AI

Whitney Zhang ’21 believes in the importance of valuing workers regardless of where they fit into an organizational chart. Zhang is a PhD student in MIT’s Department of Economics studying labor economics. She explores how the technological and managerial decisions companies make affect workers across the pay spectrum.  “I’ve been interested in economics, economic impacts, and […]

The new sites will boost Stargate’s planned capacity to nearly 7 gigawatts—about equal to the output of seven large nuclear reactors.

AI agents are evolving beyond basic single-task helpers into more powerful systems that can plan, critique, and collaborate with other agents to solve complex problems. Deep Agents—a recently introduced framework built on LangGraph—bring these capabilities to life, enabling multi-agent workflows that mirror real-world team dynamics. The challenge, however, is not just building such agents but

This post is co-written by Mark Warner, Principal Solutions Architect for Thales, Cyber Security Products. As generative AI applications make their way into production environments, they integrate with a wider range of business systems that process sensitive customer data. This integration introduces new challenges around protecting personally identifiable information (PII) while maintaining the ability to

Venture capitalist Alexa von Tobel is ready to bet on quantum computing—starting with hardware.

MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of the Renaissance Philanthropy and XTX Markets’ AI for Math grants.  Four additional MIT alumni — Anshula Gandhi ’19, Viktor Kunčak SM ’01, PhD ’07; Gireeja Ranade ’07; and Damiano Testa PhD ’05 — were also

This post was written with Sarah Ostermeier from Comet. As enterprise organizations scale their machine learning (ML) initiatives from proof of concept to production, the complexity of managing experiments, tracking model lineage, and managing reproducibility grows exponentially. This is primarily because data scientists and ML engineers constantly explore different combinations of hyperparameters, model architectures, and

Mark Zuckerberg’s company faces backlash after rowing back promises to create between 300 and 500 new jobs to man its subsidiary’s new data center.

Tech got what it wanted by electing Trump. A year later, it looks more like a suicide pact.

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials. But when it comes to designing materials with