Ron Green is an AI expert and serial entrepreneur with over 20 years of experience.
Ron is the host of Hidden Layers: Exploring the People and Tech Behind AI, and the Co-founder and Chief Technology Officer of KUNGFU.AI | Accelerate AI.
He leads a team of talented machine learning engineers and data scientists dedicated to developing advanced AI solutions.
Ron has overseen the deployment of numerous AI systems across various sectors, including healthcare, government, retail, real estate, and financial services.
What you will learn
- Why generative AI is just the beginning of a much bigger AI revolution.
- How thinking beyond generative AI unlocks its full potential.
- A fascinating look at AI’s history and where it’s heading next.
- The common misconceptions people have about AI today.
- The most exciting advancements shaping AI right now.
Transcript
Jeff Bullas
00:00:03 – 00:00:43
Hi, everyone and welcome to today’s episode. I’m here with Ron Green. He’s dialed in from Austin, Texas. I’m here in Sydney, Australia. So we’re only separated by about 14,000 kilometers for Americans. That’s about 8000 miles. So I need to translate. Ron’s got about uh 75 °F. I’ve got about 22 °C. So we, we talk, but we still talk English and we’re thrilled to have Ron Green with us, an ex AI expert and serial entrepreneur with over 20 years experience. Ron is the host of Hidden Layers: Exploring the People and Tech Behind AI, and the Co-founder and Chief Technology Officer of KUNGFU.AI | Accelerate AI.
Jeff Bullas
00:00:44 – 00:01:09
He leads a team of talented machine learning engineers and data scientists dedicated to developing advanced AI solutions. Ron has overseen the deployment of numerous AI systems across various sectors, including healthcare, government, retail, real estate, and financial services.
Let’s dive into the fascinating world of A I with Ron Green. Welcome to the show, Ron. It’s an absolute pleasure to have you here.
Ron Green
00:01:10 – 00:01:12
Thank you so much for having me, Jeff.
Jeff Bullas
00:01:13 – 00:02:03
So Ron, you’ve been, uh, involved with nine start ups and co-founded four. That sounds complicated. Um, so, but before we get into that, uh, let’s have a little look into what led you into A I and, uh, I noticed that you did a technology degree or a Bachelor of Science. I think it was, um, uh, you know, just 56 years ago now, you’re looking very young, but it’s just five or six years ago. I am 20 years old. But anyway, I’m just confusing myself here by talking. And um so Ron, what was your curiosity about science? Was it something that, you know, like you built computers when you were seven or something? Where, where is this curiosity about science? And then we’ll woven it. What the curiosity about A I is
Ron Green
00:02:04 – 00:02:58
That is a great question. I actually don’t know. I am uh going back as far as I can remember. I was just really fascinated with the space program and uh you know, the Voyager uh space craft that NASA launched and it got me really uh excited about that whole world. And I, and I was candidly, I was really drawn to computers in a way that doesn’t make sense because I just got a total thrill at writing little simple programs, getting it to do something. And that just kind of blew my mind that you could um write some code and then have this machine execute or draw pictures or, uh, make calculations. Yeah, faster than you could possibly think. And, uh, I got, I got sucked into it pretty early on and I don’t know why.
Jeff Bullas
00:02:59 – 00:03:00
So, what was your first computer?
Ron Green
00:03:01 – 00:03:43
I had a T I 99 at Texas Instruments in 99. Yeah. And it was, you know, I think, I, I, I’m almost positive that it came with, I think it came with like, something like 32 kilobytes of memory or 64 kilobytes of memory total. I mean, that was it. And you had no way to save programs. You had to, you had to buy separately. You had to buy a cassette tape player and you would, you remember that and you would, you would hit uh record to save your programs. And uh yeah, man, that was fun. But I’m glad we are done with those days.
Jeff Bullas
00:03:44 – 00:03:56
I think my first uh with a computer was actually when I started my teaching degree, which was with an Apple machine of some type. I can’t even remember what it was.
Ron Green
00:03:56 – 00:03:58
Maybe an Apple two or something like that.
Jeff Bullas
00:03:58 – 00:04:14
Something like that. There’s one other word, another computer called an Apricot or something. That was another one that came out. I’m trying to remember what that was. But anyway, so what store did you buy that from? Does that sort of chain of stores still exist at all?
Ron Green
00:04:15 – 00:04:18
I remember we got it at something called Radioshack.
