Podcast appearances and mentions of christopher nguyen

Show notesJoin Mollie and Claire in their discussion with Christopher Nguyen, the founder of UX Playbook. From a computer science dropout to a business graduate, turned self-taught designer, and now a content creator. This episode covers Chris's career journey, including his experiences in toxic work environments, his decision to quit his job during the pandemic, the process of starting and growing UX Playbook, his approach to writing and content creation, and his future plans to evolve his business into coaching designers to build their own businesses. Key discussion points include recognizing signs of an unhealthy work culture, taking ownership of one's career path, the importance of self-reflection, the role of writing and content marketing, and Chris's vision for helping designers become entrepreneurs. (1:11) - Guest Introduction The hosts introduce their guest Chris, a product designer, founder of UX Playbook, and former head of design who has worked with companies like Google, Nike, and Coca-Cola. They discuss Chris's background and career journey.(3:01) - Toxic Work EnvironmentsChris shares his experience of joining a company that turned out to be toxic, despite being warned beforehand. He describes the red flags, lack of innovation, and the 'good enough' culture that eventually led him to quit.(12:31) - Taking a Break and Starting UX Playbook After quitting his toxic job, Chris took some time off to experiment with different projects before accidentally stumbling upon the success of UX Playbook through writing online content.(19:21) - Knowing When to Leave a Job The discussion shifts to recognizing signs that it's time to leave a job, such as physical aversion to going to the office, draining interactions, and lack of excitement about the work.(23:41) - Layoffs and Career Transitions Chris offers advice for those feeling stuck in their current roles due to the fear of layoffs, emphasizing taking small steps towards exploring new opportunities.(28:21) - Chris' new venture - The Job Sprint Program Chris talks about his new program 'The Job Sprint', created to help people land their dream jobs through a tailored approach and active learning activities.(33:11) - Future Business Evolution Chris shares his vision for evolving his business beyond just serving designers, potentially helping them build their own businesses and potentially owning a stake in their ventures.Be sure to check out Chris' latest venture - https://backlog.design/?ref=bounce

Today my guest is Christopher Nguyen, Design Leader, Facilitator, and System Thinker. Chris created the UX Playbook, a set of online self-teaching courses on UX career growth, portfolio, management, and user experience. Check this out, the link is in the description. In this episode, we're talking about UX workshops. When they are most useful, how to deal with tough participants (Chris calls them “troublemakers”), and what are the key ingredients of an effective workshop?   Useful links: UX Playbook by Chris Chris on LinkedIn 10 Tips and Trick on Workshops: Ask questions, write stuff down, and keep the timelimit. Ensure participants are committed. Use fake confidence if you don't have a real one yet. Provide help; a facilitator's role is to be helpful. Infuse energy; maintain an energetic demeanor. Schedule regular breaks. Practice as many times as possible and be proactive. Don't take yourself too seriously. Employ a "What? Why? How?" approach for each activity. Gather feedback to understand areas for improvement and recognize positive aspects. Subscribe to us: Twitter LinkedIn Instagram Ning on LinkedIn Nik on LinkedIn  

Christopher Nguyen is CEO and Co-founder of Aitomatic, a startup that builds virtual advisors tailored with domain-specific expertise, primarily catering to industrial AI applications. Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

In this episode Lauren Hawker Zafer is joined by Dr. Christopher Nguyen Who is Dr. Christopher Nguyen? Dr. Christopher Nguyen, CEO and Cofounder of Aitomatic, has an extensive career in Silicon Valley and global industries. He served as the first engineering director for Gmail at Google and led Global Industrial AI at Panasonic for four years. His current work in Industrial AI is exemplified by the "Industrial Mind," a System-2 AI solution designed for problem-solving in the industrial sector.Dr. Nguyen combines human knowledge with transparent AI, focusing on maximizing Industrial AI's potential for societal impact. He holds a BS in EECS from UC Berkeley, PhD Stanford, and co-founded the Computer Engineering department at HKUST. With his latest company, ⁠Aitomatic⁠, he's hoping to redefine how companies approach AI in the context of life-critical, industrial applications. Why this Episode?

Spotlight Thirteen is a snippet from our upcoming episode: Dr. Christopher Nguyen - Why AI Needs a Human Eye! Listen to the full episode, as soon as it comes out by subscribing to Redefining AI. Who is Dr. Christopher Nguyen? Dr. Christopher Nguyen, CEO and Cofounder of Aitomatic, has an extensive career in Silicon Valley and global industries. He served as the first engineering director for Gmail at Google and led Global Industrial AI at Panasonic for four years. His current work in Industrial AI is exemplified by the "Industrial Mind," a System-2 AI solution designed for problem-solving in the industrial sector. Dr. Nguyen combines human knowledge with transparent AI, focusing on maximizing Industrial AI's potential for societal impact. He holds a BS in EECS from UC Berkeley, PhD Stanford, and co-founded the Computer Engineering department at HKUST. With his latest company, ⁠⁠Aitomatic⁠⁠, he's hoping to redefine how companies approach AI in the context of life-critical, industrial applications. Why this Episode?

The conversation this week is with Christopher Nguyen. Christopher is CEO and co-founder of Aitomatic, where he's helping businesses harness the power of knowledge-first AI for solutions in predictive maintenance, energy optimization, demand forecasting, and more. Outside of his deep experience in AI, his background includes strategic executive leadership, hands-on software engineering, management, and highly available low-latency services architecture.If you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future AppliedAI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events!Emerging Technologies NorthAppliedAI MeetupResources and Topics Mentioned in this EpisodeAitomaticAtomic Layer DepositionKnowledge transferLarge language modelAugmented IntelligenceMaxwell's equationsK1st World 2023Useful Sensors Inc.Enjoy!Your host,Justin Grammens

In this week's episode, I am joined by Dr. Christopher Nguyen. We talk about the emerging concept of "human first AI," and the changing terrain of both AI ethics, and AI development. We imagine what a human-first approach to AI might look like, and what gets in the way of developing an ethical approach to AI in the tech industry. Christopher Nguyen's career spans four decades, and he has become an industry leader in the field of Engineering broadly, and AI specifically. Since fleeing Vietnam in 1978, he has founded multiple tech companies and has played key roles in everything from building the first flash memory transistors at Intel to spearheading the development of Google Apps as its first Engineering Director. As a professor, Christopher co-founded the Computer Engineering program at the Hong Kong University of Science and Technology, or HKUST. He earned his Bachelor of Science. degree from the University of California-Berkeley, summa cum lauday, and a PhD. from Stanford University. 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.

