AI could reboot industries and make the economy more productive; it's already infusing many of the products we use daily. But a new report by more than 20 researchers from the Universities of Oxford and Cambridge, OpenAI, and the Electronic Frontier Foundation warns that the same technology creates new opportunities for criminals, political operatives, and oppressive governments--so much so that some AI research may need to be kept secret. Included in the report, The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, are four dystopian vignettes involving artificial intelligence that seem taken straight out of the Netflix science fiction show Black Mirror. An administrator for a building's robot security system spends some of her time on Facebook during the workday. There she sees an ad for a model train set and downloads a brochure for it.

Machine learning is an application of artificial intelligence that gives a system an ability to automatically learn and improve from experiences without being explicitly programmed. In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular). Based on the Stanford Computer Science course CS246 and CS35A, this book is aimed for Computer Science undergraduates, demanding no pre-requisites. This book has been published by Cambridge University Press. This book holds the prologue to statistical learning methods along with a number of R labs included.

The world is under threat from artificial intelligence and needs to do more to keep people safe, experts have urged. A new report compiled by 26 of the world's leading experts paints a terrifying picture of the world in the next 10 years. Physical attacks as well as those on our digital worlds and political system could drastically undermine the safety of humanity, it warns, and people must work together now if they want to keep the world safe. The use of artificial intelligence is likely to empower all kinds of people – including rogue states, criminals, and terrorists, the report warns. Boston Dynamics describes itself as'building dynamic robots and software for human simulation'.

Terrorists, rogue states and criminals could soon use artificial intelligence to undermine freedom and national security, warns a new report. Superhuman hacking, surveillance and persuasion are just some of the terrifying features of artificial intelligence in 2018. The security implications of'emerging technologies' were announced by 26 experts in the AI field. They predict rapid cybercrime growth, drone misuse and the unprecedented rise in the use of'bots' to manipulate everything from elections to the news agenda and social media. The security implications of'emerging technologies' were announced by 26 experts in the AI field, who forecasted rapid cybercrime growth, drone misuse and the unprecedented rise in the use of'bots' to manipulate everything from elections to the news agenda and social media'Artificial intelligence is a game changer and this report has imagined what the world could look like in the next five to ten years,' said Dr Seán Ó hÉigeartaigh, co-author and Executive Director of Cambridge University's Centre for the Study of Existential Risk.

Some say artificial intelligence is evil; others worry about AI replacing them. According to Elon Musk, CEO of Tesla and SpaceX as well as chairman at SolarCity, artificial intelligence (AI) is a "fundamental risk to the existence of human civilization," and "if you are not concerned about AI safety, you should be. Is it a good thing or a bad thing? The potential for job disruption is another area of considerable AI concern. In a 2013 study, University of Oxford researchers estimated that 47% of total U.S. employment is at risk because of "computerization." Clearly some of this computerization comes from AI technology. More recently, in a 2017 study, McKinsey suggested that "half of today's work activities could be automated by 2055, but this could happen up to 20 years earlier or later depending on the various factors, in addition to other wider economic conditions.... Activities most susceptible to automation involve physical activities in highly structured and predictable environments, as well as the collection and processing of data.

Life has gotten more convenient since 2012, when breakthroughs in machine learning triggered the ongoing frenzy of investment in artificial intelligence. Speech recognition works most of the time, for example, and you can unlock the new iPhone with your face. People with the skills to build things such systems have reaped great benefits--they've become the most prized of tech workers. But a new report on the downsides of progress in AI warns they need to pay more attention to the heavy moral burdens created by their work. It calls for urgent and active discussion of how AI technology could be misused.

Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that instance. For low class-label noise levels, especially when simple base learners are used, emphasis should be made on instances for which the disagreement rate is high. When more flexible classifiers are used and as the noise level increases, the emphasis on these uncertain instances should be reduced. In fact, at sufficiently high levels of class-label noise, the focus should be on instances on which the ensemble classifiers agree. The optimal type of emphasis can be automatically determined using cross-validation. An extensive empirical analysis using the beta distribution as emphasis function illustrates that vote-boosting is an effective method to generate ensembles that are both accurate and robust.

A standard introduction to online learning might place Online Gradient Descent at its center and then proceed to develop generalizations and extensions like Online Mirror Descent and secondorder methods. Here we explore the alternative approach of putting exponential weights (EW) first. We show that many standard methods and their regret bounds then follow as a special case by plugging in suitable surrogate losses and playing the EW posterior mean. For instance, we easily recover Online Gradient Descent by using EW with a Gaussian prior on linearized losses, and, more generally, all instances of Online Mirror Descent based on regular Bregman divergences also correspond to EW with a prior that depends on the mirror map. Furthermore, appropriate quadratic surrogate losses naturally give rise to Online Gradient Descent for strongly convex losses and to Online Newton Step. We further interpret several recent adaptive methods (iProd, Squint, and a variation of Coin Betting for experts) as a series of closely related reductions to exp-concave surrogate losses that are then handled by Exponential Weights. Finally, a benefit of our EW interpretation is that it opens up the possibility of sampling from the EW posterior distribution instead of playing the mean. As already observed by Bubeck and Eldan (2015), this recovers the best-known rate in Online Bandit Linear Optimization.

Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic. However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter. In this paper, we propose a novel actor-critic method called the guide actor-critic (GAC). GAC firstly learns a guide actor that locally maximizes the critic and then it updates the policy parameter based on the guide actor by supervised learning. Our main theoretical contributions are two folds. First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic. Second, we show that the deterministic policy gradient method is a special case of GAC when the Hessians are ignored. Through experiments, we show that our method is a promising reinforcement learning method for continuous controls.

The roles played by learning and memorization represent an important topic in deep learning research. Recent work on this subject has shown that the optimization behavior of DNNs trained on shuffled labels is qualitatively different from DNNs trained with real labels. Here, we propose a novel permutation approach that can differentiate memorization from learning in deep neural networks (DNNs) trained as usual (i.e., using the real labels to guide the learning, rather than shuffled labels). The evaluation of weather the DNN has learned and/or memorized, happens in a separate step where we compare the predictive performance of a shallow classifier trained with the features learned by the DNN, against multiple instances of the same classifier, trained on the same input, but using shuffled labels as outputs. By evaluating these shallow classifiers in validation sets that share structure with the training set, we are able to tell apart learning from memorization. Application of our permutation approach to multi-layer perceptrons and convolutional neural networks trained on image data corroborated many findings from other groups. Most importantly, our illustrations also uncovered interesting dynamic patterns about how DNNs memorize over increasing numbers of training epochs, and support the surprising result that DNNs are still able to learn, rather than only memorize, when trained with pure Gaussian noise as input.