
Machine Learning Research
Protein Families Deciphered: Machine Learning Categorizes Proteins Based on Their Functions
Convolutional neural networks separate proteins into functional families without considering their shapes.
Machine Learning Research
Convolutional neural networks separate proteins into functional families without considering their shapes.
Tech & Society
In 2021, transformers were harnessed to discover drugs, recognize speech, and paint pictures — and much more.
Business
Isomorphic aims to build its business on AlphaFold 2, an ensemble of neural networks that finds the shapes of protein molecules.
Tech & Society
DeepMind opened access to AlphaFold, a model that finds the shapes of proteins, and to its output so far — a potential cornucopia for biomedical research. The research lab, a division of Google’s parent company Alphabet, made AlphaFold freely available.
Science
Neural nets could speed up development of new materials.What’s new: A deep learning system from Sandia National Laboratories dramatically accelerated simulations that help scientists understand how changes to the design or fabrication of a material change its properties.
Tech & Society
Institutional hurdles to AI for medicine began to fall, setting the stage for widespread clinical use of deep learning in medical devices and treatments. DeepMind’s AlphaFold model determined the three-dimensional shape of a protein in just hours.
Tech & Society
Machine learning thrives on data, but information about the novel coronavirus and the illness it produces has been either thin or hard to access. Now researchers are pooling resources to share everything we do know.
Machine Learning Research
A protein’s biological function depends largely on its three-dimensional shape, but deducing its shape from its sequence of amino acids has been a longstanding problem. Researchers at DeepMind reveal how they used deep learning to solve the puzzle.
Business
Alphabet subsidiary DeepMind lost $572 million in the past year, and its losses over the last three years amounted to more than $1 billion. AI contrarian Gary Marcus used the news as an opportunity to question the direction of AI as an industry.