A new way to increase the capabilities of large language models
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.
Artificial Intelligence
“Robot, make me a chair”
An AI-driven system lets users design and build simple, multicomponent objects by describing them with words.
Read More3 Questions: Using computation to study the world’s best single-celled chemists
Assistant Professor Yunha Hwang utilizes microbial genomes to examine the language of biology. Her appointment reflects MIT’s commitment to exploring the intersection of genetics research and AI.
Read MoreDeep-learning model predicts how fruit flies form, cell by cell
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
Read MoreEnabling small language models to solve complex reasoning tasks
The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.
Read MoreNew MIT program to train military leaders for the AI age
The new certificate program will equip naval officers with skills needed to solve the military’s hardest problems.
Read MoreThe technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
Read MoreNew materials could boost the energy efficiency of microelectronics
By stacking multiple active components based on new materials on the back end of a computer chip, this new approach reduces the amount of energy wasted during computation.
Read MoreMIT affiliates named 2025 Schmidt Sciences AI2050 Fellows
Postdoc Zongyi Li, Associate Professor Tess Smidt, and seven additional alumni will be supported in the development of AI against difficult problems.
Read MoreMIT researchers “speak objects into existence” using AI and robotics
The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.
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