The AI Revolution: Innovation, History & the Future with Jared Carl

IoT_podcast_Jared_Carl
Jillian Kaplan: [00:00:00] My name is Jillian Kaplan and welcome to innovators of things. I am here with Jared Karl. Jared, welcome to the podcast.
Jared Carl: Hey, thanks for having me on.
Jillian Kaplan: Yeah. Thanks for being here. So Jared, we're here to talk about innovation. You've been in the technology space a while.
How long have you been in the technology space?
Jared Carl: Oh, do you remember the sound modems used to make when they connected to the internet?
Jillian Kaplan: Yes, I do. And I also remember, remember when your parents used to pick up the phone when you were on AOL chat and disconnect? Mom, hang
Jared Carl: up! Yeah, yes, exactly. Yeah, that was my first job.
Was troubleshooting modems connecting to the Microsoft network. So we listened to the blips, seriously bleeps and sweeps and the squeals. And we can know if it's connecting at 30 through six or 56 K or whatever, based off of the handshake that was going into it. So that was my first job in tech is working in a call center.
And I think it was a great job because it taught me how to troubleshoot, taught me how all these systems were connected together. You know, the software that powers it, right. Which is becoming very important [00:01:00] nowadays. And then it moved on from there. So that was just about. 30 years ago.
Jillian Kaplan: Okay. So what has your progression been since your call center days?
Like what else have you done in the space?
Jared Carl: Oh, let's see. I went from call center to consulting because back then all you had to do was be able to breathe and reboot a computer and you could get a great, great job in consulting. There was Joe. If I quit my job Friday afternoon before lunch, I could have a better job by the time I got home Friday night.
That's how heavy it was in the late nineties of, of errors of, of software and development. So I went from troubleshooting to consulting then did some system admin work and then consulting and then Microsoft and Dell and then further, further onto the AI world after Dell after that. So, yeah, yeah, it's pretty nice because I've gotten the opportunity to see Technology from lots of different aspects from putting it together to building it to supercomputing work here with tack here in Austin, which is just a fantastic facility for learning and in supercomputing [00:02:00] work.
So yeah, I've got, I've got to see a lot over the ages. So back in the day, we're talking about certifications, you know, what AI certifications do you want to get? I've got a stack of Microsoft certifications that don't even make it to my resume anymore. So, yeah.
Jillian Kaplan: So, so a little bit of a fun ride over your 30 year career.
And when you got into tech, you took, you know, an interesting study path, I'll say, can you share a little bit about, you know, what you studied to get here and. you know, why it might not be a normal quote unquote degree for someone in technology.
Jared Carl: So being a child of the 80s and the 90s where boys were just boys and they didn't have any issues.
And it took me until I was 40. tested for A. D. H. D. U school, I didn't have a n You know, my brother went didn't, I couldn't sit st on the video here, I'm sh because they say [00:03:00] don't, d movement anymore. Be who
chair. So anyway, after high school, did not go to college. I went to college for my bachelor's degree in the mid 2000s. I got Microsoft to pay for that, which was great. So got in business and it did it while I was working. And then later on in my career, I realized, Hey, you know, I need an advanced degree.
And I was looking at my peers and it's interesting when you start looking later in your career versus the beginning of your career, because a lot of people are going to go down the MBA path because it's a great path to go down when you're like, I just got my bachelor's degree, but I still need more, still need more knowledge.
I'm going to go get an MBA. And that's fantastic. There's great schools out there for that. I decided, you know what I've already got. Almost 20 years of experience under my belt. I don't really think I need an MBA and I'm not really sure how much additional I would learn. But one of the things I've always been interested in is learning, right?
It's like, I've got to learn, got to know more, got to move a little more. I think it's a very [00:04:00] important thing is when you look at business and AI in general is keep learning, right? And it's like, when, when we have conversations, you know, you and I and work, it's, we talk about, it's like, We don't talk to each other to respond, we talk to each other to learn, right?
