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“Co-LLM” uses a general-purpose large language model to start replying to a prompt, with a “switch variable” intervening at certain words to call upon a more accurate answer from the expert model. Credit: Alex Shipps/MIT CSAIL
Ever been asked a question you only knew part of the answer to? To give a more informed response, your best move would be to phone a friend with more knowledge on the subject.
This collaborative process can also help large language models (LLMs) improve their accuracy. Still, it’s been difficult to teach LLMs to recognize when they should collaborate with another model on an answer...
Holding the screen up to an array of magnets of different strengths can rewrite the magnetic properties of the pixels in targeted areas of the screen. Different arrays of magnets will program different images into the device. Credit: Jeremy Little, Michigan Engineering
A flexible screen inspired in part by squid can store and display encrypted images like a computer—using magnetic fields rather than electronics. The research is reported in Advanced Materials by University of Michigan engineers.
“It’s one of the first times where mechanical materials use magnetic fields for system-level encryption, information processing and computing...
New software simulates complex wave scattering for metamaterial design. Could invisibility cloaks become a reality? New research brings this science fiction concept a step closer, with a breakthrough software package that simulates how waves interact with complex materials.
A new software package developed by researchers at Macquarie University can accurately model the way waves — sound, water or light — are scattered when they meet complex configurations of particles.
This will vastly improve the ability to rapidly design metamaterials — exciting artificial materials used to amplify, block or deflect waves.
The findings, published in the journal Proceedings of the Royal Society A on 19 June 2024, demonstrated the use of TMATSOLVER — a multipole-based tool that models interac...
Newly discovered role of phase separation can help develop memory devices for energy-efficient AI computing. Phase separation, when molecules part like oil and water, works alongside oxygen diffusion to help memristors — electrical components that store information using electrical resistance — retain information even after the power is shut off, according to a University of Michigan led study recently published in Matter.
Up to this point, explanations have not fully grasped how memristors retain information without a power source, known as nonvolatile memory, because models and experiments do not match up.
“While experiments have shown devices can retain information for over 10 years, the models used in the community show that information can only be retained for a few hours,”...
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