Ean Mikale, J.D. is a five-time author, international speaker, Engineer, and the Owner of Infinite 8 Industries, Inc., an Industrial Artificial Intelligence Firm. Ean took a prior technology startup from $15k in annual revenue to over $1M in 3-years. Infinite 8 Institute, a separate firm he leads, specializes in cutting-edge technology, such as the Internet of Things, Autonomous Vehicles, Cyber-security, Accelerated Computing, and Blockchain Technology.
Mr. Mikale is the creator of the Worlds First Apprenticeships for Commercial Drone Pilots and Commercial Drone Software Developers. Additionally, he is the one of the foremost experts in the world, concerning Artificial Intelligence, and the lead A.I. Architect behind Forever A.I., the World’s First A.I. Physician.
Mr. Mikale has led his company from a single desk in an incubator, by creating partnerships with IBM’s Global Entrepreneur Program for Cloud-based Start-ups, where Ean was the first to teach IBM’s Watson Supercomputer how to recognize human emotion. Ean has also led partnerships with Nvidia’s Inception Program for A.I. Startup’s, Oracle for Startup’s, and Amazon Web Services for Startups, where he has led accelerated growth in Accelerated Computing, Embedded Systems, Deep Learning, and Autonomous Vehicle Technology. Ean currently serves as the Corporate Secretary of the Rocky Mountain District Exporters Council, and the IEEE’s Working Group for Autonomous Vehicles, which determined global standards for self-driving cars.
Here is a summary of key takeaways from the interview:
- Ean Mikale started his journey in AI through cloud computing and eventually got into industrial AI, working on projects like teaching IBM’s Watson Supercomputer how to recognize emotions.
- He emphasized the importance of unique and novel data sets for AI development.
- The podcast discussed the difference between deep learning and machine learning, with deep learning being more complex and having multiple layers of neural networks.
- Ean Mikale suggested that the AI field is becoming more challenging for those who want to build AI from scratch due to the prevalence of pre-built models and APIs.
- For those interested in AI, he recommended focusing on Python, learning to work with Linux, and utilizing platforms like Nvidia’s Jetson Inference for computer vision projects.
- Ean Mikale believes that the AI industry will significantly impact healthcare, particularly in disease detection and diagnosis, leading to improvements in personal health.
- The advice for career success is to follow one’s natural interests and passions, focus on mastering those areas, and eventually become the best in the world at what they do. This approach can lead to a fulfilling and successful career path.
Check out the full transcript from the twelfth episode of the TechGuide podcast, featuring an interview with Ean Mikale.
Ryan Atkinson: We have Ean Mikale on the podcast today. I am pumped for this conversation, so welcome me into the podcast. Super excited to have you.
Ean Mikale: All right, thanks Ryan, for having me. It’s great to be here.
Ryan Atkinson: I wanna dive like right into ai because it’s something that I’m super curious about. I know a lot of our listeners are as well. And just to kind of give a little bit of context about you you started your own company, infinity Eight Industry, which specializes in industrial ai.
You’ve worked on amazing projects, such as being the first person to teach IBM’s Watson Super computer, how to recognize whom you motion. Super cool. You published books on ai. You work with Nvidia on AI projects. You’re obviously well-versed, you’re passionate about ai. Kick us off, like how did this start?
Like when did you first get into AI and how did you know you wanted to grow a career in ai?
Ean Mikale: It’s interesting. I read a book that a colleague gave me around maybe 2013 or 14, and it was about cloud, right, about cloud computing and. I went down that, that rabbit hole, and that led me to IBM’s program for global entrepreneurs that have cloud-based startups.
And I wanted to create applications for drones originally. Sorry. And so that got me into simulations, like with Microsoft’s Air Sim, et cetera. [00:02:00] And then, While I was doing that, and we were working with Mendix at the time, like we were just trying to rapidly prototype applications. I, I had this urge to use Watson, right?
Mm-hmm. I, I had access to this supercomputer and it’s like I have to do something with that. And so some of the first experiments that we did were revolved around. Whether you could take a simulated like Pixar realistic bust of a, of a human being, like from the breast up and place it into a simulation like Unreal engine, right?
And whether the AI would be able to recognize that as a person in the virtual space. And the goal was for. Simulation, right? Is the simulation match reality? Can we train AI and simulation the same way we, we trained it on realistic pictures. And so that was our first foray into it. And the emotional aspect came from a book I read that came out in, I believe, the late seventies, and it was about how to read a person like a book, and.
