Efficiently Mastering the Game of Nogo with Deep Reinforcement
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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. -
Game Changer
Matthew Sadler and Natasha Regan Game Changer AlphaZero’s Groundbreaking Chess Strategies and the Promise of AI New In Chess 2019 Contents Explanation of symbols 6 Foreword by Garry Kasparov �������������������������������������������������������������������������������� 7 Introduction by Demis Hassabis 11 Preface 16 Introduction ������������������������������������������������������������������������������������������������������������ 19 Part I AlphaZero’s history . 23 Chapter 1 A quick tour of computer chess competition 24 Chapter 2 ZeroZeroZero ������������������������������������������������������������������������������ 33 Chapter 3 Demis Hassabis, DeepMind and AI 54 Part II Inside the box . 67 Chapter 4 How AlphaZero thinks 68 Chapter 5 AlphaZero’s style – meeting in the middle 87 Part III Themes in AlphaZero’s play . 131 Chapter 6 Introduction to our selected AlphaZero themes 132 Chapter 7 Piece mobility: outposts 137 Chapter 8 Piece mobility: activity 168 Chapter 9 Attacking the king: the march of the rook’s pawn 208 Chapter 10 Attacking the king: colour complexes 235 Chapter 11 Attacking the king: sacrifices for time, space and damage 276 Chapter 12 Attacking the king: opposite-side castling 299 Chapter 13 Attacking the king: defence 321 Part IV AlphaZero’s -
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. -
Reinforcement Learning (1) Key Concepts & Algorithms
Winter 2020 CSC 594 Topics in AI: Advanced Deep Learning 5. Deep Reinforcement Learning (1) Key Concepts & Algorithms (Most content adapted from OpenAI ‘Spinning Up’) 1 Noriko Tomuro Reinforcement Learning (RL) • Reinforcement Learning (RL) is a type of Machine Learning where an agent learns to achieve a goal by interacting with the environment -- trial and error. • RL is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. • The purpose of RL is to learn an optimal policy that maximizes the return for the sequences of agent’s actions (i.e., optimal policy). https://en.wikipedia.org/wiki/Reinforcement_learning 2 • RL have recently enjoyed a wide variety of successes, e.g. – Robot controlling in simulation as well as in the real world Strategy games such as • AlphaGO (by Google DeepMind) • Atari games https://en.wikipedia.org/wiki/Reinforcement_learning 3 Deep Reinforcement Learning (DRL) • A policy is essentially a function, which maps the agent’s each action to the expected return or reward. Deep Reinforcement Learning (DRL) uses deep neural networks for the function (and other components). https://spinningup.openai.com/en/latest/spinningup/rl_intro.html 4 5 Some Key Concepts and Terminology 1. States and Observations – A state s is a complete description of the state of the world. For now, we can think of states belonging in the environment. – An observation o is a partial description of a state, which may omit information. – A state could be fully or partially observable to the agent. If partial, the agent forms an internal state (or state estimation). https://spinningup.openai.com/en/latest/spinningup/rl_intro.html 6 • States and Observations (cont.) – In deep RL, we almost always represent states and observations by a real-valued vector, matrix, or higher-order tensor. -
OLIVAW: Mastering Othello with Neither Humans Nor a Penny Antonio Norelli, Alessandro Panconesi Dept
1 OLIVAW: Mastering Othello with neither Humans nor a Penny Antonio Norelli, Alessandro Panconesi Dept. of Computer Science, Universita` La Sapienza, Rome, Italy Abstract—We introduce OLIVAW, an AI Othello player adopt- of companies. Another aspect of the same problem is the ing the design principles of the famous AlphaGo series. The main amount of training needed. AlphaGo Zero required 4.9 million motivation behind OLIVAW was to attain exceptional competence games played during self-play. While to attain the level of in a non-trivial board game at a tiny fraction of the cost of its illustrious predecessors. In this paper, we show how the grandmaster for games like Starcraft II and Dota 2 the training AlphaGo Zero’s paradigm can be successfully applied to the required 200 years and more than 10,000 years of gameplay, popular game of Othello using only commodity hardware and respectively [7], [8]. free cloud services. While being simpler than Chess or Go, Thus one of the major problems to emerge in the wake Othello maintains a considerable search space and difficulty of these breakthroughs is whether comparable results can be in evaluating board positions. To achieve this result, OLIVAW implements some improvements inspired by recent works to attained at a much lower cost– computational and financial– accelerate the standard AlphaGo Zero learning process. The and with just commodity hardware. In this paper we take main modification implies doubling the positions collected per a small step in this direction, by showing that AlphaGo game during the training phase, by including also positions not Zero’s successful paradigm can be replicated for the game played but largely explored by the agent. -
Monte-Carlo Tree Search As Regularized Policy Optimization
Monte-Carlo tree search as regularized policy optimization Jean-Bastien Grill * 1 Florent Altche´ * 1 Yunhao Tang * 1 2 Thomas Hubert 3 Michal Valko 1 Ioannis Antonoglou 3 Remi´ Munos 1 Abstract AlphaZero employs an alternative handcrafted heuristic to achieve super-human performance on board games (Silver The combination of Monte-Carlo tree search et al., 2016). Recent MCTS-based MuZero (Schrittwieser (MCTS) with deep reinforcement learning has et al., 2019) has also led to state-of-the-art results in the led to significant advances in artificial intelli- Atari benchmarks (Bellemare et al., 2013). gence. However, AlphaZero, the current state- of-the-art MCTS algorithm, still relies on hand- Our main contribution is connecting MCTS algorithms, crafted heuristics that are only partially under- in particular the highly-successful AlphaZero, with MPO, stood. In this paper, we show that AlphaZero’s a state-of-the-art model-free policy-optimization algo- search heuristics, along with other common ones rithm (Abdolmaleki et al., 2018). Specifically, we show that such as UCT, are an approximation to the solu- the empirical visit distribution of actions in AlphaZero’s tion of a specific regularized policy optimization search procedure approximates the solution of a regularized problem. With this insight, we propose a variant policy-optimization objective. With this insight, our second of AlphaZero which uses the exact solution to contribution a modified version of AlphaZero that comes this policy optimization problem, and show exper- significant performance gains over the original algorithm, imentally that it reliably outperforms the original especially in cases where AlphaZero has been observed to algorithm in multiple domains. -
