Latest from Google AI – Separating Birdsong in the Wild for Classification

Posted by Tom Denton, Software Engineer and Scott Wisdom, Research Scientist, Google Research Birds are all around us, and just by listening, we can learn many things about our environment. Ecologists use birds to understand food systems and forest health — for example, if there are more woodpeckers in a forest, that means there’s a…

Latest from Google AI – LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything

Posted by Heng-Tze Cheng, Senior Staff Software Engineer and Romal Thoppilan, Senior Software Engineer, Google Research, Brain Team Language models are becoming more capable than ever before and are helpful in a variety of tasks — translating one language into another, summarizing a long document into a brief highlight, or answering information-seeking questions. Among these,…

Latest from MIT : Computing for ocean environments

There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. “The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures,” says Wim van…

Latest from MIT : Seeing into the future: Personalized cancer screening with artificial intelligence

While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist regarding when and how often they should be administered. On the one hand, advocates argue for the ability to save lives: Women aged 60-69 who receive mammograms, for example, have a 33 percent lower risk of dying compared to those…

Latest from MIT : Scientists make first detection of exotic “X” particles in quark-gluon plasma

In the first millionths of a second after the Big Bang, the universe was a roiling, trillion-degree plasma of quarks and gluons — elementary particles that briefly glommed together in countless combinations before cooling and settling into more stable configurations to make the neutrons and protons of ordinary matter. In the chaos before cooling, a…

Latest from MIT Tech Review – Meta’s new learning algorithm can teach AI to multi-task

If you can recognize a dog by sight, then you can probably recognize a dog when it is described to you in words. Not so for today’s artificial intelligence. Deep neural networks have become very good at identifying objects in photos and conversing in natural language, but not at the same time: there are AI…

Latest from MIT Tech Review – Sustainability starts in the design process, and AI can help

Artificial intelligence helps build physical infrastructure like modular housing, skyscrapers, and factory floors. “…many problems that we wrestle with in all forms of engineering and design are very, very complex problems…those problems are beginning to reach the limits of human capacity,” says Mike Haley, the vice president of research at Autodesk. But there’s hope with…

Latest from MIT : When should someone trust an AI assistant’s predictions?

In a busy hospital, a radiologist is using an artificial intelligence system to help her diagnose medical conditions based on patients’ X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI’s predictions? She doesn’t. Instead, she may rely on her expertise, a confidence…

Latest from Google AI – Introducing StylEx: A New Approach for Visual Explanation of Classifiers

Posted by Oran Lang and Inbar Mosseri, Software Engineers, Google Research Neural networks can perform certain tasks remarkably well, but understanding how they reach their decisions — e.g., identifying which signals in an image cause a model to determine it to be of one class and not another — is often a mystery. Explaining a…

Latest from MIT : How well do explanation methods for machine-learning models work?

Imagine a team of physicians using a neural network to detect cancer in mammogram images. Even if this machine-learning model seems to be performing well, it might be focusing on image features that are accidentally correlated with tumors, like a watermark or timestamp, rather than actual signs of tumors. To test these models, researchers use…