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Artificial intelligence has captured headlines recently for its rapidly growing energy demands, and particularly the surging electricity usage of data centers that enable the training and deployment of the latest generative AI models. But it’s not all bad news — some AI tools have the potential to reduce some forms of energy consumption and enable cleaner grids.One […]
3 Questions: How AI could optimize the power grid Read More »
Even networks long considered “untrainable” can learn effectively with a bit of a helping hand. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have shown that a brief period of alignment between neural networks, a method they call guidance, can dramatically improve the performance of architectures previously thought unsuitable for modern tasks.Their findings
Guided learning lets “untrainable” neural networks realize their potential Read More »
Most languages use word position and sentence structure to extract meaning. For example, “The cat sat on the box,” is not the same as “The box was on the cat.” Over a long text, like a financial document or a novel, the syntax of these words likely evolves. Similarly, a person might be tracking variables in
A new way to increase the capabilities of large language models Read More »
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 and Engineering (NSE), is addressing the challenge as part of his research.Sarsenbayev focuses on one
Working to eliminate barriers to adopting nuclear energy Read More »
As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a wide margin on complex tasks. Try playing Sudoku with one, for instance, where you fill in numbers one through nine in such
Enabling small language models to solve complex reasoning tasks Read More »
Two current MIT affiliates and seven additional alumni are among those named to the 2025 cohort of AI2050 Fellows. Zongyi Li, a postdoc in the MIT Computer Science and Artificial Intelligence Lab, and Tess Smidt ’12, an associate professor of electrical engineering and computer science (EECS), were both named as AI2050 Early Career Fellows. Seven additional MIT
MIT affiliates named 2025 Schmidt Sciences AI2050 Fellows Read More »
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
A smarter way for large language models to think about hard problems Read More »
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
A faster problem-solving tool that guarantees feasibility Read More »
A recent study from Oregon State University estimated that more than 3,500 animal species are at risk of extinction because of factors including habitat alterations, natural resources being overexploited, and climate change.To better understand these changes and protect vulnerable wildlife, conservationists like MIT PhD student and Computer Science and Artificial Intelligence Laboratory (CSAIL) researcher Justin Kay
3 Questions: How AI is helping us monitor and support vulnerable ecosystems Read More »
A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even