And I think that's why one of the reasons we get along is we listen to the other person. We're just not trying to spit back an answer. We're trying to find the collaborative point. So always learning, I've always enjoyed history. So I got a graduate degree in military history which was. Which was a lot of fun and opened my eyes to looking at history in a different light, rather than just, you know, memorizing airplane statistics from, from World War II to actually analyzing the geopolitical bits and stuff like that, that goes on into it.
So from the AI perspective, this is where it gets interesting because really at the graduate level and beyond of studying history, it's all about taking massive amounts of information. What is your favorite point of history? If you had to choose one, just randomly,
Jillian Kaplan: my favorite point of history. Oh man, that's a good question.
I don't know. What's my favorite point of history? Like, like [00:05:00] something that, something that happened that I'm proud of in history.
Jared Carl: Well, if we you know, there's some movies where people are like, Oh, I'd love to live in that time. Like people love going to Renaissance festivals. Right. And I'm like, Oh, I'd love to really live in the Renaissance time.
Or I love the Roaring Twenties. That sort of thing. So I love, you know, the free love of the seventies, that sort of thing.
Jillian Kaplan: Okay. Okay. I just love
Jared Carl: mucking mud. So I love the Roman era. You know, I love building roads. I love Romans.
Jillian Kaplan: Okay. Okay. I mean, for me, honestly, so I grew up like in the eighties and early nineties.
And you know, they say that we're like the last generation to not grow up on technology, you know, like that, that to me is like a very interesting, I was class of 2000 graduating from high school. That to me is like a very interesting pivotal point because We changed, you know, like the way that kids interact with technology today is so different.
Like I find that a fascinating time in history. I find like the women's suffrage movement extremely [00:06:00] fascinating and like, why did these women rise up at that time? Like I'd say those are, those are two very different. Different history, but both both fascinating
Jared Carl: and it's interesting because you do something relatively modern and you chose something relatively farther in the past because I don't think I would doubt many people are still alive that survived the suffrage.
Sure. Right. But definitely the eighties, there's still lots of people that are around, but if you look at those two things from a history standpoint, like having a history degree, there has been massive amounts of information that have been written about those. And I think it's very important. Because we look at the modern time era, right?
Of the eighties is like we need to start recording the information like you and I, who lived in that time period. We need to write it down and observe our, and put down our observations. One of the important things going back to my ADHD, right? We didn't diagnose it back then. You were just a hyperactive kid.
They treated it just with Ritalin. That was the only thing that they gave you or lithium or something like that. Having a history degree in the modern era lets me take the massive amounts of [00:07:00] data. The world is about data, right? Take those massive amounts of data, break it down into what's good. What's not, what matters, what doesn't take primary and secondary sources.
Understand what all of that is together, boil it down, put it into something nice and concise. Send it off to a professor who tells me this crap, the professor being my boss. So if you look at history from that standpoint. Right. Of being able to collect a whole bunch of data and put it together and clearly synthesize it and spit it back out.
That is extremely relevant to any industry. And I think, especially in our tech industry, right? Because we look at how much tech has changed. We look at how much tech has impacted our life. One of the things, you know, in the, in the suffrage jet movement was people were learning to be able to read. So they were able to get reading scores.
We're starting to come. Literature scores were starting to come up. They had access to printing presses. Same thing that happened during the French Revolution. Technology drastically changes the way that social aspects work inside of inside of life and inside of our world. Going back to your point, our 80s and our 90s era, [00:08:00] we didn't have a technology.
We, my mom was a writer, so we were in the early people that had a computer, no internet, right? But I was able to play computer games and stuff like that. But we were called the MTV generation because we got everything from cable network, right? We did have little handheld computer devices, which gave us the ability to start using technology in our lives normally.
So it's a very interesting pivot there when we look at history and how technology and impacts our own personal lives.
Jillian Kaplan: Yeah. So like history is like helped innovate within technology. I don't know. That was a very interesting piece of the conversation. Now here's the million dollar question. Did you get the company you work for at the time to pay for the degree?
Because you told them that this history degree was going to be important to your technology. career.
Jared Carl: Yes, I did. So I was actually able to get Microsoft to pay for portions of it, right? They don't pay for all of it, but they pay for portions of it that actually made it worthwhile for a couple of reasons.
