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.

Imagine having a continuum soft robotic arm bend around a bunch of grapes or broccoli, adjusting its grip in real time as it lifts the object. Unlike traditional rigid robots that generally aim to avoid contact with the environment as much as possible and stay far away from humans for safety reasons, this arm senses subtle forces, stretching and flexing in ways that mimic more of the compliance of a human hand. Its every motion is calculated to avoid excessive force while achieving the task efficiently. In MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Laboratory for Information and Decisions Systems (LIDS) labs, these seemingly simple movements are the culmination of complex mathematics, careful engineering, and a vision for robots that can safely interact with humans and delicate objects.

More than 300 people across academia and industry spilled into an auditorium to attend a BoltzGen seminar on Thursday, Oct. 30, hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health (MIT Jameel Clinic). Headlining the event was MIT PhD student and BoltzGen’s first author Hannes Stärk, who had announced BoltzGen just a few days prior.

When the FORTRAN programming language debuted in 1957, it transformed how scientists and engineers programmed computers. Complex calculations could suddenly be expressed in concise, math-like notation using arrays — collections of values that make it easier to describe operations on data. That simple idea evolved into today’s “tensors,” which power many of the world’s most advanced AI and scientific computing systems through modern frameworks like NumPy and PyTorch.

In an MIT classroom, a professor lectures while students diligently write down notes they will reread later to study and internalize key information ahead of an exam.

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?

Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code that’s messy, hard to change safely, and often opaque about what’s really happening under the hood. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are charting a more “modular” path ahead. 

Pulkit Agrawal, MIT EECS Associate Professor and CSAIL principal investigator, has received the Toshio Fukuda Young Professional Award from the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) for his work in “robot learning, self-supervised and sim-to-real policy learning, agile locomotion, and dexterous manipulation,” according to the organization.

The Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) and Pegatron Corporation today announced a landmark five-year research partnership aimed at developing the next generation of emotionally and physically intelligent robotic systems. The program, led by CSAIL Director and MIT Professor Daniela Rus and Alan Lin, Corporate Partner Lead at Pegatron, will run from 2026 to 2031 and is designed to redefine the capabilities of robots in human-centered environments.

Hal Abelson, MIT Class of 1922 Professor and CSAIL principal investigator, has received the 2025 Lifetime Achievement Award for Excellence from Open Education Global for helping make information technology more accessible worldwide. “Hal Abelson’s work promotes knowledge of all forms as a public good,” notes the organization in a public statement. “Hal’s work has focused on communities working together to advance and support knowledge.”