Can large language models figure out the real world?
New test could help determine if AI systems that make accurate predictions in one area can understand it well enough to apply that ability to a different area.
Economics
The sweet taste of a new idea
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
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.
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.
Making the art world more accessible
The startup NALA, which began as an MIT class project, directly matches art buyers with artists.
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.
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.
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.
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.
Large language models don’t behave like people, even though we may expect them to
A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.