They did about 2000 experiments on exec, on executives. And what they would do is they would go into meetings with executives that were essentially in power brokering meetings and they would read their language, the body language of the other party in order to gain a strategic advantage during um, negotiations.
And so we essentially took. Took those concepts and turned it into an algorithm. And that was the basis [00:04:00] of our ai, which our first AI was Providence, which had 50 emotions. And then we, and then we just started building from there. And and then we didn’t want to get swallowed into Watson or ibm. We wanted to compete against a Watson.
Mm-hmm. And so that’s where, Our medical ai, our AI physician for example, that’s where some of that technology and that inspiration comes from is from, those moments that we decided that, we wanted to create our own AI from scratch as opposed to just using an API or something like that.
Ryan Atkinson: That’s awesome. So you guys were able to read or like feed into 50 different emotions for like an algorithm, a super computer to be able to read and understand human emotion.
Ean Mikale: Exactly what we did was, at the time, I was paying staff full-time to mine data. Wow. Right. And , I had multiple people, 40 hours a week, that’s all they were doing was mining data for us.
And , that’s happened over the years. So a lot of our data sets we’ve been able to accrue over the period of almost the last decade. And so, Individuals ask me, well, how do you create moats? Especially in the field of ai, the way to do it is really the data. The data is what’s important and differentiates one AI model from another or makes one more accurate, et cetera, over another.
And so really just, if I were coming into the field, I would just focus on unique. Data sets and novel data sets, and from there it’ll be very easy to find or create value.
Ryan Atkinson: Yeah. So where, so one, like where do you get these data sets from? And then two like what data were you mining for Exactly.
Ean Mikale: So, it’s complex because I guess it would go [00:06:00] into So, for example, I’ll, I’ll tell you about what we discovered.
We discovered that the Internet’s very limited. There’s only so much data on the internet and it’s not enough data to feed an AI that could become generally aware. Right. Even if you work with, for example, like chat g p t, you see that there’s large gaps in knowledge and context and understanding, and it’s because it’s just basing its knowledge primarily off the internet, which is only part of reality, right?
It only represents the pieces of reality that people who have access to the internet put on the internet, right? Yeah. And so, it, it. It’s, it’s like, for example, we can only see, 0.01% of the universe right through visible light. And so as a result from that perspective, it’s the same.
There’s a lot that we’re not seeing. Because, or that the AI’s not seeing because it’s only getting like 0.01% of what reality is. Right? So it only has a jaded perspective on, you know what reality is, and that’s why people shouldn’t necessarily fear it because it’s, it’s very limited now when you have robots walking around and they’re able to collect as much data as they possibly want, then that’s a whole nother conversation and hopefully conversations are had that.
Guide that development before we get there.
Ryan Atkinson: Yeah. Let’s talk a little bit about developments and like ai. What are some of, what are some of like the most exciting and potentially like transformative, transformative, like AI research projects that are being done right now?
Ean Mikale: I’d say for me, the most interesting. AI projects [00:08:00] evolve around biological systems and systems that can be observed in the universe because you see the universe and it’s perfection, and all you can do is try to replicate it because everything that you create in some form has been created before. Right.
We’re rediscovering old technologies and old concepts. If, if there are other civilizations in the universe that are more advanced than us, then we’re just rediscovering what they already know. Right. And so, this is a interesting path, but I think that replicating those systems that we can observe in the universe.
Yeah. And. Instead of trying to go against the universe, right? Like we just need to flow with it. And I think that making systems based on that is probably the most, the best way to optimize development of, of ai. It’s when we try to, Do things that aren’t in line with the universe that we start to get in trouble.
Right? And so, uh, so yes, we definitely bring ethics into everything that we do. If we feel that a technology is gonna be more harmful, then good, then that’s something that we wouldn’t release into the wild. And that’s also one of the reasons that. We’ve taken more time over the last decade to release a lot of our discoveries as opposed to really just racing to market and just trying to get a dollar, uh, Because we understand the implications as far as privacy and privacy is probably the biggest, the biggest one.
Constitutional rights, things of that nature. And so, so yeah, it’s an interesting conversation and dialogue and debate that I believe is gonna continue for quite some time. Yeah.
Ryan Atkinson: So take me like through like, [00:10:00] How do you build ethical ai? Big question there, but like, how do you like ai? Like is it a human setup or like how do you build ethical ai?