One is because I was working at the time [00:09:00] and I can't remember if I was still doing engineering, high level engineering support, if I'd move over to sales, but it's like studying of history and especially studying of military history, you understand conflict, right? It was, it was, it was. Clausewitz, who said, you know, armed conflict wars, the continuation diplomacy by other means, which is true to a point, but you don't want to get to war, right?
So when we're talking to customers, just like we got to understand how to be very diplomatic to them, understand their perspective, understand how to talk to people as we expand this out to the modern day global world. We work with a lot of people from a lot of different areas with a lot of different considerations.
It doesn't mean they're a jerk when they said X, Y, Z to you. Mhm. That's just a cultural difference, right? But we don't want to go to war. We want to stay diplomatic and understand that. So, so that really helped when I explained it to him, I was like, Hey, can you pay for this? It helps me reduce conflict, but then it also helps me analyze bigger problems.
So even if I don't have a history teaching thing or doing thing here at school, it is very parallel to what I'm doing to make me a generically better person [00:10:00] because we all need to be better people.
Jillian Kaplan: I feel like I should send you in for the debate team. Cause I was expecting the answer to be full on. No, of course they didn't pay for it, but it was really interesting and it's helped me.
But you're like, Oh, I just, yeah,
Jared Carl: I completely justified it to him and I'm glad they did. So yeah, very impressed.
Jillian Kaplan: That's innovation right there. Getting, getting a company to pay for your military history degree. I love it.
Jared Carl: And the best part is I didn't lie. I
Jillian Kaplan: mean, you could have presented that, you know, to your professor, AK boss at the time and said, this is why it matters, right?
This is why the technology. You know, affects the history and vice versa. Fascinating.
Jared Carl: Yeah. I talked to a local university here called a St. Edward's University. I volunteer down there a couple of times. I was talking to my kids the other day. I was like, always follow your passion because if you can become a better communicator and a better well rounded person, it makes you more.
Able to communicate with others out there in the world. So the history degree, fortunately, just my love of history in general, no matter who I talk to, like the suffrage stuff we just [00:11:00] talked about, right? It's like, we've got cool, interesting things to talk about that you're interested in, that I'm interested in, right?
So there's always good, good parallels there for, for at least the most people you talk to. You can't always fix, you know, everything, but anyway,
Jillian Kaplan: I love it. I'm here for it.
Jared Carl: Cool.
Jillian Kaplan: So as we pivot into modern. History, modern times, I'll say, right?
Jared Carl: So boring. So boring. So boring.
Jillian Kaplan: You are an AI professional and you've been innovating in AI for longer than AI has been at the forefront.
How do you think of AI as an innovation in and of itself? Like from where it started to where it is now, cause you also have been working on it for a lot longer than it's been quote unquote, the cool kid, right?
Jared Carl: Yes. Yeah, it's, it's, AI is interesting because like you said, the concepts been around forever. Some of the earliest machine learning tools are written back in the early 80s,
Speaker 3: right?
Jared Carl: You know, PCs were starting to land on, on, on desktops and computing was starting to get more accessible to people. [00:12:00] So they wrote the codes and they realized this code is going to take way too long to run.
Like literally, it would take way too long to run to figure out the insights behind it. The really, the revolution came down when computing power started to become more accessible to others. The democratization of compute. A whole other separate podcast topic is we're going to talk about how miniaturization is the greatest factor that has powered humanity since the beginning of time, making things smaller, right?
As we make that transistor smaller and add more transistors to power more compute. That's really what started opening the door to AI. The other side of this, and this is the interesting one is the availability and access to data and knowledge, because without data, without knowledge, there is no outcome that you can power without AI, because you're basically just asking computer to make any one of a thousand different possible outcomes.
Well, you need to tell the computer just like normal people and raising kids is, you know, You've got to make the right outcome. You've got to make the right decision to make it better. And then, of course, there's the whole training bits underneath it [00:13:00] of deep learning and machine learning and ways like that have actually been able to get to it.
