Lincoln Lab unveils the most powerful AI supercomputer at any US university
The new TX-Generative AI Next (TX-GAIN) computing system at the Lincoln Laboratory Supercomputing Center (LLSC) is the most powerful AI supercomputer…
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Responding to the climate impact of generative AI
In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts…
AI system learns from many types of scientific information and runs experiments to discover new materials
Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today…
New AI system could accelerate clinical research
Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical…
Improving the workplace of the future
Whitney Zhang ’21 believes in the importance of valuing workers regardless of where they fit into an organizational chart. Zhang…
MIT affiliates win AI for Math grants to accelerate mathematical discovery
MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of…
New tool makes generative AI models more likely to create breakthrough materials
The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few…
How are MIT entrepreneurs using AI?
The Martin Trust Center for MIT Entrepreneurship strives to teach students the craft of entrepreneurship. Over the last few years,…
What does the future hold for generative AI?
When OpenAI introduced ChatGPT to the world in 2022, it brought generative artificial intelligence into the mainstream and started a…
How to build AI scaling laws for efficient LLM training and budget maximization
When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget.…