Honda Research Institute USA seeks to develop intelligent systems that use curiosity to understand people’s needs and empower human capability through cross-disciplinary research that aims to advance breakthroughs in artificial cognition.
On Wednesday, October 31 MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) will be hosting a special one-day conference with BT to convene security professionals, government officials and academic experts to talk about key issues in cybersecurity.
Congratulations to this year's Nobel Prize winners! While computer science has no formal Nobel Prize, the Association for Computing Machinery’s A.M. Turing Award is often described as “the Nobel Prize of computing.”Over the years, more than a dozen CSAIL-affiliated computer scientists have receive the award - including four of the last nine winners.
Read below for more information about some of the honorees.
Google AI’s Jeff Dean has a seemingly straightforward objective: he wants to use a collection of trainable mathematical units organized in layers to solve complicated tasks that will ultimately benefit many parts of society.
For all the progress made in self-driving technologies, there still aren’t many places where they can actually drive. Companies like Google only test their fleets in major cities where they’ve spent countless hours meticulously labeling the exact 3-D positions of lanes, curbs, off-ramps, and stop signs.
Every spring, engineering students from MIT and law students from Georgetown University overcome the distance between their institutions and disciplines in a semester-long flurry of virtual classroom meetings and late-night Google hangout sessions, culminating in presentations to policy experts in DC.
This past year MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) was at the forefront of many diverse technological innovations covering a breadth of topics, from healthcare and cybersecurity to self-driving cars.
Babies as young as 10 months can assess how much someone values a particular goal by observing how hard they are willing to work to achieve it, according to a new study from MIT and Harvard University.
Artificial intelligence (AI) in the form of “neural networks” are increasingly used in technologies like self-driving cars to be able to see and recognize objects. Such systems could even help with tasks like identifying explosives in airport security lines.
Even as robots become increasingly common, they remain incredibly difficult to make. From designing and modeling to fabricating and testing, the process is slow and costly: Even one small change can mean days or weeks of rethinking and revising important hardware. But what if there were a way to let non-experts craft different robotic designs — in one sitting?
We’ve all experienced two hugely frustrating things on YouTube: our video either suddenly gets pixelated, or it stops entirely to rebuffer. Both happen because of special algorithms that break videos into small chunks that load as you go. If your internet is slow, YouTube might make the next few seconds of video lower resolution to make sure you can still watch uninterrupted — hence, the pixelation. If you try to skip ahead to a part of the video that hasn’t loaded yet, your video has to stall in order to buffer that part.
September 26, 2018 - Vladimir Vapnik of University of London and Columbia University gave a Dertouzos Distinguished Lecture titled "Learning Using Statistical Invariants (Revision of Machine Learning Problem)"