So, and the biggest pivot recently, of course, has been the large language models. Changing of language, being able to use language in a context of the whole sentence versus just translating words, being able to interface with computer knowledge and computer machines that way, completely changed our world.
Now we are still in the peak of that. I would say we look back at when tablets came out, you know, big, big cell phones. Basically people were like, Oh, we're going to put tablets everywhere. So you saw tablets for two years and they just disappeared. You know, it's like, they're not really that big thing. You still see them around.
They're still very relevant. But I think there's going to be definite drop off of that. Now, the thing is, is for technology or AI in particular, what's next, right? I think that's the most important thing. And I completely agree with Jensen Wong that it's going to be you know, a three computer problem, right?
You get the AI, you got the, Digital twin and they got the robotics being able to motorize and use a [00:14:00] I to control robotics to do things, controlling a physical device, the physical world application of a I that's going to change our world. Even just my robotic vacuum, its ability to just randomly go around the house and vacuum to vacuuming specific areas, to using a camera on board to map out my house, to understand that this is in front of the cat box and it needs to vacuum there a little harder.
That has just changed in four years. Right. That amount of stuff that AI has affected my life has just been huge, but it's all data, right? It's all powered by data. And it's all the way that's interacting with the environment around us.
Jillian Kaplan: So you kind of touched a little bit on my next question, which is like, AI is an innovation of itself, but AI also helps people innovate, like apparently with your.
Vacuum and your kitty litter box. Is that, is that do you have favorite like AI innovation or is it your vacuum with your kitty litter box? Like, is there something in your everyday life that you feel like AI is really impacted because it's helped innovate? that [00:15:00] thing,
Jared Carl: Jillian, the number one thing is taking notes in meetings, being able to take notes and meetings, because if I'm busy taking notes on my laptop or a tablet or whatever, I'm not paying as good attention to the conversation.
So being able to have AI, listen to conversation. And just in the last year, when we started using it to now, those notes have gotten a heck of a lot better, but it took to train me. To make sure I say certain words that the AI picks it up is like, okay, Jillian, this is an action item. You need to call Fred and get this taken care of.
That triggers the AI bot to write it down specifically as an action item. So there is still some training in there mentally that we have to do as well. Some other ones I mentioned, like a self driving cars, I still think they're a little ways out of being fully self driving, but. Definite point to point, you know, in city taxis so much easier to train.
You know, here and here in Austin, Texas, we don't have to worry about training a taxi, not to hit a moose. There are no moose down here. So training it just for that training for [00:16:00] particular neighborhoods makes a lot of sense. Trucking point to point trucking, that's going to be available because it makes a lot more sense outside of that.
You know, there's all sorts of other things like talking to your phone and I don't really use that yet. So. And maybe talking to my, you know, asking my song to play speakers for me, but that's not really AI. That's just, you know voice recognition. So,
Jillian Kaplan: okay. So, so you're seeing it in your everyday life, in your work life, obviously I would agree with the notes.
I got a summary the other day and we were like teasing a coworker about something and it literally opened up with like, and the team teased so and so. Yes. Score. I was like, Oh, this is for you. I was like, wow. That, that was very specific. I don't know if that's an action item or just a little reminder
Jared Carl: that we're all still human.
Right. Yeah. We're all
Jillian Kaplan: still human. We can still have fun. Yeah, totally. On, you know, the AI piece and, and talking a little bit about, you AI is really, a lot of what it does is [00:17:00] gathering data, right. And interpreting the data. And, and is that where you feel like we're going to see the biggest impact of AI?
Like how does the AI and the data piece come together in your mind as we innovate? in the space.
Jared Carl: You know, it's interesting. When we look at data, it's a great question because how much data have we generated? You know, last, right. Last time I looked at it, it was like five to seven petabytes a day.
It's, it's around 10 petabytes if my daughters go to Taylor Swift concert.
Right. But it's serious. It's like they have numbers that shows like whenever there's a Taylor Swift concert in town, data generation on that network goes up. So they can measure this data, right? Five to 10 petabytes a day. Let's just use that as a generic number, right? Multiply that up by the years, et cetera, et cetera.
