Given that our smartphones have largely become appendages over the last decade, it’s hard to imagine that ten years ago there was no Instagram, Uber, TikTok or Tinder. The ways we move, shop, eat and communicate continue to evolve thanks to the technologies we use. It can be easy to forget how quickly things have changed - so let’s turn back the clocks and reminisce about some of the computing breakthroughs that have transformed our lives in the ’10s.
Cameras and computers together can conquer some seriously stunning feats. Giving computers vision has helped us fight wildfires in California, understand complex and treacherous roads — and even see around corners.
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are working on the problem, and have developed a new system called “Minerva” that allows multiple people to stream video over a single network with minimal buffering and pixelation.
Continue the conversation of AI and the Future of Work
After the AI and the Future of Work Congress on Nov 21, keep the conversation going! Cap off your time on campus by participating in the AI & Work of the Future Unconference on Friday, November 22.
Join fellow participants and bring your biggest questions and most innovative ideas to this half-day event. No pre-determined topics, keynotes, or panels - we’ll amplify and facilitate the AI and future of work-driven conversations that matter most to you and your organization.
Technology as a vector for positive change | Technology for a better world
CSAIL recently established the TEDxMIT series. The TEDxMIT events will feature talks about important and impactful ideas by members of the broader MIT community.
This event is organized by Daniela Rus and John Werner, in collaboration with a team of undergraduate students led by Stephanie Fu and Rucha Keklar.
MIT CSAIL unsealed a special time capsule from 1999 after a self-taught programmer Belgium solved a puzzle devised by MIT professor and famed cryptographer Ron Rivest.
From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes.
Amateur and professional musicians alike may spend hours pouring over YouTube clips to figure out exactly how to play certain parts of their favorite songs. But what if there were a way to play a video and isolate the only instrument you wanted to hear?
