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
TLDR
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.Abstract:
Artificial intelligence and machine learning capabilities are growing at an unprecedented rate. These technologies have many widely beneficial applications, ranging from machine translation to medical image analysis. Countless more such applications are being developed and can be expected over the long term. Less attention has historically been paid to the ways in which artificial intelligence can be used maliciously. This report surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats. We analyze, but do not conclusively resolve, the question of what the long-term equilibrium between attackers and defenders will be. We focus instead on what sorts of attacks we are likely to see soon if adequate defenses are not developed.read more
Citations
More filters
Journal ArticleDOI
High-performance medicine: the convergence of human and artificial intelligence
TL;DR: Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
Journal ArticleDOI
Artificial Intelligence: the global landscape of ethics guidelines.
TL;DR: A global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented.
Journal Article
The Net Delusion: The Dark Side of Internet Freedom
TL;DR: The Net Delusion: The Dark Side of Internet Freedom by Evgeny Morozov New York: Public Affairs, 2011 409 pages $16.99 [ILLUSTRATION OMITTED] as discussed by the authors.
Posted Content
CTRL: A Conditional Transformer Language Model for Controllable Generation
TL;DR: CTRL is released, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior, providing more explicit control over text generation.
Proceedings ArticleDOI
Deepfake Video Detection Using Recurrent Neural Networks
David Guera,Edward J. Delp +1 more
TL;DR: A temporal-aware pipeline to automatically detect deepfake videos is proposed that uses a convolutional neural network to extract frame-level features and a recurrent neural network that learns to classify if a video has been subject to manipulation or not.
References
More filters
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
Mastering the game of Go without human knowledge
David Silver,Julian Schrittwieser,Karen Simonyan,Ioannis Antonoglou,Aja Huang,Arthur Guez,Thomas Hubert,Lucas Baker,Matthew Lai,Adrian Bolton,Yutian Chen,Timothy P. Lillicrap,Fan Hui,Laurent Sifre,George van den Driessche,Thore Graepel,Demis Hassabis +16 more
TL;DR: An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
Journal ArticleDOI
Machine learning: Trends, perspectives, and prospects
TL;DR: The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
Proceedings ArticleDOI
Protocols for secure computations
TL;DR: This paper describes three ways of solving the millionaires’ problem by use of one-way functions (i.e., functions which are easy to evaluate but hard to invert) and discusses the complexity question “How many bits need to be exchanged for the computation”.
Journal ArticleDOI
Experimental evidence of massive-scale emotional contagion through social networks
TL;DR: The results indicate that emotions expressed by others on Facebook influence the authors' own emotions, constituting experimental evidence for massive-scale contagion via social networks, and suggest that the observation of others' positive experiences constitutes a positive experience for people.