Home Page Spotlights
Not having a college degree didn’t stop Gadi Geiger from becoming a neuroscientist—or serving as the go-to guy for career advice in the Poggio Lab.
Whether, and to what extent, what is learned in the college classroom (if left unused) is likely to be mostly forgotten a “few” years after graduation. How this work can influence practical learning engineering at scale, with examples from Kaplan.
We brought together AI researchers, entrepreneurs, and thought leaders for our second Beneficial AI Conference, held in Asilomar, California. Speakers and panelists discussed the future of AI, economic impacts, legal issues, ethics, and more...
The focus of the workshop is on the computations and learning involved in human speech understanding that are required for speech-enabled machines, following CBMM’s mission to understand intelligence in brains and replicate it in machines.
Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal ...
Daniel Rockmore - Professor of Mathematics, William H. Neukom 1964 Distinguished Professor of Computational Science, Director of the Neukom Institute for Computational Science, Dartmouth College - discusses, how do we program moral philosophy and values?
Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing social interactions. ...
The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. ...
[December 16, 2016] Read Prof. Tomaso Poggio's article in the lastest Supplement to Science - Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience - discussing the integral part multiple disciplines working together.
We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and (2) recurrent learning.
article by Larry Hardesty, MIT News Office, December 1, 2016
Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? One notable instance of this discrepancy is that tasks where the candidate hypotheses are explicitly available result ...
Recordings of the lectures from June 2016's BMM Workshop in Sestri Levante, Italy are now being uploaded to the website. Eight (8) videos are available now with more coming soon.
We present a study on two key characteristics of human syntactic annotations: anchoring and agreement.
The prize, given by Columbia University, recognizes outstanding research contributions in the field of neuroscience.