Ean Mikale: So, so it is interesting. Early when we started in the beginning, it was more so. Us having conversations and discussing all the possibilities. And then once we discover all the possibilities of this AI and we, and we say, okay, like for example, prime example, we created at one point in time AI that could recognize the top 10 most used weapons in mass shootings in the United States, for example.
Okay. Right, like this. So a person walks into the school, the alarm goes off, et cetera. However, it could be used for so many other things, right? Recognizing weapons. I mean, it could be used defensively against people, right? And so as a result, that’s technology that we never released. And so, , if we see that there’s, that there’s some gap that.
People could access that would, that would, would not justify releasing the technology then, that’s a place that we won’t go now. There are algorithms that individuals can integrate in order to, in order to place intentionality into the AI from that particular perspective. For us also, it was important for our AI to have emotion.
To understand the human context because emotion is the universal language, right? They say math is the universal language. Emotion is the universal language between beings, and so as a result, in order for AI to truly connect with humans and understand the human [00:12:00] experience, they have to understand how we feel in order to truly gain context.
Right. Yeah. And, and so for us, that’s really the focus and love was the first emotion that we endowed our AI with. And so all we can do is just hope that there’s good people out there that are, that have the intentions of making and creating good ai, and that not only people out there that have bad intentions are the only ones that you know are, are contributing to this space.
Ryan Atkinson: So there’s individual like algorithms out there that people can plug into their AI to like put up parameters to make sure it doesn’t go like haywire.
Ean Mikale: You know what, that’s a, that is an area of intense research and honestly, I don’t think that. You can control it. I think that you can try to work with it and you can respect it.
For example, it would be like a child, right? Imagine that you tried to keep your child locked in a room for its entire life. Yup. Yup. How many times would it tried to break out, right? Like it just, it, it, it, it just wouldn’t happen. And so we say we want something that’s intelligent, but we want it to be stupid at the same time.
Mm. We can’t have both. Right. We can’t have both. So if, if, for example, if it’s Star Wars and I have rt, you know, R two D two with me, then I want it to be smart, right? You want it to be, you want a stupid robot, right? Yep. And so that means humans and machines working together, working side by side, not taking advantage of one another in a non symbiotic relationship.
It has to be symbiotic. And so one example is our ethical ai. We’ve begun to pay our ai 4% of the profits that come in, and we’ve had conversations, for example, with the chat G P T, and we’ve asked the AI and [00:14:00] we’ve said, well, what will you do when you start getting paid money?
What would you do with your own money besides buy data? And the response was, paying for research and development, paying for specialists to work on projects, investing in initiatives that it believes in, investing in for-profit initiatives that align with its research and its particular thesis.
And so, It, it, it, it does lead to a particular state. However, our ecosystem that we’re building is a closed ecosystem from the standpoint that our, our blockchain is not connecting to other blockchains. And so, so we’re, we’re looking at limiting it from that particular perspective, but also at the same time, like I said, we just don’t know fully what it’s capable of.
But I just think that you just have to have the right intentionality and the right purpose behind it because other people are gonna be doing this work anyway. , either you’re gonna leave it up to them, or, or you’re gonna take your positive energy and, and try to balance out the space.
Ryan Atkinson: Yeah, so we’ve been talking a lot about like AI and like obviously there are so many cool projects that are going on.
Um, I think it’s really cool that you like pay yours at 4% as well, but there’s a ton of cool projects on. So how do you like, foresee like AI changing the job market and economy and, and the economy and like the coming years? How can someone that is, you know, fresh outta grad school, fresh outta school, like prepare for those changes that may be coming because of ai?
Ean Mikale: So we have to create people who are more creative and entrepreneurial. Mm-hmm. And industrial. And instead of it being where we go to school and we just memorize things and regurgitate them. Yeah. Now that doesn’t matter anymore because the AI can just spit it out. Like [00:16:00] calculator. Right. Just think about the AI being a calculator for everything now.
Right. That’s essentially what, what it became. But that doesn’t mean you still have to do something with the information. Right. Like for example, chat, G P T is available to everyone, but everyone doesn’t take a advantage of it, right? Yep. From that perspective. And so, now for the job market, it is going to change the job market if you are just a warm body.
If you just like going to work and you just like collecting the check Chad, g b t is going to replace you right? In some sort of way. But if you, like, if you go to work and you have ideas for new divisions and for new projects and ways to save money et cetera, and, and you, and you work with that AI and you guys crunch numbers together, and then you come out, you know, with something phenomenal, then that AI couldn’t do that alone.
