Michigan Engineering | University of Michigan Smarter use of processor speeds saves energy without compromising training speed and performance.
The post Up to 30% of the power used to train AI is wasted. Here’s how to fix it. appeared first on Michigan Engineering News.
Up to 30% of the power used to train AI is wasted. Here’s how to fix it.
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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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Four election vulnerabilities uncovered by a Michigan Engineer
All have solutions, some are implemented.
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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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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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Improving traffic signal timing with a handful of connected vehicles
Communities could reduce costs and cut vehicle emissions—all in the name of shortening your trip.
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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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U-M team receives NIH grant for collaborative research to speed ARDS diagnosis
University of Michigan researchers examine if molecular compounds in exhaled breath could lead to improved diagnosis and tracking of acute respiratory distress syndrome (ARDS).
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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.
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