What if the sounds of coughs could unlock the secrets to diagnosing diseases? 💡 Researchers at Google have created an innovative AI model called Health Acoustic Representations (HeAR), designed to identify acoustic biomarkers for diseases like tuberculosis. This groundbreaking technology has the potential to revolutionize healthcare by simplifying disease diagnosis using sound. This video delves into the development of HeAR, exploring how it leverages machine learning to analyze coughs and breathing patterns, potentially leading to faster and more accessible disease detection. Learn how HeAR could transform healthcare by enabling early diagnosis and treatment, particularly in areas with limited access to advanced medical tools. Discover the future of healthcare with HeAR and its potential to improve lives around the world. Read more → https://goo.gle/3Xa0GGM Watch more Research Bytes → https://goo.gle/3Xa2DTz Subscribe to the Google Research Channel → https://lnkd.in/eeV2KKpB
About us
From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day. We aspire to make discoveries that impact everyone, and sharing our research and tools to fuel progress in the field is fundamental to our approach.
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https://research.google/
External link for Google Research
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- Technology, Information and Internet
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Updates
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In collaboration with YouTube, we’re exploring how generative AI can empower healthcare creators to produce even more high-quality content. Learn more about how we’re piloting these tools with a select group of healthcare partners, including Cleveland Clinic and Dr. Jennifer Caudle, DO FACOFP, to gather feedback and refine their capabilities →https://goo.gle/3YJVyKm Many thanks to the team for advancing this work! ↓ VP and GM of GR: Yossi Matias Sponsors: Garth Graham, Katherine Chou, Dale Webster, Ebi Atawodi, Preeti Singh, Sunny Virmani Contributors: Viknesh Sounderajah, Abhijit Guha Roy, Divya P., Siyi Kou, and many others #AIforGood #HealthTech #GenerativeAI #GoogleResearch #YouTubeHealth
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Today we describe a model taught to read and write so it can extract and digitize the strokes of handwriting without the need for specialized equipment. It then outputs realistic looking digital handwriting that can be handled like standard digital text. https://goo.gle/3NGjat0
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When presented with many stimuli (both good or bad) the sympathetic nervous system triggers a response that prepares the body to respond. Read the latest research on how electrodermal activity can relate to stress around daily routines, holidays, & more →https://goo.gle/3YFSDCj
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Creating a more connected world starts with making information accessible and useful for everyone. From translating emails to interacting with virtual assistants, #GoogleResearch is taking practical steps to incorporate language models into tech ecosystems. Learn more about Google’s efforts for furthering language inclusivity here: https://lnkd.in/gQkQp3fR
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Whether you're a seasoned quantum pro or just starting to explore, our Quantum AI Glossary series is for you. We're breaking down complex terms into clear, concise explanations. Let's expand our #QuantumAI vocabulary together.
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Introducing Iterative BC-Max, a new technique that reduces the size of compiled binary files by optimizing inlining decisions. This approach offers several advantages over standard reinforcement learning algorithms. Read all about BC-Max at: https://goo.gle/3AcnLjG
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When we train deep learning models for genomics, what do they learn? To help answer this question, we examined the DeepVariant model to determine what insights it has developed, and we discovered some surprising concepts embedded within. Read more at https://goo.gle/4eUGNdg
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Today on the blog, we discuss how we improved the probabilistic reasoning capabilities of LLMs. By incorporating real-world context and simplifying assumptions, we show that these models can make more accurate inferences about distributions. Learn more at: https://goo.gle/4dUkzH8
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