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Human-First AI. Our guest is Christopher Nguyen (https://www.linkedin.com/in/ctnguyen/), CEO, and Co-Founder of Aitomatic (https://www.aitomatic.com/). In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse. If you like this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you like this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat (https://www.augmentedpodcast.co/80). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations in industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Human-First AI. Our guest is Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse. Augmented is a podcast for industrial leaders, process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Christopher, how are you? And welcome. CHRISTOPHER: Hi, Trond. How are you? TROND: I'm doing great. I thought we would jump into a pretty important subject here on human-first AI, which seems like a juxtaposition of two contradictory terms, but it might be one of the most important types of conversations that we are having these days. I wanted to introduce you quickly before we jump into this. So here's what I've understood, and you correct me if I'm wrong, but you are originally from Vietnam. This is back in the late '70s that you then arrived in the U.S. and have spent many years in Silicon Valley mostly. Berkeley, undergrad engineering, computer science, and then Stanford Ph.D. in electrical engineering. You're a sort of a combination, I guess, of a hacker, professor, builder. Fairly typical up until this point of a very successful, accomplished sort of Silicon Valley immigrant entrepreneur, I would say, and technologist. And then I guess Google Apps is something to point out. You were one of the first engineering directors and were part of Gmail, and Calendar, and a bunch of different apps there. But now you are the CEO and co-founder of Aitomatic. What we are here to talk about is, I guess, what you have learned even in just the last five years, which I'm thrilled to hear about. But let me ask you this first, what is the most formational and formative experience that you've had in these years? So obviously, immigrant background and then a lot of years in Silicon Valley, what does that give us? CHRISTOPHER: I guess I can draw from a lot of events. I've always had mentors. I can point out phases of my life and one particular name that was my mentor. But I guess in my formative years, I was kind of unlucky to be a refugee but then lucky to then end up in Silicon Valley at the very beginning of the PC revolution. And my first PC was a TI-99/4A that basically the whole household could afford. And I picked it up, and I have not stopped hacking ever since. So I've been at this for a very long time. TROND: So you've been at this, which is good because actually, good hacking turns out takes a while. But there's more than that, right? So the story of the last five years that's interesting to me because a lot of people learn or at least think they learn most things early. And you're saying you have learned some really fundamental things in the last five years. And this has to do with Silicon Valley and its potential blindness to certain things. Can you line that up for us? What is it that Silicon Valley does really well, and what is it that you have discovered that might be an opportunity to improve upon? CHRISTOPHER: Well, I learn new things every four or five years. I actually like to say that every four or five years, I look back, and I say, "I was so stupid five years ago." [laughs] So that's been the case. TROND: That's a very humbling but perhaps a very smart knowledge acquisition strategy, right? CHRISTOPHER: Yeah. And in the most recent five years...so before co-founding Aitomatic, which is my latest project and really with the same team...and I can talk a lot more about that. We've worked with each other for about ten years now. But in the intervening time, there's a four-and-a-half-year block when we were part of Panasonic. So we had a company called Arimo that was acquired by Panasonic for our machine learning AI skills and software. And I would say if you look at my entire history, even though I did start with my degree in semiconductor all the way down to device physics and Intel and so on, but in terms of a professional working career, that was the first time we actually faced the physical world as a Silicon Valley team. And anybody who's observed Silicon Valley in the last 15-20 years, certainly ten years, has seen a marked change in terms of the shift from hardware to software. And my friend Marc Andreessen likes to say, "Software is eating the world." If you look at education, you know, the degrees people are getting, it has shifted entirely from engineering all the way to computer science. And the punch line, I guess, the observation is that we Silicon Valley people do not get physical. We don't understand the manufacturing world. We don't know how to do HVAC and so on. And so when we build software, we tend to go for the digital stuff. TROND: Christopher, it's almost surprising given the initial thrust of Silicon Valley was, of course, hardware. So it's not surprising to me, I guess because I've been observing it as well. But it is striking more than surprising that a region goes through paradigms. CHRISTOPHER: Yeah. Yeah. And it's a global trend. It's the offshoring of low-end, shall we say, low-value manufacturing and so on. And we're discovering that we actually went a little too far. So we don't have the skill set, the expertise anymore. And it's become a geopolitical risk. TROND: Right. Well, a little bit too far, maybe, or not far enough. Or, I mean, tell us what it is that you're losing when you lose the hardware perspective, particularly in this day and age with the opportunities that we're about to talk about. CHRISTOPHER: Well, I can talk specifically about the things that touch my immediate spheres. Maybe you can think abstractly about the lack of tooling expertise and manufacturing know-how, and so on. But as part of Panasonic, the acquisition was all about taking a Silicon Valley team and injecting AI, machine learning across the enterprise. And so we were part of that global AI team reporting to the CTO office. And we found out very quickly that a lot of the software techniques, the machine learning, for example, when you think about people saying data is the fuel for machine learning and specifically labeled data, right? In the digital world, the Google place that I came from, it was very easy to launch a digital experiment and collect labels, decisions made by users. You can launch that in the morning, and by evening you're building examples. You can't do that in the physical world. Atoms move a lot more slowly. And so when you try to do something like predictive maintenance, you don't have enough failure examples to train machine learning models from. So all of the techniques, all of the algorithms that we say we developed from machine learning that seem to work so well, it turns out it worked so well because the problem space that we worked on has been entirely digital, and they all fail when it comes to manufacturing, the things that you can touch and feel, you know, cars that move and so on. TROND: I want to ask you this, Christopher, because the first company you helped co-found was, in fact, a contract manufacturer. Do you think that reflecting on this long career of yours and these various experiences, what was it that convinced you before others? I mean, you're not the only one now in the Valley that has started to focus on manufacturing and including hardware again, but it is rare still. What does it require to not just think about manufacturing but actually start to do compute for manufacturing? Is it just a matter of coming up with techniques? Or is it a whole kind of awareness that takes longer? So, in your case, you've been aware of manufacturing, acutely aware of it for decades. CHRISTOPHER: I would say there are two things, one is obvious, and the other was actually surprising to me. The obvious one is, of course, knowledge and experience. When we work on sonar technology that shoots a beam down an echogram that comes back to detect fish in the ocean, it's very necessary, not just convenient, but necessary for the engineers that work on that to understand the physics of sound waves travel underwater, and so on. So that education, I have long debates, and it's not just recently. When we were trying to structure a syllabus for a new university, I had long debates with my machine-learning friends, and they said, "We don't need physics." And I said, "We need physics." That's one thing. But you can concretely identify you need to know this. You need to know this. So if you're going to do this, learn the following thing. The thing that was more unexpected for me in the last five years as I sort of sound this bell of saying, hey, we need to modify our approach; we need to optimize our algorithms for this world, is a cultural barrier. It's kind of like the story of if you have a hammer, you want to go look for nails. So Silicon Valley today does not want to look for screwdrivers yet for this world. TROND: So you're saying Silicon Valley has kind of canceled the physical world? If you want to be really sort of parabolic about this, it's like software is eating the world, meaning software is what counts, and it's so efficient. Why go outside this paradigm, basically? If there's a problem that apparently can't be fixed by software, it's not a valuable problem. CHRISTOPHER: Or I can't solve that problem with my current approach. I just have to squint at it the right way. I have to tweak the problem this way and so on despite the fact that it's sort of an insurmountable challenge if you tried to do so. And concretely, it is like, just give me enough data, and I'll solve it. And if you don't have enough data, you know what? Go back and get more data. [chuckles] That's what I myself literally said. But people don't have the luxury of going back to get more data. They have to go to market in six months, and so on. TROND: Right. And so manufacturing...and I can think of many use cases where obviously failure, for example, is not something...you don't really want to go looking for more failure than you have or artificially create failure in order to stress test something unless that's a very safe way of doing so. So predictive maintenance then seems like a, I guess, a little bit of a safer space. But what is it about that particular problem that then lends itself to this other approach to automating labeling? Or what exactly is it that you are advocating one should do to bridge to digital and the physical AIs? CHRISTOPHER: I actually disagree that it is a safer space. TROND: Oh, it's not a safer space to you. CHRISTOPHER: That itself there's a story in that, so let's break that down. TROND: Let's do it. CHRISTOPHER: So, again, when I say Silicon Valley, it is a symbol for a larger ecosystem that is primarily software and digital. And when I say we, because I've worn many hats, I have multiple wes, including academia; I've been a professor as well. When we approach the predictive maintenance problem, if you approach it as machine learning, you got to say, "Do this with machine learning," the first thing you ask for...let's say I'm a data scientist; I'm an AI engineer. You have this physical problem. It doesn't matter what it is; just give me the dataset. And the data set must have rows and columns, and the rows are all the input variables. And then there should be some kind of column label. And in this case, it'll be a history of failures of compressors failing, you know, if the variables are such, then it must be a compressor. If the variables are such, it must be the air filter, and so on. And it turns out when you ask for that kind of data, you get ten rows. [laughs] That's not enough to do machine learning on. So then people, you know, machine learning folks who say they've done predictive maintenance, they actually have not done predictive maintenance. That's the twist. What they have done is anomaly detection, which machine learning can do because, with anomaly detection, I do not need that failure label. It just gives me all the sensor data. What anomaly detection really does is it learns the normal patterns. If you give it a year's worth of data, it'll say, okay, now I've seen a year's worth of data. If something comes along that is different from the past patterns; I will tell you that it's different. That's only halfway to predictive maintenance. That is detecting that something is different today. That is very different from, and it isn't predicting, hey, that compressor is likely to fail about a month from now. And that when we were part of Panasonic, it turns out the first way...and we solved it exactly the way I've described. We did it with the anomaly detection. And then we threw it over the wall to the engineer experts and said, "Well, now that you have this alert, go figure out what may be wrong." And half of the time, they came back and said, "Oh, come on, it was just a maintenance event. Why are you bothering me with this?" TROND: But, Christopher, leveraging human domain expertise sounds like a great idea. But it can't possibly be as scalable as just leveraging software. So how do you work with that? And what are the gains that you're making? CHRISTOPHER: I can show you the messenger exchange I had with another machine-learning friend of mine who said exactly the same thing yesterday, less than 24 hours ago. TROND: [laughs] CHRISTOPHER: He said, "That's too labor-intensive." And I can show you the screen. TROND: And how do you disprove this? CHRISTOPHER: Well, [chuckles] it's not so much disproving, but the assumption that involving humans is labor-intensive is only true if you can't automate it. So the key is to figure out a way, and 10-20 years ago, there was limited technology to automate or extract human knowledge, expert systems, and so on. But today, technologies...the understanding of natural language and so on, machine learning itself has enabled that. That turns out to be the easier problem to solve. So you take that new tool, and you apply it to this harder physical problem. TROND: So let's go to a hard, physical problem. You and I talked about this earlier, and let's share it with people. So I was out fishing in Norway this summer. And I, unfortunately, didn't get very much fish, which obviously was disappointing on many levels. And I was a little surprised, I guess, of the lack of fish, perhaps. But I was using sonar to at least identify different areas where people had claimed that there were various types of fish. But I wasn't, I guess, using it in a very advanced way, and we weren't trained there in the boat. So we sort of had some sensors, but we were not approaching it the right way. So that helped me...and I know you work with Furuno, and Garmin is the other obviously player in this. So fish identification and detection through sonar technology is now the game, I guess, in fishery and, as it turns out, even for individuals trying to fish these days. What is that all about? And how can that be automated, and what are the processes that you've been able to put in place there? CHRISTOPHER: By the way, that's a perfect segue into it. I can give a plug perhaps for this conference that I'm on the organizing committee called Knowledge-First World. And Furuno is going to be presenting their work exactly, talking a lot about what you're talking about. This is kind of coming up in November. It is the first conference of its kind because this is AI Silicon Valley meets the physical world. I think you're talking about the fish-finding technology from companies like Furuno, and they're the world's largest market share in marine navigation and so on. And the human experts in this are actually not even the engineers that build these instruments; it's the fishermen, right? The fishermen who have been using this for a very long time combine it with their local knowledge, you know, warm water, cold water, time of day, and so on. And then, after a while, they recognize patterns that come back in this echogram that match mackerel, or tuna, or sardines, and so on. And Furuno wants to capture that knowledge somehow and then put that model into the fish-finding machine that you and I would hold. And then, instead of seeing this jumbled mess of the echogram data, we would actually see a video of fish, for example. It's been transformed by this algorithm. TROND: So, I mean, I do wish that we lived in a world where there was so much fish that we didn't have to do this. But I'm going to join your experiment here. And so what you're telling me is by working with these experts who are indeed fishermen, they're not experts in sonar, or they're not experts in any kind of engineering technology, those are obviously the labelers, but they are themselves giving the first solutions for how they are thinking about the ocean using these technologies. And then somehow, you are turning that into an automatable, an augmented solution, essentially, that then can find fish in the future without those fishermen somehow being involved the next time around because you're building a model around it. CHRISTOPHER: I'll give you a concrete explanation, a simplified version of how it works, without talking about the more advanced techniques that are proprietary to Furuno. The conceptual approach is very, very easy to understand, and I'll talk about it from the machine learning perspective. Let's say if I did have a million echograms, and each echogram, each of these things, even 100,000, is well-labeled. Somebody has painstakingly gone through the task of saying, okay, I'm going to circle this, and that is fish. And that is algae, and that's sand, and that's marble. And by the way, this is a fish, and this is mackerel, and so on. If somebody has gone through the trouble of doing that, then I can, from a human point of view, just run an algorithm and train it. And then it'll work for that particular region, for that particular time. Okay, well, we need to go collect more data, one for Japan, the North Coast, and one for Southwestern. So that's kind of a lot of work to collect essentially what this pixel data is, this raw data. When you present it to an experienced fisherman, he or she would say, "Well, you see these bubbles here, these circles here with a squiggly line..." So they're describing it in terms of human concepts. And then, if you sit with them for a day or two, you begin to pick up these things. You don't need 100,000-pixel images. You need these conceptual descriptions. TROND: So you're using the most advanced AI there is, which is the human being, and you're using them working with these sonar-type technologies. And you're able to extract very, very advanced models from it. CHRISTOPHER: The key technology punch line here is if you have a model that understands the word circle and squiggly line, which we didn't before, but more recently, we begin to have models, you know, there are these advances called large language models. You may have heard of GPT-3 and DALL-E and so on, you know, some amazing demonstrations coming out of OpenAI and Google. In a very simplified way, we have models that understand the world now. They don't need raw pixels. These base models are trained from raw pixels, but then these larger models understand concepts. So then, we can give directions at this conceptual level so that they can train other models. That's sort of the magic trick. TROND: So it's a magic trick, but it is still a difficult world, the world of manufacturing, because it is physical. Give me some other examples. So you worked with Panasonic. You're working with Furuno in marine navigation there and fishermen's knowledge. How does this work in other fields like robotics, or with car manufacturing, or indeed with Panasonic with kind of, I don't know, battery production or anything that they do with electronics? CHRISTOPHER: So, to give you an example, you mentioned a few things that we worked on, you know, robotics in manufacturing, robotics arm, sort of the manufacturing side, and the consistency of battery sheets coming off the Panasonic manufacturing line in Sparks, Nevada as well as energy optimization at Westinghouse. They supply into data centers, and buildings, and so on. And so again, in every one of these examples, you've got human expertise. And, of course, this is much more prevalent in Asia because Asia is still building things, but some of that is coming back to the U.S. There are usually a few experts. And by the way, this is not about thousands of manufacturing line personnel. This is about three or four experts that are available in the entire company. And they would be able to give heuristics. –They will be able to describe at the conceptual level how they make their decisions. And if you have the technology to capture that in a very efficient way, again, coming back to the idea that if you make them do the work or if you automate their work, but in a very painstaking way like thousands of different rules, that's not a good proposition. But if you have some way to automate the automation, automate the capturing of that knowledge, you've got something that can bridge this physical, digital divide. