Speeding up drug discovery with diffusion generative models
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
Computer science and technology
A method for designing neural networks optimally suited for certain tasks
With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.
Learning to grow machine-learning models
New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.
Detailed images from space offer clearer picture of drought effects on plants
J-WAFS researchers are using remote sensing observations to build high-resolution systems to monitor drought.
Mining the right transition metals in a vast chemical space
Computational chemists design better ways of discovering and designing materials for energy applications.
New method accelerates data retrieval in huge databases
Researchers used machine learning to build faster and more efficient hash functions, which are a key component of databases.
Matthew Kearney: Bringing AI and philosophy into dialogue
The computer science and philosophy double-major aims to advance the field of AI ethics.
Creating a versatile vaccine to take on Covid-19 in its many guises
Aided by machine learning, scientists are working to develop a vaccine that would be effective against all SARS-CoV-2 strains.
New insights into training dynamics of deep classifiers
MIT researchers uncover the structural properties and dynamics of deep classifiers, offering novel explanations for optimization, generalization, and approximation in deep networks.
Large language models are biased. Can logic help save them?
MIT researchers trained logic-aware language models to reduce harmful stereotypes like gender and racial biases.