Page 11: Research news on Trustworthy machine learning

Trustworthy machine learning addresses methods for training and deploying models that are secure, privacy-preserving, and robust to manipulation. Work in this area develops federated and decentralized learning schemes, cryptographic and homomorphic encryption frameworks, and privacy-preserving compression to protect data and models. It also studies adversarial example generation and defenses, certified unlearning, bias and spurious correlation mitigation, and the use of synthetic and filtered data. Applications span fraud and cyberattack detection, fake news and deception detection, and secure automation systems.

Hi Tech & Innovation

Researcher develops generative learning model to predict falls

In a study published in the journal Information Systems Research, Texas Tech University's Shuo Yu and his collaborators developed a generative machine learning model to detect instability before a fall occurs. The hope is ...

Energy & Green Tech

Practical changes could reduce AI energy demand by up to 90%

Artificial intelligence (AI) can be made more sustainable by making practical changes, such as reducing the number of decimal places used in AI models, shortening responses, and using smaller AI models, according to research ...

Machine learning & AI

New method can teach AI to admit uncertainty

In high-stakes situations like health care—or weeknight "Jeopardy!"—it can be safer to say "I don't know" than to answer incorrectly. Doctors, game show contestants, and standardized test-takers understand this, but most ...

Energy & Green Tech

AI model shortens the development time of new materials

Time-consuming testing and computer simulations are bottlenecks in the design of new materials. A thesis from the University of Gothenburg aims to develop an AI model that can efficiently determine the durability and strength ...

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