Institution
IBM
Company•Armonk, New York, United States•
About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.
Papers published on a yearly basis
Papers
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The Turing Institute1, University of Oxford2, Naver Corporation3, Pierre-and-Marie-Curie University4, Digital Europe5, Delft University of Technology6, Umeå University7, Technische Universität München8, University of Turin9, IBM10, University of Padua11, University of Edinburgh12, Bocconi University13, Catholic University of Leuven14, ETH Zurich15
TL;DR: The core opportunities and risks of AI for society are introduced; a synthesis of five ethical principles that should undergird its development and adoption are presented; and 20 concrete recommendations are offered to serve as a firm foundation for the establishment of a Good AI Society.
Abstract: This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
855 citations
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IBM1
TL;DR: This paper formalizes the sample selection bias problem in machine learning terms and study analytically and experimentally how a number of well-known classifier learning methods are affected by it.
Abstract: Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model is expected to make predictions. In many practical situations, however, this assumption is violated, in a problem known in econometrics as sample selection bias. In this paper, we formalize the sample selection bias problem in machine learning terms and study analytically and experimentally how a number of well-known classifier learning methods are affected by it. We also present a bias correction method that is particularly useful for classifier evaluation under sample selection bias.
854 citations
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TL;DR: It is proved that to solve Consensus, any failure detector has to provide at least as much information as W, and W is indeed the weakest failure detector for solving Consensus in asynchronous systems with a majority of correct processes.
Abstract: We determine what information about failures is necessary and sufficient to solve Consensus in asynchronous distributed systems subject to crash failures. In Chandra and Toueg [1996], it is shown that W, a failure detector that provides surprisingly little information about which processes have crashed, is sufficient to solve Consensus in asynchronous systems with a majority of correct processes. In this paper, we prove that to solve Consensus, any failure detector has to provide at least as much information as W. Thus, W is indeed the weakest failure detector for solving Consensus in asynchronous systems with a majority of correct processes.
853 citations
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IBM1
TL;DR: The latest version of TD-Gammon is now estimated to play at a strong master level that is extremely close to the world's best human players.
Abstract: TD-Gammon is a neural network that is able to teach itself to play backgammon solely by playing against itself and learning from the results, based on the TD(») reinforcement learning algorithm (Sutton 1988). Despite starting from random initial weights (and hence random initial strategy), TD-Gammon achieves a surprisingly strong level of play. With zero knowledge built in at the start of learning (i.e., given only a "raw" description of the board state), the network learns to play at a strong intermediate level. Furthermore, when a set of hand-crafted features is added to the network's input representation, the result is a truly staggering level of performance: the latest version of TD-Gammon is now estimated to play at a strong master level that is extremely close to the world's best human players.
852 citations
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IBM1
TL;DR: In this paper, the same authors showed that polypyrrole, poly-N-methylpyrron and poly-phenyl pyrrole polymers can be electrochemically driven between the oxidized (conducting) form and the neutral (insulating) form.
852 citations
Authors
Showing all 134658 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Anil K. Jain | 183 | 1016 | 192151 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Rodney S. Ruoff | 164 | 666 | 194902 |
Tobin J. Marks | 159 | 1621 | 111604 |
Jean M. J. Fréchet | 154 | 726 | 90295 |
Albert-László Barabási | 152 | 438 | 200119 |
György Buzsáki | 150 | 446 | 96433 |
Stanislas Dehaene | 149 | 456 | 86539 |
Philip S. Yu | 148 | 1914 | 107374 |
James M. Tour | 143 | 859 | 91364 |
Thomas P. Russell | 141 | 1012 | 80055 |
Naomi J. Halas | 140 | 435 | 82040 |
Steven G. Louie | 137 | 777 | 88794 |
Daphne Koller | 135 | 367 | 71073 |