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Google and Korean Baduk (Chinese Go) Association (KBA) today held the “Google Deepmind Challenge Match Press Briefing” to announce the details on the upcoming Go games between Sedol Lee (9-dan) and AlphaGo, a computer AI by Google DeepMind.
Digital Baby Project's Aim: Computers That See Like Humans
"On Wednesday, DeepMind, a research organization that operates under the umbrella of Alphabet, reported that a program combining two separate algorithms had soundly defeated a high-ranking professional Go player in a series of five matches."
On Friday, Jan. 15th, 2016, the Center for Brains, Minds & Machines hosted a day of talks displaying the collaboration and interaction between Neuroscientists and Computer Scientists at MIT. The largest auditorium in building 46 was packed with interest.
We have assembled a stellar list of speakers at the intersection of Neuroscience and AI from both sides of Vassar Street who will give an account of how this multi-disciplinary interaction affects their work.
Prof. Matt Wilson will be teaching an ethics course during MIT IAP 2016, see details included below. CBMM grad students and postdocs, who have not yet completed a similar ethics course, are encouraged to register.
Machines that learn like people Algorithms could learn to recognize objects from a few examples, not millions; may better model human cognition.
“Our findings are hard to reconcile with the idea that music piggybacks entirely on neural machinery that is optimized for other functions, because the neural responses we see are highly specific to music,” says Nancy Kanwisher.
Researchers from the Kanwisher and McDermott Labs discuss research which identified a neural population highly selective for music.
“With all the progress in machine learning, it’s amazing what you can do with lots of data and faster computers,” said Joshua B. Tenenbaum.
"Human-level concept learning through probabilistic program induction" by Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum has been published in the Dec. 11, 2015 issue of Science.
"For the first time we think we have a machine system that can learn a large class of visual concepts in ways that are hard to distinguish from human learners," said Joshua Tenenbaum
Prof. Joshua Tenenbaum (CBMM Research Thrust Leader) and Tejas Kulkarni (CBMM Siemens Graduate Fellow) are helping to organize a workshop for NIPS 2015, the Workshop on "Black Box Learning and Inference". Abstract submission deadline: Oct. 2, 2015
Prof. Sam Gershman (CBMM, Harvard) and Prof Noah Goodman (CBMM, Stanford) are helping organize this workshop. Abstract submission deadline: Oct. 28, 2015