David McCandless turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world.
The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. 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.
Armed with new computer systems that can crunch massive amounts of data more quickly, Baidu has built a new "deep learning" speech recognition system that it says works better than current systems that power Google, Apple, Microsoft, and others.
Le Monde.fr version mobile - Dans un entretien à la BBC, l'astrophysicien britannique Stephen Hawking craint que les humains, limités par une lente évolution biologique, ne puissent rivaliser avec l'intelligence artificielle.
Police departments are increasingly turning to predictive analytics to help them fight crime, and the early returns are positive, with double-digit drops in crime rates reported in many cities. So what’s next? According to big data analytics experts, police departments could spend their time and money more effectively by giving deep learning algorithms a role in the dispatch room.
Hui Wang has seen the nature of online fraud change a lot in the 11 years she’s been at PayPal. In fact, a continuous evolution of methods is kind of the nature of cybercrime. As the good guys catch onto one approach, the bad guys try to avoid detection by using another. Today, said Wang, PayPal’s senior…
In episode five of Talking Machines, we hear the first part of our conversation with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU). Ryan introduces us to the ideas in tensor factorization methods for learning latent variable models (which is both a tongue twister and and one of the new tools in ML). To find out more on the topic, the paper Tensor decompositions for learning latent variable models is a good place
Adam Gibson, an independent software engineer based in San Francisco, doesn’t want this new technology locked inside the biggest names on the net. He believes deep learning techniques should be available to any website, company, or developer interested in using them. And that’s why he’s launching a new startup called Skymind.
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