Open AccessPosted Content
A Berkeley View of Systems Challenges for AI
Ion Stoica,Dawn Song,Raluca Ada Popa,David A. Patterson,Michael W. Mahoney,Randy H. Katz,Anthony D. Joseph,Michael I. Jordan,Joseph M. Hellerstein,Joseph E. Gonzalez,Ken Goldberg,Ali Ghodsi,David E. Culler,Pieter Abbeel +13 more
TLDR
This paper proposes several open research directions in systems, architectures, and security that can address challenges and help unlock AI's potential to improve lives and society.Abstract:
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production. These changes have been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems software and architectures, and by the broad accessibility of these technologies.
The next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (often mission-critical) decisions on our behalf, often in highly personalized contexts. Realizing this promise, however, raises daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, that are robust against sophisticated adversaries, and that can process ever increasing amounts of data across organizations and individuals without compromising confidentiality. These challenges will be exacerbated by the end of the Moore's Law, which will constrain the amount of data these technologies can store and process. In this paper, we propose several open research directions in systems, architectures, and security that can address these challenges and help unlock AI's potential to improve lives and society.read more
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Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
TL;DR: A comprehensive survey of the recent research efforts on edge intelligence can be found in this paper, where the authors review the background and motivation for AI running at the network edge and provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the edge.
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Deep Reinforcement Learning: An Overview
TL;DR: This work discusses core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration, and important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn.
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Deep Learning: A Critical Appraisal
TL;DR: Ten concerns for deep learning are presented, and it is suggested that deep learning must be supplemented by other techniques if the authors are to reach artificial general intelligence.
Journal ArticleDOI
Federated Learning via Over-the-Air Computation
TL;DR: A novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel and providing a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection.
Posted ContentDOI
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Miles Brundage,Shahar Avin,Jack Clark,Helen Toner,Peter Eckersley,Ben Garfinkel,Allan Dafoe,Paul Scharre,Thomas Zeitzoff,Bobby Filar,Hyrum S. Anderson,Heather M. Roff,Gregory C. Allen,Jacob Steinhardt,Carrick Flynn,Seán Ó hÉigeartaigh,Simon Beard,Haydn Belfield,Sebastian Farquhar,Clare Lyle,Rebecca Crootof,Owain Evans,Michael Page,Joanna J. Bryson,Roman V. Yampolskiy,Dario Amodei +25 more
TL;DR: The following organisations are named on the report: Future of Humanity Institute, University of Oxford, Centre for the Study of Existential Risk, Universityof Cambridge, Center for a New American Security, Electronic Frontier Foundation, OpenAI.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.