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com, and pick up the book in a bookstore near you. TROND: How stable is that kind of model knowledge? Because I'm just thinking about it in the long run here, are these physical domain experts that are giving up a little bit of their superpower are they still needed then in a future scenario when you do have such a model? Or will it never be as advanced as they are? Or is it actually going to be still kind of an interface that's going to jump between machines and human knowledge kind of in a continuous loop here? CHRISTOPHER: Yeah, in the near term, it turns out we're not working on replacing experts as much as scaling experts. Almost every case we've worked on, companies are in trouble largely because the experts are very, very few and far between, and they're retiring. They're leaving. And that needs to be scaled somehow. In the case of, for example, the cold chain industry all of Japan servicing the supermarkets, you know, there's 7-ELEVEN, there's FamilyMart, and so on, there are three experts who can read the sensor data and infer what's likely to fail in the next month. So in the near term, it's really we need these humans, and we need more of them. TROND: I'm glad to hear that even that is a bit of a contrarian message. So you're saying physical infrastructure and the physical world matters. You're saying humans matter. [laughs] It's interesting. Yeah, that's contrarian in Silicon Valley, I'll tell you that. CHRISTOPHER: It is. And, in fact, related to that problem, Hussmann, which is a refrigeration company, commercial refrigeration supplies to supermarkets. It was a subsidiary of Panasonic. It has a really hard time getting enough service personnel, and they have to set up their own universities, if you will, to train them. And these are jobs that pay very well. But everybody wants to be in software these days. Coming back to the human element, I think that long-term I'm an optimist, not a blind optimist but a rational one. I think we're still going to need humans to direct machines. The machine learning stuff is data that reflects the past, so patterns of the past, and you try to project that in the future. But we're always trying to effect some change to the status quo. Tomorrow should be a better day than today. So is that human intent that is still, at least at present, lacking in machines? And so we need humans to direct that. TROND: So what is the tomorrow of manufacturing then? How fast are we going to get there? Because you're saying, well, Silicon Valley has a bit of a learning journey. But there is language model technology or progress in language models that now can be implemented in software and, through humans, can be useful in manufacturing already today. And they're scattered examples, and you're putting on an event to show this. What is the path forward here, and how long is this process? And will it be an exponential kind of situation here where you can truly integrate amazing levels of human insight into these machine models? Or will it take a while of tinkering before you're going to make any breakthroughs? Because one thing is the breakthrough in understanding human language, but what you're saying here is even if you're working only with a few experts, you have to take domain by domain, I'm assuming, and build these models, like you said, painstakingly with each expert in each domain. And then, yes, you can put that picture together. But the question is, how complex of a picture is it that you need to put together? Is it like mapping the DNA, or is it bigger? Or what kind of a process are we looking at here? CHRISTOPHER: If we look at it from the dimension of, say, knowledge-based automation, in a sense, it is a continuation. I believe everything is like an s-curve. So there's acceleration, and then there's maturity, and so on. But if you look back in the past, which is sort of instructive for the future, we've always had human knowledge-based automation. I remember the first SMT, the Surface Mount Technology, SMT wave soldering machine back in the early '90s. That was a company that I helped co-found. It was about programming the positioning of these chips that would just come down onto the solder wave. And that was human knowledge for saying, move it up half a millimeter here and half a millimeter there. But of course, the instructions there are very micro and very specific. What machine learning is doing...I don't mean to sort of bash machine learning too much. I'm just saying culturally, there's this new tool really that has come along, and we just need to apply the tool the right way. Machine learning itself is contributing to what I described earlier, that is, now, finally, machines can understand us at the conceptual level that they don't have to be so, so dumb as to say, move a millimeter here, and if you give them the wrong instruction, they'll do exactly that. But we can communicate with them in terms of circles and lines, and so on. So the way I see it is that it's still a continuous line. But what we are able to automate, what we're able to ask our machines to do, is accelerating in terms of their understanding of these instructions. So if you can imagine what would happen when this becomes, let's say, ubiquitous, the ability to do this, and I see this happening over the next...Certainly, the base technology is already there, and the application always takes about a decade. TROND: Well, the application takes a decade. But you told me earlier that humans should at least have this key role in this knowledge-first application approach until 2100, you said, just to throw out a number out there. That's, to some people, really far away. But the question is, what are you saying comes after that? I know you throw that number out. But if you are going to make a distinction between a laborious process of painful progress that does progress, you know, in each individual context that you have applied to human and labeled it, and understood a little case, what are we looking at, whether it is 2100, 2075, or 2025? What will happen at that moment? And is it really a moment that you're talking about when machines suddenly will grasp something very, very generic, sort of the good old moment of singularity, or are you talking about something different? CHRISTOPHER: Yeah, I certainly don't think it's a moment. And, again, the HP-11C has always calculated Pi far faster and with more digits than I have. So in that sense, in that particular narrow sense, it's always been more intelligent than I am. TROND: Yeah. Well, no one was questioning whether a calculator could do better calculations than a human. For a long time -- CHRISTOPHER: Hang on. There's something more profound to think about because we keep saying, well, the minute we do something, it's okay; that's not intelligence. But what I'm getting to is the word that I would refer to is hyper-evolution. So there's not a replacement of humans by machines. There's always been augmentation, and intelligence is not going to be different. It is a little disturbing to think about for some of us, for a lot of us, but it's not any different from wearing my glasses. Or I was taking a walk earlier this morning listening to your podcast, and I was thinking how a pair of shoes as an augmented device would seem very, very strange to humans living, say, 500 years ago, the pair of shoes that I was walking with. So I think in terms of augmenting human intelligence, there are companies that are working on plugging in to the degree that that seems natural or disturbing. It is inevitable. TROND: Well, I mean, if you just think about the internet, which nowadays, it has become a trope to think about the internet. I mean, not enough people think about the internet as a revolutionary technology which it, of course, is and has been, but it is changing. But whether you're thinking about shoes, or the steam engine, or nuclear power, or whatever it is, the moment it's introduced, and people think they understand it, which most people don't, and few of us do, it seems trivial because it's there. CHRISTOPHER: That's right. TROND: But your point is until it's there, it's not trivial at all. And so the process that you've been describing might sound trivial, or it might sound complex, but the moment it's solved or is apparently solved to people, we all assume that was easy. So there's something unfair about how knowledge progresses, I guess. CHRISTOPHER: That's right. That's right. We always think, yeah, this thing that you describe or I describe is very, very strange. And then it happens, and you say, "Of course, that's not that interesting. Tell me about the future." TROND: Well, I guess the same thing has happened to cell phones. They were kind of a strange thing that some people were using. It was like, okay, well, how useful is it to talk to people without sitting by your desk or in the corner of your house? CHRISTOPHER: I totally remember when we were saying, "Why the hell would I want to be disturbed every moment of the day?" [laughs] I don't want the phone with me, and now I -- TROND: Right. But then we went through the last decade or so where we were saying, "I can't believe my life before the phone." And then maybe now the last two, three years, I would say a lot of people I talk to or even my kids, they're like, "What's the big deal here? It's just a smartphone," because they live with a smartphone. And they've always had it. CHRISTOPHER: They say, "How did you get around without Google Maps?" And then somebody says, "We used maps." And I said, "Before Google Maps." [laughter] TROND: Yeah. So I guess the future here is an elusive concept. But I just want to challenge you one more time then on manufacturing because manufacturing, for now, is a highly physical exercise. And, of course, there's virtual manufacturing as well, and it builds on a lot of these techniques and machine learning and other things. How do you see manufacturing as an industry evolve? Is it, like you said, for 75 years, it's going to be largely very recognizable? Is it going to look the same? Is it going to feel the same? Is the management structure the way engineers are approaching it, and the way workers are working? Are we going to recognize all these things? Or is it going to be a little bit like the cell phone, and we're like, well, of course, it's different. But it's not that different, and it's not really a big deal to most people. CHRISTOPHER: Did you say five years or 50 years? TROND: Well, I mean, you give me the timeframe. CHRISTOPHER: Well, in 5 years, we will definitely recognize it, but in 50 years, we will not TROND: In 50 years, it's going to be completely different, look different, feel different; factories are all going to be different. CHRISTOPHER: Right, right. I mean, the cliché is that we always overestimate what happens in 5 and underestimate what happens in 50. But the trend, though, is there's this recurring bundling and unbundling of industries; it's a cycle. Some people think it's just, you know, they live ten years, and they say it's a trend, but it actually goes back and forth. But they're sort of increasing specialization of expertise. So, for example, the supply chain over the last 30 years, we got in trouble because of that because it has become so discrete if you want to use one friendly word, but you can also say fragmented in another word. Like, everybody has been focused on just one specialization, and then something like COVID happens and then oh my God, that was all built very precisely for a particular way of living. And nobody's in the office anymore, and we live at home, and that disrupts the supply chain. I think if you project 50 years out, we will learn to essentially matrix the whole industry. You talked about the management of these things. The whole supply chain, from branding all the way down to raw materials, is it better to be completely vertically integrated to be part of this whole mesh network? I think the future is going to be far more distributed. But there'll be fits and starts. TROND: So then my last question is, let's say I buy into that. Okay, let's talk about that for a second; the future is distributed or decentralized, whatever that means. Does that lessen or make globalization even more important and global standardization, I guess, across all geographical territories? I'm just trying to bring us back to where you started with, which was in the U.S., Silicon Valley optimized for software and started thinking that software was eating the world. But then, by outsourcing all of the manufacturing to Asia, it forgot some essential learning, which is that when manufacturing evolves, the next wave looks slightly different. And in order to learn that, you actually need to do it. So does that lesson tell you anything about how the next wave of matrix or decentralization is going to occur? Is it going to be...so one thought would be that it is physically distributed, but a lot of the insights are still shared. So, in other words, you still need global insight sharing, and all of that is happening. If you don't have that, you're going to have pockets that are...they might be very decentralized and could even be super advanced, but they're not going to be the same. They're going to be different, and they're going to be different paths and trajectories in different parts of the world. How do you see this? Do you think that our technology paradigms are necessarily converging along the path of some sort of global master technology and manufacturing? Or are we looking at scattered different pictures that are all decentralized, but yet, I don't know, from a bird's eye view, it kind of looks like a matrix? CHRISTOPHER: I think your question is broader than just manufacturing, although manufacturing is a significant example of that, right? TROND: It's maybe a key example and certainly under-communicated. And on this podcast, we want to emphasize manufacturing, but you're right, yes. CHRISTOPHER: The word globalization is very loaded. There's the supposedly positive effect in the long run. But who is it that said...is it Keynes that said, "In the long run, we're all dead?" [laughs] In the short run, the dislocations are very real. A skill set of a single human being can't just shift from hardware to software, from manufacturing to AI, within a few months. But I think your question is, let's take it seriously on a scale of, say, decades. I think about it in terms of value creation. There will always be some kind of disparity. Nature does not like uniformity. Uniformity is coldness; it is death. There have to be some gradients. You're very good at something; I'm very good at something else. And that happens at the scale of cities and nations as well. TROND: And that's what triggers trade, too, right? CHRISTOPHER: Exactly. TROND: Because if we weren't different, then there would be no incentive to trade. CHRISTOPHER: So when we think about manufacturing coming back to the U.S., and we can use the word...it is correct in one sense, but it's incorrect in another sense. We're not going back to manufacturing that I did. We're not going back to surface mount technology. In other words, the value creation...if we follow the trajectory of manufacturing alone and try to learn that history, what happens is that manufacturing has gotten better and better. Before, we were outsourcing the cheap stuff. We don't want to do that. But then that cheap stuff, you know, people over there build automation and skills, and so on. And so that becomes actually advanced technology. So in a sense, what we're really doing is we're saying, hey, let's go advanced at this layer. I think it's going to be that give and take of where value creation takes place, of course, layered with geopolitical issues and so on. TROND: I guess I'm just throwing in there the wedge that you don't really know beforehand. And it was Keynes, the economist, that said that the only thing that matters is the short term because, in the end, we are all dead eventually. But the point is you don't really know. Ultimately, what China learned from manufacturing pretty pedestrian stuff turned out to be really fundamental in the second wave. So I'm just wondering, is it possible to preempt that because you say, oh, well, the U.S. is just going to manufacture advanced things, and then you pick a few things, and you start manufacturing them. But if you're missing part of the production process, what if that was the real advancement? I guess that is what happened. CHRISTOPHER: Okay. So when I say that, I think about the example of my friend who spent, you know, again, we were a Ph.D. group at Stanford together. And whereas I went off to academia and did startups and so on, he stayed at Intel for like 32 years. He's one of the world's foremost experts in semiconductor process optimization. So that's another example where human expertise, even though semiconductor manufacturing is highly automated, you still need these experts to actually optimize these things. He's gone off to TSMC after three decades of being very happy at one place. So what I'm getting to is it is actually knowable what are the secret recipes, where the choke points are, what matters, and so on. And interestingly, it does reside in the human brain. But when I say manufacturing coming back to the U.S. and advanced manufacturing, we are picking and choosing. We're doing battery manufacturing. We're doing semiconductor, and we're not doing wave soldering. So I think it is possible to also see this trend that anybody who's done something and going through four or five iterations of that for a long time will become the world's expert at it. I think that is inevitable. You talk of construction, for example; interestingly, this company in Malaysia that is called Renong that is going throughout Southeast Asia; they are the construction company of the region because they've been doing it for so long. I think that is very, very predictable, but it does require the express investment in that direction. And that's something that Asia has done pretty well. TROND: Well, these are fascinating things. We're not going to solve them all on this podcast. But definitely, becoming an expert in something is important, whether you're an individual, or a company, or a country for sure. What that means keeps changing. So just stay alert, and stay in touch with both AI and humans and manufacturing to boot. It's a mix of those three, I guess. In our conversation, that's the secret to unlocking parts of the future. Thank you, Christopher, for enlightening us on these matters. I appreciate it. CHRISTOPHER: It's my pleasure. TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Human-First AI. Our guest was Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talked about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame. My takeaway is that physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat as our guest. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners. The augmented podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production and logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co. Please share this show with colleagues who care about where industry and especially about how industrial tech is going. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and on YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: Christopher Nguyen.

