Figure 1: Typical double descent of the condition number (y axis) of a random data matrixdistributed asN(0,1): the condition number is worse whenn=d, better ifn > d(on the rightofn=d) and also better ifn < d(on the left ofn=d).
An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology’s grandest challenges — determining a protein’s 3D shape from its amino-acid sequence.
Recent advances give theoretical insight into why deep learning networks are successful. CBMM Director Tomaso Poggio, postdoc Andrzej Banburski and graduate student Qianli Liao look into the "black box" of deep learning to provide insights.
The RSS Keynote on July 15 was delivered by Josh Tenenbaum, Professor of Computational Cognitive Science at MIT BCS, CSAIL. Titled “It’s all in your head: Intuitive physics, planning, and problem-solving in brains, minds and machines”.
"Are we really going to bet that we can go back to life as normal without proper coronavirus tracking in place?" NYTimes article calls upon CBMM Memo 106, by Shai Shalev-Swartz and Amnon Shashua, for the discussion about herd immunity.
In this online webinar, Profs. Amnon Shashua and Shai Shalev-Shwartz will discuss their memorandum recently released covering their analysis of a risk-based selective quarantine model on Tuesday, March 31, 2020 @ 1:00pm ET. Registration required.
Manuscript released to the public assesses the safety of dividing the population into high- and low-risk to achieve herd immunity and whether the health system can support this. Information for the decision makers of the world.
Demis Hassabis and Amnon Shashua, CBMM External Advisory Committee members, are the 2020 Dan David Prize winners for artificial intelligence. They share $1M for work on learning from experience in gaming and intelligent real-time vision, respectively.