Trends in Physical AI Innovations

The future of scientific research just shifted into overdrive Researchers at North Carolina State University have developed an AI-powered autonomous laboratory that accelerates materials discovery by 1,000%, fundamentally changing how we approach some of humanity's most pressing challenges The breakthrough lies in replacing traditional steady-state experiments with dynamic flow experiments, where chemical mixtures are continuously varied through the system and monitored in real time Instead of capturing a single snapshot, this approach creates "a full movie of the reaction as it happens," generating 20 data points where conventional methods would produce just one This isn't just an incremental improvement, it's a paradigm shift The system's streaming-data approach enables machine learning algorithms to "make smarter, faster decisions, honing in on optimal materials and processes in a fraction of the time" The implications ripple across industries critical for our future: - Faster battery development for electric vehicles - Accelerated solar panel efficiency improvements - Rapid advancement in sustainable manufacturing materials. Professor Milad Abolhasani, who led this research, envisions a future where "scientists could discover breakthrough materials for clean energy, new electronics, or sustainable chemicals in days instead of years, using just a fraction of the materials and generating far less waste" We're witnessing AI evolve from analyzing existing data to actively conducting scientific research itself This autonomous lab represents the convergence of artificial intelligence with physical experimentation, a combination that could redefine the pace of innovation across multiple sectors The research, published in Nature Magazine Chemical Engineering, demonstrates that AI's greatest impact may not be in replacing human tasks, but in amplifying human capability to solve complex problems at unprecedented speed Read more about this breakthrough: https://lnkd.in/dkQKaD-d

If you’ve been paying attention these last 5 years, you probably noticed a parade of robotics startups close their doors. But you may also have noticed in the last 12 months a resurgence of humanoid robotics companies, like Physical Intelligence, Sanctuary AI, Figure AI. (A nod to Boston Dynamics whose Atlas is 11 years old.) When I stand back and squint, I’m seeing a trend. Humanoid robots are undeniably cool, but are particularly difficult to extract useful work from. Thanks to recent developments in reinforcement learning, we are closer than we’ve ever been, but we still have a little ways to go. Watch this space. I’m expecting to see a handful of unconventional algorithmic advances in the next two years. They will be focused on handling the idiosyncrasies and chaos of the physical world and will run on an onboard processor.

“𝗜𝗳 𝗜 𝘄𝗲𝗿𝗲 𝟮𝟬 𝗮𝗴𝗮𝗶𝗻, 𝗜’𝗱 𝘀𝘁𝘂𝗱𝘆 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝘀𝗰𝗶𝗲𝗻𝗰𝗲𝘀.” – 𝗝𝗲𝗻𝘀𝗲𝗻 𝗛𝘂𝗮𝗻𝗴, 𝗖𝗘𝗢 𝗼𝗳 𝗡𝘃𝗶𝗱𝗶𝗮 It’s not every day that the CEO of the world’s most valuable tech company points away from software and toward physics, chemistry, and engineering. Jensen Huang believes the next wave of innovation is Physical AI, where AI systems reason about and interact with the real world. This shift demands a strong foundation in the physical sciences, not just data and code. Why? Because Physical AI isn’t just about generating language or images, it’s about understanding friction, force, motion, and material behavior. It’s the kind of intelligence needed for: ▫️ Advanced robotics ▫️ Smarter manufacturing ▫️ Real-world decision-making ▫️ Complex healthcare technologies As AI moves beyond screens and into physical systems, the ability to model, predict, and manipulate the physical world becomes essential. 🔗 https://lnkd.in/gBfTa8_t 𝗪𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘀𝘁𝘂𝗱𝘆 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘁𝗵𝗶𝗻𝗴 𝗶𝗳 𝘆𝗼𝘂 𝘄𝗲𝗿𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗰𝗼𝗹𝗹𝗲𝗴𝗲 𝘁𝗼𝗱𝗮𝘆? 𝗔𝗻𝗱 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗶𝘀 𝘀𝗵𝗶𝗳𝘁 𝘄𝗶𝗹𝗹 𝗰𝗵𝗮𝗻𝗴𝗲 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘁𝗲𝗮𝗰𝗵 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗮𝗻𝗱 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀?