Jul 13, 2018
In the second part of this epic podcast, Andy and Dave continue their discussion with research from MIT, Vienna University of Technology, and Boston University, which uses human brainwaves and hand gestures to instantly correct robot mistakes. The research uses a combination of electroencephalogram (EEG, brain signals) and electromyogram (EMG, muscle signals) in combination to allow a human (without training) to provide corrective input to a robot while it performs tasks. On a related topic, MIT’s Picower Institute for Learning and Memory demonstrated the rules for human brain plasticity, by showing that when one synapse connection strengthens, the immediately neighboring synapses weaken; while suspected for some time, this research showed for the first time how this balance works. Then, research from Stanford and Berkley introduces a Taskonomy, a system for disentangling task transfer learning. This structured approach maps out 25 different visual tasks to identify the conditions under which transfer learning works from one task to another; such a structure would allow data in some dimensions to compensate for the lack of data in other dimensions. Next up, OpenAI has developed an AI tool for spotting photoshopped photos, by examining three types of manipulation techniques (splicing, copy-move, and removal), and by also examining local noise features. Researchers at Stanford have used machine learning to recreate the periodic table of elements after providing the system with a database of chemical formulae. And finally, Andy and Dave wrap up with a selection of papers and other media, including CNAS’s AI: What Every Policymaker Needs to Know; a beautifully-done tutorial on machine learning; the Question for AI by Nilsson; Nonserviam by Lem; IPI’s Governing AI; the US Congressional Hearing on the Power of AI; and Twitch Plays Robotics.