We are #hiring across AI Research, Product, Engineering, and GTM 👀 Join the team that is defining what Physical AI is and building a physical world model that learns from sensors and works across every asset, use case, and workflow. 🔗 Apply today: lnkd.in/dxi-3XRH

Newton’s embeddings turn a window of raw sensor readings into a single point, placed so that moments that look alike in the physical world land near each other. This public dataset, PAMAP2, shows data recorded when a group of nine people wore three motion sensors, on the wrist, the chest and the ankle, while going through twelve everyday activities. This visualization tool helps our team see the data in more detail and isolate different states: Rest, walk, run, cycle, stairs and housework each settle into their own region. Seeing why a model groups two moments together is the first step toward understanding what it tells you about the physical world. Built with Newton on the PAMAP2 physical activity dataset (Reiss and Stricker, 2012).

When people hear Physical AI, they picture a humanoid robot. Our CTO, Nick Gillian, makes the case that most of the real opportunity lies in existing equipment rather than in deploying a humanoid workforce. Every factory is streaming radar, video, vibration, and current. Newton is one model that reads all of it, catches what’s off in real time, and adapts to machines it’s never seen, right at the edge. Full conversation with @AI_in_the_AM ↓

If you’re at Long Horizon, head over to the Frontier Stage! Our Cofounder and Chief Scientist, @_jaimelien_ will be on stage at 2:55 pm.

An AI model correctly identified a slow, regular heartbeat in patient monitoring data. Then it incorrectly concluded the readings ruled out a serious cardiac conduction abnormality. Even when an AI model is given the right conclusion about a physical system, it can still be wrong about why. In new research with @encord_team, announced today at the Long Horizon Summit alongside the launch of Encord Data Labs, we tested three AI models on sensor data from patient monitoring (see the graph below), building HVAC systems, and atmospheric CO₂. Each model was given the correct finding, and domain experts reviewed its explanation against the underlying signals. The research found that all three produced flawed explanations of the evidence behind it: → An HVAC airflow obstruction was recognized, but sensor readings were misread to rule out other faults → Seasonal CO₂ patterns were recognized, but the physical processes driving them were reversed → A slow heartbeat was recognized, but pulse data was misread to rule out heart block A plausible explanation isn’t the same as an accurate one, and accuracy matters when real-world decisions depend on it. With Encord Data Labs, we’re pairing Newton’s native understanding of sensor data with expert evaluation to ground AI reasoning in measurable evidence. Read the full findings on our blog: archetypeai.io/blog/archetyp…

Operational State Monitoring agent helps you understand the hidden operating conditions that drive machine performance. It allows operators to: 📊 Continuously monitor machines and industrial processes 🔎 Identify changing operating states from multimodal sensor data 💡 Provide real-time visibility into machine behavior, surfacing equipment degradation, process instability, and the operating envelopes that drive reliability, quality, and throughput Learn more about our physical world model, Newton's, capabilities here: archetypeai.io/agents

Unplanned downtime cost the world’s 500 largest companies roughly $1.4 trillion in 2024, about 11% of their annual revenue, according to Siemens. Factories, grids, and fleets have been monitoring sensor data to detect these failures early for years. What’s been missing is the intelligence layer that can read those signals and help operators take action before a line stops. Our Co-founder and COO, Brandon Barbello, explains why the first wave of AI models struggled to understand the physical world and what the next wave needs to get right. Read the full piece in Forbes. forbes.com/councils/forbeste…

ICYMI: Newton, our physical world model, was named one of the Next Big Things in Tech in Foundational AI 🚀 Thanks to our talented team that made this possible! Learn more about the award and this year’s winners: fastcompany.com/91612044/fou…

Most products are finished the day they ship. Physical AI makes a different kind of object possible, one whose behavior keeps unfolding through use, shaped by the people and places around it. That idea sits at the center of Living Objects: Designing Products That Grow With Us, the talk our co-founder and Chief Design Officer Leonardo Giusti is giving at KIKK Festival on Oct. 23. At KIKK, Leo will share three objects developed by Archetype AI and the MIT Design Intelligence Lab: a lamp with memory, a radio that gives voice to the conditions around it, and a set of tags that connect everyday objects. Each one is designed to stay open-ended. The question he’ll leave the room with: what should be decided in advance, and what should be left for life to shape?

RT @ipoupyrev: Say “AI in the physical world,” and everyone pictures a robot. The robot is the least interesting part of the story. The op…

Physical systems are becoming increasingly complex, and it takes more and more specialized expertise to run them. At the same time, experienced workers are retiring, and hiring in physical industries like manufacturing is hard. The push for more energy, more GPUs, and more data centers only widens that gap. On Inside the Silicon Mind, our CEO @ipoupyrev says the answer is to pair AI with the workers and operators who run these systems. Operators bring the expertise, and agents turn data into insights. Watch the full episode: youtu.be/ZpOOpAOpc_Y?si=cvPD…

A new customer shouldn’t mean a new model. But that’s the trap for a lot of Physical AI and robotics startups. Each new site brings new machines, new sensors, and new operating conditions. With custom ML, that means labeling new data and building another model. Newton works differently. It’s one pretrained model that works across sensors, machines, and sites, and adapts from a handful of examples. In one manufacturing deployment, a single Newton model ran zero-shot across every motor type, from different sizes and manufacturers, and caught gearbox faults early. So your tenth customer runs on the same foundation as your first. Need a new capability? Add a Newton Agent on the same model. Start building on Newton: archetypeai.io/build-with-us

Physical AI can’t run like an LLM in a data center. There isn’t enough power for that. An alternative @ipoupyrev is proposing: train in the cloud, deploy to the edge, run online or fully offline. Decisions happen in real time, and your data stays yours.

Our Chief Scientist @_jaimelien_ joins the panel at Long Horizon in San Francisco on Oct. 20 to take on one question: “Will World Models Eat Physical AI?” “World model” is a broad term; some are built to generate or simulate the physical world. Newton is a physical world model that learns from real-world sensor data, so it’s built to understand what is actually happening on a machine or a factory floor. That’s the view Jaime brings to the debate. ➡️ In SF? Join us: archetypeai.io/resources/eve…

The most critical operational events often have very little historical data, making them difficult for traditional AI systems to understand. Our Rare Event Detection agent, powered by Newton, learns the unique patterns associated with known faults, failures, or operational events from just a handful of labeled examples, enabling teams to identify critical conditions before they become larger operational problems. Learn more: archetypeai.io/agents

Happening this week 👀 ⬇️

Imagine asking a motor how it’s doing, and an AI agent giving you a response in natural language. That’s how @ipoupyrev described the type of world model we’re building on Inside the Silicon Mind. Sensors already measure things like temperature, rotation speed, vibration, current and flow. Physical AI is capable of reading those signals and tells you what’s going on inside the machine, in words you can act on. Ivan and the hosts get into how it all works. Have a listen: youtube.com/watch?v=ZpOOpAOp…