Show Notes Christopher Nguyen CEO and Co-Founder of Aitomatic Hacker, Professor, Builder/Founder w/ successful exits, Leader, Knowledge-First ML Creator @h1st_ai, ex-GoogleApps, http://bit.ly/scholar-ctn   ‣ Strategic executive leadership [Google, Panasonic] ‣ CEO/CTO/VP Eng, successful startup+corp experience [Agenda-Asia, Arimo, Aitomatic] ‣ Hands-on software engineering management - built & led teams of '00s ‣ Machine Learning/AI, Extreme Internet-scale, highly available, low-latency service architectures ‣ Quantitative finance, applied statistics   We talk about          What's it like to successful start and exit several companies? What is a Knowledge-First App Engine? How do you train a cyber-security system against something that has not happened yet? What is open-source project Human-First AI. What are some of the top strategic technology trends you are seeing now and believe will be there in 2023 and 2024?     Connect with Christopher Nguyen https://www.linkedin.com/in/ctnguyen/ https://www.aitomatic.com/ ctn@alumni.stanford.org

Artificial Intelligence and machine learning are transforming manufacturing as we know it. In this podcast, Panasonic's veteran AI expert shares how companies today can harness the AI capacities of tomorrow.

Christopher Nguyen is CEO and cofounder of Aitomatic, a startup that uses a knowledge-first approach to build and deploy machine learning solutions, with a focus on industrial applications (manufacturing and other physical settings).Join us at K1st World, a fantastic symposium and networking event slated for November 16 & 17. Use the discount code GRADIENTFLOW60 to attend in person or online.Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Android • Spotify • Stitcher • Google • AntennaPod • RSS.Detailed show notes can be found on The Data Exchange web site.

Christopher Nguyen is a UX Designer, entrepreneur, content creator and product design leader who has come back to Vietnam multiple times on his journey so far, and joins the podcast to discuss design, content creation, outsourcing, learning from setbacks, self development and much more. Topics include ⁃ Why Vietnam holds such a special place for Chris and why he has continued to come back ⁃ The importance of networking, outsourcing and interacting with like minded individuals, creators and entrepreneurs ⁃ Having the self awareness to tailor your strengths and weaknesses to your particular field or project ⁃ His key lessons amongst a lengthy career in various fields such as design and consultancy ⁃ Having the courage to try something new, experimenting and following an interest ⁃ Adding a healthy amount of pressure to situations in order to enhance performance If you enjoyed this conversation, you can find out more about ComebaCK at @thecomebackwithck on Instagram.

"When machine learning algorithms that come from the digital world meet the physical world, there's a lot of challenges that are unresolved." Today's episode is a chat with Christopher Nguyen, CEO of AItomatic.  "When we became part of Panasonic, we essentially operated out of the global CTO office. And so we became this oracle desk. People come and say, Can you sprinkle a little AI on my product and transform the revenue flow or something?" AITomatic is a technology company that is addressing a huge shortcoming in industrial digital areas. Machine learning models and artificial intelligence solutions in industry need to incorporate domain expertise from real human experts, or the results from the solutions are often worse than having no insight at all. The process of capturing and using human domain knowledge isn't scalable,  and is fraught with social and cultural problems. AITomatic is developing new methods and tools to capture and incorporate deep domain expertise into industrial digital solutions. "We learned that most machine learning based predictive maintenance approaches out there are actually worse than nothing." With a career spanning four decades, Christopher Nguyen's tech bona fides are second to none. Since fleeing Vietnam in 1978, this multiple-time tech founder has played key roles in 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. "We stopped making things in North America in the last few years.. And now we're saying, let's bring it all back. And there's a generational gap between the software generation and the people who make things." LinkedIn profiles Personal: https://www.linkedin.com/in/ctnguyen/ Business: https://www.linkedin.com/company/aitomatic/ Twitter handles: @pentagoniac @aitomatic Facebook pages: Https://facebook.com/ Website: https://aitomatic.com

Summary Machine learning is a data-hungry approach to problem solving. Unfortunately, there are a number of problems that would benefit from the automation provided by artificial intelligence capabilities that don’t come with troves of data to build from. Christopher Nguyen and his team at Aitomatic are working to address the "cold start" problem for ML by letting humans generate models by sharing their expertise through natural language. In this episode he explains how that works, the various ways that we can start to layer machine learning capabilities on top of each other, as well as the risks involved in doing so without incorporating lessons learned in the growth of the software industry. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Your host is Tobias Macey and today I’m interviewing Christopher Nguyen about how to address the cold start problem for ML/AI projects Interview Introduction How did you get involved in machine learning? Can you describe what the "cold start" or "small data" problem is and its impact on an organization’s ability to invest in machine learning? What are some examples of use cases where ML is a viable solution but there is a corresponding lack of usable data? How does the model design influence the data requirements to build it? (e.g. statistical model vs. deep learning, etc.) What are the available options for addressing a lack of data for ML? What are the characteristics of a given data set that make it suitable for ML use cases? Can you describe what you are building at Aitomatic and how it helps to address the cold start problem? How have the design and goals of the product changed since you first started working on it? What are some of the education challenges that you face when working with organizations to help them understand how to think about ML/AI investment and practical limitations? What are the most interesting, innovative, or unexpected ways that you have seen Aitomatic/H1st used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Aitomatic/H1st? When is a human/knowledge driven approach to ML development the wrong choice? What do you have planned for the future of Aitomatic? Contact Info LinkedIn @pentagoniac on Twitter Google Scholar Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Aitomatic Human First AI Knowledge First World Symposium Atari 800 Cold start problem Scale AI Snorkel AI Podcast Episode Anomaly Detection Expert Systems ICML == International Conference on Machine Learning NIST == National Institute of Standards and Technology Multi-modal Model SVM == Support Vector Machine Tensorflow Pytorch Podcast.__init__ Episode OSS Capital DALL-E The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Today, at our show we have Christopher Nguyen who is a leader in the AI space and co-founder of Aitomatic. You will enjoy the conversation as we geek out with Christopher and cover the following topics: Learn how Chris started his career as a software engineer at HP, worked at Google and now heading Aitomatic. What is Aitomatic and how it's different from other AI players in the market? How human and AI can work together instead of AI replacing humans. How do you think AI will help us scale human productivity? Data is becoming a bigger problem for companies compared to automation. Mostly, it's not about big data but about linking few data sources. How companies can use automation to solve their smaller data problems? What will be the inflection point the world using more AI and Human combination? Where is Aitomatic is heading? Visit www.analyticstodaypodcast.com to listen to more episodes of our show. Leave your feedback on iTunes: https://podcasts.apple.com/us/podcast/analyticstoday-podcast/id1044308732

Take charge of your life. Christopher Nguyen is a self-taught product design leader, founder, and creator. Formally, he was the Head of Design at Thailand's first FinTech unicorn and held other design leadership positions. Chris Nguyen, from the UK living in Vietnam, talks about the journey that he went through to be able to take charge of the life he has right now. The challenges he went through shaped him to be a design leader, teaching others the ropes for them to be able to take control over their own career. Chris and I have a lot in common on a mindset level so this episode flew by like no other. Watch the full episode here: Follow his journey in LinkedIn: https://www.linkedin.com/in/cjhnguyen/ On YouTube: https://www.youtube.com/c/semigrownkid On Instagram: https://www.instagram.com/semigrownkid/

MRI (magnetic resonance imaging) tests are incredibly helpful to aid in diagnosis. Dr. Deborah Kwon, Director of Cardiac MRI at Cleveland Clinic speaks with Dr. Christopher Nguyen, PhD, Director of MRI Research in the Advanced Imaging and Simulation Center for MRI, about the future and advancements in cardiac MRI.

MRI (magnetic resonance imaging) tests are an incredibly helpful test for your care team to learn about your heart. Dr. Deborah Kwon, Director of Cardiac MRI at Cleveland Clinic talks with Christopher Nguyen, PhD, Director of MRI Research in the Advanced Imaging and Simulation Center for MRI, about the benefits of cardiac MRI and how they are working together improve this technology and the patient experience.

With a career spanning four decades, Christopher Nguyen's tech bona fides are second to none. Since fleeing Vietnam in 1978, this multiple-time tech founder has played key roles in 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 and Matthew discuss how machine learning is being used and generated from the Engineers experience and data. Christopher tells us what Knowledge-First AI is and how it is similar and different from expert systems. To learn more about Christopher or to get into contact with him, check out his LinkedIn, https://www.linkedin.com/in/ctnguyen/

How can industrial organizations become sophisticated with AI's potential to build powerful capabilities and deliver fast revenue? Leveraging domain expertise is the key. Instead of spending tons of cash to get more data, Aitomatic will help you level up your AI's competitive edge.   In this episode, we are sitting down with Christopher Nguyen, CEO and Co-Founder of Aitomatic, a knowledge-first app engine for industrial IoT that assists industrial companies in using their domain expertise to build more effective algorithms. In this talk, we discuss why only 9% of manufacturers currently use AI in their business processes and how they can overcome data availability challenges. We also discuss which use cases are best suited for a human-first AI development platform and which work well with a traditional black box training approach.   Key Takeaways:  Why are we not seeing AI takeoff in the industrial space as often as we see it in other consumer markets?  What is predictive maintenance and predictive analytics? How does he effectively deal with the fundamental challenge of very small data sets? What approach does he use to address the problem with algorithms?   

In this video, we speak with Christopher Nguyen, CEO of Aitomatic, an automotive cybersecurity firm, about the threats and challenges facing the automotive industry. Nguyen explains how machine learning incorporates physical elements and human experience into its learning processes. However, machine learning also introduces new cyber risks that must be addressed. Nguyen notes that as vehicles have become more connected and autonomous, they have also become more vulnerable to cyber-attacks. Hackers could potentially gain access to steering, braking or other critical systems and cause physical harm. “The automotive industry is really focused on ensuring the cyber resilience and security of vehicles to prevent any catastrophic events,” says Nguyen. Aitomatic uses techniques like anomaly detection, predictive modeling and sensor fusion to help detect threats and protect connected vehicles. Nguyen highlights the need for constant vigilance and adaptation to the evolving techniques of malicious actors. With lives at stake, the automotive industry is working to stay one step ahead of cybercriminals through new tools, standards and information sharing across companies. Overall, this video provides a sobering look at the dangers of an increasingly connected automotive world. But with experts like Nguyen and companies like Aitomatic working to build security into new systems, there is hope that a secure and autonomous vehicle future is on the horizon.

We talk to Christopher Nguyen from Aitomatic about cyber security and threats and challenges of cyber security in the automotive industry. Chris explains about machine learning and how this is being used to incorporate physical elements and human experience in the learning. He also speaks about what the automotive industry is doing to prevent cyber … Continue reading Ep. 69 Tackling Automotive Cyber Security →

Christopher Nguyen, CEO and Co-Founder of Aitomatic (www.aitomatic.com) discusses the predictive maintenance uses of A.I. in manufacturing. The company also provides A.I. for energy optimization, demand forecasting, avionics user experience, automotive cybersecurity, automotive cabin experience, smart home experience, and marine sonar imaging. Learn more about your ad choices. Visit megaphone.fm/adchoices

Christopher Nguyen is the CEO and cofounder of Aitomatic, where he aims to redefine how companies approach AI in the context of life-critical, industrial applications.   As a multiple-time tech founder, Christopher has played key roles across a wide range of technology startups. He has also operated in large corporations where he has built the first flash memory transistors at Intel and spearheaded 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.    In this episode, Christopher and Aaron discuss AI's shortcomings, the mission of Aitomatic, and how diverse experiences keep life engaging. Sign up for a Weekly Email that will Expand Your Mind.   ​​Christopher Nguyen's Challenge; Separate the what and the why when problem solving.   Connect with ​​Christopher Nguyen Linknedin Aitomatic Website If you liked this interview, check out our interview with Tom Galluzzo where we discuss GPT-3, robotics, and entrepreneurship.  Underwritten by Piper Creative Piper Creative makes creating podcasts, vlogs, and videos easy.    How? Click here and Learn more.   We work with Fortune 500s, medium-sized companies, and entrepreneurs.   Follow Piper as we grow YouTube Subscribe on iTunes | Stitcher | Overcast | Spotify

Christopher Nguyen, serial entrepreneur and CEO of Aitomatic, realized big data isn't the only answer when training AI models. In fact, when doing preventive or predictive maintenance on industrial equipment, only small data is available. He and his team asked what if instead of relying on automated data collection we codify expertise in the heads of a small number of experienced technicians. And thus human-first AI was born. Aitomatic was launched in 2021 to productize the new field. It builds on Christopher's legacy of innovation having spent time in academia, at Google, and other startups including Arimo before its acquisition by Panasonic.Listen and learn...Why human-first vs. data-first AI may disrupt traditional approaches to machine learning.How automation problems in physical-first vs. digital-first industries require different solutions.How to build machine learning models when there isn t enough data.Why the world is in short supply of human expertise.How people feel about having their jobs automated away.Why the topic of ethical AI is controversial.The science behind neuromorphic computing.References in today's episode...AitomaticChristopher on TwitterGordon Wilson, CEO of Rain Neuromorphics, on AI and the Future of WorkThanks to Tess Hau from Tess Ventures for the introduction to Christopher! 