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. -
Artificial Intelligence and Big Data – Innovation Landscape Brief
ARTIFICIAL INTELLIGENCE AND BIG DATA INNOVATION LANDSCAPE BRIEF © IRENA 2019 Unless otherwise stated, material in this publication may be freely used, shared, copied, reproduced, printed and/or stored, provided that appropriate acknowledgement is given of IRENA as the source and copyright holder. Material in this publication that is attributed to third parties may be subject to separate terms of use and restrictions, and appropriate permissions from these third parties may need to be secured before any use of such material. ISBN 978-92-9260-143-0 Citation: IRENA (2019), Innovation landscape brief: Artificial intelligence and big data, International Renewable Energy Agency, Abu Dhabi. ACKNOWLEDGEMENTS This report was prepared by the Innovation team at IRENA’s Innovation and Technology Centre (IITC) with text authored by Sean Ratka, Arina Anisie, Francisco Boshell and Elena Ocenic. This report benefited from the input and review of experts: Marc Peters (IBM), Neil Hughes (EPRI), Stephen Marland (National Grid), Stephen Woodhouse (Pöyry), Luiz Barroso (PSR) and Dongxia Zhang (SGCC), along with Emanuele Taibi, Nadeem Goussous, Javier Sesma and Paul Komor (IRENA). Report available online: www.irena.org/publications For questions or to provide feedback: [email protected] DISCLAIMER This publication and the material herein are provided “as is”. All reasonable precautions have been taken by IRENA to verify the reliability of the material in this publication. However, neither IRENA nor any of its officials, agents, data or other third- party content providers provides a warranty of any kind, either expressed or implied, and they accept no responsibility or liability for any consequence of use of the publication or material herein. -
Machine Theory of Mind
Machine Theory of Mind Neil C. Rabinowitz∗ Frank Perbet H. Francis Song DeepMind DeepMind DeepMind [email protected] [email protected] [email protected] Chiyuan Zhang S. M. Ali Eslami Matthew Botvinick Google Brain DeepMind DeepMind [email protected] [email protected] [email protected] Abstract 1. Introduction Theory of mind (ToM; Premack & Woodruff, For all the excitement surrounding deep learning and deep 1978) broadly refers to humans’ ability to rep- reinforcement learning at present, there is a concern from resent the mental states of others, including their some quarters that our understanding of these systems is desires, beliefs, and intentions. We propose to lagging behind. Neural networks are regularly described train a machine to build such models too. We de- as opaque, uninterpretable black-boxes. Even if we have sign a Theory of Mind neural network – a ToM- a complete description of their weights, it’s hard to get a net – which uses meta-learning to build models handle on what patterns they’re exploiting, and where they of the agents it encounters, from observations might go wrong. As artificial agents enter the human world, of their behaviour alone. Through this process, the demand that we be able to understand them is growing it acquires a strong prior model for agents’ be- louder. haviour, as well as the ability to bootstrap to Let us stop and ask: what does it actually mean to “un- richer predictions about agents’ characteristics derstand” another agent? As humans, we face this chal- and mental states using only a small number of lenge every day, as we engage with other humans whose behavioural observations. -
Towards Incremental Agent Enhancement for Evolving Games
Evaluating Reinforcement Learning Algorithms For Evolving Military Games James Chao*, Jonathan Sato*, Crisrael Lucero, Doug S. Lange Naval Information Warfare Center Pacific *Equal Contribution ffi[email protected] Abstract games in 2013 (Mnih et al. 2013), Google DeepMind devel- oped AlphaGo (Silver et al. 2016) that defeated world cham- In this paper, we evaluate reinforcement learning algorithms pion Lee Sedol in the game of Go using supervised learning for military board games. Currently, machine learning ap- and reinforcement learning. One year later, AlphaGo Zero proaches to most games assume certain aspects of the game (Silver et al. 2017b) was able to defeat AlphaGo with no remain static. This methodology results in a lack of algorithm robustness and a drastic drop in performance upon chang- human knowledge and pure reinforcement learning. Soon ing in-game mechanics. To this end, we will evaluate general after, AlphaZero (Silver et al. 2017a) generalized AlphaGo game playing (Diego Perez-Liebana 2018) AI algorithms on Zero to be able to play more games including Chess, Shogi, evolving military games. and Go, creating a more generalized AI to apply to differ- ent problems. In 2018, OpenAI Five used five Long Short- term Memory (Hochreiter and Schmidhuber 1997) neural Introduction networks and a Proximal Policy Optimization (Schulman et al. 2017) method to defeat a professional DotA team, each AlphaZero (Silver et al. 2017a) described an approach that LSTM acting as a player in a team to collaborate and achieve trained an AI agent through self-play to achieve super- a common goal. AlphaStar used a transformer (Vaswani et human performance. -
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: .