AI

Computer-aided design (CAD) systems are tried-and-true tools used to design many of the physical objects we use each day. But CAD software requires extensive expertise to master, and many tools incorporate such a high level of detail they don’t lend themselves to brainstorming or rapid prototyping. In an effort to make design faster and more […]

Hannah Wong told staff she is moving on to her “next chapter.” The company will be running an executive search to find a replacement, according to a memo.

Hannah Wong told staff she is moving on to her “next chapter.” The company will be running an executive search to find a replacement, according to a memo.

Today, out of an estimated 1 trillion species on Earth, 99.999 percent are considered microbial — bacteria, archaea, viruses, and single-celled eukaryotes. For much of our planet’s history, microbes ruled the Earth, able to live and thrive in the most extreme of environments. Researchers have only just begun in the last few decades to contend

Today, out of an estimated 1 trillion species on Earth, 99.999 percent are considered microbial — bacteria, archaea, viruses, and single-celled eukaryotes. For much of our planet’s history, microbes ruled the Earth, able to live and thrive in the most extreme of environments. Researchers have only just begun in the last few decades to contend

What if there were a way to solve one of the most significant obstacles to the use of nuclear energy — the disposal of high-level nuclear waste (HLW)? Dauren Sarsenbayev, a third-year doctoral student at the MIT Department of Nuclear Science and Engineering (NSE), is addressing the challenge as part of his research. Sarsenbayev focuses on

Foundation model training has reached an inflection point where traditional checkpoint-based recovery methods are becoming a bottleneck to efficiency and cost-effectiveness. As models grow to trillions of parameters and training clusters expand to thousands of AI accelerators, even minor disruptions can result in significant costs and delays. In this post, we introduce checkpointless training on

Modern AI infrastructure serves multiple concurrent workloads on the same cluster, from foundation model (FM) pre-training and fine-tuning to production inference and evaluation. In this shared environment, the demands for AI accelerators fluctuates continuously as inference workloads scale with traffic patterns, and experiments complete and release resources. Despite this dynamic availability of AI accelerators, traditional

Large Language Model (LLM) agents have revolutionized how we approach complex, multi-step tasks by combining the reasoning capabilities of foundation models with specialized tools and domain expertise. While single-agent systems using frameworks like ReAct work well for straightforward tasks, real-world challenges often require multiple specialized agents working in coordination. Think about planning a business trip:

Enterprise organizations are rapidly moving beyond generative AI experiments to production deployments and complex agentic AI solutions, facing new challenges in scaling, security, governance, and operational efficiency. This blog post series introduces generative AI operations (GenAIOps), the application of DevOps principles to generative AI solutions, and demonstrates how to implement it for applications powered by