Paul Liang
@pliang279 • 9,030 subscribers
Assistant Professor MIT @medialab @MITEECS @nlp_mit || Foundations of self-evolving multisensory AI to enhance the human experience.
Videos
Most of today's AI can see the world, but it doesn’t **feel** it. Capturing the sense of touch is crucial for dexterous robotic manipulation, user modeling, and understanding physical interactions. Introducing OpenTouch: bringing full-hand tactile sensing into real-world AI🖐️ OpenTouch is collected in-the-wild using tactile sensing gloves, hand pose tracking gloves, and egocentric glasses. It includes: • 5 hours of real-world data, • 3 hours densely annotated contact-rich interactions, • 2,900 curated interaction clips, • across 800 objects, 14 environments, and 29 grasp types. all open at:
Paul Liang47,158 просмотров • 3 месяцев назад
Despite much progress in AI, the ability for AI to 'smell' like humans remains elusive. Smell AIs 🤖👃can be used for allergen sensing (e.g., peanuts or gluten in food), hormone detection for health, safety & environmental monitoring, quality control in manufacturing, and more. As a step towards AI for smell, our group is releasing **SmellNet,** a massive open dataset to advance AI smell-recognition in real-world settings. Using portable gas and chemical sensors, we collected 180,000 time steps of 50 substances (spanning nuts, spices, herbs, fruits, and vegetables) with 50 hours of data. SmellNet enables the training of AI models for real-time classification of substances based on their smell alone - see video below, where even subtle differences between cumin, cloves, and oregano can be detected. Check out our paper and open-source data & code for the smell AI revolution! paper: data & code: w Dewei, Carol, David [email protected] MIT Media Lab MIT EECS
Paul Liang20,600 просмотров • 1 год назад
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