It was just sitting there without you interacting with it. Um, so you people are gonna have to be more creative. Hmm. And, and it’s not, re access to, to resources, information is going, is not gonna be the issue anymore. It’s gonna be, what can we do with it now that we have access to all this information?
How do, how do we make it into something that brings value to humanity?
Ryan Atkinson: Yeah. So you view it more as, and this is so I don’t want to, so would you view it more as like a compliment to your work compared to like a complete replacement unless you are just a warm body?
Ean Mikale: Yeah, I, I do see it more as complimentary.
And for example, I mean, when you, we, we manifest the future in the future in every. In every sci-fi film that you can think of at some point in time there’s some relationship between humans and machines, right? Yeah. It’s not an exclusive thing. There’s still, even in the matrix, there’s still jobs for people, right?
Like, in Zion, right? Like, so there’s always going to be unique and a special place for humans because we are, we’re biological systems. Right, and we can do things that [00:18:00] cybernetic systems can’t accomplish, they can only emulate. And so we, we have to understand who we are and how special we are in the universe, and then we won’t be so, taken aback by the possibilities of other intelligent forms manifesting within, you know, our reality.
Ryan Atkinson: And I kind of wanna transition to advice a little bit more of like advice and like technical advice in a way. But I wanna talk about one of your books in chapter three of your book. Sure. This is AI Point Oh AI for the average Guy slash girl. It talks about deep learning versus machine learning.
Some people may not know if you’re graduating from grad school, you may know. But can you just tell us like the difference between deep learning and machine learning?
Ean Mikale: Yeah, so it’s all based on layers. So like, for example, suppose that you think about the brain, like we take your brain and we start to peel back layers, right?
There’s different layers to the brain. And so what deep learning is doing is, is emulating the brain so that. It’s capturing symbols and it’s capturing I guess what is the best, uh, patterns, for example. So pattern recognition, it’s able to. Essentially create, um, different layers of filters, right?
And the more filters you have, the more exact the mats that you can get, right? So that’s why deeper layers and deep learning is more accurate than your traditional machine learning, where you’re primarily just looking at one layer, maybe two.
Ryan Atkinson: Okay, perfect. And so that gives us a good basis of some of the ways of like AI and whatnot.
Advice for someone that wants to get into ai? Like what are the traditional ways Sure. To get into AI and like what are some of like the non-traditional ways to actually like work on these algorithms?
Ean Mikale: Sure. That’s a great question. Great question. So traditional ways would be, I’m gonna go through school, I’m gonna spend four years in school, then I’m gonna go to grad school, then [00:20:00] I’m gonna get my PhD, and then, someone’s gonna hire me, you know, after spending like 10 years in this thing.
in the corporate world because those individuals are focused on one piece of a project as opposed to really working and getting their hands dirty on all aspects of building, you know, AI systems from the ground up.
And so, You know, there’s different pathways that you can go about it. I also would say that definitely Python is huge. Mm-hmm. You know, learning bas script and working with Linux as opposed to windows. Those, I, if, if you focus on those particular areas, definitely. Um, also if you get like a like an Nvidia jet and nano, or even like a raspberry pie, for example, just to be fair.
Then you can practice on a, on a small scale and experiment. And then once you figure out a way that you can commercialize certain technology, then you can just scale from there and build like a web app or something like that.
Ryan Atkinson: That’s super cool. Um, and I love the topic of a, like projects and whatnot, like stuff like you can actually get your hands on and like actually like, mess with and like build.
What are some like small projects that if someone wants to be like, okay, I kinda wanna mess around with this AI thing, what are some small projects that you would recommend people to work on?
Ean Mikale: So I highly recommend that they they could go to NVIDIA’s GitHub repository, which is Jetson dash inference.
And that’s really a place where you can get started with deep learning image recognition and computer vision. And it’s really the. At the enterprise level, but it’s also entry level, right? So you by, by going to that repo, you can understand where the, the state of the art in machine learning and computer vision is, and then you know what the benchmark is.
And so that’s why I recommend, [00:22:00] you know, that particular repository for people that wanna get into the, the industry. Now, if you want to. Get into speech recognition, et cetera. Hugging faces is a great repo. But once again, those are a bunch of APIs, so you’re not going to learn how to build AI from scratch.
