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Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
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AI in health should be regulated, but don’t forget about the algorithms, researchers say
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
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Researchers reduce bias in AI models while preserving or improving accuracy
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
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Study: Some language reward models exhibit political bias
Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.
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Enabling AI to explain its predictions in plain language
Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.