Microsoft’s John Langford discusses multiworld testing at QCon NYC last June. The contextual bandit system increased clickthrough by 25 percent - and a few months later, Microsoft turned it into an open source Multiworld Testing Decision Service built on the Vowpal Wabbit machine learning system, that you can run on Azure.
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In 2016, Google started using DeepMind’s reinforcement learning to save power in some of its data centers by learning how to optimize around 120 different settings like how the fans and cooling systems run, adding up to a 15 percent improvement in power usage efficiency.Īnd without anyone really noticing, back in January 2016 Microsoft started using a very specific subset of reinforcement learning called contextual bandits to pick the personalized headlines for MSN.com something multiple machine learning systems had failed to improve. Reinforcement learning is the reason Microsoft just bought Maluuba, which Microsoft plans to use it to aid in understanding natural language for search and chatbots, as a stepping stone to general intelligence.Ĭommercial deployments are far rarer, though. Developers at a hackathon built a smart trash can be called AutoTrash that used reinforcement learning to sort compostable and recyclable rubbish into the right compartments. It’s how University of California Berkeley’s BRETT robot learns how to move its hands and arms to perform physical tasks like stacking blocks or screwing the lid onto a bottle, in just three hours (or ten minutes if it’s told where the objects are that it’s going to work with, and where they need to end up). Reinforcement learning is how DeepMind created the AlphaGo system that beat a high-ranking Go player (and has recently been winning online Go matches anonymously). It learns how to solve problems rather than being taught what solutions look like.
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It isn’t told by a trainer what to do and it learns what actions to take to get the highest reward in the situation by trial and error, even when the reward isn’t obvious and immediate. Reinforcement learning is where an agent learns by interacting with its environment.
But another technique, reinforcement learning, is just starting to make its way out of the research lab.
Almost every machine learning breakthrough you hear about (and most of what’s currently called “artificial intelligence”) is supervised learning where you start with a curated and labeled data set.
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