$124B Data Problem: How Synthetic Data Accelerates AI

When your AI has to make decisions in the real world, the data you don’t have can hurt people.

 

DiffuseDrive CEO Balint Pasztor joins IT Visionaries to unpack the $124B data scarcity problem holding back autonomous systems — and how synthetic data (done right) can compress years of data collection into hours.

 

We dig into: why edge cases matter (and how to safely create them), closing the sim-to-real gap with diffusion models, preventing model drift, and building a data engine for physical AI across defense, robotics, and industrial automation. If you care about self-driving cars, drones, QA on the factory floor—or just shipping AI that survives the messiness of reality—this one’s for you.

 

Key Moments:

00:00 Introduction to Autonomous Driving Challenges

00:26 Meet Balint Pasztor and Diffuse Drive

01:14 The Importance of Synthetic Data

06:39 The Role of Synthetic Data in AI Training

18:07 Understanding Diffusion Models

23:28 Challenges in Real-World Data Collection

26:46 Three Steps to Improve AI Performance

32:13 Overcoming Non-Obvious Data Challenges

36:00 Balancing Data Quantity and Quality

40:28 Future of Autonomous Systems and Physical AI

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