Newton vs. LLMs: The case for Physical AI

​Large language models have transformed digital work — from customer service to coding and financial analysis. But walk onto a factory floor, power plant, or construction site and the picture looks different.

​The physical world doesn't speak in text. It operates in signals — vibration, temperature, current, and motion. And understanding those signals at scale requires a fundamentally different kind of AI.

​In this virtual event, we’ll explore how our foundation model, Newton, was designed to understand the physical world in ways that language models can’t — by understanding real-time multimodal sensor data. This unlocks new classes of problems from identifying anomalies in critical infrastructure and optimizing sophisticated machinery to coordinating complex physical systems where robots and humans interact.

​What you’ll walk away with:

• ​The limits of LLMs on physical data

• ​What a physical AI foundation model requires to generalize across domains

• ​The latest research and techniques behind Newton

• ​How Newton compares to traditional approaches

​The session will also include a demo of Newton and the Archetype AI platform.