Episode Details

Connect with Christopher Nguyen

Website: www.aitomatic.com. 

Lisa Ryan: Hey, it's Lisa Ryan. Welcome to the Manufacturers' Network Podcast. I'm excited to introduce our guest today, Christopher Nguyen. With a decades-long career, Christopher's tech bona fides are second to none. Since fleeing Vietnam in 1978, this multiple-time tech founder has played key roles and everything from building the first flash memory transistors at Intel to spearheading the development of Google Apps as its first engineering director. Today he's become an outspoken proponent of the emerging field of Ai engineering and a thought leader in the space of ethical human-centric Ai. With his latest company Aitomatic, he's hoping to redefine how companies approach Ai in the context of life-critical industrial applications. Christopher, welcome to the show.

Christopher Nguyen: Hi, Lisa thanks for having me.

Lisa Ryan: Share with us a little bit about your background and what led you to do what you're doing now with Ai.

Christopher Nguyen: The most relevant thing about what I'm doing now can be considered a failure, starting after my previous company's acquisition by a company called Panasonic. We all know Panasonic as a global engine. However, many people don't realize that Panasonic is less of a consumer company than an industrial company in manufacturing, avionics, and automotive. 

The acquisition of my previous company was the apply Ai machine learning to that global engine. Very quickly, we found that a lot of our, let me call it Silicon Valley techniques of digital-first companies like Google and Facebook, and Twitter run into apparent limitations when it comes to dealing with the physical world. The discussion or debate between atoms versus bits, and we've had to develop a whole bunch of techniques that involve leveraging a lot of human knowledge and expertise. We are automating all of that with machine learning to solve these industrial problems. That's the thesis of Aitomatic, the company.

Lisa Ryan: So how do you do that when you talk about taking that human knowledge? How are you taking what we do almost automatically as human beings and turning that into machine learning?

Christopher Nguyen: Maybe I can share why we do that because too many of us today, that is counterintuitive. We thought the future is only data-driven, and we only collect enough data with sensors on machines, and then we feed them and do these machine learning algorithms, and they'll know and don't predict they'll do everything for us.

It turns out that doesn't apply not today and enough for a very long time to the physical industry. Take the problem of looking at sensors on a machine by refrigeration system and then trying to predict in advance. Is this likely to fail over the next two weeks? Is a compressor going to conk out or something like that? To do that, we still rely on human expertise because it's not in the data we're collecting. It's in their life experience. 30-40 years of seeing various refrigeration systems, models, operating conditions, and so on and building up instead of intuitions in their minds over time. We failed trying to do it the other way. We succeeded in incorporating human knowledge. That's the reason we do that. I can talk about how we do that.

Lisa Ryan: That's interesting because when you have somebody that's been in the job for 20 or 30 years and, as you said, that's that feeling that intuition and being able to take a human feeling and turn it into data, that's just fascinating. If there's an easy way to describe how that happens, that would be great.

Christopher Nguyen: If we learn like humans, we're building learning machines. We can either learn from examples, or we could le