ComputerLearning
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Tenacious robot ashamed of creator's performance, shows mankind how it's done (video)
Looks like researchers have made another step towards taking Skynet live: giving robots the groundwork for gloating. A Swiss team of misguided geniuses have developed learning algorithms that allow robot-kind to learn from human mistakes. Earthlings guide the robot through a flawed attempt at completing a task, such as catapulting a ball into a paper basket; the machine then extrapolates its goal, what went wrong in the human-guided example, and how to succeed, via trial and error. Rather than presuming human demonstrations represent a job well done, this new algorithm assumes all human examples are failures, ultimately using their bad examples to help the 'bot one-up its creators. Thankfully, the new algorithm is only being used with a single hyper-learning appendage; heaven forbid it should ever learn how to use the robot-internet.
Computer learning and computational neuroscience icon Dr. Leslie Valiant wins Turing Award
We've seen recently that computers are more than capable of kicking humanoids to the curb when it comes to winning fame and fortune, but it's still we humans who dole out the prizes, and one very brainy humanoid just won the best prize in computer science. That person is Leslie Valiant, and the prize is the fabled A.M. Turing Award. Dr. Valiant currently teaches at Harvard and over the years developed numerous algorithms and models for parsing and computer learning, including work to understand computational neuroscience. His achievements have helped make those machines smarter and better at thinking like we humans, but he's as of yet been unsuccessful in teaching them the most important thing: how to love.
