5 Big Predictions for Artificial Intelligence in 2017

Last year was huge for advancements in artificial intelligence and machine learning. But 2017 may well deliver even more. Here are five key things to look forward to. Positive reinforcement AlphaGo’s historic victory against one of the best Go players of all time, Lee Sedol, was a landmark for the field of AI, and especially for the technique known as deep reinforcement learning. Reinforcement learning takes inspiration from the ways that animals learn how certain behaviors tend to result in a positive or negative outcome. Using this approach, a computer can, say, figure out how to navigate a maze by trial and error and then associate the positive outcome—exiting the maze—with the actions that led up to it. This lets a machine learn without instruction or even explicit examples. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how play the game at an expert level. The hope is that reinforcement learning will now prove useful in many real-world situations. And the recent release of several simulated environments should spur progress on the necessary algorithms by increasing the range of skills computers can acquire this way. In 2017, we are likely to see attempts to apply reinforcement learning to problems such as automated driving and industrial robotics. Google has already boasted of using deep reinforcement learning to make its data centers more efficient. But the approach remains experimental, and it still requires time-consuming simulation, so it’ll be interesting to see how effectively it can be deployed. Dueling neural networks At the banner AI academic gathering held recently in Barcelona, the Neural Information Processing Systems conference, much of the buzz was about a new machine-learning technique known as generative adversarial networks.

Read More: Technology Review


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