School of Humanities Arts and Social Sciences

Artificial Intelligence

MIT Department of Economics to launch James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work

With support from the Stone Foundation, the center will advance cutting-edge research and inform policy.

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Artificial IntelligenceMachine Learning

Bridging philosophy and AI to explore computing ethics

In a new MIT course co-taught by EECS and philosophy professors, students tackle moral dilemmas of the digital age.

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Artificial Intelligence

Aligning AI with human values

“We need to both ensure humans reap AI’s benefits and that we don’t lose control of the technology,” says senior Audrey Lorvo.

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Artificial IntelligenceIndustry

Introducing the MIT Generative AI Impact Consortium

The consortium will bring researchers and industry together to focus on impact.

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Artificial IntelligenceMachine Learning

Making the art world more accessible

The startup NALA, which began as an MIT class project, directly matches art buyers with artists.

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Artificial IntelligenceIndustry

MIT welcomes Frida Polli as its next visiting innovation scholar

The neuroscientist turned entrepreneur will be hosted by the MIT Schwarzman College of Computing and focus on advancing the intersection of behavioral science and AI across MIT.

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Artificial Intelligence

MIT affiliates named 2024 Schmidt Futures AI2050 Fellows

Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.

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Artificial Intelligence

What do we know about the economics of AI?

Nobel laureate Daron Acemoglu has long studied technology-driven growth. Here’s how he’s thinking about AI’s effect on the economy.

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Artificial Intelligence

Four from MIT named 2025 Rhodes Scholars

Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.

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Artificial IntelligenceMachine Learning

Despite its impressive output, generative AI doesn’t have a coherent understanding of the world

Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.

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