Tag: Generative AI
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Generative AI uses more energy on non-English text
Models can use up to 15 times more energy to process and generate text in some of the world’s most widely spoken languages, often with lower-quality output.
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Optimizing AI energy consumption throughout its entire lifecycle
Funded with $1.83M from DARPA, University of Michigan Engineering leads a project that aims to cut AI’s energy needs by up to half.
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AI training energy reduced up to 50% with U-M software
NVIDIA, the world’s leading AI computing company, is evaluating how to add the energy-saving software to its open-source training platform.
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AI energy use: New tools show which model consumes the most power, and why
Software can help developers assess AI model energy use, which is necessary to lower costs and strain on the grid.
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AI in manufacturing: 10 teams launch projects with internal funding
Seed-networking events foster collaboration in key research areas.
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AI tool predicts battery cycle life with just a few days’ data
A ‘learner,’ ‘interpreter’ and ‘oracle’ work together with minimal experiments to draw parallels between historical data and new battery designs.
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byLLM: adding AI to software without the prompt engineering
When using byLLM, developers completed tasks over three times faster and wrote 45% fewer lines of code when creating applications like chatbots and virtual assistants.
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Large language models and research progress: A Q&A with Ricardo Vinuesa
Guidelines for responsible LLM use to help, not hinder, research progress.
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New AI minor launches for students across disciplines
The minor enables students from disciplines outside computer science to explore and apply AI in their own fields.
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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.