Jeff Bullas
00:04:22 – 00:04:25
Yeah. Ok. Does Red Shack? Exit today.
Ron Green
00:04:26 – 00:04:37
You know, I honestly don’t know. I know I went through some hard times there for a bit but, uh, I don’t remember if it’s still, uh, still in business and you’ll see a lot of them if it is, you know,
Jeff Bullas
00:04:37 – 00:04:53
radioshack. In other words, they allow you to buy, they make your own radios. Didn’t you remember how you are? I would actually, I actually built my own right, first radio actually. Um, oh, that’s awesome. Yeah. So I’m a little bit older than you. Like, I’m actually 100 and one but I’m just looking,
Ron Green
00:04:53 – 00:04:55
I’m just 100
Jeff Bullas
00:04:58 – 00:05:09
now. So you sort of bought your own computer. So, what prompted you to start a science degree is because that was your natural inclination, seeing the most obvious choice for you to do?
Ron Green
00:05:09 – 00:05:45
Yeah. You know, I mean, I, I’ve almost never told anybody this, uh, certainly on a podcast but I was, I was in college and I didn’t know what I wanted to do and I was looking to transfer degrees and this is a, this is 100% a true story. Um, I was, uh, at the University of Texas at Austin and they have a fantastic computer science program there. But I didn’t know that. And I was talking to the girlfriend of one of my friends and she said, well, you’ve always liked computers. Why aren’t you just majoring in computer science? And the funny thing about that? Is. I was like, yeah, sure, I’ll do it. I had no idea how hard it was gonna be.
Ron Green
00:05:45 – 00:06:14
And the funny thing is this is actually how I got into artificial intelligence because at the end of that degree I was so burned out. I mean, I was just exhausted. I went in the last semester and I thought to myself, well, I don’t know what I’m gonna do professionally but it’s not gonna involve computers because I was just done. I was just completely fried and then I had an elective. I took introduction to artificial intelligence and within like, two weeks fell in love and I was like, oh,
Ron Green
00:06:14 – 00:06:35
oh, this is what I’m gonna do the rest of my life because it just felt so, um, incredible this idea that you would, that we might somehow collectively attempt to build computers that had humanlike intelligence. My, my biggest worry at the time is that I was too late for it. You know, this is a long time ago. But,
Jeff Bullas
00:06:35 – 00:06:50
So what era was that? Because sort of a r is like some of the eras has actually been a couple of winters of A r apparently. So, that’s right. So, was that, um, look, I don’t know the exact time frame, but I’ve actually, yeah,
Ron Green
00:06:50 – 00:07:47
So, the first day of winter kind of happened in the seventies, 19 seventies. Um, and it was a good decade. It was probably the early eighties. Before it kind of resurged again. And um money and, and time and researchers investigated it again. I built my first neural network in 1992. Um Right about the time the field was entering the second A I winner and interest in it was dying. It didn’t really discourage me. I went to the University of Sussex in England and got a master’s in um A I and adaptive systems. But with the.com bubble uh in the late nineties and early two thousands, um by the early two thousands, almost nobody was doing commercial work with artificial intelligence and especially not with neural networks. And today, you know, in 2024
Ron Green
00:07:48 – 00:08:30
everything that most people think about when they think about artificial intelligence chat G BT, um stable diffusion, you know, mid journey, all these different uh programs, they’re all based upon deep learning, which is artificial neural networks. But 20 years ago, the neural networks were considered kind of a dead end, a dead, a dead path. And, and um nobody was really interested in uh putting a bunch of time, Romani into something like that. It wasn’t really until 2012 with a computer vision competition that um neural networks had a big breakthrough and the resurgence started again.
Jeff Bullas
00:08:31 – 00:08:50
So tell us a bit about your experience going to the other side of the world, you know, England. Um UK. Um And yeah, that’s quite a big move to go and do a university degree in another country. Luckily they speak English.
