Approach could provide scientists with a quicker, gentler way to gauge the health of bird populations

Groups Conversations All groups and messages Sign in Machine Learning, Deep Learning and AI Conversations About Privacy • Terms     Taking AI-based transformations to the next level with prompt markup languages 0 views Skip to first unread message Eyal Estrin unread, 10:19 PM (15 minutes ago)    to https://www.cio.com/article/4246977/taking-ai-based-transformations-to-the-next-level-with-prompt-markup-languages.html Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

AWS Marketplace now supports usage-based metering for Amazon SageMaker bidirectional streaming inference, so model providers can charge for what customers actually consume during a live stream. Deepgram is the launch partner, bringing usage-based speech-to-text and text-to-speech billing to AWS Marketplace.

ITanaka. Nanoinformatics, 75–95 (Springer, SINGAPORE, 2018). Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: A review on methods and applications of machine learning for materials design. Engineering. 2019;5:1017–26. https://doi.org/10.1016/j.eng.2019.02.011. Chen C-T, Gu GX. Machine learning for composite materials. MRS Commun. 2019;9:556566 https://doi.org/10.1557/mrc.2019.32. Chen L, Pilania G, Batra R, Huan TD, Kim C, Kuenneth C, et al. Polymer informatics: Current status and critical next steps. Mater Sci Eng R: Rep. 2021;144:100595. https://doi.org/10.1016/j.mser.2020.100595. Doan Tran H, Kim C, Chen L, Chandrasekaran A, Batra R, Venkatram S, et al. Machine-learning predictions of polymer properties with polymer genome. J Appl Phys. 2020;128:171104. https://doi.org/10.1063/5.0023759. Tanaka I, Rajan K, Wolverton C. Data-centric science for materials innovation. MRS Bull. 2018;43:659663 https://doi.org/10.1557/mrs.2018.205. von Lilienfeld OA, Ramakrishnan R, Rupp M, Knoll A. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties. Int J Quantum Chem. 2015;115:1084–93. https://doi.org/10.1002/qua.24912. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: recent applications and prospects. npj Comput Mater. 2017;3:54 https://doi.org/10.1038/s41524-017-0056-5. Wang CC, Pilania G, Boggs SA, Kumar S, Breneman C, Ramprasad R. Computational strategies for polymer dielectrics design. Polymer. 2014;55:979–88. https://doi.org/10.1016/j.polymer.2013.12.069. Kadupitiya J,...

Job Description The lab is part of the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) and is based at SciLifeLab Stockholm and KTH. We develop and apply computational a

This post shows how to keep the AWS Agent Registry current automatically. Using AWS CloudTrail, Amazon EventBridge, AWS Lambda, and AgentCore Identity, an event-driven pipeline detects MCP server changes and pushes updated tool metadata to the registry with no stored secrets, across single- and multi-account environments.

Weight-Space Programming (WSP) enables reasoning agents to directly inspect and modify neural network parameters, offering an alternative to gradient-based model adaptation. However, its capabilities, cross-task generalization, and practical limitations remain insufficiently understood. We systemati

Learn how AssemblyAI runs cost-optimized speech-to-text inference at scale on AWS, using Amazon ECS, Amazon EC2 Spot GPU instances, and an Amazon SQS-based retry architecture to achieve 70% cost savings while maintaining SLA commitments across European Regions.