But that is historical data. They usually cannot go back and retrain on that data or figure out from that data because there's just so much of it. So we need to [00:18:00] intercept the data at the source it is generated. In this case, the cell phones in the car's case at the car in I. O. T. Space in business camera sensors in manufacturing environments, temperature sensors oil and gas platforms, right?
The impact of not only the external temperature, but the internal temperature as well as the pressure down the road and being able to figure out like when the pressure spikes on the other side of the platform. It's going to cause an issue all the way down here. We never knew that before. So AI is really about building insights from that historical data.
They're going to impact the way that we run and manage operations today. And again, shorten that time to decision to as close to zero as possible, and then empower the humans to be able to do it. Because that really is where I think we're going to see the biggest benefit. is when humans are able to interact with machines in plain and common language, and the machines are able to generate those outcomes that are better as good or as better than what a human can do.
Being able to [00:19:00] type into a machine and say, why is my car misfiring? Bam. You've got an error on cylinder one. That's great. You know, how do I fix it? Replace the spark plug because this other sensor also said bad. You got a low voltage in your, in your lead. That is great. That was 500 diagnosis problem. Just taking it to just give them to the mechanic.
So I think it's going to be a interesting, interesting world where we're going to go to, and it really starts taking off into some of these other areas that we haven't seen before.
Jillian Kaplan: The use case you just described is really an augmenter, right? Like we're using it to augment what a human would take a lot more time to figure out, right?
Because obviously you still need to take it to the mechanic or whoever to get the spark. I don't know what you just said. Spark plug replaced,
Speaker 3: spark
Jillian Kaplan: plug replaced. But you know, being able to find that out quicker is. Maybe safer for the person driving it knows not to drive it at that point and get it right to the mechanic and tell them, Hey, this is the [00:20:00] problem.
Like, can you fix it? That's
Jared Carl: one of the things we hear is like, Oh, AI is going to come take our jobs. You know, we heard the same thing about putting the cotton gin in the field. Oh, it's going to go take the human's jobs. The, the, the car, when it came out, Oh, it's going to take the job of all these horse people that are feeding your horses, you know, stuff like that.
There are a lot of jobs. That can be done better by machines. There's a lot of, especially now safety jobs that you don't wanna send a human in there to do. Yep. So the, the, the phrase, and I agree with this is AI may, AI's probably not gonna take your job, but the person who uses AI will because they're going to be so much more productive because.
I don't have to sit there and write my notes at the end of every day. I can get onto my next meeting because I got 12 others after this, but I can send out the notes and the action items and then flag my outlook to pick me up on that later. So being able to use AI for those tools makes so much better coders, being able to code better and debug their own code faster with AI [00:21:00] knowledge, they're going to generate better and more code down the road.
So it's higher quality and it's faster time. The resolution are kind of some of the really key factors here that we're going to start making a really interesting changes.
Jillian Kaplan: I love that. Yeah, I love, I, I haven't heard that exact expression is before like AI won't take your job at the person that understands AI and can use it well, but I feel like that makes a lot of sense, especially with like everything that I'm seeing.
I also think that like people that can innovate like yourself and are innovators are more From what I've seen more apt to adopt AI because they're like, not as afraid to go outside of the norm and the way things have always been done, right? I'm going
Jared Carl: to be honest. So Jillian, I want my career to be done before quantum hits.
I don't want to learn another programming language, another compute technology. done when quantum hits, right? Quantum is going to change the world. That is going to be revolutionary because it's going to break so many things that we're used to. Encryption [00:22:00] and blockchain, potentially crypto is going to be so affected by quantum.
You know how it's going to work. And of course all the other, just basically what feels like fantasy and quantum you know, just, it's just going to change the way, the way we work, but you're right. You know, if we don't adopt innovation, innovation, innovation is going to go without you. I mean, just just flat out.
Be honest. You know, we look at health care and what they're going to start doing with AI already doing with a, you know, assisted analysis of x rays. Oh my God. Have you seen the cost of health care? If they can make that health care cheaper or better, I doubt they'll make it cheaper because their margins are really thin nowadays anyway.
