Cerebral cavernous malformation (CCM) patients treated with oral drug, REC-994, showed reduction in total cerebral and brainstem lesion volume.
Category: robotics/AI – Page 289
Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis
Hamilton-Jacobi (HJ) reachability is a rigorous mathematical framework that enables robots to simultaneously detect unsafe states and generate actions that prevent future failures. While in theory, HJ reachability can synthesize safe controllers for nonlinear systems and nonconvex constraints.
In practice, it has been limited to hand-engineered collision
Avoidance constraints modeled via low-dimensional state-space representations and first-principles dynamics. In this work, our goal is to generalize safe robot controllers to prevent failures that are hard—if not impossible—to write down by hand, but can be intuitively identified from high-dimensional observations:
Google Launches Gemini 2.0 Pro LLM
In today’s AI news, Google launched its much-anticipated new flagship AI model, Gemini 2.0 Pro Experimental, on Wednesday. The announcement was part of a series of other AI model releases. The company is also making its reasoning model, Gemini 2.0 Flash Thinking, available in the Gemini app.
In other advancements, LinkedIn is testing a new job-hunting tool that uses a custom large language model to comb through huge quantities of data to help people find prospective roles. The company believes that artificial intelligence will help users unearth new roles they might have missed in the typical search process.
S Deep Research feature, which can autonomously browse the web and create research reports. ‘ + s up from hitting $50 million ARR, or the yearly value of last month s case for why they are the best positioned to take over TikTok And, in this episode, a16z Partner Marc Andrusko chats with Mastercard’s Chief AI and Data Officer Greg Ulrich about Mastercard’s long history of using AI, the opportunities (and potential risks) associated with integrating generative AI into fraud detection, determining what tech to employ based on use cases, and the best advice he’s ever gotten.
Then, power your AI transformation with an insightful keynote from Scott Guthrie, Executive Vice President, Cloud + AI Group at Microsoft, and other industry experts. Watch this keynote presentation from NYC stop on Microsoft’s AI Tour.
We close out with this insightful discussion with Malcolm Gladwell and Ric Lewis, SVP of Infrastructure at IBM. Learn how hardware capabilities enable the matrix math behind large language models and how AI is transforming industries—from banking to your local coffee shop.
Thats all for today, but AI is moving fast — like, comment, and subscribe for more AI news! Please vote for me in the Entrepreneur of Impact Competition today! Thank you for supporting my partners and I — it’s how I keep Neural News Network free.
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Compact camera can identify objects at the speed of light
Collaboration can be a beautiful thing, especially when people work together to create something new. Take, for example, a longstanding collaboration between Arka Majumdar, a University of Washington (UW) professor of electrical and computer engineering and physics, and Felix Heide, an assistant professor of computer science at Princeton University.
Together, they and their students have produced some eye-popping research, including shrinking a camera down to the size of a grain of salt while still capturing crisp, clear images.
Now, the pair is building on this work, publishing a paper in Science Advances that describes a new kind of compact camera engineered for computer vision—a type of artificial intelligence that allows computers to recognize objects in images and video.
Encrypted optical information in nonlinear chaotic systems uncovered using neural networks
Optical information encoded in holograms is transferred by means of ultrashort laser filaments propagating in highly nonlinear and turbulent media. After propagation, the initial optical information is completely scrambled and cannot be retrieved by any experimental or physical modeling system. Yet, we demonstrate that neural networks trained on experimental data provide a robust way to fully recover the original hologram images. Remarkably, our approach demonstrates the ability to decode intricate spatial information, marking a significant advancement in information retrieval from chaotic media, with applications in secure free-space optical communications and cryptography.
Novel AI boosts breast cancer detection in mammograms
A few prospective studies have suggested that AI use in mammography screening increases cancer detection. However, cancer detection supported with AI should not predominately identify indolent cancers or occur at the expense of more false positives; instead, AI usage should increase the detection of clinically relevant cancers.
About the study
In the present study, researchers assessed the performance of cancer screening measures in the MASAI trial. The trial was designed to compare AI-supported mammography screening with standard double-reading.