Tag: Machine Learning
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Between rain and snow, machine learning finds 9 precipitation types
Leveraging 1.5M minutes of precipitation data and a nonlinear method to handle complex relationships between variables, the team created a new classification system
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Quantum chemistry: Making key simulation approach more accurate
Density functional theory is limited by a mystery at its heart: the universal exchange-correlation functional. U-M researchers are trying to uncover it.
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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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Improving AI models: Automated tool detects silent errors in deep learning training
TrainCheck uses training invariants to find the root cause of hard-to-detect errors before they cause downstream problems, saving time and resources.
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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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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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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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Versatile knee exo for safer lifting
Helping out the quad muscles kept study participants lifting safely despite fatigue, with an algorithm that smoothly shifts between lifting and carrying tasks.
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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.
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Understanding attention in large language models
How do chatbots based on the transformer architecture decide what to pay attention to in a conversation? They’ve made their own machine learning algorithms to tell them.
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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.