Matthew Putman, this week’s guest on the AI Podcast, knows that the devil is in the details. That’s why he’s the co-founder and CEO of Nanotronics, a Brooklyn-based company providing precision manufacturing enhanced by AI, automation and 3D imaging.
He sat down with AI Podcast host Noah Kravitz to discuss how running deep learning networks in real time on factory floors produces the best possible products. And how Nanotronics models and equipment are finding success in fields ranging from the semiconductor industry to genome sequencing.
Key Points From This Episode:
Nanotronics develops universal AI models that can be customized depending on individual customers’ processes and deployments.
The AI models that Nanotronics deploys at a customer site can be communicated directly from the GPU to the machine, without the cloud, to ensure security and speed.
When the new Nanotronics factory is finished (pictured, above), they’ll use their own deep learning models to ensure precision manufacturing as they construct their equipment.
- “It’s a great advantage to our customers to actually have a smaller footprint because we have a computationally driven system, rather than a system that requires a lot of very expensive large hardware” — Matthew Putman [7:14]
- “We can adjust actual controls in real time to make corrective actions for any type of anomalies that occur. It’s not so important to us what the absolute value is on each of the stations, it’s that by the end, the product has the most reproducibility and highest quality possible” — Matthew Putman [8:47]
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