A new MIT study finds “health knowledge graphs,” which show relationships between symptoms and diseases and are intended to help with clinical diagnosis, can fall short for certain conditions and patient populations. The results also suggest ways to boost their performance.
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.
More than 50 million Americans suffer from sleep disorders, and diseases including Parkinson’s and Alzheimer’s can also disrupt sleep. Diagnosing and monitoring these conditions usually requires attaching electrodes and a variety of other sensors to patients, which can further disrupt their sleep.
Today’s 3-D printers have a resolution of 600 dots per inch, which means that they could pack a billion tiny cubes of different materials into a volume that measures just 1.67 cubic inches. Such precise control of printed objects’ microstructure gives designers commensurate control of the objects’ physical properties — such as their density or strength, or the way they deform when subjected to stresses. But evaluating the physical effects of every possible combination of even just two materials, for an object consisting of tens of billions of cubes, would be prohibitively time consuming.
The data captured by today’s digital cameras is often treated as the raw material of a final image. Before uploading pictures to social networking sites, even casual cellphone photographers might spend a minute or two balancing color and tuning contrast, with one of the many popular image-processing programs now available.
In recent years engineers have been developing new technologies to enable robots and humans to move faster and jump higher. Soft, elastic materials store energy in these devices, which, if released carefully, enable elegant dynamic motions. Robots leap over obstacles and prosthetics empower sprinting. A fundamental challenge remains in developing these technologies. Scientists spend long hours building and testing prototypes that can reliably move in specific ways so that, for example, a robot lands right-side up upon landing a jump.
On October 16, 2019, Prof. David Patterson, UC Berkeley professor emeritus, Google distinguished engineer, and RISC-V Foundation vice-chair, gave a Dertouzos Distinguished Lecture at CSAIL / MIT, entitled 'Domain Specific Architectures for Deep Neural Networks: Three Generations of Tensor Processing Units (TPUs).'
Septmeber 18, 2019 - Prof. Yoshua Bengio, Prof. University of Montreal, and Scientific Director, Mila, gave a Dertouzos Distinguished Lecture at CSAIL entitled 'Learning High-Level Representations for Agents'
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)"