A computer model of vision created by MIT neuroscientists designed these images that can stimulate very high activity in individual neurons.  Image: Pouya Bashivan
May 2, 2019 - 1:30 pm
Study shows that artificial neural networks can be used to drive brain activity. Anne Trafton | MIT News Office MIT neuroscientists have performed the most rigorous testing yet of computational models that mimic the brain’s visual cortex. Using their current best model of the brain’s visual neural network, the researchers designed a new way to precisely control individual neurons and populations of neurons in the middle of that network. In an...
MIT and Liberty Mutual Insurance announced a $25 million, five-year collaboration to support intelligence research at a meeting on Tuesday attended by Liberty Mutual Chairman and CEO David Long (left) and MIT President L. Rafael Reif.  Photo: Rose Lincoln
April 30, 2019 - 1:30 pm
Company announces $25 million, five-year collaboration. MIT and Liberty Mutual Insurance today announced a $25 million, five-year collaboration to support artificial intelligence research in computer vision, computer language understanding, data privacy and security, and risk-aware decision making, among other topics.  The new collaboration launched today at a meeting between leadership from both institutions, including Liberty Mutual Chairman...
Artistic rendering of artificial intelligence and deep learning visual recognition systems. Image by Christine Daniloff/MIT
April 29, 2019 - 5:30 pm
by Sabbi Lall Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there’s a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for AI developers, such as those improving self-driving car navigation. While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition...
Photo of Blake Richards
April 26, 2019 - 4:00 pm
Singleton Auditorium(46-3002)
Blake Richards, Assistant Professor, Associate Fellow of the Canadian Institute for Advanced Research (CIFAR)
Theoretical and empirical results in the neural networks literature demonstrate that effective learning at a real-world scale requires changes to synaptic weights that approximate the gradient of a global loss function. For neuroscientists, this means that the brain must have mechanisms...
robotic fingers shaping foam simulating sushi rice
April 23, 2019 - 12:00 pm
By Julio Cachila Robots are very useful. They are used in industrial applications, manufacturing, and assembly. Now, a new system will teach them to do something more refined. Something like molding sticky rice into that edible little thing called sushi. A group of MIT researchers, namely Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, and Antonio Torralba, have developed a system that improves a robot’s ability to mold certain...
April 16, 2019 - 4:00 pm
MIT 46-5165
Kohitij Kar
Title: Recurrent computations during visual object perception—investigating within and beyond the primate ventral stream
Recurrent circuits are ubiquitous in the primate ventral stream, that supports core object recognition — primate’s ability to rapidly categorize objects. While...
This work created by Sarah Schwettmann is on display in the MIT Museum Studio.  Image courtesy of Sarah Schwettmann
April 16, 2019 - 12:30 pm
PhD student Sarah Schwettmann explains how the study of visual perception can translate students’ creativity across domains. by Connie Blaszczyk | Center for Art, Science, and Technology Computational neuroscientist Sarah Schwettmann is one of three instructors behind the cross-disciplinary course 9.S52/9.S916 (Vision in Art and Neuroscience), which introduces students to core concepts in visual perception through the lenses of art and...
April 8, 2019 - 9:00 am
Together, deep learning and symbolic reasoning create a program that learns in a remarkably humanlike way. by Will Knight Excerpt: "Over the decades since the inception of artificial intelligence, research in the field has fallen into two main camps. The “symbolists” have sought to build intelligent machines by coding in logical rules and representations of the world. The “connectionists” have sought to construct artificial neural networks,...
Photo of Jon Bloom
April 2, 2019 - 4:00 pm
MIT Building 46-3002 (Singleton Auditorium)
Dr. Jon Bloom, Broad Institute
Abstract:  When trained to minimize reconstruction error, a linear autoencoder (LAE) learns the subspace spanned by the top principal directions but cannot learn the principal directions themselves. In this talk, I'll explain how this observation became the focus of a project on representation...
April 2, 2019 - 1:15 pm
Researchers combine statistical and symbolic artificial intelligence techniques to speed learning and improve transparency. Kim Martineau | MIT Quest for Intelligence A child who has never seen a pink elephant can still describe one — unlike a computer. “The computer learns from data,” says Jiajun Wu, a PhD student at MIT. “The ability to generalize and recognize something you’ve never seen before — a pink elephant — is very hard for machines.”...
photo of Prof. Antonio Torralba
April 1, 2019 - 11:45 am
Machine Learning and Artificial Intelligence (AI) will impact several sectors in a big way in the next ten years, especially healthcare, with several jobs to be affected and many other interesting jobs to emerge, an expert told Gulf Times. “Healthcare is an area where machine learning will have a huge impact," explained Dr Antonio Torralba, a professor of electrical engineering and computer science at the Massachusetts Institute of Technology (...
March 26, 2019 - 4:00 pm
Xavier Boix Bosch
Abstract: Recent progress in computer vision has led to new unresolved questions about their emergent properties. Understanding the emergent behaviour of computer vision algorithms can fuel the engineering of computer vision and help understand biological intelligence. In this talk, I will discuss...
March 22, 2019 - 4:00 pm
Julio Martinez-Trujillo
Abstract: The brain’s memory systems are like time machines for thought: they transport sensory experiences from the past to the present, to guide our current decisions and actions. Memories have been classified into long-term, stored for time intervals of days, months, or years, and short-term,...
Perception of a familiar face, such as Scarlett Johansson, is more robust than for unfamiliar faces, such as German celebrity Karoline Herferth.  Photos: Wikimedia Commons
March 22, 2019 - 12:00 pm
McGovern Institute researchers find that the brain starts to register gender and age before recognizing a face. Sabbi Lall | McGovern Institute for Brain Research Our brains are incredibly good at processing faces, and even have specific regions specialized for this function. But what face dimensions are we observing? Do we observe general properties first, then look at the details? Or are dimensions such as gender or other identity details...