Ron Green
00:08:51 – 00:09:48
That was uh I’m not gonna lie. That was a big part of why I ended up in England. Um Sussex had an amazing program and I was really interested in sort of this, this mixture of approaches. Um I was in the inaugural um class there where it’s called uh evolutionary and adaptive systems at Sussex. And I can’t recommend the program enough and we focused on things like genetic algorithms, neural networks, um uh you know, simulated and kneeling genetic programs, robotics. All of these different approaches that were at the intersection of sort of adaptive dynamical systems and artificial intelligence. And it was an incredibly uh productive experience. Although, you know what’s funny is in grad school, um uh
Ron Green
00:09:48 – 00:10:35
neural networks, even at that point in time, were fairly obscure. Um but nobody used the term artificial intelligence because it had kind of been discredited. And so, um literally, it has probably been almost 30 years. It’s really in the last couple of years that A I has come back as a term where people don’t ask you to, to sort of distinguish between machine learning and artificial intelligence. Like there’s enough progress being made uh that, that distinction and, you know, is not really required anymore, which I find fascinating because in grad school, it was a little bit looked down at Poland as, as maybe sort of a dead end um path.
Jeff Bullas
00:10:36 – 00:10:45
Yes. Um the thing I struggle with is actually the name of artificial intelligence. In fact, I think it’s been misnamed, but
Ron Green
00:10:45 – 00:10:45
a
Jeff Bullas
00:10:45 – 00:10:45
lot
Ron Green
00:10:45 – 00:10:46
of people feel that way
Jeff Bullas
00:10:47 – 00:10:57
because for me, it is actually not artificial intelligence. It’s actually just the machines that actually capture the intelligence, wisdom, and creativity of humanity.
Ron Green
00:10:57 – 00:10:58
I would agree with that
Jeff Bullas
00:10:58 – 00:11:28
and just turn it to zeros and ones and then program it. So it makes sense to you. Average human. Now today with Chat G BT is so A I was democratized two years ago, coming up, November 30. So the anniversary of A I Chat G BT, they’re almost synonymous. Um uh is in uh actually four days, five days for you because we live in the future. So
Ron Green
00:11:28 – 00:11:30
That’s right. That’s right.
Jeff Bullas
00:11:30 – 00:12:07
Um So uh the thing I, the other thing that intrigues me about and it’s been brought up about A. It is that being good at it almost involves you almost need to be a polymath or A I helps us be a polymath because it’s the intersection of technology and the arts and philosophy. And the list goes on. And so with your degree, you did, was there a combination of multiple disciplines within that degree?
Ron Green
00:12:08 – 00:13:03
Yeah, there really was um the, the skills that you need to work in, you know, sort of modern artificial intelligence today. It’s kind of a blend. Um and A It is of course just software. It’s not magic. So you really need strong software. Skills you need to be able to, for the most part, work in Python as a programming language. But with modern A I with deep learning systems, it’s increasingly um um uh a matter of sort of three different primary branches of math, calculus, linear algebra and statistics and statistical approaches are probably the aspect of math that have grown the most. It’s having the biggest influence on modern uh artificial intelligence where
Ron Green
00:13:04 – 00:13:57
calculus in linear algebra, you know, you know, it’s kind of funny most of modern A I most of deep learning is just matrix multiplication. So, you know, if you squint, most of modern A I is just linear algebra. But uh the statistical realm is where most, I think the interesting work is being done algorithmically. And then um obviously, if you’re good at math and you’re good at programming, well, that’s not gonna help you much if you don’t understand the domain you’re working in, right? So if you’re working on uh you know, in healthcare biotech or maybe you’re trying to build systems that can uh generate music or generate images, you need to work and understand that tho with domain experts and understand those domains as well. It’s not enough to just come in with, you know, strong computer science and math capabilities.
Jeff Bullas
00:13:57 – 00:14:21
Yeah. And you’ve mentioned a word a couple of times in there called algorithms, which I thought was actually a fairly new term, but actually algorithm is, is the word actually about 4000 years old. And I heard a lovely description of what an algorithm is. It’s just the processes that you need to follow to solve a problem. That’s it.
Ron Green
00:14:21 – 00:14:38
That’s it. No, that’s absolutely right. I mean, an algorithm, you know, you could say uh you know the instructions for uh packing your car for a trip and putting the luggage in there. You know, if you wrote down the steps to do that, that would be an algorithm. It’s nothing more complicated than that.
Jeff Bullas
00:14:38 – 00:14:43
Sometimes you feel like you do need calculus to actually get an actual box into the car, isn’t it?
Ron Green
00:14:43 – 00:14:45
Yeah, sometimes it’s like Tetris that is
Jeff Bullas
00:14:45 – 00:14:45
for
Ron Green
00:14:45 – 00:14:47
sure.
