AI with AI
Episode 1.35: How to Train Your DrAIgon (for good, not for bad)
In recent news, Andy and Dave discuss a recent Brookings report on the view of AI and robots based on internet search data; a Chatham House report on AI anticipates disruption; Microsoft computes the future with its vision and principles on AI; the first major AI patent filings from DeepMind are revealed; biomimicry returns, with IBM using "analog" synapses to improve neural net implementation, and Stanford U researchers develop an artificial sensory nervous system; and Berkley Deep Drive provides the largest self-driving car dataset for free public download. Next, the topic of "hard exploration games with sparse rewards" returns, with a Deep Curiosity Search approach from the University of Wyoming, where the AI gets more freedom and reward from exploring ("curiosity") than from performing tasks as dictated by the researchers. From Cognition Expo 18, work from Martinez-Plumed attempts to "Forecast AI," but largely highlights the challenges in making comparisons due to the neglected, or un-reported, aspects of developments, such as the data, human oversight, computing cycles, and much more. From the Google AI Blog, researchers improve deep learning performance by finding and describing the transformation policies of the data and using that information to increase the amount and diversity of the training dataset. Then, Andy and Dave discuss attempts to use drone surveillance to identify violent individuals (for good reasons only, not for bad ones). And in a more sporty application, "AI enthusiast" Chintan Trivedi describes his efforts to train a bot to play a soccer video game, by observing his playing. Finally, Andy recommends an NSF workshop report, a book on AI: Foundations of Computational Agents, Permutation City, and over 100 video hours of the CogX 2018 conference.