Computer science and technology
Computer science and technology

Vana is letting users own a piece of the AI models trained on their data

More than 1 million people are contributing their data to Vana’s decentralized network, which started as an MIT class project.

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.

For this computer scientist, MIT Open Learning was the start of a life-changing journey

Ana Trišović, who studies the democratization of AI, reflects on a career path that she began as a student downloading free MIT resources in Serbia.

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.

At the core of problem-solving

Stuart Levine ’97, director of MIT’s BioMicro Center, keeps departmental researchers at the forefront of systems biology.

“An AI future that honors dignity for everyone”

As artificial intelligence develops, we must ask vital questions about ourselves and our society, Ben Vinson III contends in the 2025 Compton Lecture.

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.

AI system predicts protein fragments that can bind to or inhibit a target

FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.

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.

AI model deciphers the code in proteins that tells them where to go

Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.