work of the future
Work of the Future Event of the Year
The 4th annual Congress was a virtual event that featured the final report from the MIT Task Force on the Work of the Future. Hosted by MIT's Task Force on Work of the Future, CSAIL, and Initiative on the Digital Economy, this year's Congress highlighted research findings from the MIT Task Force on Work of the Future's final report released in November 2020. Given the rapidly changing environment brought on by Covid-19, this topic is more important and relevant than ever.
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biggest tech breakthroughs
CSAIL article

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.

work of the future

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.

TEDx MIT
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.

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Teaching machines to see 3D
MIT news article

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.