DeepMind gets good at games (and choosing them) – plus more bits and bytes from the world of machine learning

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Roundup If you can't get enough of machine learning news then here's a roundup of extra tidbits to keep your addiction ticking away. Read on to learn more about how DeepMind is helping Google's Play Store, and a new virtual environment to train agents safely from OpenAI. An AI recommendation system for the Google Play Store: Deepmind are helping Android users find new apps in the Google Play Store with the help of machine learning. "We started collaborating with the Play store to help develop and improve systems that determine the relevance of an app with respect to the user," the London-based lab said this week. Engineers built a model known as a candidate generator.


Last Week in AI

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Every week, my team at Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Gaming is one of the areas in which AI has shown the most progress in the last few years. From checker to Go to StarCraft, AI programs has regularly achieved superhuman performance and shown signs of creativity.


Weekly Papers Multi-Label Deep Forest (MLDF); Huawei UK Critiques DeepMind α-Rank

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Close to a thousand machine learning papers are published each and every week. On Fridays, Synced selects seven studies from the last seven days that present topical, innovative or otherwise interesting or important research that we believe may be of special interest to our readers. Author: Liang Yang, Xi-Zhu Wu, Yuan Jiang, Zhi-Hua Zhou from National Key Laboratory for Novel Software Technology, Nanjing University Abstract: In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these methods highly depends on the precise choice of model depth.


Ian Osband on Twitter

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This feels like a real breakthrough: https://arxiv.org/abs/1911.08265 Take the same basic algorithm as AlphaZero, but now *learning* its own simulator.


DeepMind Unveils MuZero, a New Agent that Mastered Chess, Shogi, Atari and Go Without Knowing the Rules Plow

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Games have become one of most efficient vehicles for evaluating artificial intelligence(AI) algorithms. For decades, games have built complex competition, collaboration, planning and strategic dynamics that are a reflection of the most sophisticated tasks that AI agents face in the real world. From Chess, to Go to StarCraft, games have become a great lab to evaluate the capabilities of AI agents in a safe and responsible manner. However, most of those great milestones started with agents that were trained on the rules of the game. There is a complementary subset of scenarios in which agents are presented with a new environment without prior knowledge of its dynamics.