3 Questions: Honing robot perception and mapping
Walking to a friend’s house or browsing the aisles of a grocery store might feel like simple tasks, but they in fact require sophisticated capabilities. That's because humans are able…
On the Stepwise Nature of Self-Supervised Learning
Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase…
Learning the language of molecules to predict their properties
Discovering new materials and drugs typically involves a manual, trial-and-error process that can take decades and cost millions of dollars. To streamline this process, scientists often use machine learning to…
MIT scientists build a system that can generate AI models for biology research
Is it possible to build machine-learning models without machine-learning expertise? Jim Collins, the Termeer Professor of Medical Engineering and Science in the Department of Biological Engineering at MIT and the…
Educating national security leaders on artificial intelligence
Understanding artificial intelligence and how it relates to matters of national security has become a top priority for military and government leaders in recent years. A new three-day custom program…
When computer vision works more like a brain, it sees more like people do
From cameras to self-driving cars, many of today’s technologies depend on artificial intelligence to extract meaning from visual information. Today’s AI technology has artificial neural networks at its core, and…
Content Customization in WordPress with CyberSEO Pro
WordPress users frequently leverage the robust capabilities of the CyberSEO Pro plugin for content syndication. Often it is necessary to modify or remove certain parts of the imported HTML pages.…
Researchers teach an AI to write better chart captions
A new dataset can help scientists develop automatic systems that generate richer, more descriptive captions for online charts.
Generating 3D Molecular Conformers via Equivariant Coarse-Graining and Aggregated Attention
<!-- --> Figure 1: CoarsenConf architecture. <!-- (I) The encoder $q_phi(z| X, mathcal{R})$ takes the fine-grained (FG) ground truth conformer $X$, RDKit approximate conformer $mathcal{R}$ , and coarse-grained (CG) conformer…