Managing a power grid is like trying to solve an enormous puzzle. Grid operators must ensure the proper amount of power is flowing to the right areas at the exact time when it is needed, and they must do this in a way that minimizes costs without overloading physical infrastructure. Even more, they must solve this complicated problem repeatedly, as rapidly as possible, to meet constantly changing demand. To help crack this consistent conundrum, MIT researchers developed a […]

Ver mais

Large language models (LLMs) are mainly trained to generate text responses to user queries or prompts, with complex reasoning under the hood that not only involves language generation by predicting each next token in the output sequence, but also entails a deep understanding of the linguistic patterns surrounding the user input text.

Ver mais

Caroline Uhler is an Andrew (1956) and Erna Viterbi Professor of Engineering at MIT; a professor of electrical engineering and computer science in the Institute for Data, Science, and Society (IDSS); and director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, where she is also a core institute and scientific leadership team member.  Uhler is interested in all the methods by which scientists can uncover causality in biological systems, ranging from causal discovery […]

Ver mais

Author(s): Luca Derumier Originally published on Towards AI. Attention Is All You Need You’re at a crowded party. Music playing, dozens of conversations happening simultaneously, glasses clinking. Yet somehow, when someone across the room mentions your name, you hear it. When your friend starts telling a story, you can follow it despite the chaos. Your brain is constantly, automatically deciding what deserves your attention and what can be ignored. Image generated by Imagen 4.This article delves into the […]

Ver mais

To make large language models (LLMs) more accurate when answering harder questions, researchers can let the model spend more time thinking about potential solutions. But common approaches that give LLMs this capability set a fixed computational budget for every problem, regardless of how complex it is. This means the LLM might waste computational resources on simpler questions or be unable to tackle intricate problems that require more reasoning. To address this, MIT researchers developed a smarter way to allocate […]

Ver mais

Strange as it may sound, large language models (LLMs) can be leveraged for data analysis tasks, including specific scenarios such as time series analysis.

Ver mais