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News

3 Questions: John Leonard on the future of autonomous vehicles

MIT Task Force on the Work of the Future examines job changes in the AV transition and how training can help workers move into careers that support mobility systems.

News

What to Expect When You’re Expecting Robots

Book co-authored by Associate Professor Julie Shah and Laura Major SM ’05 explores a future populated with robot helpers.

News

Researchers present bold ideas for AI at MIT Generative AI Impact Consortium kickoff event

Presentations targeted high-impact intersections of AI and other areas, such as health care, business, and education.

News

“Hot Topics in Computing” convenes experts to discuss future of computing

Last week CSAIL hosted the first “Hot Topics in Computing” speaker series, a new monthly forum where computing experts hold discussions with community members on various topics in the computer science field.

News

Decoding Vision

How does the brain process an image? Research by CSAIL Principal Investigator Ruth Rosenholtz offers a new mathematical model for how the brain summarizes information received from the retina. According to Rosenholtz's model, the brain creates a statistical summary of each different aspect of the image at hand, while most models show the brain identifying boundaries, alignment and placing shapes until the brain can recognize an object.

Read more on Rosenholtz's work here.

News

A more effective way to train machines for uncertain, real-world situations

Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.

News

Robots dress humans without the full picture

MIT researchers design a robot that has a trick or two up its sleeve.

News

Pushing Computer Limits

CSAIL Associate Professor Scott Aaronson is delving into the next frontier of computing: quantum computing. The wild west of the computer world, quantum computing is a new means of expanding the boundaries of information processing.

News

New method uses crowdsourced feedback to help train robots

Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.

News

To build a better AI helper, start by modeling the irrational behavior of humans

A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.
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MIT CSAIL

Massachusetts Institute of Technology

Computer Science & Artificial Intelligence Laboratory

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