Insights      Technology      Applied AI      Episode 71: AI and the Future of Manufacturing

Episode 71: AI and the Future of Manufacturing

Manufacturing hasn’t really changed all of that much in the past 75 years, with one key exception. Today we’re capturing vast amounts of data about every single process that takes place on the factory floor. And while that data is proving very useful, we’re only just beginning to be able to use it to optimize decision-making and create better products. In this episode, Jon Prial talks with Matthew Putman, the Co-Founder and CEO of Nanotronics. They talk about how miniaturization, sensors, and robotics are different than traditional consumer-facing ML solutions. They also discuss the difference between hardware control systems with sensors and software processes, as well as where to focus your efforts for optimization.

You’ll Hear About

  • The uses of machine learning and artificial intelligence in process management
  • How manufacturing is evolving with the explosion of data
  • Using artificial intelligence to create new manufacturing processes

[sc name=”share-podcast”]

Who Is Matthew Putman? 

Matthew Putman is an American scientist and the CEO and Co-Founder of Nanotronics, a company that provides ultra high-resolution images for industry and science. He’s also a Professor and Researcher at Colombia University. He was previously an owner and Vice President of Development for Tech Pro, Inc., which was acquired by Roper Industries in March 2008. Matthew has lectured at The University of Paris, USC, University of Michigan, and The Technical University of Sao Paulo. He holds 5 patents, and has published over 20 technical papers and has a Ph.D. in applied physics.

You can find him here on LinkedIn.

Read more like this

From Static to Adaptive: Scaling AI Reasoning Without the Waste

2025 has been the year of reasoning models. OpenAI released o1 and...

Why Georgian Invested in Replit

We are excited to announce our latest investment in Replit’s $250 million…

A Practical Guide to Reducing Latency and Costs in Agentic AI Applications

Scaling companies that are actively integrating Large Language Models (LLMs) into their…