This week's guest is one of the top technologists working in the field of Artificial Intelligence, Christopher Nguyen. With a career spanning four decades, Christopher's tech bona fides are second to none.Since fleeing Vietnam in 1978, this multiple-time tech founder has played key roles in 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.You may already know that my new book, That Little Voice in Your Head, is due to release on May 26th. It is my deepest dive yet into understanding and changing your brain (with a technology metaphor driving the whole thing, to boot). I'm certainly proud of it, but I know that new media coverage will soon transition from talking about the crucially important message of my last book, Scary Smart.  For that reason, I had to have Chris on the show to give this discussion a whole new level of color and depth that none of us can afford to ignore.Listen as we discuss:How the interconnected of machines made the internet and is making the future.Christopher's history as a refugee fleeting Vietnam to how he became a top techie.How can a developer write a code that creates an independently intelligent entity?The NYTimes coverage of the "Cat Paper" that broke AI discussion into the mainstream.So much of life and the world is predictable and, therefore, algorithmically replicable.Are creativity, consciousness, emotions, and art in the cards for intelligent machines?Being thoughtful of how machines are deployed is an existential priority.A profound thought: Intention must come second to possible impact.Do guns or the people using them kill people? The answer is both.Instagram: @mo_gawdatFacebook: @mo.gawdat.officialTwitter: @mgawdatLinkedIn: /in/mogawdatYouTube: @mogawdatofficialWebsite: mogawdat.comConnect with Christopher Nguyen on Twitter @aitomatic and his website, aitomatic.comDon't forget to subscribe to Slo Mo for new episodes every Sunday. Only with your help can we reach One Billion Happy #onebillionhappy.