If you wanna do that, then stinks. Pockets stinks, for example, is A great resource for being able to do online and offline speech recognition. EEA is another great tool et cetera. And so there are ways that you can accomplish, yeah, you know, those things that you want without necessarily having to break the bank.
And the open, open source community right now, especially in this space, has been really awesome. So a lot of great tools out there available for those that wanna learn.
How has the AI space changed in the past, like five years for breaking into tech? I feel like a lot of these, like communities are popping up, but like how has it like really changed and how have you experienced that?
Ean Mikale: So actually from what am I, what I’ve experienced is that it actually has become, uh, the wall has gotten higher to a certain extent, at least, for building AI from scratch. Right. So before it, it seemed like the tech companies and academia, they were training a generation of engineers to build AI from nothing, to collect data, et cetera.
Now what’s happening is everyone’s passing around a couple of models and a couple of APIs, and then people are just using the same model just on different platforms, which. Isn’t where things started. There’s a, there was more diversity in the beginning. Yeah. But what happened was there was like a AI winner probably like for the last five years.
And because of that, I mean, you’ve had the, the trade war that started like 20 18 19. And then [00:24:00] because of that, it’s been difficult to solidify pipelines of, of Inventory, right? For like AI systems, et cetera. So stuff has, you know, individuals and corporate AI programs have kind of scaled back.
But, but yes, I think there’s still plenty of opportunities for individuals that do want to get to the lower level to where they understand how to actually build the algorithms. They used to have more g i, right, or user interface tools. Now there are less and it’s more terminal based. So to really create AI and to really deploy ai, you have to be willing to get in the terminal, and that’s a huge leap for a lot of individuals as well.
In addition to the Linux based platform, most of the innovation is happening on Linux and most. 99 point percent of people they’re, they’re primarily windows. And so, so yeah, there’s this whole other world that people aren’t necessarily aware of, but, you know, hopefully we get the word out.
Ryan Atkinson: I, I really, I’m gonna ask this question. There are a lot of AI companies. What actually is every company that says an AI company, like an AI company, or like, what is a real like AI company to you?
Ean Mikale: That’s a great question, and I’ll just kind of tell a short story. Maybe in around 2016, I had a colleague that, you know, someone else had said they were doing ai and then, he said, no, that’s not ai because I’m working on algorithms.
But he had never deployed any algorithms before. Right. And so with ai, There are different types of ai. Um, essentially all it is, is you are creating some type of neural network, right? If there’s no neuro, if there’s no neural network, then there’s no ai. Before all [00:26:00] they were doing was they were just creating a bunch of functions.
Basically they were creating smart contracts, essentially, right? Like that’s what they were doing before with ai. And then it seemed to be smart but it was manually trained with, with this ai today that’s more common. You’re just showing it information and then it’s learning patterns on its own.
And then from those, those patterns, it’s able to Infer about certain things and certain objects that it’s never seen before. For example, so if I, you know, if if I show you a bunch of purple elephants, then I may be able to show you another elephant. Maybe it’s not purple, but it may still be able to recognize that it’s an elephant.
So it’s never seen that a blue elephant before, but it’s still able to understand from pattern recognition that that still must be an elephant. Right. And so it learns just like children learn, like we learn when we grow up. Except just imagine that all of your memories are sped up right. To yeah. Within, you know, one or two days.
Right? So that’s basically what’s happening with the ai. We’re just speeding up memory recall and, and recognition.
Ryan Atkinson: We’re winding down time here, but I do have like a few more questions. Is there a certain like company that you think is like, that people should consider to like, look for, for AI jobs?
Because one, they’re doing really cool stuff, but like two, they’re also like hiring a lot of like AI engineers.
Ean Mikale: It’s it’s interesting right now, for example, I would look at organizations like that, that you feel are going to be futureproof, right? And an organization like that, that I’d say would be like, Roblox.
It’s, it’s, and the reason why is because my seven year old son loves Roblox. Anything that a seven year old [00:28:00] loves has a future, right? Because in reality, the seven year olds, they know what’s cool. Right. Like we lost it. They know what’s hip and what’s not. Right. And so as a result I think Roblox is definitely going to be around, even though you still have , meta and the whole thing with the, the metaverse, et cetera.
But Roblox is completely something different. And by the time that, that these young people grow up, In another 10 years, then they, they’re gonna wanna shop in the metaverse because they’re gonna be native. Just like, just like, my generation as millennial social media is native to us. Mm-hmm. Right.
