Finding Mastery with Dr. Michael Gervais - Decoding the Dark Web, Artificial Intelligence, and Deep Learning | NASA CTIO, Dr. Chris Mattmann

Episode Date: March 2, 2022

This week’s conversation is with Dr. Chris Mattmann, the Chief Technology and Innovation Officer at the NASA Jet Propulsion Laboratory, also known as JPL.His work has helped NASA explore space, and helped journalists and governments track international financial crime amongst the world’s elite across the globe.Chris is best known for a 20 year career inventing the most downloaded software on the planet culminating from his membership on the Apache Software Foundation Board of Directors (2013-18), creating technology that powers all the data systems in industry including his pioneering work building the Tika library. Tika, the “digital babel fish” is the key technology that solved the Panama Papers and won the Pulitzer Prize in journalism in 2017. Chris is a frequent keynote speaker in government, academia and industry and his work helped to define the field of data science.At its core, this conversation is an exploration of what it means to innovate - it's about deep learning (one of the core tenets of growth) and the duality of technological progression in the modern world._________________Subscribe to our Youtube Channel for more powerful conversations at the intersection of high performance, leadership, and meaning: https://www.youtube.com/c/FindingMasteryGet exclusive discounts and support our amazing sponsors! Go to: https://findingmastery.com/sponsors/Subscribe to the Finding Mastery newsletter for weekly high performance insights: https://www.findingmastery.com/newsletter Download Dr. Mike's Morning Mindset Routine! https://www.findingmastery.com/morningmindsetFollow us on Instagram, LinkedIn, and X.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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track international financial crime amongst the world's elite across the globe. Very cool. I mean, the range of Chris's experiences is incredible. He's best known for a 20-year career inventing the most downloaded software on the planet, culminating from his membership on the Apache Software Foundation Board of Directors from 2013 to 2018, creating technology that powers all the data systems in industry, including his pioneering work building the Tika library. Tika, the digital babble fish, as it's known, is the key technology that solved the Panama Papers, and it won the Pulitzer Prize in journalism in 2017. So Chris is a frequent

keynote speaker in government, academia, and industry, and his work helped to define the field of data science. So at its core, this conversation is about deep learning, one of the core tenets of growth and innovation for sure. And with that, let's get right into this week's conversation with Dr. Chris Mattman. Chris, how are you? Mike, I'm doing good. Feels like a Monday, but in a good way, not the Garfield way. Garfield. I mean, we're going to bring out jokes already. I love it. Do you enjoy kind of silly jokes, Garfield being a lead off there? 100%. Yeah. In fact, when I wrote my book on machine learning recently, the guy who wrote the foreword was the head of AI or applied AI at Google, Scott Penberthy.

And his comment to me was, Chris, I just love all of your terrible dad jokes in your book. Every chapter has them. So. Do you remember any of them off the top of your head? You know, I mean, they range from basically the kids playing sports or kids watching something about, you know, poop on TV or something like that. And then figuring out a way to kind of get that into training data or, you know, things in machine learning, things like that, you know, things that a dad would encounter with three kids running around talking about all sorts of nonsense. I am in my house. I, we can't get away from these nuts jokes. It's like, I've got a 13 year old, like these, that's our excellent, excellent jokes. Excellent. I it's, it, that is, that is like on the scale that's above a five. So, all right, good. So one of the things that I loved when I was learning about your history is that you've got range. You're one of the polymaths that can go wide and deep in lots of different areas. And so I really don't know where to start with you. So it's fun that we started with bad jokes. And it's probably fitting for your personality and your career.

But maybe we can just start with what I'm really interested in right now is the dark web. And then move into some AI and some machine learning. But can we start with the dark web? And I'm not sure one of our producers wasn't even aware what the dark web was which was shocking to me and at the same time it's like oh okay well maybe we need to open that up just a little bit for a conversation for more people sure yeah yeah well and and you know decade ago i wasn't exactly sure what it was either um what's really interesting is that there's a public internet that's the internet that we've all transact on we put our our content out on, we're going to publish the podcast. There's also what's

called the deep web on the public internet. That's the web behind logging in and forms. So if you're going to log into your bank, you can't see it without transacting with it and things like that. So that's the deep web. So 3% of the actual public internet is the internet. You can see 90 plus percent of that, maybe 97% of that, the remainder is what we call the deep web, the web behind forms and JavaScript and all that. And then the dark web isn't even that. The dark web was invented in sort of the early 2010 time frame. And basically what it was invented at was a anonymized communication protocol. It came from the Department of Defense in the US. They wanted basically a way to kind of anonymously and

securely communicate between different folks in the battlefield. That protocol, it's called the Onion Protocol. And there's a technology around it called tour, uh, which allows you to sort of operate on that protocol as an entirely different internet, uh, you know, not subject to, uh, you know, basically Google's and all of that of the world. And so, yeah. And so I became deeply involved in that, um, circa 2014, because basically what the government had found, like many things, you know, and so people talk about Mike, well, you know, should you ever invent things because they could be used for wrongdoing? Well, that's kind of what happened in this case. The bad guys, what they found is that there's a pretty good protocol. And in fact,

in the Snowden papers, you know, Edward Snowden, what they found and some of that information that he leaked out was that even the government circa 2012, the NSA, the National Security Agency in the US didn't have the capability to kind of crack in or spy on or read or know what's going on on that dark web, that anonymized Internet, you know, based on that protocol and Tor and the onion protocol. And so basically what they were finding is that, you know, people who were doing human trafficking, people that were, would, you know, sell weapons and arms, people that would put out kind of their electronics, they were going on that protocol. They were getting a Tor browser, which allowed you to, it's kind of like your Chrome or your Mozilla, the way you browse the internet, except that's the way you browse the dark web. And they were standing up marketplaces there. One of the most famous ones was the one you might've heard from Ross Albright. If you've watched, if you're a Netflix fan and you heard about the Silk Road, that was one to basically buy illegal pharmaceuticals and drugs and things like that. So they were standing up those

marketplaces and doing the dirt, you know, basically. And in 2014, which is sort of the second decade of my career, I got heavily involved with the government because I was actually building technology and trying to figure out how to take those technology dollars and put it back at NASA, work for space and things like that. And so, yeah, we started basically building a Google for that dark web. Now, I don't want to paint the picture that, oh, it's only this, you know, wild west and evil people are on there. Again, it grew out of the need to communicate anonymously, to do things,

in support of even U.S. priorities and other things. But yeah, that's basically, and so it exists today. You know, there are capabilities to kind of look at it. It's still very heavily secure, anonymized. And so bad stuff still happens on it today. And maybe in the daily life, people might be familiar with it because you might have seen commercials on TV from your credit reporting agencies like Experian or whatever that says you can buy a dark web scan. And so one of the biggest uses of

it today is what happens with all these data leaks, security breaches and stuff in the IT world is they will take all those logins, passwords and things that people have acquired, and they'll dump them on the dark web. And because again, it's this anonymized secure marketplace in a way, and people will transact and buy stolen data, stolen logins and stuff like that. And that's why credit reporting agencies and other places want to scan it. And then if you just make it more practical for me really quickly, was that where Napster and BitTorrent, is that where they lived? Great question. Great question, Mike. It wasn't, but it's sort of a similar kind of idea. So 30 years ago, there was an idea to create an internet or a protocol, which is a way of basically having computers talk to one another.

70 years ago, one of the most famous protocols, know, 70 years ago, one of the most famous protocols, you know, 60, 70 years ago, one of the most famous protocols were being discussed at the time would lay the foundation for something that was eventually called the ARPANET, which was the way that computers at their very core could be networked together. And the early sort of, you know, ways of the internet,

eventually they found utility on top of that by sending email or figuring out ways to send electronic mail on that. But even computers networked together could send messages to one another, talk about their health, say where they are. And the early internet was basically a way to kind of bring computers together in that way, to bring academic and government communities and eventually commercial communities together. Those are called protocols, which are ways for electronic devices to communicate. So to your question, Napster, BitTorrent and all that, they certainly were their own protocols. They were invented kind of in the 90s, early 2000s, and they were ways to kind of share

files, distribute, again, very common properties, leaked data, or, you know, before the record industry or before the movie industry got really into digital rights before the DCMA, the Digital Copyright Millennium, you know, act before all of that, this is where people would come and they would share Yeah, they would share mp3 files, they would share movies that they maybe they leaked, or they got off. So that all existed. And there was a whole set of that. But those were different protocols. In fact, they were less secure.

