Benchmarking framework reveals major safety risks of using AI in lab experiments
While artificial intelligence (AI) models have proved useful in some areas of science, like predicting 3D protein structures, a new study shows that it should not yet be trusted in many lab experiments. The study, published in Nature Machine Intelligence, revealed that all of the large-language models (LLMs) and vision-language models (VLMs) tested fell short on lab safety knowledge. Overtrusting these AI models for help in lab experiments can put researchers at risk.
As artificial intelligence models become increasingly prevalent and are integrated into diverse sectors like health care, finance, education, transportation, and entertainment, understanding how they work under the hood is critical. Interpreting the mechanisms underlying AI models enables us to audit them for safety and biases, with the potential to deepen…
From their early days at MIT, and even before, Emma Liu '22, MNG '22, Yo-whan “John” Kim '22, MNG '22, and Clemente Ocejo '21, MNG '22 knew they wanted to perform computational research and explore artificial intelligence and machine learning. “Since high school, I’ve been into deep learning and was…
What research can be pursued with small models trained to complete true programs? Typically, researchers study program synthesis via large language models (LLMs) which introduce issues such as knowing what is in or out of distribution, understanding fine-tuning effects, understanding the effects of tokenization, and higher demand on compute and…