Among the expected advancements in artificial intelligence (AI) for 2017 is progress in applying deep reinforcement learning to real-world challenges such as automated driving and industrial robotics.
Algorithmic innovation in reinforcement learning will be driven by the recent release of several simulated environments.
Meanwhile, generative adversarial networks are expected to advance the ability of computers to learn from unlabeled data and produce extremely realistic simulations.
A third likely trend is an explosion of Chinese innovation in AI and machine learning, with investors funding AI-focused startups and China's government pledging to allocate about $15 billion in AI funding by 2018.
Also expected this year is further evolution of AI systems' language-recognition and generation capabilities, built on techniques that have enabled significant progress in voice and image recognition.
Some researchers also predict an inevitable backlash this year against the heavy hype of AI technologies in 2016. They say the problem of hype is the unavoidable sense of disappointment when major breakthroughs fail to materialize, leading to the failure of overvalued startups.
From Technology Review
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