Guided learning lets “untrainable” neural networks realize their potential
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
Artificial Intelligence
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.
Read MoreA “scientific sandbox” lets researchers explore the evolution of vision systems
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
Read More“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 MoreWorking to eliminate barriers to adopting nuclear energy
Nuclear waste continues to be a bottleneck in the widespread use of nuclear energy, so doctoral student Dauren Sarsenbayev is developing models to address the problem.
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.
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