But if they can make that better, that changes the way that things are going to work or make it safer for you, right? Like fall detections in Yeah. And then in the hospital rooms, being able to understand if this person is going to come back and see us sooner than later because of certain aspects of their life.
Don't mix these two things because of your genetics, you know, those sorts of things I think will be absolutely huge. And hopefully that will make everybody's life better you know, from a health care standpoint.
Jillian Kaplan: Yeah, yeah. I mean, you're right. [00:23:00] Innovation is going to happen whether you're. An innovator and want to be on the bus or you're not going to be on the bus and then you're just going to be left behind with everything like not just innovation and AI, but in all aspects of everything because things are always, always, always changing.
Jared Carl: This
Jillian Kaplan: was awesome.
Jared Carl: But here's the thing. It's completely your choice if you want to use it or not. Nobody's forcing you to use it. You know, but to me, it's like if you don't use it and then somebody else does and they end up getting the job, not not you, but you know, you in general, right? It's like, well, you maybe should have used AI because you're staying on top of things and again, goes back to that always be learning, right?
Always be learning about ways to do your job better, to live your life better, you know, those sorts of things. And then of course, oh God, AI failures. AI is not perfect guys. It ain't. My fridge told me the other day I needed more baby food. There has not been baby food in my fridge, you know, a long time, so don't always believe it.
That's why you're [00:24:00] supposed to always check your notes afterwards. So it's, it's still got a ways to go on, on fixing that. So yeah. Yeah.
Jillian Kaplan: I mean, I would totally agree with that, like as well, because, you know, it's learning from us. And we're not perfect either, right? So like we have to consider that for sure.
And I think that people that are willing to innovate and do new things and adopt AI in their lives and in their jobs are going to see a lot of positive come out of it, even though it might be a little bit harder in the beginning, but a lot of things are right there a little harder in the beginning.
And then over time, it. It makes sense and it's worth it.
Jared Carl: Yeah. And that's what we always say. Keep the human in the loop still you know, self driving cars, right? The self driving cars becoming reality. I think it's fantastic that the car could get you there safer, et cetera, et cetera. Usually, but they're still not perfect, right?
You still hear crashes of, of people, you know, not paying attention. They fully trust their self driving cars and it, it hits something right. It's not ready yet for prime time globally. Right. But be [00:25:00] aware of it. Right. Again, if you're going to use technology, you should know its strengths and its weaknesses that it could break.
It could not break. So just pay attention to it.
Jillian Kaplan: Yeah. Yeah. A hundred percent. Yeah. I mean, I got it. I got a new car recently and it self parks. And I, my husband is like, you need to use this. I'm like, but I'm nervous to your point. I'm like, it's new. It's different. But I know that once I learn it, it will make my life easier because like, Hey, just pull up, you press the button.
I'm like, Oh, okay. Yeah, exactly. Yeah. But it's, it's even for someone like me that likes technology and likes innovation, learning things the first time is always a little sticky. So.
Jared Carl: Right. How do you incorporate the technology into your life? That as a person, it makes your life better, but you don't have to alter the way you do things.
You know, that's a, that's a, it's an aspect of AI. It's like, are you just forcing yourself to use AI or does it naturally fit into your life cycle? Yeah. Yeah, cause parking I'm fine with parking. I suck at parallel parking. So, you know,
Jillian Kaplan: parallel park for you.
Jared Carl: Exactly. The early parallel [00:26:00] parking systems were horrible.
Jillian Kaplan: Yeah.
Jared Carl: So, yeah. I mean, they're probably still better than I was, but anyway yeah, I
Jillian Kaplan: digress. We digress. Yeah.
Jared Carl: That's a different story. Yeah.
Jillian Kaplan: Awesome. Well, this is an awesome conversation. Thank you so much for joining me on the podcast.
Jared Carl: Oh, my pleasure. Thanks for having me on. I hope people get something out of it or at least laugh a little bit.
Jillian Kaplan: Perfect. Love it. They will do both. Get something out of it and laugh.
Jared Carl: There we go.
Jillian Kaplan: Thank you.
Jared Carl: Thanks. Appreciate your time.