- forcement learning algorithm that has achieved superhuman performance in the games of Go, Chess, and Shogi with no prior knowledge other than the rules of the game. This success naturally begs the question whether it is possible to develop similar high-performance reinforcement learning algorithms for generic sequential decision-making problems (beyond two-player games), using only the constraints of the environment as the “rules.” To address this challenge, we start by taking steps towards developing a formal understanding of AGZ. AGZ includes two key innovations: (1) it learns a policy (represented as a neural network) using super- vised learning with cross-entropy loss from samples generated via Monte-Carlo Tree Search (MCTS); (2) it uses self-play to learn without training data. We argue that the self-play in AGZ corresponds to learning a Nash equilibrium for the two-player game; and the supervised learning with MCTS is attempting to learn the policy corresponding to the Nash equilibrium, by establishing a novel bound on the difference between the expected return achieved by two policies in terms of the expected KL divergence (cross-entropy) of their induced distributions. To extend AGZ to generic sequential decision-making problems, we introduce a robust MDP framework, in which the agent and nature effectively play a zero-sum game: the agent aims to take actions to maximize reward while nature seeks state transitions, subject to the constraints of that environment, that minimize the agent’s reward. -
AI for Broadcsaters, Future Has Already Begun…
AI for Broadcsaters, future has already begun… By Dr. Veysel Binbay, Specialist Engineer @ ABU Technology & Innovation Department 0 Dr. Veysel Binbay I have been working as Specialist Engineer at ABU Technology and Innovation Department for one year, before that I had worked at TRT (Turkish Radio and Television Corporation) for more than 20 years as a broadcast engineer, and also as an IT Director. I have wide experience on Radio and TV broadcasting technologies, including IT systems also. My experience includes to design, to setup, and to operate analogue/hybrid/digital radio and TV broadcast systems. I have also experienced on IT Networks. 1/25 What is Artificial Intelligence ? • Programs that behave externally like humans? • Programs that operate internally as humans do? • Computational systems that behave intelligently? 2 Some Definitions Trials for AI: The exciting new effort to make computers think … machines with minds, in the full literal sense. Haugeland, 1985 3 Some Definitions Trials for AI: The study of mental faculties through the use of computational models. Charniak and McDermott, 1985 A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes. Schalkoff, 1990 4 Some Definitions Trials for AI: The study of how to make computers do things at which, at the moment, people are better. Rich & Knight, 1991 5 It’s obviously hard to define… (since we don’t have a commonly agreed definition of intelligence itself yet)… Lets try to focus to benefits, and solve definition problem later… 6 Brief history of AI 7 Brief history of AI . The history of AI begins with the following article: . -
Efficiently Mastering the Game of Nogo with Deep Reinforcement
electronics Article Efficiently Mastering the Game of NoGo with Deep Reinforcement Learning Supported by Domain Knowledge Yifan Gao 1,*,† and Lezhou Wu 2,† 1 College of Medicine and Biological Information Engineering, Northeastern University, Liaoning 110819, China 2 College of Information Science and Engineering, Northeastern University, Liaoning 110819, China; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Computer games have been regarded as an important field of artificial intelligence (AI) for a long time. The AlphaZero structure has been successful in the game of Go, beating the top professional human players and becoming the baseline method in computer games. However, the AlphaZero training process requires tremendous computing resources, imposing additional difficulties for the AlphaZero-based AI. In this paper, we propose NoGoZero+ to improve the AlphaZero process and apply it to a game similar to Go, NoGo. NoGoZero+ employs several innovative features to improve training speed and performance, and most improvement strategies can be transferred to other nonspecific areas. This paper compares it with the original AlphaZero process, and results show that NoGoZero+ increases the training speed to about six times that of the original AlphaZero process. Moreover, in the experiment, our agent beat the original AlphaZero agent with a score of 81:19 after only being trained by 20,000 self-play games’ data (small in quantity compared with Citation: Gao, Y.; Wu, L. Efficiently 120,000 self-play games’ data consumed by the original AlphaZero). The NoGo game program based Mastering the Game of NoGo with on NoGoZero+ was the runner-up in the 2020 China Computer Game Championship (CCGC) with Deep Reinforcement Learning limited resources, defeating many AlphaZero-based programs. -