One of the reasons they got taken down is they didn't take the government. And obviously, these laws were passed to basically say that stuff was illegal because people didn't want their IP and their copyrighted stuff kind of getting out there. But because they were not as hard to crack, there were protocols that work, but they didn't take security first. They didn't last as long. And actually, the record industry, the movie industry, or whatever, they disrupted all of that. You know, they disrupted it by basically building big platforms in which you're willing to pay 99 cents for a song or even 14 bucks for a movie. It keeps it in a digital wallet for you. You know, it's easy to track. You can search it.

You know, hell, I'm looking over here at my, you know, Blu-ray DVD collection and it's useless, right? I'm never going to use it again because of that. So they did such a good job of disrupting that, that it kind of eliminated those de novo protocols and things that people were doing. But in this case with the dark web, there is no alternative, you know, for that type of secure communication. So it persists and, you know, people do things a lot of times bad on it. So. And so is the, is the browser for the dark web called Tor? is that how you get on the dark web? Yeah, yeah. Tor, it stands for the Onion router.

And remember I said, Onion is the underlying protocol for the dark web. And that's exactly how you get on, Mike. And yeah, it looks, and in fact, I think it was an open source project, which means that a big community came together and they build the software and they disseminate it for free, you know, typically under a more permissive license. So you don't have to pay them to necessarily do it. But basically, yeah, you can download it, it kind of looks like Chrome. And the difference is instead of typing in a URL and a very easy name in your mind, you know, like, give me guns dot, you know, tour, you know, or whatever, right? You're going to input these weird looking and it's still it's, you know, you're going

to input these weird looking what they call onion URLs, which are, you know, you have to have indexes and directories of them and things like that. And it's not as common as the Internet. And it's not as sort of easy to navigate and they like it that way. That's one of the things they like about it. They don't want it to be easy to search and find and that's by design.

So I think there's a nice segue to get into blockchain and Bitcoin and the platforms, but I don't want to miss a philosophy that you shared, which is remarkable, that every good technology has a dark side. That's a remarkable philosophy and a bit like every Marvel character has a villain. So there's that yin and yang and you're beautifully picking up on an ancient idea. How does that impact innovation for you? Because you innovate, you create that. I mean, you're the chief technology innovation officer at one of the more significant government agencies across the planet. So how do you wrestle with that philosophy?

Yeah, you know, it's basically, and I don't know if this will be controversial and I will see. I mean, I don't lose a wink of sleep over it, to tell you the truth. I really don't because because the way I think about it is sort of like this, Mike. So some of the stuff I've helped invent technology called Hadoop. It basically is the reason that it drove down the costs on banking, that medical companies and big biopharma, that even mRNA vaccines could be made. The power that that platform provides to transact, do low cost, basically big data processing to analyze information has all of these amazing, great uses. And so I was on the board of Apache when that was made. I was a contributor to that project. Apache

is the home for a lot of that. I'll call web one, web two software before we get into blockchain and crypto, which is web three. But yeah, obviously here's something that, you know, maybe I don't totally agree with, you know, that was used for, that's pretty much the technology that the NSA used to build a big data center, you know, to spy on people, you know, and collect their phone records and what came out later and, you know, all of these things. And so, you know, people say, well, how do you grapple with that? Like you just said, and I said, well, but look at all the good it did, you know, and, and, you know, like I would, I'm going to go to the dad jokes, not the dad jokes, but I'm going to explain it like I would to my 12 and a half year old. You could take a spoon

and kill somebody. Does that mean we shouldn't have spoons? You know, like, I mean, that's obviously an extreme, you know, analogy, but it's the truth, you know, in my mind. So, I mean, another example, we might talk about this here and we might not, but the audience can, you know, Google it. I invented a technology called Tika. That's basically the technology that was used to sniff out and solve the Panama Papers conspiracy, which is all these, you know, international public figures hiding their money, right, and evading taxes and putting their stuff in the cave. And it was a technology that allowed reporters to basically deeply report what was going on in that and at least raise public awareness.

And, you know, what people should I'm not making judgments, you know, or anything, but it at least changed the public discourse, you know, now could Tika be used for bad things? I mean, certainly it could be used by scammers to eat. Tika is a technology that allows you to quickly sort through, sift through any type of file, figure out what type of file it is and extract out the people, the places, the things in there, the connections between them. So there are lots of bad uses for that. Like imagine, you know, someone getting our data in the US or, you know, a bunch of spies and information about them that could really affect them or catch them or reveal their, you know, like basically where they live and their connect. It certainly could be used for bad things as well.

You could imagine it in the hands of the bad guys, you know, too. And, you know, does that mean I shouldn't have invented it? Heck no, because, you know, we need to keep creating. The final thing I'll say, you know, it's that line from Jurassic Park and you're going to find pop culture, you know, the first the first set of them. I like the new Jurassic World, but I'll quote Jurassic Park. You know, it's like looking at Dr. Ian Malcolm, you know, who sits there

and he's talking to Sam Neill and he said, you stood on the shoulders of giants. You took all that DNA research, you know, to the next level, but you didn't ask yourself, should you? Well, I'm of the mindset they should have, you know, and if we can get dinosaurs, that'd be great.

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answer as much as like the content of the answer as I did the position and clarity that you're working from. So you've thought about it. And when you when you're working to get clarity, is it intuitive, automatic, like, of course, it's this, and then it makes sense in your mind? Or do you write and talk and think it through? Like, what is your process to get the clarity that you just had? That is a fantastic question. That is a fit. So I'm half Sicilian, half Irish.

So this is, you know, I'll do. Hey, that's both of us then. That's both of us? Okay, all right. Well, that is super crazy. And maybe that's another reason. Which side was Irish? Which side was Irish? My mother's side and my mother's side.

Oh, no, no. I was thinking of my grandma. My mom's side is Italian. Oh, OK. Got it. Yeah, I'm the I'm the reverse. And a longer story is how I found that out. We'll save for the next episode. But regardless, I'd say the fire inside of me that Italian side wants to like, has no problem with decision making can spot on do it with or without, you know, you could think of a slider bar of the amount of information before doing it. And it's probably in the lower third where I feel like I can, but over, I'd say maybe the Irish side or the other times, and maybe even just my scientist training, you have a PhD in computer science and, you know, I did learn science, even though my degrees in computers and all that, um, I could prescribe bits and bytes.

Um, but, uh, you know, uh, uh, that side is more, I would say the ladder of what you're talking, well, it is what you're talking about. I want to take in information. I want to, you know, I want to revise my hypotheses. I want to take in more information. I want to see, and I've adapted, you know, my positions on things over time as, as, as science allows us to do, I would actually say that doesn't mean, and you know, the thing I hate sometimes, Mike, I'll just share that with you real quick, is that doesn't mean we should be paralyzed by analysis. You know, it just like everything in life, you know, in moderation, a little bit of analysis, a little

bit of decision making, but don't, you know, don't only have one of the extreme, you know what I mean? So, so how do you feel pressure? Is it when you need to make a decision fast, or you feel like you just can't quite get enough information to have the accuracy when you need to make a decision fast or you feel like you just can't quite get enough information to have the accuracy that you need? Those are two, that's only one lens of pressure. Pressure, there's lots of other different sources of it, but just on decision-making, how does it happen for you? That's great. Now, one's the scarce resource time. I mean, it depends, right? We have our time on this earth, time is infinite, but you know, humans, you could think about time through a lens of a scarce resource. And, you know, the field that I'm in certainly doesn't necessitate in all cases that

time, you know, that time should drive that, you know, because I do a lot of research, you know, time is there, you know, but it's not necessarily the driver. So, you know, I would say it's more of the latter, you know, not necessarily time as a scarce resource that's sort of driving, you know, that element of decision making. Okay, so let's go back to Tika for a minute. And before we get into Web3, where does that idea come from? And then materially, how do you build something like that? And I don't mean coding, but do you pull people in? Do you go to the shed and start writing and chopping wood?

The shed is more for chopping wood, I guess. But how do you, can you just walk through that? Because it's been a, a world shifting technology. Yeah. Yeah. Great. Well, so for, for me, um, where it came out of was like probably my, my first decade of, of real professional work at JPL. And I started there just straight in school. I was, you know, I basically, I grew up in a trailer in Santa Clarita, California. It's about an hour North of Los Angeles. I tell people, listen, you can't just slip out. I grew up in a trailer. I grew up in a trailer.

Well, I'll get, this is context. That's important. That's gonna, I think kind of set this, you know, tone, but yeah, I mean, I, I did like basically, um, you know, I tell people, you know, so I went to this very private, expensive institution, but it's an actual nice story. You know, it wasn't that I necessarily grew up from from trailer to USC. Is that what you're talking about? From trailer to USC. Right.

And I looked around and I saw the people with their fancy cars and stuff like that and dreamt about what could be, you know, and again, I'm not making any judgments calls. These are all dear. Many of them are dear friends of mine, you know, that I knew again, I'm not making any judgments calls. These are all dear, many of them are dear friends of mine, you know, that I knew, and I don't care what background you came from. There's good everywhere, but I'm just saying, at least for me, you know, I am not, I came through a thin funnel, you know, it's not like everybody comes out of the funnel that I come in or I came from and, you know, had the opportunity. I wouldn't even say it was all driven by me, but the opportunity to, to do this. So, so yeah, I grew, had the opportunity. I wouldn't even say it was all driven by me,

but the opportunity to do this. So, so yeah, I grew up in a trailer. I had a beautiful family that loved me dearly. We just didn't have a lot of money. We didn't have very much money at all. We didn't have very much of anything. So I, I love sports. When I realized I couldn't be an athlete, you know, because I'm five, nine and everyone around me was growing when I was playing high school football, but I wasn't, uh, you know, I, my last year I invested in my mind. I said, well, how am I going to make it? How am I going to get the hell out of Santa Corita and do something, you know, because it was a great place. We had magic mountain, just six flags, the only one on the West coast, you know, or whatever, six flags. And we had them all.

And you had them all, but you also had some paintball. It was early in paintballing up there. I don't know if you know that. It certainly was very early. And we had some cool music videos that were made like smashing pumpkins and things like that. So before you, I love this concept. I want, so you, you scanned and you said, right, I see cool kids or whatever, sport, sport, sport. That's not going to be my path. And then you went, I want to know how you did that analysis and said, I need to invest my mind. They're investing in their body. I've never heard it said that way. But then right before that, what did mom and dad do? What was, what was happening there that they, you know, that they, the, the best that they were able to offer the family was living in a trailer. And I guess like,

I don't know if trailer life is good or not good. I've just, I've just fallen the stereotype that, you know, it's not great, but I don't know if that's the case for you. Yeah. Well, so my mother, um, my mother had a lot of mental illness. And so she was this had schizophrenia. And so she was in and out of the hospital throughout her life. I knew what a 5150 was when I was about six years old. And that's basically where the police, well, they call it a psychiatric evaluation team or a pet team come and in the old days, they used to come with the police sometimes. And they would make an assessment that, you know, this person, it's probably either a danger to themselves or others. And we need to put them on what they call a 5150 or a 72 hour hold and evaluate them. That happened probably more than a dozen times, you know. And so now when my mother, when that wasn't happening to my mother,

and as if you research or folks research schizophrenia, when the folks aren't having a episode, you wouldn't know the difference between them and any other, you know, parents, but then, you know, when they are having an episode, they could be standing outside naked, getting picked up by the police, you know, and things like that. And so unfortunately, that that happened. I love my mother deeply to the day she died in 2019. You know, most recently, right before the pandemic. And in her, you know, later years, she did take her meds and was, you know, more, I'd say cogent. And I had a nice relationship through with her my whole life. Up through her. Now, my father and so my mother was a homemaker. You know, when she that wasn't happening, she helped take care Now my father, and so my mother was a homemaker. Um, you know, when she,

that wasn't happening, she helped take care of my grandmother lived with us. Uh, it's very, you know, raised six children, you know, very competent and helped us out a lot. Uh, and in fact was probably the most stable there. My father, um, had started a business. Uh, he had a printing business and about midway through my, right before I entered teenage years, he lost his business. And that was part of the reason we lost our home. We went from being sort of middle-class living, I think in Saugus in Santa Clarita, which is more of a middle-class place. We moved to a trailer park in Canyon country, which is, you know, at the time was a little

less, I mean, it was less affluent. And my father, when that happened, became a car salesman. He was, you know, and again, like just setting the context, this is in the 80s, you know, or whatever, early 90s. And so my father also was older. So he was, you know, like I was the kid who didn't have the parents in their 40s. You know, my parents were in their 60s, you know, and things. So they're a little older, you know, so my father was, and so, yeah, he would, I remember

he loved the guy I'd ever, you know, seen this maybe again, maybe this still exists, but my dad just ate pie and ice cream every day. He was gone all day at the car. He came home at nine at night, you know, you can't live off a pie and ice cream kids. That'll, that'll kill you. But yeah, so not, not good for the heart and not good for other, you know, body functions. So, okay. So you grew up with, um, inside of a family with mental health issues.

Yeah. And so how did you, yeah. How did you manage that? Because there's, there's people now that finally are talking about, you know, their own mental health. And we also understand that mental health is a family concern. It's not just, just the one individual, whether it's addiction or anxiety, depression, schizophrenia, bipolar, borderline histrionic, whatever it might be that it's a real thing. So how did you manage it as a kid? Yeah. And believe it or not, like audience, as you thing. So how did you manage it as a kid?

Yeah. And believe it or not, like audience, as you're listening to us right now, I am going to get back to Mike's question too, about Tika, because this all relates and watch us connect these dots here. So here we go. So here's what happens. You asked me the prior question of like, how did I know that I needed to build up my mind and things like that. And in part, you also just asked me, how did you deal with the mental illness? And, you know, what's going on? And all that? Well, part of it was building a self defense mechanism inside my to basically tell myself, like, I had a brother, my brother is great. He was one of the funniest guys I ever know. But my brother was taking a little bit different path than me. He was more athletic and hanging out with people. I was more of a introvert, you know, and we flipped later in life, which is kind of funny. I'm more of an extrovert

now, but I built up a self-defense mechanism inside. I said, look, I love my parents, but they're not going to get me out of here. And my goal was to, again, like escape. I hate to say it, like, you know, the 97%, you know, white folk and whatever, lack of diversity and get to a place like LA where I was born. And I would visit my uncle, my uncle lived in Highland Park, which is, you know, in Northeast Los Angeles. And I just love being out there. And I wanted to get out there and away from where I was at and also just escape it. So I built a self defense mechanism inside, very much focused on my academics, again, realized I wasn't going to get out of there with my athleticism, although I was great at sports stats and I still am, you know, I love sports and I

follow them deeply, but yeah, that's, that's what I did. And so, uh, yeah, so basically my last, my senior year, I got into computers a lot more. I joined the yearbook. I did the sports editing for that. I learned a bunch of like Adobe and other stuff, got on computers that weren't like Apple two E's, which if you go look that up, those are terrible computers. I had that in a Mac classic at home, like an old piece of crap that didn't work. And so, yeah, I got on real computer, started to learn it, but I didn't know when I got to USC and I got in and I bought, got a bunch of scholarships to get in there and get the hell out. I didn't know I was going to study computers. I knew I was good at math and I like computers, but I got there. And basically what happened was

just quickly summarizing a few years there. I had a bunch of imposter syndrome again, also not just from looking at everyone around and it's, it's not only like this, I'm being facetious, but driving nice cars, coming from families that, you know, actually know what's going on with their children and, you know, have the means to support her. I had like imposter syndrome, you know, when I got there and I was like, do I belong here? Am I good at this? You know, I don't know. But I dedicated myself, you know, and, and I went through this transition of like proving to myself in a couple of years that I was the first one in my class at SC undergrad from like 98 to, you know, 2000 in engineering that had a job. I had a job when I was a sophomore. I got a job at JPL. I stayed up in the computer lab at night. I didn't know

what JPL was at the time, but I knew it was money. And some people said it was good. And the people that I met there, I met an earth scientist who hired me, a guy named Rob Raskin. And he said, I need programmers, you know, to basically work on this like earthquake database. And can you help me? And I, like I said, I needed a job. I had proved to myself I could be there. I had a job and I was like, okay, I've got a job. I can do this. And then I started enjoying life and becoming part of the pop culture valet. I started going to parties, hanging out, realizing it wasn't only about school. And then I dipped a little. I got too into that. And then I kind of brought it back around. But that got me to JPL. That also taught me a lot too. And JPL taught me this and school taught me this and getting out on my own, but

realizing you can't do it on your own. And this is how I'm going to bring it back. I was on a team of people that wasn't just USC. It was people that who had full-time jobs there who were like five or 10 years ahead of me, you know, at JPL. And I became part of a team of these exceptional individuals that were doing things and the world was changing at the time. So everybody had started to get these things called iPhones, which you could take these amazing pictures. And JPL and Caltech, funny enough, invented the patent for iPhone cameras, by the way. So the camera in your iPhone is from JPL, Caltech, a CMOS thing. So that was happening. Everyone became a sensor. Well, all of the science missions at NASA, JPL, Earth Science and Planet were fundamentally

changing too. So instead of after 10 years capturing a few DVDs worth of data, they were moving into the era of in three months, a mission called Orbiting Carbon Observatory in 2005, capturing 150 terabytes, which was tens of thousands of DVDs of data captured in the first three months. So the sensors, the data was taking a lot more data. The missions had to process more information. And I was sitting there a part of this team, how the hell are we going to do this? And so here's the answer to your question,

how I got involved in this stuff. I stayed at USC initially driven financially. I didn't want to be the smartest person in the world. I passed with the minimum GPA for undergrad 3.2. Master's, I go in to get the $10,000 raise that you got at the time for getting a master's at JPL thinking I could do it in 12 to whatever months. And I did. But I met and got familiar with research there. And I was like, wow, reading papers, building off of other technology, building off of people, learning about open source and this movement that was happening at the time of communities, building stuff together. And what I did is I became one of the biggest, probably the biggest open source advocate, you know, in my generation at JPL saying, hey, we need to put these communities together of people at IBM, Microsoft, Google, who are building this tech,

because they're having the same problem. The early days of social media, fire hose of information, just like science, just like everywhere. They were all having the same problems. I couldn't do it all alone. And that's how I got involved eventually and met people at the Apache Software Foundation and said, hey, we need to be using this technology at JPL and NASA. That's the only way we can handle all of this. We have to throw out the way we were doing in building systems. And ultimately, over about a decade, we kind of did that. We changed the way and infused a ton of that software technology into our missions.

And that was the way that we scaled. And that's how I got involved in basically building that technology. Now, the final little missing piece is that one of the key issues that they had in data And that was the way that we scaled. And that's how I got involved in basically building that technology. Now, the final little missing piece is that one of the key issues that they had in data at the time and data systems was searching for it. And so search engines, the early days of search engines, because not only amassing all this data, but you got to find it. And so just like Google was happening at the time, Yahoo and the evolution of search engines, you can watch the little graph, you know, manage more data, need to find it fast, people's expectations quicker, their attentions go

away. And now we're in the dearth of that, right? You know, one minute attention span, what was happening then was search, we needed a way to search all that information. And so part of the tech I got involved with building with search engines, and we needed that for NASA data systems and things like that too to find our data. One of the key parts of the search engine, and this is where Tika comes in, quickly identifying a file and its type no matter what type. There are thousands of types of files on the internet, PowerPoint files, images, Word documents. So no matter what the type, quickly identify what the file's type is, do it automatically.

Once you identify that, extract text, extract information about that file, who created it, when, where, and in any language. And that was that, what I just said, that fundamental concept is Tika. And so at the time, that was my favorite thing to work on. And so I was working on a big open source search engine project called Nudge. Open source at Apache was trying to be like Google, commoditize the technology, not just for NASA, but for everybody, a university, whatever. So you didn't have to buy Google all the time. And that was my favorite part of working on it.

And so that's what got pulled out into its own library. And that's what became Tika. So we did it. Sorry, it took longer, but we did it. did it no very cool you dropped some gems in there and one of the gems is that the imposter syndrome bit which um i think the context clue that you left or left us with is that you wanted to prove to yourself so that seems like that was the inoculation or the strategy to work with the imposter syndrome, which is like, so just for reference, imposter syndrome is like, what are they going to think of me? They're going to find out at one point. And it's this private worry about, you know,

how they're going to view you and you're going to be exposed at one point. And then you said, I needed to prove to myself. So can you just pull on that and open that idea up just a little bit? I needed to prove to myself. What does that mean to you? Yeah, well, what it means is I'm the laziest person in the world. And I tell people that and they're like, what? I said, yeah, I mean, I studied software architecture. I don't want to build things that are already built. I want to put them together. And that's the only way, you know, one of the questions you asked me too, Mike, is how

do you do teams or how do you just do big things? You know, do you put teams together? Do you do it all yourself? And in the beginning, you think you can do it all yourself. But then what you realize is you need to copy others. And it kind of even goes back to that Jurassic Park quote, Dr. Ian Malcolm. They didn't invent the ability to recreate dinosaurs by all of a sudden in reinventing DNA helix

and cloning and all. They built off of the shoulders of others. And so for me, you know, I am the laziest person in the world. I mean, there are some people, they just, they could make the same painting over and over again because it gives them something. It's, you know, it doesn't matter

that it's repetitive or whatever. But for me, like it does, you know, like once I've done something once I kind of like, I kind of just want to move on to the next thing. Or if I'm going to do that again, I just want to copy and paste it because that's how, in some ways, like I said, you grow. So for me, when I was trying to challenge myself, you know, to, to, to do these things or to prove to myself that I was doing it's me constantly fighting the internal, Chris, you're bored, you don't want to do this, or, you know, maybe I have ADHD, I don't know, I've never been diagnosed. But it's just the constant desire to not chase the next thing. But you know, interest myself with it, you know, or,

or keep the interest up. So that, that's what it is. That's what it is in some ways for me, Mike. And you know, it's funny, I'm going to make a joke here. Not a dad joke. It's a little bit of a dad joke, but my teams now, they always joke with me that I'm like the old school, you know, I'm in my forties, but I'm the old school, right? Okay, that's fine. Machine learning and anything. So you look at the new technologies, they're, in my opinion, chasing the next things, the people in my organization, whether they use PyTorch or whatever the next machine learning, deep learning technologies are, fast AI, they use all the new ones. I typically find one and try, now what's so funny, I try and master it. So I'll pick the technology that's like five years old or whatever,

but I'll deeply understand it. And I joke with like the next generation. I'm like, yeah, you guys are just like me. You just want to chase, you know, the old thing. But now I'm even like when I do commit and go all in, you know, and I'm going to do this thing, I do. And I typically pick the old technology now.

It's so funny. I flipped a little, but, but it's me fighting myself inside. Finding Mastery is brought to you by Cozy Earth. Over the years, I've learned that recovery doesn't just happen when we sleep. It starts with how we transition and wind down. And that's why I've built intentional routines into the way that I close my day. And Cozy Earth has become a new part of that. Their bedding, it's incredibly soft, like next level soft. And what surprised me the most is how much it actually helps regulate temperature. I tend to run warm at night and these sheets have helped me sleep cooler and more consistently, which has made a meaningful difference in how I show up the next day for myself, my family, and our team here at Finding Mastery. It's become part of my nightly routine.

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and use the code findingmastery at checkout for 20% off your first order. That's calderalab, C-A-L-D-E-R-L-A-B.com slash FINDINGMASTERY. I love to understand how you work to master, you know, anything. And so your process for deeply learning and understanding something, what do you do? And I don't know if it's organic or if it's meaning more spontaneous, or you've got a methodology that you follow. I'm going to relate this a little to design. It doesn't have to be software design.

It could be building architecture, could be anything. There are people that can immediately look at a situation and generalize and develop what they call a product line or more of a reference architecture for doing things. They can look at a situation, even the first one they've seen and be like, here are the general parts and components of it. And here's how I would repeat those or scale them or do more of them or whatever. I am not one of those people. I can do that. But my process is, and I don't favor that, I favor building something.

So the other side of that, and it's not exclusive, but it usually falls into these two camps. The other side is you build something, it's not efficient, it's not the right way. You build a house and you learn a lot, you know, about, about doing it. And maybe the next one you build is more efficient. You know, you don't make the same mistakes. You make a little less of them. You build another one and then, you know, all that. So for me, most of my process where I'm successful is I look at

something or something that I want to disrupt in a domain or technology or just something I want to do. And I create with, I create, I build the first version of whatever. And in fact, and again, it goes to my style. Like I want to work on that, that new thing or whatever, but once I've done it, I'm, you know, the next time I want to spend less time doing it. And then the next time after that, I want to spend less time doing it and building off of. So that's my process. Like real quick, just related to the book I just wrote, Machine Learning with TensorFlow.

TensorFlow is Google's machine learning technology. And again, it is a little older one. It's not the newest one or whatever. But I, you know, I published the book on Tika 10 years ago. I wrote this book with a publishing company called Manning and as a Manning author, I'll get, you know, they'll send me, oh, you want this book and you can get books and they'll send you, you know, things to read that are written by other Manning authors. And so I got a copy of this book, Machine Learning with TensorFlow, the first edition. And I was like, you know, I want to know what the heck all these folks that work

for me, I want to deeply understand it, you know, because I don't, I don't get it all. And it's been so long since I've taken a math class or but I mean, I kind of get the concepts, but I didn't deeply understand it. So I grabbed that first edition of that book. And it's like, build a computer vision algorithm, you know, to do facial identification, but it didn't tell you how it gave you a bunch of like nuggets. Like if it's teaching you about computer vision and what that means in machine learning, it gives you a suggestion at the end to go do it, you know, build the facial identification system. Because if you can do computer vision and pick between different people's faces, you can build a facial identification system. Okay. So it left a

bunch of nuggets like that in the book. And I could show you the copy. I don't have it in front of me, but I could go dig it up. I take a picture for you. My copy of that book is scribbled on, noted on. I've got white pages in and out of it. And what I eventually had, Michael, was about all the material I needed after going through that book for a new book, because I built, created, did everything it suggested. And then I knew how to do it better. And so I wrote the second edition of that book. And that's what I do. That's my process. And so even Tika, I joke with people, it's a great library. It's still used and whatever. I wrote the first version of that. I haven't written a lick of code in Tika in real code in probably five years. And that's

great because the next generation is doing something with it and I've moved on, you know, to the next things. So. Okay. You know what I love? It's so refreshing. You are the CTO of like, what I said, one of the, no, CTIO, I guess it is chief technology and innovation officer of one of the more significant, serious institutions on the planet. And you're like, yeah, you know, um, I really don't know the stuff that my employees know. I love that. It's really refreshing.

So like, is that normal for you just to have this way of like, I don't know things I'm going to go learn doubling down on your learning capability, as opposed to doubling down on your knowledge, you know, um, if you will. Yeah. Yeah. That's, that's exactly, that's exactly what I do, Mike. And, you know, you want imposter syndrome, you know, again, I got a PhD walking around these places, this place while I used to walk around the place, we haven't walked around in quite a while, but some of us, but you know, at JPL, you walk around and it's like, it's like when people tell me they go to Google or whatever, you know, it's like, well, I'm the, you know, I came from Harvard and I have a PhD, but I go to Google and that's where it's really hard. You know? I mean, I feel

the same way at JPL, you know, I'm walking around these amazing people who are literally putting rovers on Mars. I'm part of that. You know, I still have some imposter syndrome, but yeah, like every now and then I got to tell myself, you know, I I'm sick of hearing about, um, generative adversarial networks and not knowing what the hell they are, you know, like I'm going to learn this, you know? And, and when I do, I'm done. Like I said, I spent two and a half years. I spoke at the biggest AI machine learning. People thought that I had been there for a decade. Oh, Chris. No, I just went really deep for two and a half years. I learned it. I feel good. I feel comfortable with it. And now I'm into Web3, blockchain and crypto. Okay. Yeah. Because I read somewhere that your job,

the way you described your job is to envision the future and look into new technologies, right? And like, so I'm loving this conversation because you're talking about doubling down on learning. You're talking about not pretending anything. And you're talking about the standards being really high. You're talking about proving to yourself that you can figure something out and that you can go the distance. Your mind, your working memory is really high. You're talking about proving to yourself that you can figure something out and that you can go the distance. Your working memory is really high. The speed of your mind is obviously very fast. And I think that part of your genius is that you go deep into one thing and then scaffold that

on to the other things that you know. But it's not like the width that you have or the horizon of knowledge that you have is probably most compelling because of the depth on many verticals. And so, right, you can go wide, but you can go deep and the depth is over time has been significant. So all that being said, and you've got a high EQ, so you've got a lot going on for you here, Chris. It's pretty cool. And then so, but all that being said is that this process of learning is the crown jewel

for you. And so can you do another pass for folks that aren't trying to figure out technology or whatever? And you're saying, listen, if you really want to get good at learning, here's a couple of things to consider. What would you, how would you guide us? Yeah. Yeah.

And I've tried to generalize this a little bit. So, so one of the other hats I wear is periodically, I'm a, I'm a professor. I'm an adjunct professor at the university of Southern California. So I'm my alma mater. I love it. You know, big USC Trojans fan in all areas and very blessed to have gone there. So I teach a graduate class in data science maybe every couple of years. And when I teach it, I try and do what you just said, Mike, which is I generalize. It really follows a formula, even of my learning. It's a class,

and I'm not going to get too technical here. Basic purpose of the class is to teach people how to analyze data, how to visualize it, how to tell data stories, and even how to prepare and arrange and clean it up so that you can visualize it, tell stories about it, and make hypotheses, do everything. And that's not specific to data or technology. That's life, baby. That's what we need to do. We need to assess things, look at data, make decisions. And so the pattern of the class, the way that it works is the first assignment I say is I pick an interesting data set or something that's novel. We've done, the last one we did was like email phishing, you know, like the Nigerian prince, you know,

it's a little old and out of the pop culture, but it still kind of shows the point. I've done UFOs. You know, there are media stories out there about, you know, me researching UFOs that's either through my class, just data that's interesting to me. And the first assignment is others to look at that data and derive sort of unforeseen features about it, or just things that aren't part of the default data set, but as some derived knowledge or information that you would have to get by going deep. That's like assignment one, usually takes like about a month. They do it in teams, whatever. Assignment two is basically once

you've derived something about the data set, transform it, process it, change it in some way, combine it with other data, you know, take the UFO data and then over and overlay it or intersect it with places where there are, you know, abuse of alcoholism and see if there's a correlation, you know, are people drunk when they're seeing these things? Is it by airports, you know, mash it with other stuff. That's assignment two. Take that, you know, new data and mash it with other stuff and look at it again. The third is, okay, you've got some interesting insights, hypotheses, and whatever.

You've looked deeply at this data. And that took about a month too. The third usually takes about a couple weeks weeks is okay. Tell someone about it, you know, show someone in a very easy to understand way, not deeply technical, why your hypothesis that, you know, UFO sightings typically happen by, you know, airports and also statistically in places where there are higher abuse of alcoholism than in other counties, you know, make it, make a case why, and show some visualization, you know, make the New York Times page, you know, with the beautiful visualizations about why the little, you know, stories,

that's the third assignment. And, you know, usually have about two or three weeks, a little less time to do that. That pattern, you know, can be applied to lots of different things, you know, to just to learning in general and is the basis of, in my opinion, learning. It's finding interesting data, deriving new features about it, mashing it up with other interesting data, and then telling people about it. So. Very cool, which is exactly what you described about going deep in particular areas and then presenting at a conference on AI. Those three steps are at play. Okay.

Very cool. I love it. And the teach one, reach one. Reach one, teach one. How does that saying go? Teach one, reach one. So if you can explain something, you're actually learning quite a bit, but you're also bringing other folks along. So there's something from a social aspect that's happening there as well okay so you're gonna drop the ufo thing

i've i've read that you're like yeah it's what's it called the drake um hypothesis is that sure the drake equation yep the drake equation that which is that yeah there's a good chance right better than good chance there's a strong chance that there's other life in this huge universe of ours. Maybe you can add some nuance there, some texture there, but also like, have we seen them? Have we seen UFOs? The way I explain that, the way I explain that is sort of the following, Mike. I haven't seen one. I would love to see one. That said, if you look at the evidence in places like Roswell, if you look at Project Blue Book, you know, the first deep study of this led by the University of Colorado for the government and the Air Force, you know, in that sort of post-1947 to 1960 era. If you look at, you know, sightings over

Mexico, you know, things like that, there are certainly things that no matter what anyone tells you, we can't explain. Hell, there are 700 cases from Project Blue Book or something that you can explain and even the newer things, right? So what this comes down to, again, is like training our neural networks inside of our brains, and assessing with some level of to, again, is like training our neural networks inside of our brains and assessing with some level of confidence whether that is true or not, but not with 100% certainty. I can't tell you. I don't, if I had a gray alien right here and someone was watching this podcast in video

format and I say, hey, Mike, check out my gray alien. You'd be like, Chris, is that a deep fit? Even if you believed me, what's going to happen is, is the local sheriff going to come pick me up and then you don't see me again. You know what I mean? Like, like, like, I don't know, you know, is that that's certainly possible. So what I tell people, all the studying that I've done, and I've done a lot, I've, I've even gone physically to places and touch things and look to things, you know, in places like Roswell and things like that. I spent some time down in Roswell on a project. And so to understand that, secondarily, I had the opportunity to understand the

communal zeitgeist around what, you know, the reputation and what they believe and whatever. So I'm curious what your take on it was, but keep going. Keep going. Yeah. Well, I'm, I'm, I'm close and I can even jump to that, but yeah, my, my point is all of this stuff comes down to a leap of faith. So the people that tell you there's no aliens and Roswell was a complete joke and this and that they're just as wrong as the, the people that tell you, well, this is an alien. I've got him right here. You know what I mean? Like it all boils down to a leap of faith and my leap of faith. It's a religion. And, you know, has anyone seen God? Can anyone produce God or Allah or, you know, whoever for me, you know, no. So this all comes down to a leap of faith. Right. And, and so for me, my leap of faith is

more likely and not that there are. You know, I believe. And whether they've been here or not, I'd like to think that they have based on some of this stuff like let's talk about Roswell. You know how hard it was to convince people in the 40s? And I studied U.S. history and world history in the 30s and the 40s. You know how hard it was to, like, get them to lie, especially in a military town. One of the biggest ones that had the 509th, you know, the, the only nuclear, the place where they dropped the bomb, how the reputation of those people during the forties. And it wasn't like today where everyone distrusts the government back then everyone trusted the government and for like 400 people to all tell a version of something that sounded, I mean, that would be pretty hard for something

not to have happened. Right. You know? So I don't know. I mean, Occam's razor tells me something happened. Can I produce it? No, I can't show it. You know, I can't. And all that stuff's gone. You can go, go through the, you know, the, the, the planes or, you know, the, you know, the, the ranchers, you know, you can go back to Mack Brazel's farm. You go surf all that and look at it. It's all dirt. There's nothing there. And it's been 60 years, you know, what are you going to do?

So, you know, plus 70, 80, right? I don't know. So, yeah. Yeah. It felt like when I was there that it was a folklore that they were living on and it was almost just part of the dna and but there was not a deep fascination like yeah they're here they came it was it was like i don't know my grandparents said something but um do you want to buy a keychain that has an alien on it you know it's a

little bit like that for me it's like the alien jerky on the way to vegas you know it's i always gotta stop and get that alien jerky in barstow or whatever. Yeah. Right. Yeah. Exactly. Okay. So do you, so take that framework and then apply it to spirituality and your take on God, Allah, you know, a higher being where, where do you land in that thinking? Yeah. Well, my leap of faith and, you know, maybe, you know, wrong or right, but my personal leap of faith on is I do believe in a God, I don't go to church, you know, in the traditional sense, but there are just things that have happened in my life to me, you know, just things that I can't explain. That's very, you know, internal or personal, you know, things that

shouldn't have happened, or by all, you know, accounts, you know, should have happened, not just that I wanted to happen, but just things and even outside of me, and in other people and other things, people I've seen be, you know, healed, but shouldn't have been healed by things, just just things like that. And then the other side of it, I'll just I'll just share, you know, is that I've, that's all the positive, you know, evidence, evidence, but you know, belief for me., that's all the positive, you know, evidence, not evidence, but, you know, belief for me. And then I've seen stuff that actually scares, scares the hell out of me. My, I've seen some stuff where, you know, I think that, you know, the other side, there could be evil, you know, that is indescribable. And, you know, to be honest, I'll tell you, there's a

reason I think The Exorcist is the scariest movie, you know, still, and maybe not this generation, I don't think, but I, you know, I'll watch it on Halloween, but it scares the hell out of me still, you know, and there's a relationship between that and mental illness, like, especially if you've seen, I hate to relate this back to pop culture, but it's the way I do things, exorcist three, and what happened in that movie, and how they, you know, went through people that had mental illness and things. And there've just been things that have happened in my life, you know, with my mom and other things that just, I'm just like, my mom's not religious, but she's talking about, you know, Jesus and stuff she shouldn't have known, or, you know, it's just weird. And, and so things like that, both on the positive and negative end make me do believe that, you know,

there's both good and evil and there probably is a God out there, but, but that's just me. There you go. Okay. Good segue into AI, right? Which is, um, this intelligence that is building. It's like, we're tickling the dragon and we've been tickling the dragon for a while. And, um, it depends on where you, where you orientate. Are you optimistic? Are you pessimistic? Are you more cynical? Do you have hope? You know, like it depends on some psychology about how you think about the future of AI. But I've read some, some research that nine years we're going to have, and it probably in your world is more like two years. Cause you guys play with things so much faster than like, you know, between you and DARPA, who knows what you're really doing,

but in nine years, we're going to have the, um, the first computer that will be smarter than the smartest human. And whether that's true or not, you know, um, I don't, don't quote me on this, the, the timeline there, but what is your, what is your take? What do we need to know about AI from your, from your perspective? Obviously, you come from a position of hope and optimism, but what do we need to know about AI? Well, that it's real, that people like, you know, we need to be very concerned and it's dangerous, shouldn't be dismissed outright. Because in his field and what he's working on and like, just like you said about me and

what he sees with, you know, EVs and cars and computer vision and neural link and brain stuff, he sees it across many verticals, you know, and their impact. And he is so much farther ahead, uh, I think, and so even hearing that, I don't dismiss that outright that there's a be afraid of this, you know, element to it. That said, you know, it kind of depends still where you land on like the climate thing, you know, whether if in 12 years, everything's going to be, or whatever that, you know, gone to crap and there's no going back or whether there's a chance to disrupt. And we only assess things in the current framework of what we understand.

I remember the famous saying, or it's not even a joke, it's a saying, but, you know, if you look at the amount of rocket fuel in the late 1800s that they believed were necessary to get off the planet, and then you actually looked at what they ended up getting off the planet with, much, much different. And those were the best mathematicians, scientists at the time and whatever, because again, science, understanding all this stuff constantly evolves. Same in the context of AI, what's real today, the ability to automate lots of things. I won't say anything, but lots of things. And the scariness that that actually puts on people, their skills,

their life disruption, the taking away of their jobs, on people, their skills, their life disruption, the taking away of their jobs, you know, and all of that. And it's not to say, again, like coming at it from my optimist perspective, it's not to say that that's not the natural way of things. It shouldn't have happened, that we should be riding around in horses and buggies still. No, like I think all of this stuff should happen. So the automation, I'm not super afraid of it. We need to be empathetic. We need to take in, you know, don't tell truckers they need to learn to code.

That's a dismissive thing. We need to, you know, transition people's skills. And to be honest, that's what really scares me, Mike, is we, from a policy perspective, from a thinking perspective, not enough thought is being put into that. The automation is real. The fact that any human out of the loop decision-making can have bad outcomes. So can AI identify people and, you know, better than humans can in their faces and stuff? Absolutely. Does that mean that it doesn't have rounding

errors and bias that might identify the wrong people? I mean, hell, DNA fingerprinting has been around in criminology and for a long time and is in practice use. Does it mean that it's 100% right? What happens when something's 95% right and there's still 5% error? You put people in jail that shouldn't be. You pin a crime on them. They didn't commit, things like that. Well, the same is true in facial identification, right? That AI machine. The trolley car dilemma. What do you do in those situations? So you're erring on the side of like, we're going to figure it out. I'm concerned, though, that we're not upskilling people at the rate that the technology is coming. And then so what do we do with that 75% of people that could be displaced? Like, what happens?

Do we go to dystopia? Well, the jobs, I don't think so. The jobs that they can have and should have and that we need from them is that we don't do a good job. And Andrew Yang nailed this in his campaign, you know, during the last political campaign, not the one for mayor, but the one when he ran for president. He said data is the new oil. And there's a lot in that. And what is he saying? Well, right. So to get to oil, we got to have crude and we got to take the crude and we got to refine it, right. You know, into leaded, unleaded, and then, you know, high octane, you know, we got to go through this whole process to do that. So we take unstructured crude and we turn it into structured

oil that we can then power and do energy with. same thing with data um and the same thing with ai imagine ai is the car ai runs on gas that's data and there's an infinite supply on that we just we spent you know the first part of the episode talking about the dark web the deep web all that the web itself is an infinite supply near infinite t TikTok, this, that, all the social media, but everything else, science of data. But it's unstructured. And we need to refine it. We need to take the crude and turn it into structure. Because the thing about the AI car that it wants is it wants very structured labeled training data, because it needs to learn patterns, how to repeat, how to gamify that

against each other, you know, and have just the same battles we have in our mind. Should I do this? What would happen if I do this? And then game planning and scenarios, that's what AI does. The same thing, but it needs very structured interpretations of the outcomes and the input, you know, to get to those outcomes. And that doesn't exist. It's again, unstructured. So we need ways of making it structured. And what are the ways of making it structured? That's where people come in. We have not found a way to automate or replace that, to take unstructured and put that syntactic, experiential, human, I can look at unstructured to make sense of it, right? That's where all the humans are. We call that subject matter experts. We shouldn't replace truckers tomorrow. We should glean

knowledge over the next 18 to 24 months from them. And even post, once we have automated trucks that are driving, the humans need to be in the loop, reviewing things that those trucks did not train on, adapting, tweaking, changing weights. That's the job for all of them. It should be a high paid, high skilled job. And, and that, in my opinion, is how we start to, and during that time, they'll learn just like I did. And wow, we looped this all the way back up, doing things, doing and going deep allows you to generalize and repeat. And so in my mind, that's how we do it. Say that one more time. Generalize. Say that one more time. Doing things and going deep allows you to generalize and repeat. You know, it's the story

of me. You know, I could have been a trucker, you know, and whatever. But you build, you build, you generalize, you repeat. That's how we need to use our human beings, you know, and work together with AI. I love what you just said. Can you give us a history from web 1.0 all the way to three? First web one, put data out there on the web, hypermedia, universities, governments putting out information they want you to read. Web 1.5 was, what could we do with it? And that's where Tim Berners-Lee, you might've heard Sir Tim Berners-Lee came in and came up with the idea of the semantic web. The problem and the failure in his idea, he's a brilliant guy, was he believed you and I, when we were putting data out there in hypermedia on our webpages, Mike, would annotate it and make

all the intelligent agents have the annotations around the data that we need for those agents to be able to, how to make an appointment, how to help us. Nobody was willing to do that because no one wanted to write XML around their stuff. Web 1.5 came, Zuckerberg, and this goes into Web 2. What they did is they tricked us through Facebook. They mined us to get those annotations and social media and Twitter that they needed for Berners-Lee's idea for the semantic web by sharing pictures with Ma and Grandpa about your kid and annotating the pictures, tagging them.

They got all that semantic data that they needed. And that's why you see the super advancements in intelligent agents, intelligent assistants, all that semantic data that they needed. And that's why you see the super advancements in intelligent agents, intelligent assistants, all that everything Berners-Lee envisioned in that Scientific American article exists today because of social media and what they did. They mined us, but they now, this is where you got to go back and forth. Does that mean I think Zuckerberg's an evil person? No. Or Sandberg or whatever. I read that. What book is it sitting over here? An Ugly Truth by those two New York Times reporters recently. And they really want you to hate them. You know, they this stuff, man, I loved Facebook. I thought it was great. Um, to share pictures with relatives that I don't get to see, they get to see my kids grow up. That was what I got out of it. Now what they got out of it, which is what they wanted was all those annotations, that network, that graph,

and look at what they did with it. Now they know, you know, my wife's pregnant before anyone else knows, you know, they know all this. And then the next generation, like, so what is that? It's refinement. It's what Yang was talking about on that end, but it's now controlled by in web two, that was controlled by, you know, six companies, the G whatever, not the G, but what are they, the company, the C20, you know, all that. In Web3, and this is what's causing everybody's head to explode, they want to democratize that. They still want us all to be creators,

but they want us all to own those annotations and that transactions and all that in a decentralized way. That's what the blockchain is. Okay, keep going. Talk to us about Web 3.0, what we need to understand about blockchain. I mean, I love the concept. Is it here to stay? Is it something that we should be tripling down on? I'm a little concerned. I have not gone in on it. I've got some money on Ethereum. So listen, you can tell that I don't know what I'm doing. So can you just teach about Web 3.0 and some of the currency models? So now that we've got that history, so now we're in the Web 3.0 era where we're

democratizing really those annotations that we needed, really that transactions, you know, history, what happened, but not, you know, the C20 or, you know, whatever, own it. Everybody owns it. And so the ways to do that are through what they call blockchain platforms nowadays, which are the foundation for Web3. What is the blockchain? Basically, the blockchain is a transactional ledger. Not in which everyone has all information about everybody and where they live and things like that, just how they transact kind of on the web, how they annotate things, what they buy, what the relationship in the network that things like Tika that I invented to help discern out of unstructured is now kept in a structured way. The people, the places, the

things, the connections together between them. And why is that sort of important in the context of this? So you hear a lot of people talking about this with respect to currency and cryptocurrency and these volatile markets. And they certainly are volatile, whether you're doing Bitcoin, which is a particular type of blockchain, or Ethereum, which is another type of blockchain. Having those in a structured way is a good thing, you know, because otherwise, you have to invest all of this sort of technology and capability to periodically discern that same information, which you need anyways, right? To make bets on things, to have assessed with confidence strategy and policy that we need to be doing.

It's just that today we've heard about these things in the context of like purchasing and buying power or because people see it as sort of a get rich quick scheme. And in some cases, if you're willing to roll the dice, it is, But over the long term, I'm less sort of bullish on the fact that I'm going to take a bunch of money out of my bank account in US dollars and put it all into Bitcoin and Ethereum, and suddenly I'm going to become a millionaire. I'm less bullish on that. I'm more bullish on sort of the technology capabilities and what the blockchain itself provides.

Because it really is a representation of, like I cited Yang before, of refinement of crude into data that we actually need. And what you could do with that, all sorts of AI, all sorts of automation, all sorts of foundational things that we can do once we have that capability. And the blockchain is so much further along today than it was five years ago. I mean, five years ago, I looked at this, I went to a conference at UCLA, this big blockchain conference, and it smelt to me like the early days of the web, people fighting over the protocols, like we talked about, whether you were using Ethereum versus Bitcoin. Transacting took seven minutes. I mean, literally to do a Bitcoin transaction, you know, five years ago, you waited seven

minutes, you know, for your purchase to come through and, you know, things like that. So that obviously not scalable, you know, you can't use it now today. The, you know, the big platforms like Ethereum itself is worth nine figures, the simple value in that platform and reserving space on it or reserving or purchasing some utility off of it, alone, you're purchasing a piece of that wealth. And it is an asset class. It's something. The same for Bitcoin. But not even the foundational stuff, Mike, but the actual other verticals that they've added on top. OpenSea as a marketplace to distribute art. OpenSea just had a nine-figure evaluation.

