Amazon Science
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SharpZO enables edge AI fine-tuning using only forward passes. The approach achieves 7% higher accuracy than existing low-memory methods and converges in as little as one-tenth the time: https://amzn.to/4pLQek8
Fine-tuning vision-language models on memory-constrained devices
A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than existing techniques.
www.amazon.science
January 12, 2026 at 8:33 PM
SharpZO enables edge AI fine-tuning using only forward passes. The approach achieves 7% higher accuracy than existing low-memory methods and converges in as little as one-tenth the time: https://amzn.to/4pLQek8
Reposted by Amazon Science
No data, no AI, no progress. My @AmazonScience article explores how multi-layered mapping + petabyte-scale cloud infrastructure helps save lives in time of crisis. Building AI without addressing the fundamental data divide means solving the wrong problems. amazon.science/blog/why-ai-...
Why AI for good depends on good data
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
amazon.science
October 14, 2025 at 2:02 PM
No data, no AI, no progress. My @AmazonScience article explores how multi-layered mapping + petabyte-scale cloud infrastructure helps save lives in time of crisis. Building AI without addressing the fundamental data divide means solving the wrong problems. amazon.science/blog/why-ai-...
"Normcore agents" are trained by Amazon's AGI Lab to chain together hundreds of micro-interactions to execute customer requests. In reinforcement learning gyms, agents practice atomic behaviors across dozens of application domains, learning to execute complex workflows with near-perfect reliability:
The unseen work of building reliable AI agents
"Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.
www.amazon.science
January 8, 2026 at 5:27 PM
"Normcore agents" are trained by Amazon's AGI Lab to chain together hundreds of micro-interactions to execute customer requests. In reinforcement learning gyms, agents practice atomic behaviors across dozens of application domains, learning to execute complex workflows with near-perfect reliability: