AI

This post was co-authored with Jingwei Zuo from TII. We are excited to announce the availability of the Technology Innovation Institute (TII)’s Falcon-H1 models on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, developers and data scientists can now use six instruction-tuned Falcon-H1 models (0.5B, 1.5B, 1.5B-Deep, 3B, 7B, and 34B) on AWS, and have access […]

This post was written with Avdhesh Paliwal of Oldcastle APG. Oldcastle APG, one of the largest global networks of manufacturers in the architectural products industry, was grappling with an inefficient and labor-intensive process for handling proof of delivery (POD) documents, known as ship tickets. The company was processing 100,000–300,000 ship tickets per month across more

London Stock Exchange Group (LSEG) is a global provider of financial markets data and infrastructure. It operates the London Stock Exchange and manages international equity, fixed income, and derivative markets. The group also develops capital markets software, offers real-time and reference data products, and provides extensive post-trade services. This post was co-authored with Charles Kellaway

AI agents are transforming enterprise applications across industries, from customer service to complex decision workflows. As organizations scale these deployments, they face a fundamental question: how can you improve trust in an AI application? The challenge is transparency. AI agents can make decisions on behalf of users, invoke tools dynamically, and follow reasoning paths that

The U.S. Department of Energy’s National Nuclear Security Administration (DoE/NNSA) recently announced that it has selected MIT to establish a new research center dedicated to advancing the predictive simulation of extreme environments, such as those encountered in hypersonic flight and atmospheric re-entry. The center will be part of the fourth phase of NNSA’s Predictive Science Academic Alliance

The ever elusive first lady has emerged with a brief to exert thought leadership over AI, for the children. Some insiders are excited; others won’t touch the subject with a 10-foot pole.

As generative AI continues to transform how enterprises operate—and develop net new innovations—the infrastructure demands for training and deploying AI models have grown exponentially. Traditional infrastructure approaches are struggling to keep pace with today’s computational requirements, network demands, and resilience needs of modern AI workloads. At AWS, we’re also seeing a transformation across the technology

At a weekend hackathon in San Francisco, more than 100 coders gathered to test whether they could beat AI—and win a $12,500 cash prize.

In her new book, feminist author Laura Bates explores how sexbots, AI assistants, and deepfakes are reinventing misogyny and harming women.

As organizations scale their AI infrastructure to support trillion-parameter models, they face a difficult trade-off: reduced training time with lower cost or faster training time with a higher cost. When they checkpoint frequently to speed up recovery time and minimize lost training time, they incur in substantially higher storage cost. And when they checkpoint infrequently,