Home Page Spotlights

Photo of David Vogel
On Wed., April 12, 2017, David S. Vogel, an award-winning predictive modeling scientist, will discuss state-of-the-art machine learning techniques and the application of the these techniques to healthcare, recommendation systems, and finance.
Figure 7 Same as Figure 3, but all weights are collected from Layer 5
Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local minima. However, the practical observation is that, at least for the most successful Deep Convolutional Neural Networks (DCNNs)...
CBMM Memo 061: Full interpretation of minimal images
The goal in this work is to model the process of ‘full interpretation’ of object images, which is the ability to identify and localize all semantic features and parts that are recognized by human observers. The task is approached by dividing the ...
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
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. A class of deep convolutional networks represent an important special case...
The Convergence of Machine Learning and Artificial Intelligence Towards Enabling Autonomous Driving
Prof. Amnon Shashua, Co-founder, CTO and Chairman of Mobileye, talks about the machine learning technology behind Mobileye's success in the field of autonomous driving.
Mobileye logo
"Intel agreed on Monday to pay $15.3 billion for Mobileye, an Israeli technology company that specializes in making sensors and cameras for autonomous cars, as the global microchip giant tries to expand its reach in the fast-growing sector."
Prof. Jacob Feldman (Rutgers) will be speaking on Friday, April 7, 2017.
On March 23, 2017, select academic and industry leaders will convene at CBMM to provide guidance, advice, and direction for all of the Center's activities, consistent with its vision, goals, and objectives.
AAAI Spring Symposium Series Science of Intelligence: Computational Principles of Natural and Artificial Intelligence
How we may be able to replicate intelligence in machines, and how the brain produces intelligent behavior- is one of the greatest challenges in science and technology. There are many aspects of human intelligence which have been impossible so far to...
Marine Biology Lab, Woods Hole, MA
Applications are now due by Fri., April 7, 2017. This course aims to cross-educate computer engineers and neuroscientists; it is appropriate for graduate students, postdocs, and faculty in computer science and/or neuroscience.
Lung-on-a-chip, a product of Convergence research, quickly screens drugs for effectiveness and safety.
Two highly esteemed MIT professors publish a letter in Science magazine urging the new US administration to recognize the role of science and technology in the country’s infrastructure.
Gadi Geiger
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...
Speech representation, perception and recognition workshop
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.