MIT scientists investigate memorization risk in the age of clinical AI
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
Privacy
The approach maintains an AI model’s accuracy while ensuring attackers can’t extract secret information.
Read MoreBridging 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.
Read MoreIntroducing the MIT Generative AI Impact Consortium
The consortium will bring researchers and industry together to focus on impact.
Read MoreNew security protocol shields data from attackers during cloud-based computation
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
Read MoreResearchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
Read More3 Questions: How to prove humanity online
AI agents could soon become indistinguishable from humans online. Could “personhood credentials” protect people against digital imposters?
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