@amazon.science
The latest news and research from Amazon's science community.
@amazon.science🎤 Can you out-rhyme the AI agent? Stop by the Amazon booth at @colmweb.org for a live AI rap battle demo, plus talks on multi-agent systems and responsible AI for science: https://amzn.to/4jsMYKM #COLM2026
@amazon.scienceAI-powered smart glasses that guide delivery drivers from van to the customer's doorstep, hands-free. Hear from Amazon applied scientist Yelin Kim on the computer vision and edge AI behind it at @colmweb.org. #COLM2026.
@amazon.scienceAmazon Scholar and UT Austin professor @mattlease.bsky.social explores how to harness AI for scientific discovery while assessing potential risks.
Catch his Expo Talk at @colmweb.org Thursday at 1pm PT. #COLM2026
@amazon.scienceDay 2 at @colmweb.org. Stop by the Amazon booth for talks on multi-agent orchestration, visual reasoning, and LLM agent evaluation: https://amzn.to/4jsMYKM #COLM2026
@mattlease.bsky.socialReposted by @amazon.scienceLooking forward to presenting this talk on Responsible AI for Science on Thursday at the 3rd Annual Conference on Language Modeling (COLM)! @colmweb.org #colm2026
@amazon.science
@nsfsimonscosmicai.bsky.social
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@utaustin.bsky.social
@amazon.scienceDay 1 at @colmweb.org. We're here all week with live research talks, meet-the-scientist sessions, and conversations on what's next for language models. Stop by the Amazon booth. #COLM2026
More info: https://amzn.to/4jsMYKM
@amazon.science"One workload is power-bound. The next workload is memory-bandwidth-bound. The next is memory-bound. It's one of the most interesting hardware design problems that we've seen in ages." Amazon SVP Peter DeSantis sat down with SemiAnalysis founder Dylan Patel at #AIInfraSummit:
@amazon.scienceAmazon Bio Discovery developed three AI approaches to accelerate antibody drug design: MochiBind (sequence-based affinity ranking), CA-MAP (developability prediction with batch effect correction), and an agent-guided design system with 46 lab-validated hits against a novel cancer target.
@amazon.scienceYears of iterating against the same benchmarks should, by textbook logic, produce overfitting. It largely doesn't. New research explains why: strategies that generalize can be expressed in too compact a form to allow memorization, while the ones that overfit don't survive a compression.
@aaroth.bsky.socialReposted by @amazon.scienceA blog post on some neat work with @zstevenwu.bsky.social and Martin Bertran: www.amazon.science/blog/why-don...
