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…
During early development, tissues and organs begin to bloom through the shifting, splitting, and growing of many thousands of cells. A team of MIT engineers has now developed a way to predict, minute by minute, how individual cells will fold,…
OpenAI team has released their openai/circuit-sparsity model on Hugging Face and the openai/circuit_sparsity toolkit on GitHub. The release packages the models and circuits from the paper ‘Weight-sparse transformers have interpretable circuits‘. What is a weight sparse transformer? The models…
Everyone talks about LLMs—but today’s AI ecosystem is far bigger than just language models. Behind the scenes, a whole family of specialized architectures is quietly transforming how machines see, plan, act, segment, represent concepts, and even run efficiently on small…
Can a 3B model deliver 30B class reasoning by fixing the training recipe instead of scaling parameters? Nanbeige LLM Lab at Boss Zhipin has released Nanbeige4-3B, a 3B parameter small language model family trained with an unusually heavy emphasis on…
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…
Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images. For instance, to determine how the size of the brain’s…
Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative…
In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint. The energy demands of generative AI are expected to continue increasing dramatically over…
The new TX-Generative AI Next (TX-GAIN) computing system at the Lincoln Laboratory Supercomputing Center  (LLSC) is the most powerful AI supercomputer at any U.S. university. With its recent ranking from  TOP500, which biannually publishes a list of the top supercomputers in…