AI Chips: What They Are and Why They Matter
APRIL 2020 AI Chips: What They Are and Why They Matter An AI Chips Reference AUTHORS Saif M. Khan Alexander Mann Table of Contents Introduction and Summary 3 The Laws of Chip Innovation 7 Transistor Shrinkage: Moore’s Law 7 Efficiency and Speed Improvements 8 Increasing Transistor Density Unlocks Improved Designs for Efficiency and Speed 9 Transistor Design is Reaching Fundamental Size Limits 10 The Slowing of Moore’s Law and the Decline of General-Purpose Chips 10 The Economies of Scale of General-Purpose Chips 10 Costs are Increasing Faster than the Semiconductor Market 11 The Semiconductor Industry’s Growth Rate is Unlikely to Increase 14 Chip Improvements as Moore’s Law Slows 15 Transistor Improvements Continue, but are Slowing 16 Improved Transistor Density Enables Specialization 18 The AI Chip Zoo 19 AI Chip Types 20 AI Chip Benchmarks 22 The Value of State-of-the-Art AI Chips 23 The Efficiency of State-of-the-Art AI Chips Translates into Cost-Effectiveness 23 Compute-Intensive AI Algorithms are Bottlenecked by Chip Costs and Speed 26 U.S. and Chinese AI Chips and Implications for National Competitiveness 27 Appendix A: Basics of Semiconductors and Chips 31 Appendix B: How AI Chips Work 33 Parallel Computing 33 Low-Precision Computing 34 Memory Optimization 35 Domain-Specific Languages 36 Appendix C: AI Chip Benchmarking Studies 37 Appendix D: Chip Economics Model 39 Chip Transistor Density, Design Costs, and Energy Costs 40 Foundry, Assembly, Test and Packaging Costs 41 Acknowledgments 44 Center for Security and Emerging Technology | 2 Introduction and Summary Artificial intelligence will play an important role in national and international security in the years to come. -
ELF Opengo: an Analysis and Open Reimplementation of Alphazero
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero Yuandong Tian 1 Jerry Ma * 1 Qucheng Gong * 1 Shubho Sengupta * 1 Zhuoyuan Chen 1 James Pinkerton 1 C. Lawrence Zitnick 1 Abstract However, these advances in playing ability come at signifi- The AlphaGo, AlphaGo Zero, and AlphaZero cant computational expense. A single training run requires series of algorithms are remarkable demonstra- millions of selfplay games and days of training on thousands tions of deep reinforcement learning’s capabili- of TPUs, which is an unattainable level of compute for the ties, achieving superhuman performance in the majority of the research community. When combined with complex game of Go with progressively increas- the unavailability of code and models, the result is that the ing autonomy. However, many obstacles remain approach is very difficult, if not impossible, to reproduce, in the understanding of and usability of these study, improve upon, and extend. promising approaches by the research commu- In this paper, we propose ELF OpenGo, an open-source nity. Toward elucidating unresolved mysteries reimplementation of the AlphaZero (Silver et al., 2018) and facilitating future research, we propose ELF algorithm for the game of Go. We then apply ELF OpenGo OpenGo, an open-source reimplementation of the toward the following three additional contributions. AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate First, we train a superhuman model for ELF OpenGo. Af- superhuman performance with a perfect (20:0) ter running our AlphaZero-style training software on 2,000 record against global top professionals. We ap- GPUs for 9 days, our 20-block model has achieved super- ply ELF OpenGo to conduct extensive ablation human performance that is arguably comparable to the 20- studies, and to identify and analyze numerous in- block models described in Silver et al.(2017) and Silver teresting phenomena in both the model training et al.(2018).