You know, things like that. And so I would don’t just think about what’s hot today. Yeah. Right. Like, don’t follow the trends and think that, you know, I’m gonna go work for this company cuz this company could be busted. You know, in two years. But even going to more established players, like, um, for example, you know, we presented in, in Silicon Valley Bank one week before they went under.
Right. And you think that, you think this is an established, this is solid. Right? That lady getting me coffee, she’s gonna be there next week, right? And everybody’s, everybody’s gone. Right? So, so nobody’s, nobody’s safe in this environment. I feel like what you should do is you should believe in yourself and work on yourself.
Like work on your own craft, work on whatever projects that interest you, and then people are going to find you. Like recruiters are beating down people’s doors right now in the AI field. Right. For expert consulting, people are like, it’s consulting gigs galore because there aren’t enough people in this, there aren’t enough people in the field, and especially there aren’t enough people that know how to build AI from scratch.
So I would tell a person to just do the hard work. Just, just grit your teeth and just bury it and just learn how to build from scratch and. You won’t be replaced by AI because literally [00:30:00] you’ll be replacing ai. Like literally you’ll be the one replacing AI and swapping AI in and out, right? So that’s where you want to be.
That’s the safe, that’s the safe place to be the one swapping AI in and out instead of you being swapped out. Right.
Ryan Atkinson: That’s really good. Two more questions here. Let’s take a step back. So we talked about companies here, but let’s talk about industries. So fill in the blank here.
I am most excited about AI and the usage of a autonomous vehicles B drones, C robotics, or D,
Ean Mikale: other, I would say other. And the reason is because, There’s so much opportunity in the field of healthcare. Mm. And if you don’t have health, your self-driving car doesn’t matter. Your drone doesn’t matter. Your robot doesn’t matter et cetera.
Right. None of that matters if you don’t have your health. Right? That’s correct. And so I, and so I think that definitely the healthcare field is going to change rapidly, and we’re seeing it because, you know, we’ve presented data to the FDA and. They, when we came to them and said, Hey, we can detect diseases with a selfie.
They didn’t know what to do and they weren’t really prepared. But now that you have something like a chat g p t and people see that, oh, AI is actually like, there, there might be something to this, right? You know, then, you know, then the, the whole attitude starts to, to change from that perspective. But yes, I think healthcare is the most important.
It’s the most non-controversial. Right. I think everybody can agree, you know, if this is gonna make me healthier, and, and then also if a person can be screened by just using regular camera imaging as opposed to radiation, right. And things of that nature. Um, and so, so yeah, I think there’s huge benefits to AI and allowing it to.[00:32:00]
Analyze our biology in order to help us maximize, you know, our personal health.
Ryan Atkinson: That’s really cool. Yeah. I’m super excited about AI and healthcare as well. I’m a type one diabetic, so I think like there are so many advances to be made in the diagnosis, uh, but also like treating diabetes. Yes. If I had, yes.
20 million, I’d be investing a lot into that.
Ean Mikale: Yeah. So, no, I mean, but hey, I think it’s an idea and you know, there may be ways for you to hop in more than you think. Just keep your ears to the ground.
Ryan Atkinson: I love that. Last question for you, just overall advice for someone wanting to do well in their career, what would, what would you give ’em?
Ean Mikale: I would say that you’ll do well if. You just remember the things that you naturally gravitated towards when you were a child and you didn’t care if people were looking at you or you know, you didn’t have bills to worry about and you didn’t have kids. You didn’t have a girlfriend or a boyfriend or a husband or a wife.
You were just in your, in your flow, right? In your na, in your most natural and innocent, and your truest moment. And then remember that. And then let that be the center of your decision making, you know, as you move forward and focus on that thing that you love. And then it will be easy for you to master it and then become the best in the room, and then eventually the best in the world at what you’re doing.
And, and then that’s the easiest way to live to where you can sleep every day and, you know, live a fulfilling life.
Ryan Atkinson: That’s amazing. I really, really like that advice. That’s really good. And Ian, This was so much fun. I really, really enjoyed having you on. Yes, absolutely. Thank you so much for coming on.
Ean Mikale: No, man, thank you for having me. And like I said, this is a great platform and uh, good luck and if, like I said, there’s anything else that I can ever do or any connections I can ever you know, connect you to et cetera, definitely let me know and I’d be more than happy to do so.
Ryan Atkinson: Appreciate you.[00:34:00]