The Newton World Model: How We’re Building AI That Understands Your Physical World
Across manufacturing, energy, and other major industrial sectors, experienced operators are retiring faster than they can be replaced. The machines they understood are getting more complex. Industrial systems have outgrown human bandwidth, generating more data than any team can understand in real time.
Fifty years of system identification, statistical process control, and physics-informed modeling haven't closed the gap. Neither has a single global AI model — every machine is unique, every environment is different, and every asset evolves over time in ways no training corpus can capture.
Operational intelligence requires both a global foundation and local adaptation. Join our Chief Scientist, Jaime Lien, for a discussion of the Newton World Model — our Physical AI architecture that enables machines to learn their own dynamics directly from sensor data, the way a skilled operator does after years of experience.
We'll walk through the research, demonstrate the approach in action, and introduce our latest Newton Fine-Tuning capability — turning your operational data into a model adapted to your specific machines and operations.
You'll learn:
Why a global foundation and local adaptation are essential for Physical AI
How machines can autonomously discover their own operational ontology from sensor observation alone — without labels, predefined states, or domain expertise
How universal architecture and machine-specific adaptation combine to make this scalable
What Newton Fine-Tuning enables today, and where autonomous adaptation is headed next