Latest from Google AI – Digitizing Smell: Using Molecular Maps to Understand Odor

Posted by Richard C. Gerkin, Google Research, and Alexander B. Wiltschko, Google Did you ever try to measure a smell? …Until you can measure their likenesses and differences you can have no science of odor. If you are ambitious to found a new science, measure a smell.— Alexander Graham Bell, 1914. How can we measure…

Latest from MIT : Analyzing the potential of AlphaFold in drug discovery

Over the past few decades, very few new antibiotics have been developed, largely because current methods for screening potential drugs are prohibitively expensive and time-consuming. One promising new strategy is to use computational models, which offer a potentially faster and cheaper way to identify new drugs. A new study from MIT reveals the potential and…

Latest from MIT : Using machine learning to identify undiagnosable cancers

The first step in choosing the appropriate treatment for a cancer patient is to identify their specific type of cancer, including determining the primary site — the organ or part of the body where the cancer begins. In rare cases, the origin of a cancer cannot be determined, even with extensive testing. Although these cancers…

Latest from Google AI – Announcing the Patent Phrase Similarity Dataset

Posted Grigor Aslanyan, Software Engineer, Google Patent documents typically use legal and highly technical language, with context-dependent terms that may have meanings quite different from colloquial usage and even between different documents. The process of using traditional patent search methods (e.g., keyword searching) to search through the corpus of over one hundred million patent documents…

Latest from MIT Tech Review – What does GPT-3 “know” about me? 

For a reporter who covers AI, one of the biggest stories this year has been the rise of large language models. These are AI models that produce text a human might have written—sometimes so convincingly they have tricked people into thinking they are sentient.  These models’ power comes from troves of publicly available human-created text…

Latest from MIT : AI that can learn the patterns of human language

Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do. But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system…

UC Berkeley – Reverse engineering the NTK: towards first-principles architecture design

Foundational works showed how to find the kernel corresponding to a wide network. We find the inverse mapping, showing how to find the wide network corresponding to a given kernel. Deep neural networks have enabled technological wonders ranging from voice recognition to machine transition to protein engineering, but their design and application is nonetheless notoriously…

Latest from MIT Tech Review – I Was There When: AI helped create a vaccine

I Was There When is an oral history project that’s part of the In Machines We Trust podcast. It features stories of how breakthroughs and watershed moments in artificial intelligence and computing happened, as told by the people who witnessed them. In this episode we meet Dave Johnson, the chief data and artificial intelligence officer…

Latest from Google AI – High-Definition Segmentation in Google Meet

Posted by Tingbo Hou and Juhyun Lee, Software Engineers, Google In recent years video conferencing has played an increasingly important role in both work and personal communication for many users. Over the past two years, we have enhanced this experience in Google Meet by introducing privacy-preserving machine learning (ML) powered background features, also known as…

Latest from MIT : Taking a magnifying glass to data center operations

When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in…