AI

Lara Ozkan, an MIT senior from Oradell, New Jersey, has been selected as a 2025 Marshall Scholar and will begin graduate studies in the United Kingdom next fall. Funded by the British government, the Marshall Scholarship awards American students of high academic achievement with the opportunity to pursue graduate studies in any field at any […]

Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliver personalized experiences to customers using

Warehouses full of servers are hungry for power, no matter who supplies it.

Traditional hierarchies hold businesses back. Instead, teams need to combine human and artificial intelligence to succeed.

If you missed our second live, subscriber-only Q&A with WIRED’s AI columnist Reece Rogers, you can watch the replay here.

Five MIT faculty members and two additional alumni were recently named to the 2024 cohort of AI2050 Fellows. The honor is announced annually by Schmidt Futures, Eric and Wendy Schmidt’s philanthropic initiative that aims to accelerate scientific innovation.  Conceived and co-chaired by Eric Schmidt and James Manyika, AI2050 is a philanthropic initiative aimed at helping

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows

Amazon SageMaker HyperPod is designed to support large-scale machine learning (ML) operations, providing a robust environment for training foundation models (FMs) over extended periods. Multiple users — such as ML researchers, software engineers, data scientists, and cluster administrators — can work concurrently on the same cluster, each managing their own jobs and files without interfering

This post was written with Darrel Cherry, Dan Siddall, and Rany ElHousieny of Clearwater Analytics. As global trading volumes rise rapidly each year, capital markets firms are facing the need to manage large and diverse datasets to stay ahead. These datasets aren’t just expansive in volume; they’re critical in driving strategy development, enhancing execution, and

Twitch, the world’s leading live-streaming platform, has over 105 million average monthly visitors. As part of Amazon, Twitch advertising is handled by the ad sales organization at Amazon. New ad products across diverse markets involve a complex web of announcements, training, and documentation, making it difficult for sales teams to find precise information quickly. In