DiffuseDrive is filling the data gaps holding back Physical AI

What happened

Training AI to operate in the physical world requires enormous amounts of data, but many situations autonomous systems need to recognise are rare, dangerous, or hard to capture in the real world. Hungarian startup DiffuseDrive is tackling that data gap by generating synthetic training data for physical AI systems, helping companies expose their models to scenarios their existing datasets don't contain.

Vecsei leads the engineering of the company’s AI systems and the transition of research into scalable production technology. Before joining DiffuseDrive, Vecsei spent several years working in machine learning and AI engineering, including leading an ML R&D organisation that grew from a small team into a department of more than 30 people.

He studied at the Budapest University of Technology and Economics, specialising in applied computer science. Schmid is AI Research Lead at DiffuseDrive, working at the intersection of the company’s research and production engineering.

Originally from Germany, he has specialised in machine learning since 2020. Why Physical AI has a data problem DiffuseDrive aims to solve the problem of data scarcity in physical AI across applications including defence, aerospace, mining, and autonomous vehicles. DiffuseDrive has built a system that can generate those missing data points and inject them directly into customers' datasets, helping improve downstream applications such as autonomous drones, vehicles, and mining systems.

Key facts

• Training AI to operate in the physical world — requires: enormous amounts of data, but many situations autonomous systems need to recognise are rare, dangerous, or hard to capture in the real world