Tag: Generative AI
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This digital hand enables hands-free virtual reality
More than just a stand-in, the AI-powered agent can complete tasks by following simple voice commands that don’t include nitty-gritty details.
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AI system discovers visual categories while adapting to new contexts
Open ad-hoc categorization approach combines language guidance with visual clustering to learn contextualized features for flexible image interpretation.
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AI leaderboards can be trustworthy by following these tips
Methods for ranking chess players and athletes don’t always translate to AI. U-M researchers identify and outline best practices.
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Conference: Scientific discovery in the age of AI
Experts from academia, industry and government discussed the growing utility of generative AI in science and what’s coming next.
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AI symposium: Michigan Engineering speakers share how they use AI in research
In addition to making predictions and scientific discoveries, engineers at the MIDAS symposium discussed improving AI’s interpretability and preventing misuse.
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U-Michigan announces most advanced AI research complex with historic Los Alamos alliance
The planned facility for high-performance computing and AI research has secured $100M from the state.
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AI, computation and scientific discovery: A Q&A with Karthik Duraisamy
The director of the Michigan Institute for Computational Discovery and Engineering discusses the institute’s past and future.
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Up to 30% of the power used to train AI is wasted. Here’s how to fix it.
Smarter use of processor speeds saves energy without compromising training speed and performance.
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Real-time descriptions of surroundings for people who are blind
The quick and clear mental image of the real world helps people who are blind or have low vision focus on other tasks, or just enjoy the things around them.
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OptoGPT for improving solar cells, smart windows, telescopes and more
Taking advantage of the transformer neural networks that power large language models, engineers can get recipes for materials with the optical properties they need.
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Biases in large image-text AI model favor wealthier, Western perspectives
AI model that pairs text, images performs poorly on lower-income or non-Western images, potentially increasing inequality in digital technology representation.