So whether they're great today, it doesn't matter because in a year they're going to be even greater, their capabilities. What you see in using blockchain and these platforms to distribute tickets or access, to provide you access to special events. And the biggest bullish thing I am, and I'll sort of end at this for me, is the relationship of that to virtual reality and where we are going with the future of work. You know, JPL is stricken by this, just like many tech big companies and everything, our population distributed in the pandemic.

You know, everyone used to live here. You had to be close to the lab or somewhat close. You know, I mean, you could be the furthest people away might have lived in the mountains a couple hours away in Wrightwood or on the way to Vegas, but you know, or, you know, in the South Bay, but you still live within a couple hours, you lived in the five county LA area, you don't anymore, I'd say, you know, 40, maybe 50% of our population doesn't we have a hybrid workforce, that's not going to change. That's how people advance got a house, you know, moved on in their lives. So they're not going to give that up.

We need VR. We need more immersive experiences. We need more connections. And I've been heavily researching that in the context of my job at JPL and my teams to basically figure out how we put on these headsets, get in, feel, touch, see realistic representations of each other, connect. But also what I'm excited about is the relationship between the blockchain and creating those experiences. So when we had a company, All Hands before, I might have handed you a physical award and said, good job, Mike, and you took that award back to your desk, or maybe the award included something you'd go spend at the cafeteria, get a free lunch, go to the employee store, get some swag. That is an NFT on the blockchain today. And it's driving, now I'll hand you that digital NFT, you'll put it in

your wallet, you might transact with it, you could sell a piece of it, a derivative element, you could give someone the lunch and somebody else the employee store thing. Or it might drive you to come back and put some foot traffic on our workplace. I might be able to drive you to come back to the employee store or come to JPL for that couple of days and come back just to leverage your thing. So JPL obviously is doing that in a small scale compared to big brands like Chipotle and everywhere else, which is using that same playbook combination between NFTs, you know, Web3, crypto and VR to drive traffic to events, to bring people together and to do the future of work. So that's why I'm bullish in all that.

Are you on the metaverse? I'm totally on the metaverse. I, you know, the other thing, you know, the other thing is these devices like so, you know, Facebook meta makes a device for VR that it's called the quest, the quest to. And in my mind, it's changed the game. It is cheaper than a cell phone. So now you go back to people like with my history that grew up. It's a birthday present 300 bucks. So I mean, even I could have been the trailer got one of these, you know, it's, it's, it's a lot of money, but I could get it for my birthday or whatever. So now it's not just high tech, you know, rich, affluent people that get it. We're seeing these in classrooms, you know, classrooms could buy 20 of them now, you know, whatever. So it's not the most comfortable thing to wear all the time. And being in there for, you know, many hours, we still need to figure all that out. And we don't know all that yet. But what I can

tell you is this type of experience in a zoom or two dimensional is just flat out thrown out the way if you use it for nothing else than just to meet people, there are free, easy to use capable technologies in there in which you and I could shake hands, high five, dance. We could go to a forest and a campfire. I could show you JPL and walk you around. It's just blown away by that experience. And so, yeah, I am totally in the metaverse. I believe in it.

I see why billions, tens of billions of dollars are being invested in it. Yeah. Can I buy a Finding Mastery studio in the metaverse? And then you and I have a, I don't know how that really works, but could we have a podcast in the metaverse? We totally could. We totally could.

We totally could. But you and I would look like avatars. We don't, I wouldn't see that you've got, you know, I'd see that you have a beard, but I wouldn't see like the texture of your beard. So that's a special thing, Mike. So, and, and I think there's a marketing flaw for Oculus and meta and also, you know, magically even everyone else that shows, you know, I don't want to pick on any one company, but anyone that only shows you the metaverse in which you're these sort of cartoonistic avatars is making a mistake because what they could also show is that no,

actually there are apps like spatial, uh, you know, and, and vision, you know, a number of apps in which the first step is actually scanning your face on your iPhone and having a realistic 3d representation of your face. So when I meet with people in these apps and maybe it's that pan generational thing for me, I don't know. I want to see, yeah, the cartoonish thing doesn't do it for me. And I tend to shy away. I like to see realistic or, and you can, so. Yeah. Very cool. Hey, listen, I want to just say, thank you. I want to honor your time. I could sit with you for hours. Um, and I'm excited to see JPL through the metaverse at some point, you know, with the

headset. So, uh, that would be a fun, I'd like to take you off on that offer. I'd like to do it. I'd like to take you around JPL on it. So let's do it. You're a legend. And so I just want to say thank you. Any parting best practices that you would love? One, two, three quick hits for folks to say, hey, listen, this has been really important in my life. And you might find it to be important in yours as well. I've got two and they're part of the general theme, but the first one is succeed at every level. And I see too much of this. And even it's a battle inside of myself, one of my neural network GANs running against each other fighting. Don't jump to the next level without succeeding at every level. And lots of people need to understand that because when it's time to go to the next level without succeeding at every level, you know, and lots of people need to understand that, like, because when it's time to go to the next level, if you succeeded at every

level, it'll naturally happen. And it'll just, you won't be pushing succeed at every level. The other is, and I see a lot of this today, that I think people could benefit from those who do decide. And so the, by the, by the way, the, these, the first one was a manager. One of my dear friends at JPL told me that one, and it's born true in my career. The second one is from the open source world. Those who do decide, I see too many people today who Mike, you and I are digging a hole and they're standing around the hole telling us how we could dig the hole better. And, and so those who do decide, baby, no, you want to do it better, get down here and dig the hole, you know, and those who do decide

those two, you know, and then obviously succeed at every level, those two practices, I think will do well to help you in your career. Hot damn, because you're doing that on Instagram, you've lost like, like a lot of weight, dude, dude running like I like I love I love how real you are in this conversation you know you could hide behind the big desk that you sit behind but you don't you're just authentic and real and honest and open and curious and it's so refreshing and I watch you on Instagram I mean you're ripping on the fitness thing right now. So like, it's really fun, dude. Thanks so much. Yeah.

Well, the first part of it, Mike was keto. My wife got me on that, the ketogenic diet. It's a great thing. It definitely caused me to realize I didn't need all the sugar and everything else in my life, although I'm Italian. So it's hard. But that said, that stuff wasn't sustainable for me forever. So you have to balance it like everything in life with exercise. And yeah, I've been, I run around the Rose Bowl every day and thanks for noticing. And, uh, I I've lost

like a hundred pounds. It works. Go figure, you know, calorie control, you know, moderation and exercise. We could all use a lot more of that in our lives. So go figure what's your hand, what's your Instagram, Instagram handle. Uh, it's Chris Mattman, all one word C H R I S M A T T M A N N. So that's where can we find your book? Where's the, where do you want to drive us? It's Chris Matman, all one word, C-H-R-I-S-M-A-T-T-M-A-N-N. So that's me. Where can we find your book? Where do you want to drive us? Matman, M-A-T-T-M-A-N-N.ai.

I am now an AI on the metaverse. So yeah, just go to matman.ai. It's all there. That's my website. You are a legend. Thank you for your time. Thank you for sharing your wisdom. I've loved every part of this conversation and looking forward to next time. Thank you. And likewise, thanks for having me,

Mike. And it's been a pleasure. And thank you so much. All right. Thank you so much for diving into another episode of Finding Mastery with us. Our team loves creating this podcast and sharing these conversations with you. If you're looking for even more insights, we have a newsletter we send out every Wednesday. Punch over to findingmastery.com slash newsletter to sign up. The show wouldn't be possible without our sponsors and we take our recommendations seriously. And the team is very thoughtful about making sure we love and endorse every product you hear on the show. If you want to check out any of our sponsor offers you heard about in this episode, you can find those deals at findingmastery.com slash sponsors. And remember, no one does it alone. The door here at

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