Christopher Nguyen is the CEO of Aitomatic, which provides knowledge-first AI for industrial automation. Chad talks with Christopher about why having a physical sciences background matters for this work, if we have artificial intelligence, why we still need people, and working in knowledge-first AI instead of knowledge-second, knowledge-third, or no knowledge at all. Data reflects the world. Aitomatic (https://www.aitomatic.com/) Follow Aitomatic on Twitter (https://twitter.com/aitomatic) or LinkedIn (https://www.linkedin.com/company/aitomatic/). Follow Christopher on Twitter (https://twitter.com/pentagoniac) or LinkedIn (https://www.linkedin.com/in/ctnguyen/). Follow thoughtbot on Twitter (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Become a Sponsor (https://thoughtbot.com/sponsorship) of Giant Robots! Transcript: CHAD: This is the Giant Robots Smashing Into Other Giant Robots Podcast, where we explore the design, development, and business of great products. I'm your host, Chad Pytel. And with me today is Christopher Nguyen, CEO of Aitomatic, which provides knowledge-first AI for industrial automation. Christopher, thanks for joining me. CHRISTOPHER: Thank you. CHAD: So I was prepping for this interview, and I noticed something that jumped out at me that we have in common, and that is your first computer was the TI-99C/4A. CHRISTOPHER: No kidding. CHAD: And that was also my first computer. CHRISTOPHER: Oh, okay. CHAD: [laughs] CHRISTOPHER: You got no storage, correct? CHAD: No storage; everything was off of the solid-state disks. And I remember I was a little late to it. My parents actually got it for me. I think I was 9 or 10. And my parents got it for me at a garage sale. And so all I had was the manual and the basic manual that came with it. And because it had no storage, I needed to type in the programs that were in the back of that book from scratch, and there was no way to save them. So you would type them in -- [laughs] CHRISTOPHER: Oh my God. Every single day the same code over and over again. And hopefully, you don't turn it off. CHAD: [laughs] Exactly. There definitely were times where it would just be on in my room because I didn't want to lose what I had spent all day typing in. CHRISTOPHER: Yeah, yeah, I remember my proudest moment was my sister walked into the living room...and there was no monitor, and you connected it directly to the TV. CHAD: To the TV, yeah. CHRISTOPHER: And younger people may not even know the term character graphics, which is you pick in your book the character space, and then you put them together into a graphic image. And I painstakingly, on graph paper, created a car and converted it to hex and then poked it into these characters and put them together. And my sister walked in like, "Oh my God, you made a car." [laughter] CHAD: That was a good time. It was difficult back then. I feel like I learned a lot in an environment where I see people learning. Today it's a lot more of a complicated environment. They're much higher up the stack than we were back then. And, I don't know, I feel like I actually sort of had it easy. CHRISTOPHER: Well, in many ways, that very abstraction to...you see jobs like to talk about higher software abstraction to make you more productive. I think it's absolutely that powerful. And Marc Andreessen, my friend, likes to talk about how software is eating the world. But it turns out there's one perspective where people have gone up the stack a little too far, too fast, and too much. We're still physical in the industry that I work in. You know, our previous company was acquired by Panasonic. And I've been working on industrial AI for the last four and a half years. And it's very hard for us to find people with the right physics or electro engineering background and the right science understanding to help automate and build some of these systems because everybody's in software now. CHAD: Why does physical sciences background matter for this work? CHRISTOPHER: Let me give you a couple of examples. One example is one of our customers is a very large global conglomerate doing marine navigation and marine sensors. And one of the products they do is fish finding so that amateurs like you and me would go hold one of these systems and shoot it down straight to the ocean. A sonar beam goes down, kind of like submarines. But hopefully, an image would come back. And so to build a system to convert all of that into something other than jumbled what they call echograms, maybe convert to a fish image, you have to build a lot of machine intelligence, AI, machine learning, and so on. But just to understand the data and make the right decisions about how to do that, you need to understand the physics of sound wave echoes in the ocean. If you can't do that and you got to work with another engineer to tell you how to do that, it really slows things down a lot. So knowing the equation but also having a physical intuition for how it all works can make or break the success of an engineer working on something like that. Another example is we worked on avionics. Don't blame me for this, but if you have had poor experience with Wi-Fi on a plane, we may be involved in one way or another, Panasonic Avionics. CHAD: [laughs] CHRISTOPHER: But the antenna array that sits on top of the plane to receive satellite signal and sends out a signal, so you can expect there's some kind of optimization involved. It's not just line of sight. If there's a cloud coming nearby, then there's some distortion, and there's some optimization needed to take place. Again, an understanding of...at least if you remember, if not an expert in college physics, about antenna radiation pattern and so on, which help tremendously a data scientist or an engineer working on that problem whereas somebody who's a pure computer scientist would struggle a lot and probably give up with that problem. CHAD: Yeah, this may be a little bit of a facetious question or leading question; I'm not sure which, but if we have artificial intelligence, why do we need people to do this stuff? CHRISTOPHER: [laughs] Well, I have a broader, you know, I've thought about that a lot. And I'll answer it in the broad sense, but I think you can specialize it. The problem with machine learning, at least today and I really think for a very long time for the rest of the century at least, is that it is trained on data. And data is past examples. And when I say past, I include the present. In other words, whatever it is that our algorithms learn, they learn the world as it is. Now, we're always trying to change the world in some way. We're always trying to change the world to what we wish it to be, not what it is. And so it's the humans that express that aspiration. I want my machine to behave better in some way. Or I want my algorithms not to have this built-in bias when it makes a decision that affects someone's life. If it's pure machine learning and data, it will indeed reflect all the decisions that have ever been made, and it'll have all those built-in biases. So there's a big topic there to unpack and who's responsible for doing what. But I think coming back to your question, we'll always need humans to express what it is that is the world that you want in the next minute, the next day, the next week, or the next 50 years. CHAD: So let's talk more about the ethics or the biases that can be baked into AI. How do you prevent that at Aitomatic? CHRISTOPHER: As I said, this is a big topic. But let me begin by saying that actually, most of us don't know what we mean when we say bias, or to put it more broadly, we don't agree on the meaning. The word bias in colloquial conversation always comes with a negative connotation on the one hand. On the other hand, in machine learning, bias is inherent. You cannot have machine work without bias. So clearly, those two words must mean something slightly different even though they reflect the same thing, the same underlying physics, if you will. So first, before people get into what they think is a very well-informed debate, they must first agree on a framework for terms that they're using. Now, of course, I can accommodate and say, okay, I think I know what you mean by that term. And so, let's take the colloquial meaning of bias. And when we say bias, we usually mean some built-in prejudice, it may be implicit, or it may be explicit that causes a human or machine to make a decision that discriminates against someone. And here's the thing, we've got to think about intent versus impact. Is it okay for the effect to be quote, unquote, "biased" if I didn't intend it, or it doesn't matter what my intent was, and it's only the impact that matters? That's another dimension that people have to agree or even agree to disagree on before they start going into these circular arguments. But let's focus on, for now, let's say it's the impact that matters. It doesn't matter what the intent is, particularly because machines, as of present, there is no intent. So, for example, when the Uber vehicle a number of years ago hit and killed a bicyclist, there was no traceable intent, certainly not in the system design to cause that to happen. But yet it happened, and the person did die. So coming back to your question, I know that I've neglected the question because I'm unpacking a lot of things that otherwise an answer would make no sense, or it would not have the sense meant. So coming back, how do we prevent bias as an effect from happening in our system? And an answer that I would propose is to stop thinking about it in terms of point answers; in other words, it's not that...people say...well, myself I even said earlier it's in the data. Well, if it's in the data, does that absolve the people who build the algorithms? And if it's in the algorithms, does that absolve the people who use it? I had a conversation with some friends from Europe, and they said, "In America, you guys are so obsessed with blaming the user." Guns don't kill people; people kill people. But I think to answer your question in a very thoughtful manner; we must first accept the responsibility throughout the entire chain and agree on what it is the outcome that we want to have, at least effect. And then the responsibility falls on all chains, all parts of the chain. And one day, it may be, hey, you got to tune the algorithm a certain way. Another day may be, hey, collect this kind of data. And another day, it might be make sure that when you finally help with the decision, that you tweak it a certain way to affect the outcome that you want. I think what I've described is the most intellectually honest statement. And somebody listening to this is going to have a perspective that disagrees vehemently with one of the things I just said because they don't want that responsibility. CHAD: I like it, though, because it recognizes that we're creating it. It may be a tool, and tools can be used for anything. But as the creators of that tool, we do have responsibility for...well, I think we have responsibility for what that is going to do, and if not us, then who? CHRISTOPHER: That's right. Yeah. But if you follow the debate, you will find that there are absolutists who say, "That's not my problem. That's the user, or the decision-maker, or the data provider. But my algorithms I have to optimize in this way, and it's going to output exactly what the data told it to. The rest is your problem." CHAD: So it strikes me in hearing you describe what's involved, especially at the state that machine learning is at now; it probably varies or what you are going to do specifically varies based on what you're trying to achieve. And maybe even the industry that it's in like avionics and what you need to do there may be different than energy. CHRISTOPHER: Yep, or more broadly, physical industries versus the plane falls out of the air, or a car hits somebody, somebody actually dies. If you get a particular algorithm wrong at Google, maybe you click on the wrong ad. So I really advocate thinking about the impact and not just the basic algorithms. CHAD: Yeah, so tell me more about the actual product or services that Aitomatic provides and also who the customers are. CHRISTOPHER: I think what we discussed is quite relevant to that. I think it does lead in a very real perspective directly into that. We do what's called knowledge-first AI. And that knowledge-first as opposed to knowledge-second and knowledge-third or no knowledge at all, there are very strong schools of thought that say, "With sufficient data, we can create AI to do everything." Data is reflecting the world. As I mentioned, it's in the past as it is, not as what we want it to be. When you apply it to some of the concrete things that we do, let's take a use case like predictive maintenance of equipment, you want to be able to save cost and even to save lives. You want to replace things, service things before they actually fail. Failure is very costly. It's far more costly than the equipment itself. Today, the state of the art is preventive maintenance, not predictive. Preventive means, let's just every six months, every one year replace all the lights because it's too costly to replace them one by one when they fail. Lots of industries today still do what's called reactive maintenance, you know, fix it when it fails. So predictive maintenance is the state of the art. The challenge is how do you get data and train enough machine intelligence to essentially predict? And the prediction precisely means the following: can you tell me with some probability that this compressor for this HVAC system, this air conditioning system may fail within the next month? And it turns out machine learning cannot do that. CHAD: Oh, that's the twist. CHRISTOPHER: Exactly. [laughter] And I know a lot of people listening are going to sit up and say, "Christopher doesn't know what the hell he's talking about." CHAD: [laughs] CHRISTOPHER: But I really know. I really know what the hell I'm talking about because we've been part of an industrial giant. I'll tell you what machine learning can do and what it cannot do. What it can do is with the data that's available...the main punch line, the main reason here is that there are not enough past examples of actual failures of certain types. There's a lot of data. We're swimming in data, but we're not actually swimming in cleanly recorded failures that are well classified. And machine learning is about learning from past examples, except today, algorithms need a lot of past examples, tens of thousands, hundreds of thousands, or even millions of past examples, in order for it to discover those repeating patterns. So we have a lot of data at places like Panasonic, Samsung, Intel, GE, all the physical industries, but these are just sensor data that's recording mostly normal operation. When a failure happens, that tends to be rare. Hopefully, failures are rare, and then they're very specific. So it turns out that what's called the labeled data is insufficient for machine learning. So what machine learning can do is do what's called anomaly detection. And that is look at all the normal patterns, and then when something abnormal appears on the horizon to say, "Hey, something is weird. I haven't seen this before." But it cannot identify what it is, which is only half of predictive maintenance because you have to identify what the problem is so you can replace that compressor or that filter. And it turns out humans are very good. Human experts are very good at that second part. The first improvement might be to say let's get machine learning to detect anomalies and then let's get human experts to actually do fault prediction. And after you do this for a while, which is what we did at Panasonic in the last three, four years across the global AI units, we said, "Well, wait a minute. Why are we making these very expensive?" Human experts do this if we can somehow codify their domain expertise. And so that's what Aitomatic is. We have developed a bunch of techniques, algorithms, and systems that run as SaaS software to help people codify their domain expertise, combine it with machine learning, and then deploy the whole thing as a system. CHAD: The codified expertise, there's a word for that, right? CHRISTOPHER: Probably you're referring to expert systems. CHAD: Yes. Yes. CHRISTOPHER: Yeah. Expert systems is one way to codify domain expertise. At the very basic level, you and I wrote actual BASIC programs before. You can think of that as codifying your human knowledge. You're telling the computer exactly what to do. So expert systems of the past is one way to do so. But what I'm referring to is a more evolved and more advanced perspective on that, which is how do you codify it in such a way that you can seamlessly combine with machine learning? Expert systems and machine learning act like two islands that don't meet. But how do you do it in such a way that you can codify human knowledge and then benefit as more data comes in, absolutely move into this idea of asymptotically this world where data tells you everything? Which it never will. And so the way we do that, the naive way, as I mentioned, is simply to just write it down as a bunch of rules. And the problem is rules conflict with each other. We, humans, work on heuristics. Whatever it is you tell me to do, you could be an expert, and you start teaching me, and you say, "Okay, so here are the rules." And then once I learn the rules, you say, "Well, and there are some exceptions." [laughs] And then, can you tell me all the exceptions? No, you can't. You have to use judgment. Okay, well, what is that? So the way we codify it is you can think of that evolution. I'll give you one concrete example from the machine learning perspective so people that are machine learning experts can see how we do things that are different. There's something in the machine learning process called the loss function. Have you heard of that term? CHAD: No. Yeah. CHRISTOPHER: So it's very simple. Training, which I'm sure everybody has heard, is really about how do I tweak the parameters inside the algorithm so that eventually, it gives the correct answer? So this process is repeated millions of times or hundreds of thousands of times. But let's say the first time, it gives you a random answer, but you know what the right answer should be. These are training examples. So you compute an error. If you output a five and the answer is actually six, so I say, "Oh, you're off by one, positive one," and so on. So there's a loss function, and in this case, it's simply the subtraction of one. And then that signal, that number one, is somehow fed back into the training system that says, "Well, you were close, but you're off by one." And the next time, maybe you're off by 0.5, next time maybe you're off by -2, and so on and so forth. That value is computable, what's called a loss function. That's machine learning because you have all these examples. Well, human knowledge can be applied as a loss function too. A simple example is that you don't have all the data examples, but you have a physical equation. If you throw a ball in the air, it follows a parabolic pattern, and we can model that exactly, an elliptic equation. That is a way to produce the correct answer, but there's no resistance there. And so, we can apply that function back as a loss function to encode that human knowledge. Of course, things are not always as simple as a parabolic equation. But a human expert can say, "The temperature on this can never exceed 23. If it exceeds 23, life is going to end as we know it because you're going to have a disaster." You can put into the loss function an equation that says if your predictor is greater than 23, give it a very high loss. Give it a very strong signal that this cannot be. And so your machine learning function being trained can get that signal coming back and adjust the parameters appropriately. So that's just one example of how we codify human knowledge in a way that is more than just expert systems. CHAD: That's really cool. Now, is there a way, once you have the system up and running and it is making decisions, to then feedback into that cycle and improve the model itself? CHRISTOPHER: Oh, absolutely, yeah. I think there's a parallel to what I say during training to also while it's in production, both in real-time, meaning one example at a time, as well as in batch after you've done a bunch of these. In fact, the first successful predictive maintenance system we deployed when we were part of Panasonic employs a human being that they feedback at. So our system would try to learn as much as it can and then try to predict the probability of failure of some piece of equipment. And the human being at the other end would say, "Okay, yeah, that looks reasonable." But a lot of times, they would say, "Clearly wrong. Look at this sensor over here. The pressure is high, and you didn't take that into account." So that's a process that we use both to certainly improve the output itself but also the feedback to improve our predictive AI. Mid-Roll Ad I wanted to tell you all about something I've been working on quietly for the past year or so, and that's AgencyU. AgencyU is a membership-based program where I work one-on-one with a small group of agency founders and leaders toward their business goals. We do one-on-one coaching sessions and also monthly group meetings. We start with goal setting, advice, and problem-solving based on my experiences over the last 18 years of running thoughtbot. As we progress as a group, we all get to know each other more. And many of the AgencyU members are now working on client projects together and even referring work to each other. Whether you're struggling to grow an agency, taking it to the next level and having growing pains, or a solo founder who just needs someone to talk to, in my 18 years of leading and growing thoughtbot, I've seen and learned from a lot of different situations, and I'd be happy to work with you. Learn more and sign up today at thoughtbot.com/agencyu. That's A-G-E-N-C-Y, the letter U. CHAD: So on the customer side, whether you can share specific customers or not, what kinds of companies are your customers? CHRISTOPHER: So I've mentioned in passing a number, so Panasonic is one of our customers. When I say Panasonic, Panasonic is a global giant, so it's run as individual companies. So, for example, avionics automotive coaching, how a fish gets from the ocean to your table, Panasonic has a big market share in making sure that everywhere in the chain that fish is refrigerated. So it's called the cold supply chain or cold chain. Supermarkets their refrigeration systems keep our food fresh, and if that goes down in an unplanned manner, then they lose entire days or weeks of sales. I mentioned the example of Furuno, F-U-R-U-N-O. If you go to some marina, say Half Moon Bay, California, you would see on the masts most of the navigation equipment is a Furuno, the white and blue logo. So we help them with those systems and fish finding systems. As well as off the coast of Japan, there's a practice called fixed-net fishing. What that is is miles and miles of netting. And large schools of fish would swim from different gates, A into B. And once they get to B, it's set in such a way that they cannot go back to A. But it's very large that they feel like they're swimming in the ocean still and eventually to trap C. And so Furuno is working on techniques to both detect what kind of fish is flowing through as well as actually count or estimate the number so the fishermen can determine exactly when to go and collect their catch. So I can go on. There are lots of these really interesting physics-related and physical use cases. CHAD: So is Aitomatic actually spun off from Panasonic? CHRISTOPHER: Spin-off, I think legally speaking, that is not the correct term because we're independent. Panasonic does not own shares. But in terms of our working relationship as customer and vendor, it's as good as it ever was. CHAD: What went into that decision-making process to do that? CHRISTOPHER: To do the so-called spin-off? CHAD: Yeah. CHRISTOPHER: Lots of things. CHAD: I'm sure it was a complicated decision. [laughs] CHRISTOPHER: Like we used to say at Google, to decide where to put a data center, lots of things have to intersect just the right way, including the alignment of the stars. In our case, it's a number of things. Number one, the business model just, as I said, at a very high level, it makes a lot of sense for us to be an independent company otherwise inside a...if we're a small unit inside a parent company, the business incentives are very different from if you're a startup, that's one. And the change is positive for both sides. Number two, in terms of venture capital, as you know, today, once you're an independent company, you can access a very large amount of scale in such a way that even a global giant doesn't have the same model to fund. Number three, certainly, the scope of the business we want to be able to apply...everything that I talk to you here is actually an open-source project. We have something called human-first AI rather than just knowledge-first, and so being able to put it out into the open-source and being able to have other people contribute to it is much easier as an independent startup than if it's a business unit. And then finally, of course, aspirations, myself and the rest of the team, we can move a lot faster. People are more passionate about the ownership of what they do. It's a much better setup as an independent company. CHAD: Were there things from Panasonic, either in culture or the way that the business works, that even though you had the opportunity to be independent, you said, "Hey, that was pretty good. Let's keep that going"? CHRISTOPHER: Well, I can comment on the culture of Panasonic itself. It's something that I was surprised by. This is 100 years old. The anniversary was in 2018. I gave a talk in Tokyo. So a 100-year old conglomerate. Japan might seem very stodgy, and, sorry to say, in many ways, it is. But I was very impressed. And I say this as a headline in cocktail conversation. I say the culture of engineering at Panasonic is far more like the Google that I knew than it is different; in other words, very little empire-building. People are very engineering-driven. There are a lot of cordial discussions and so on when people go into a meeting. I was very impressed by this. The Japanese engineers in Panasonic were always really well prepared. By the time they got to the meeting, even though they are in this context our customers, they will come with a slide deck like 30 slides talking through the entire use case. And they thought about this, they thought about that. And so I'm sitting there just absorbing it, just learning the whole thing. I really enjoyed that part of being part of Panasonic. And many of those folks are now lifelong friends of mine. CHAD: And so that's something that you've tried to maintain, that engineering-focused culture and great place. CHRISTOPHER: Well, when we were acquired by Panasonic, both Tsuga-san, the CEO, and Miyabe-san, the CTO, said the following, he said, "We want you to infect Panasonic, not the other way around." [laughs] From their perspective, we had this Silicon Valley setup. And they want this innovation, a fresh startup, not just the algorithms but also the culture. And they were true to their word. We kept an office, our own unit, kept their office in Downtown Mountain View. And folks were sent in to pick up our ways and means. What I enjoyed, the part that I just shared with you, is what I didn't expect to learn but what I did learn in retrospect. CHAD: As you set out on everything you want to achieve, what are you worried about? What do you think the biggest hurdles are going to be that you need to overcome to make a successful business, successful product? CHRISTOPHER: Well, I've done this multiple times. So people like to say, "You've seen this movie before," but of course, every movie is told differently, and the scenes are different, the actors are different, and so on. Of course, the times are different. So concretely, our immediate next hurdle you have to have proof points along the way. So we've got good revenues already. As a startup less than one-year-old, we have unusually good revenues but mainly because of our deep relationships in this particular industry. The next concrete proof point is a series of things, metrics that says we have a good product-market fit. And, of course, product-market fit means more than just a great product idea. It's a great product idea that is executed in a way that the market wants it in the next quarter, not ten years from now. So product-market fit is that iteration, and we're quite fortunate to have already customers what we call design partners that we work with. So hearing from that diverse set is pretty good confidence that if they want it, then other people will want it as well. And then after that, certainly after in timing but in the doing now, is scaling our sales efforts, our sales volume beyond just the founder-led volume that we currently have, so building the sales team and so on. But these are things that I will say are generally understood. But it does have to still be; you just got to sweat it. You got to do it. It doesn't happen automatically. I think the much bigger challenge that I see, and maybe it's an opportunity depending on how you think about it, is I'll call it a cultural barrier. Silicon Valley, in particular, the academic side of us...and you may know I used to be a professor, so when I say academic, I'm talking about myself as well. So any criticism is self-directed. CHAD: [laughs] CHRISTOPHER: We tend to be purists. The purism of today, if I can use that term, is data. And so, whenever I talk about knowledge-first AI, it offends the sensibilities of some people. They say, "You mean you're going back to expert systems. You mean you are not going to be extolling the virtues of machine learning and so on." And I have to explain data is nice if you have it, but 90% of the world doesn't have the data. And you do need to come up with these new techniques to combine human knowledge with machine learning. We look forward to being the Vanguard of that revolution, if you will, that say maybe it's a step backward. I think of it as a step forward of really harmoniously combining human knowledge and machine data to build what we call AI systems, these powerful systems that we're purporting to build. And that's almost directly at odds with the school of thought where people say, "Eventually, we'll have all the data." [laughs] And maybe, as you stated at the beginning, we don't need humans anymore. I will fight that battle. CHAD: The customers that you talked about, a lot of them seem to be pretty big enterprises. So as you talk about scaling sales beyond the founder-led sales that you're doing now, are you continuing to sell to enterprises? Or do you ultimately envision the product being accessible to any company? CHRISTOPHER: Well, I would say both. But I say that in a very careful sense because it's very important building businesses with a focus. And so let me break down what I mean by both, not just from some ambitious thing, you know, A and B. We will focus on enterprise as a matter of business. And the reason for that is A, that's where the money is but B, but more importantly, it's also where the readiness is. We've gone through...it's amazing. It's been a decade since that first New York Times, what I call the cats' paper about the Google Brain Project. We've gone through a decade of the hype and everything, but this vast physical industry, the industrial of the world, is ready. When I say ready, it means that people are now sophisticated. They don't look at it with wide eyes and say, "Please sprinkle a little bit of AI on my system." So they have teams, and they can benefit from what we do at the scale of what I've just described. But the reason I say both is because, quite happily, it is an open-source project. Our roadmap is designed with our design partners, but once it's out there, the system can be contributed to by others. The nature of open source is such that people tend to use it more than contribute. That's fine. So I think a lot of the smaller companies and smaller teams, once they overcome this cultural barrier of applying knowledge as opposed to pure data, I think they can really take advantage of our technology. CHAD: I'm glad you segued there because I was going to bring us there, too, which is that that open source that you've made available was it ever a question whether you could build a business where you were also open-sourcing the software behind it? CHRISTOPHER: It was absolutely a question 10 years ago. The industry has evolved. And now you and I talked about the TI-99/4A. I was already writing what's called public domain software before the term open source. Ten years ago, CIOs would say, "Why would I do away with the relationship with a big company like a Microsoft or an Oracle in favor of this unreliable, unknown open source?" It turns out, as we now look back, it was nothing to do with the business model; it was the immaturity of open source. Today, it is the opposite. Today, people don't worry about the lock-in with a vendor whose source code that they don't have. But I think equally important, source code is no longer a competitive advantage. Let me say that again. Source code is no longer that intellectual property. CIOs today want to be able to have the peace of mind that if some company locks them out or the company becomes defunct, that the engineers still have access to that source code so that they can build it. But that is not the real value. Amazon, Microsoft Azure, and GCP, Google have proven that people are very willing to pay for some experts to run operationally these systems so that they can concentrate on what they do best. So every day today, you know, every month, we're sending checks to AWS. They're running something that my team can easily run but probably at a much higher cost. But even at cost parity, I would like my team members to be focused on knowledge-first AI rather than the running of an email system or the running of some compute. So likewise, the value that our customers get from us is not the source code. But they're very willing for us to run this big industrial AI system so that they can focus on the actual work of codifying their expert knowledge. And by the way, I probably gave too long an answer to that. Another way is simply to look at the public market; there are very well rewarded companies that are entirely open source. CHAD: Yeah. Well, thank you. That was great. Thank you for stopping by and sharing with me. I really appreciate it. If folks want to find out more about Aitomatic or get in touch with you or follow along, where are all the places that they can do that? CHRISTOPHER: I think the website, aitomatic.com And it's just like automatic, except that it starts with A-I-. So I think the website is a great place to start to contact us. CHAD: Wonderful. Thank you again. CHRISTOPHER: Awesome. Thank you. CHAD: You can subscribe to the show and find notes for this episode at giantrobots.fm. If you have questions or comments, email us at hosts@giantrobots.fm. And you can find me on Twitter @cpytel. This podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. Thanks for listening, and see you next time. ANNOUNCER: This podcast was brought to you by thoughtbot. thoughtbot is your expert design and development partner. Let's make your product and team a success. Special Guest: Christopher Nguyen .

