Personalization features can make LLMs more agreeable
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
Project AI Evidence will connect governments, tech companies, and nonprofits with world-class economists at MIT and across J-PAL’s global network to evaluate and improve AI solutions.
Read MoreAssociate Professor Rafael Gómez-Bombarelli has spent his career applying AI to improve scientific discovery. Now he believes we are at an inflection point.
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Read MoreMIT Sports Lab researchers are applying AI technologies to help figure skaters improve. They also have thoughts on whether five-rotation jumps are humanly possible.
Read MoreRemoving just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.
Read MoreMIT faculty join The Curiosity Desk to discuss football, math, Olympic figure skating, AI and the quest to cure ovarian cancer.
Read MoreEnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.
Read MoreHe joins Nikos Trichakis in guiding the cross-cutting initiative of the MIT Schwarzman College of Computing.
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