Michigan Engineering | University of Michigan Smarter use of processor speeds saves energy without compromising training speed and performance.
Up to 30% of the power used to train AI is wasted. Here’s how to fix it.
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Precision health and advanced communications: €9M ($10M) for bio-inspired nanoparticles on demand
Advanced microscopy techniques and AI models will help design complex nanoparticles for specific biological targets with less trial and error.
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Accounting for bias in medical data helps prevent AI from amplifying racial disparity
Some sick Black patients are likely labeled as “healthy” in AI datasets due to inequitable medical testing.
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$15M for game theory with AI agents, quantum semiconductors for microelectronics and photonics
The DoD funds efforts to incorporate AI agents into game theory and develop microelectronics that can withstand a hot day on Venus or carry quantum information.
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‘You all are going to create things that astonish us’
OpenAI CEO Sam Altman spoke to University of Michigan students about the future of AI and its implications for education and industry.
The post ‘You all are going to create things that astonish us’ appeared first on Michigan Engineering News.
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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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AI chips could get a sense of time
Timekeeping in the brain is done with neurons that relax at different rates after receiving a signal; now memristors—hardware analogues of neurons—can do that too.
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Auto industry deadlines loom for impaired-driver detection tech, U-M offers a low-cost solution
Current technologies already in use could help prevent crashes and deaths linked to impaired driving.
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Widely used AI tool for early sepsis detection may be cribbing doctors’ suspicions
When using only data collected before patients with sepsis received treatments or medical tests, the model’s accuracy was no better than a coin toss.
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Clinicians could be fooled by biased AI, despite explanations
Regulators pinned their hopes on clinicians being able to spot flaws in explanations of an AI model’s logic, but a study suggests this isn’t a safe approach.