Dr. Christopher Nguyen is a renowned geriatric neuropsychologist.  He defines Neuropsychology and ways that neuropsychologists assess, diagnose, and treat cognitive and behavioral issues related to the brain.  He and several of his colleagues are translating assessment tools from English to Vietnamese and creating culturally relevant questions.  We also discuss barriers to help-seeking among individuals of Vietnamese and AAPI-descent, and how we can engage in conversations with our Asian elders about planning for their future care. ========================================== Bio: Dr. Chris Nguyen is a geriatric neuropsychologist and assistant professor at The Ohio State University Wexner Medical Center. He completed his doctorate degree at the University of Iowa, an internship at the Ann Arbor VA Healthcare System, and a fellowship at the University of Oklahoma Health Sciences Center. He conducts neuropsychological evaluations with a wide range of patient populations and has a particular interest in dementia and other neurodegenerative diseases.  His research interests include topics in cognitive aging, decision making, civil capacities, and cross-cultural considerations in neuropsychology. Chris is a member of the American Psychological Association Committee on Aging and was recently elected to serve as President-Elect of the Asian Neuropsychological Association.  In his free time, Chris enjoys a good cup of coffee and spends time with family and friends.

Christopher Nguyen is CEO and co-founder of Aitomatic, a startup building a platform for Industrial AI applications. Christopher previously held executive and leadership roles at organizations tasked with building machine learning solutions for traditional enterprises. Our conversation centered around what Christopher terms, AI Engineering – a new discipline concerned with the qualitative and quantitative design, construction, and operation of systems with artificial-intelligence capabilities.Download a FREE copy of our recent Data Engineering Survey Results:  https://gradientflow.com/2022desurveySubscribe: Apple • Android • Spotify • Stitcher • Google • AntennaPod • RSS.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

