Can large language models figure out the real world?
New test could help determine if AI systems that make accurate predictions in one area can understand it well enough to apply that ability to a different area.
New test could help determine if AI systems that make accurate predictions in one area can understand it well enough to apply that ability to a different area.
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
MIT engineers used a machine-learning model to design nanoparticles that can deliver RNA to cells more efficiently.
The team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.
As large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.
New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.
By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.
This new approach could lead to enhanced AI models for drug and materials discovery.
Neural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.