<span class="vcard">Adam Zewe | MIT News</span>
Adam Zewe | MIT News

“Periodic table of machine learning” could fuel AI discovery

Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones.

Making AI-generated code more accurate in any language

A new technique automatically guides an LLM toward outputs that adhere to the rules of whatever programming language or other format is being used.

A faster way to solve complex planning problems

By eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.

New method efficiently safeguards sensitive AI training data

The approach maintains an AI model’s accuracy while ensuring attackers can’t extract secret information.

Could LLMs help design our next medicines and materials?

A new method lets users ask, in plain language, for a new molecule with certain properties, and receive a detailed description of how to synthesize it.

New method assesses and improves the reliability of radiologists’ diagnostic reports

The framework helps clinicians choose phrases that more accurately reflect the likelihood that certain conditions are present in X-rays.

Researchers teach LLMs to solve complex planning challenges

This new framework leverages a model’s reasoning abilities to create a “smart assistant” that finds the optimal solution to multistep problems.

AI tool generates high-quality images faster than state-of-the-art approaches

Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.

Robotic helper making mistakes? Just nudge it in the right direction

New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.

Like human brains, large language models reason about diverse data in a general way

A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.