Please join first author Yuan Lu and Guest Editor Jan Staessen as they discuss the article "National Trends and Disparities in Hospitalization for Acute Hypertension Among Medicare Beneficiaries (1999-2019)." Dr. Carolyn Lam: Welcome to Circulation on the Run: your weekly podcast, summary and backstage pass to the journal and it's editors. We're your co-hosts. I'm Dr. Carolyn Lam, associate editor from the National Heart Center and Duke National University of Singapore. Dr. Greg Hundley: And I'm Dr. Greg Hundley, associate editor, and director of Pauley Heart Center at VCU health in Richmond, Virginia. Dr. Carolyn Lam: Greg, today's feature discussion is about the national trends and disparities and hospitalizations for hypertensive emergencies among Medicare beneficiaries. Isn't that interesting? We're going to just dig deep into this issue, but not before we discuss the other papers in today's issue. I'm going to let you go first today while I get a coffee and listen. Dr. Greg Hundley: Oh, thanks so much, Carolyn. My first paper comes to us from the world of preclinical science and it's from professor Christoff Maack from University Clinic Wursburg. Carolyn, I don't have a quiz for you, so I'm going to give a little break this week, but this particular paper is about Barth syndrome. Barth syndrome is caused by mutations of the gene encoding taffazin, which catalyzes maturation of mitochondrial cardiolipin and often manifests with systolic dysfunction during early infancy. Now beyond the first months of life, Barth syndrome cardiomyopathy typically transitions to a phenotype of diastolic dysfunction with preserved ejection fraction, one of your favorites, blunted contractile reserve during exercise and arrhythmic vulnerability. Previous studies traced Barth syndrome cardiomyopathy to mitochondrial formation of reactive oxygen species. Since mitochondrial function and reactive oxygen species formation are regulated by excitation contraction coupling, these authors wanted to use integrated analysis of mechano-energetic coupling to delineate the pathomechanisms of Barth syndrome cardiomyopathy. Dr. Carolyn Lam: Oh, I love the way you explained that so clearly, Greg. Thanks. So what did they find? Dr. Greg Hundley: Right, Carolyn. Well, first defective mitochondrial calcium uptake prevented Krebs cycle activation during beta adrenergic stimulation, abolishing NADH regeneration for ATP production and lowering antioxidative NADPH. Second, Carolyn, mitochondrial calcium deficiency provided the substrate for ventricular arrhythmias and contributed to blunted inotropic reserve during beta adrenergic stimulation. And finally, these changes occurred without any increase of reactive oxygen species formation in or omission from mitochondria. So Carolyn what's the take home here? Well, first beyond the first months of life, when systolic dysfunction dominates, Barth syndrome cardiomyopathy is reminiscent of heart failure with preserved rather than reduced ejection fraction presenting with progressive diastolic and moderate systolic dysfunction without relevant left ventricular dilation. Next, defective mitochondrial calcium uptake contributes to inability of Barth syndrome patients to increase stroke volume during exertion and their vulnerability to ventricular arrhythmias. Lastly, treatment with cardiac glycosides, which could favor mechano-energetic uncoupling should be discouraged in patients with Barth syndrome and left ventricular ejection fractions greater than 40%. Dr. Carolyn Lam: Oh, how interesting. I need to chew over that one a bit more. Wow, thanks. But you know, I've got a paper too. It's also talking about energetic basis in the presence of heart failure with preserved ejection fraction, but this time looking at transient pulmonary congestion during exercise, which is recognized as an emerging and important determinant of reduced exercise capacity in HFpEF. These authors, led by Dr. Lewis from University of Oxford center for clinical magnetic resonance research sought to determine if an abnormal cardiac energetic state underpins this process of transient problem congestion in HFpEF. Dr. Carolyn Lam: To investigate this, they designed and conducted a basket trial covering the physiological spectrum of HFpEF severity. They non-invasively assess cardiac energetics in this cohort using phosphorous magnetic resonance spectroscopy and combined real time free breathing volumetric assessment of whole heart mechanics, as well as a novel pulmonary proton density, magnetic resonance imaging sequence to detect lung congestion, both at rest and during submaximal exercise. Now, Greg, I know you had a look at this paper and magnetic resonance imaging, and spectroscopy is your expertise. So no quiz here, but could you maybe just share a little bit about how novel this approach is that they took? Dr. Greg Hundley: You bet. Carolyn, thanks so much for the intro on that and so beautifully described. What's novel here is they were able to combine imaging in real time, so the heart contracting and relaxing, and then simultaneously obtain the metabolic information by bringing in the spectroscopy component. So really just splashing, as they might say in Oxford, just wonderful presentation, and I cannot wait to hear what they found. Dr. Carolyn Lam: Well, they recruited patients across the spectrum of diastolic dysfunction and HFpEF, meaning they had controls. They had nine patients with type two diabetes, 14 patients with HFpEF and nine patients with severe diastolic dysfunction due to cardiac amyloidosis. What they found was that a gradient of myocardial energetic deficit existed across the spectrum of HFpEF. Even at low workload, the energetic deficit was related to a markedly abnormal exercise response in all four cardiac chambers, which was associated with detectable pulmonary congestion. The findings really support an energetic basis for transient pulmonary congestion in HFpEF with the implication that manipulating myocardial energy metabolism may be a promising strategy to improve cardiac function and reduce pulmonary congestion in HFpEF. This is discussed in a beautiful editorial by Drs. Jennifer Hole, Christopher Nguyen and Greg Lewis. Dr. Greg Hundley: Great presentation, Carolyn, and obviously love that MRI/MRS combo. Carolyn, these investigators in this next paper led by Dr. Sara Ranjbarvaziri from Stanford University School of Medicine performed a comprehensive multi-omics profile of the molecular. So transcripts metabolites, complex lipids and ultra structural and functional components of hypertrophic cardiomyopathy energetics using myocardial samples from 27 hypertrophic cardiomyopathy patients and 13 controls really is the donor heart. Dr. Carolyn Lam: Wow, it's really all about energetics today, isn't it? So what did they see, Greg? Dr. Greg Hundley: Right, Carolyn. So hypertrophic cardiomyopathy hearts showed evidence of global energetic decompensation manifested by a decrease in high energy phosphate metabolites (ATP, ADP, phosphocreatine) and a reduction in mitochondrial genes involved in the creatine kinase and ATP synthesis. Accompanying these metabolic arrangements, quantitative electron microscopy showed an increased fraction of severely damaged mitochondria with reduced crystal density coinciding with reduced citrate synthase activity and mitochondrial oxidative respiration. These mitochondrial abnormalities were associated with elevated reactive oxygen species and reduced antioxidant defenses. However, despite significant mitochondrial injury, the hypertrophic cardiomyopathy hearts failed to up-regulate mitophagic clearance. Dr. Greg Hundley: So Carolyn, in summary, the findings of this study suggest that perturbed metabolic signaling and mitochondrial dysfunction are common pathogenic mechanisms in patients with hypertrophic cardiomyopathy, and these results highlight potential new drug targets for attenuation of the clinical disease through improving metabolic function and reducing myocardial injury. Dr. Carolyn Lam: Wow, what an interesting issue of our journal. There's even more. There's an exchange of letters between Drs. Naeije and Claessen about determinants of exercise capacity in chronic thromboembolic pulmonary hypertension. There's a "Pathways to Discovery" paper: a beautiful interview with Dr. Heinrich Taegtmeyer entitled,"A foot soldier in cardiac metabolism." Dr. Greg Hundley: Right, Carolyn, and I've got a research letter from Professor Marston entitled "The cardiovascular benefit of lowering LDL cholesterol to below 40 milligrams per deciliter." Well, what a great issue, very metabolic, and how about we get onto that feature discussion? Dr. Carolyn Lam: Let's go, Greg. Dr. Greg Hundley: Welcome listeners to our feature discussion today. We have a paper that is going to address some issues pertaining to high blood pressure, or hypertension. With us, we have Dr. Yuan Lu from Yale University in New Haven, Connecticut. We also have a guest editor to help us review this paper, Dr. Jan Staessen from University Louvain in Belgium. Welcome to you both and Yuan, will start with you. Could you describe for us some of the background that went into formulating your hypothesis and then state for us the hypothesis that you wanted to address with this research? Dr. Yuan Lu: Sure. Thank you, Greg. We conducted this study because we see that recent data show hypertension control in the US population has not improved in the last decades, and there are widening disparities. Also last year, the surgeon general issued a call to action to make hypertension control a national priority. So, we wanted to better understand whether the country has made any progress in preventing hospitalization for acute hypertension. That is including hypertension emergency, hypertension urgency, and hypertension crisis, which also refers to acute blood pressure elevation that is often associated with target organ damage and requires urgent intervention. We have the data from the Center for Medicare/Medicaid, which allow us to look at the trends of hospitalization for acute hypertension over the last 20 years and we hypothesize we may also see some reverse progress in hospitalization rate for acute hypertension, and there may differences by population subgroups like age, sex, race, and dual eligible status. Dr. Greg Hundley: Very nice. So you've described for us a little bit about perhaps the study population, but maybe clarify a little further: What was the study population and then what was your study design? Dr. Yuan Lu: Yeah, sure. The study population includes all Medicare fee-for-service beneficiaries 65 years and older enrolled in the fee-for-service plan for at least one month from January 1999 to December 2019 using the Medicare denominator files. We also study population subgroups by age, sex, race and ethnicity and dual eligible status. Specifically the racial and ethnic subgroups include Asian, blacks, Hispanics, North American native, white, and others. Dual eligible refers to beneficiary eligible for both Medicare and Medicaid. This study design is a serial cross sectional analysis of these Medicare beneficiaries between 1999 and 2019 over the last 20 years. Dr. Greg Hundley: Excellent. Yuan, what did you find? Dr. Yuan Lu: We actually have three major findings. First, we found that in Medicare beneficiaries 65 years and older, hospitalization rate for acute hypertension increased more than double in the last 20 years. Second, we found that there are widening disparities. When we look at all the population subgroups, we found black adults having the highest hospitalization rate in 2019 across age, sex, race, and dual eligible subgroup. And finally, when we look at the outcome among people hospitalized, we found that during the same period, the rate of 30 day and 90 day mortality and readmission among hospitalized beneficiaries improved and decreased significantly. So this is the main findings, and we can also talk about implications of that later. Dr. Greg Hundley: Very nice. And did you find any differences between men and women? Dr. Yuan Lu: Yes. We also looked at the difference between men and women, and we found that actually the hospitalization rate is higher among females compared to men. So more hospitalizations for acute hypertension among women than men. Dr. Greg Hundley: Given this relatively large Medicare/Medicaid database and cross-sectional design, were you able to investigate any relationships between these hospitalizations and perhaps social determinants of health? Dr. Yuan Lu: For this one, we haven't looked into that detail. This is just showing the overall picture, like how the hospitalization rate changed over time in the overall population and by different population subgroups. What you mentioned is an important issue and should definitely be a future study to look at whether social determine have moderated the relationship between the hospitalization. Speaker 3: Excellent. Well, listeners, now we're going to turn to our guest editor and you'll hear us talk a little bit sometimes about associate editors. We have a team that will review many papers, but when we receive a paper that might contain an associate editor or an associate editors institution, we actually at Circulation turn to someone completely outside of the realm of the associate editors and the editor in chief. These are called guest editors. With us today, we have Dr. Jan Staessen from Belgium who served as the guest editor. He's been working in this task for several years. Jan, often you are referred papers from the American Heart Association. What attracted you to this particular paper and how do you put Yuan's results in the context with other studies that have focused on high blood pressure research? Dr. Jan Staessen: Well, I've almost 40 years of research in clinical medicine and in population science, and some of my work has been done in Sub-Saharan Africa. So when I read the summary of the paper, I was immediately struck by the bad results, so to speak, for black people. This triggered my attention and I really thought this message must be made public on a much larger scale because there is a lot of possibility for prevention. Hypertension is a chronic disease, and if you wait until you have an emergency or until you have target organ damage, you have gone in too late. So really this paper cries for better prevention in the US. And I was really also amazed when I compared this US data with what happens in our country. We don't see any, almost no hospitalizations for acute hypertension or for hypertensive emergencies. So there is quite a difference. Dr. Jan Staessen: Going further on that, I was wondering whether there should not be more research on access to primary care in the US because people go to the emergency room, but that's not a place where you treat or manage hypertension. It should be managed in primary care with making people aware of the problem. It's still the silent killer, the main cause of cardiovascular disease, 8 million deaths each year. So this really triggered my attention and I really wanted this paper to be published. Dr. Greg Hundley: Very nice. Jan, I heard you mention the word awareness. How have you observed perhaps differences in healthcare delivery in Belgium that might heighten awareness? You mentioned primary care, but are there any other mechanisms in place that heighten awareness or the importance? Dr. Jan Staessen: I think people in Belgium, the general public, knows that hypertension is a dangerous condition. That it should be well treated. We have a very well built primary care network, so every person can go to a primary care physician. Part of the normal examination in the office of a primary care physician is a blood pressure measurement. That's almost routine in Belgium. And then of course not all patients are treated to go. Certainly keeping in mind the new US guidelines that aim for lower targets, now recently confirmed in the Chinese study, you have to sprint three cells. And then the recent Chinese study that have been published to the New England. So these are issues to be considered. I also have colleagues working in Texas close to the Mexican border at the university place there, and she's telling me how primary care is default in that area. Dr. Jan Staessen: I think this is perhaps part of the social divide in the US. This might have to be addressed. It's not only a problem in the US, it's also a problem in other countries. There is always a social divide and those who have less money, less income. These are the people who fell out in the beginning and then they don't see primary care physicians. Dr. Jan Staessen: Belgium, for instance, all medicines are almost free. Because hypertension is a chronic condition prevention should not only start at age 65. Hypertension prevention should really start at a young age, middle age, whenever this diagnosis of high blood pressure diagnosis is confirmed. Use blood pressure monitoring, which is not so popular in the US, but you can also use home blood pressure monitoring. Then you have to start first telling your patients how to improve their lifestyle. When that is not sufficient, you have to start anti hypertensive drug treatment. We have a wide array of anti hypertensive drugs that can be easily combined. If you find the right combination, then you go to combination tablets because fewer tablets means better patient adherence. Dr. Greg Hundley: Yuan we will turn back to you. In the last minutes here, could you describe some of your thoughts regarding what you think is the next research study that needs to be performed in this sphere of hypertension investigation? Dr. Yuan Lu: Sure. Greg, in order to answer your question, let me step back a little bit, just to talk about the implication of the main message from this paper, and then we can tie it to the next following study. We found that the marked increase in hospitalization rate for acute hypertension actually represented many more people suffering a potential catastrophic event that should be preventable. I truly agree with what Dr. Staessen said, hypertension should be mostly treated in outpatient setting rather than in the hospital. We also find the lack of progress in reducing racial disparity in hospitalization. These findings highlight needs for new approaches to address both the medical and non-medical factors, including the social determinants in health, system racism that can contribute to this disparity. When we look at the outcome, we found the outcome for mortality and remission improved over time. Dr. Yuan Lu: This means progress has been made in improving outcomes once people are hospitalized for an acute illness. The issue is more about prevention of hospitalization. Based on this implication, I think in a future study we need better evidence to understand how we can do a better job in the prevention of acute hypertension admissions. For example, we need the study to understand who is at risk for acute hypertensive admissions, and how can this event be preempted. If we could better understand who these people are, phenotype this patient better and predict their risk of hospitalization for acute hypertension, we may do a better job in preventing this event from happening. Dr. Greg Hundley: Very nice. And Jan, do you have anything to add? Dr. Jan Staessen: Yes. I think every effort should go to prevention in most countries. I looked at the statistics, and more than 90% of the healthcare budget is spent in treating established disease, often irreversible disease like MI or chronic kidney dysfunction. I think then you come in too late. So of the healthcare budget in my mind, much more should go to the preventive issues and probably rolling out an effective primary care because that's the place where hypertension has to be diagnosed and hypertension treatment has to be started. Dr. Greg Hundley: Excellent. Well, listeners, we've heard a wonderful discussion today regarding some of the issues pertaining to hypertension and abrupt admission to emergency rooms for conditions pertaining to hypertension, really getting almost out of control. We want to thank Dr. Yuan Lu from Yale New Haven and also our guest editor, Dr. Jan Staessen from Louvain in Belgium. On behalf of Carolyn and myself, we want to wish you a great week and we will catch you next week on the run. This program is copyright of the American Heart Association, 2021. The opinions express by speakers in this podcast are their own and not necessarily those of the editors or of the American Heart Association for more visit aha journals.org.

ComebaCK chats to Christopher Nguyen, one of the hosts of the Ranting Bananas podcast. We discuss themes from the podcast, why it's important to get uncomfortable, and aims for the future. You can find out more about Christopher at 'The Ranting Bananas' Podcast, and more about ComebaCK at @thecomebackwithck on Instagram and www.thecomebackwithck.wordpress.com. (P.S. Apologies for the slight technical issues in this episode). 

In this episode of the Data Exchange I speak with Christopher Nguyen, CEO of Arimo (a Panasonic company). I first met Christopher in the early days of Apache Spark, Arimo was one of the first companies to embrace Spark and make it a central component of their data platform. He was also an early proponent of exploring deep learning for enterprise applications. A serial entrepreneur, Christopher was also an Engineering Director at Google where he was responsible for Google Apps.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

"Machine learning is to big data as human learning is to life experience," says Christopher Nguyen, the co-founder and CEO of big data intelligence company Adatao. Sure, but then, what IS big data? (especially as it's become a buzzword that captures so many things)... On this episode of the a16z Podcast, Nguyen puts on his former computer science professor hat to describe 'big data' in relation to 'machine learning' -- as well as what comes next with 'deep learning'. Finally, the former Google exec shares how Hadoop and Spark evolved from the efforts of companies dealing with massive amounts of real-time information; what we need to make machine learning a property of every application (why would we even want to?); and how we can make all this intelligence accessible to everyone. ––– The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.