Jeff Bullas
00:14:48 – 00:15:10
I was on a trip recently and um there was one of our friends who called himself an expert car packer. So uh it was funny. She says I packed the car, don’t talk to me. And that was telling his wife me, my partner, I’m going ok. So
Ron Green
00:15:11 – 00:15:11
butt out,
Jeff Bullas
00:15:12 – 00:15:20
I just stand and watch with, you know, a slight smirk. Um When things aren’t going well and going. No, I’m not saying, hey,
Ron Green
00:15:20 – 00:15:27
you know what if somebody wants to pack the car, the more power to them. I’m, I’m fine to sit back and watch. I have no problem with that.
Jeff Bullas
00:15:28 – 00:15:40
So anyway, um then we had another experience where someone else was packing the car. And then I’m saying again, I went good and right there’s a certain tension in traveling sometimes isn’t there so. Well
Ron Green
00:15:40 – 00:15:43
yeah that’s right, that’s for sure.
Jeff Bullas
00:15:44 – 00:16:12
Yeah. Alright, so let’s get back to business instead of just going down the car parking route which could be, well we could talk about travel then but that’d be fun but anyway let’s get into um your A I first A I deven and then you there’s two that start ups and I think we’ll just touch on these. Um You got MB and you’ve got Kung Fu A I, so which was first and then we’ll get on to Kung Fu A I.
Ron Green
00:16:13 – 00:16:55
Well, Kung Fu A I actually came first. So Kung Fu A I is a uh engineering and strategy firm. We’re seven years old. We have focused on A I exclusively since our founding. And basically, it’s very simple. We help companies adopt and implement artificial intelligence. So we do everything from A I strategy development to uh custom A I model design and development. Um And it’s funny, you know, at the time you go back seven years ago, a lot of people thought we were crazy. Uh you know, thinking that I really had much of a future at that point, but it
Ron Green
00:16:55 – 00:17:44
honestly, it really was, it did not take that much time, that much insight. It was pretty clear based upon uh the white papers that were being, being published and the successes that we were seeing getting, uh, computer vision and natural language processing models into production that, um, a, I was going to be a really, really, um, legitimate focus for businesses soon and we, and our thought was all right. Well, most businesses aren’t gonna start, they’re, they’re gonna need help and, and we can be that company and now, you know, here seven years later things are, are really taken off and, um, it’s, it’s really, you know, nice to see it all come to fruition. And uh and Beau is a separate company that um
Ron Green
00:17:45 – 00:18:28
uh some of the co Kung Fu A I co-founders started and very briefly and bee is an architecture construction engineering company that is bringing artificial intelligence to that space to basically streamline a bunch of processes that architects do. Like, for example, if an architect changes in their design, it’s very common that they will print all the designs out and you’ll have to go through visually and compare documents, could be hundreds of pages to look for changes or look for uh mistakes, all of that can be automated now using computer vision capabilities. And so that company is focused on sort of revolutionizing that space.
Jeff Bullas
00:18:29 – 00:19:27
Yeah, that’s um it’s just fascinating. Some stuff I wrote but maybe a year ago, I looked at some of the early architecture designs created by artificial A I to create buildings which are very organic and then it was great to do a design but then can you build it. Um That’s, that’s the other question, the other big question. But I, I spoke at uh I did the famous Iranian architecture built this building in Baku Azerbaijan. It’s the most gorgeous building in the world I’ve ever seen. You can walk on it. You can, I spoke in it luckily, and I’m very thankful for that opportunity, but it’s the most gorgeous piece of art. Um That’s a building that I’ve ever seen. And so organic architecture seems to be gaining quite a lot of momentum from my amateur outside looking in.
Ron Green
00:19:28 – 00:20:12
But no, I think that’s, I think that’s very true. And I think part of it is um the, the generative A I um technologies are making that more and more feasible to sort of imagined, blending different approaches and styles and having um you know, these are radically beautiful generative architectures produced by these models. But you, you bring up a good point which is, you know, we’re still in the early stages of this and not all of these gorgeous systems are necessarily um uh structurally gonna be able to be built. But that’s um yeah, that’s a problem. I’m very confident we’re gonna knock out here in the next few years. Yeah,
Jeff Bullas
00:20:12 – 00:20:16
you must, it’s a very interesting ST in, from being involved in starting up that film.
Ron Green
00:20:17 – 00:20:55
Oh, absolutely. We, you know, th th this is what I would tell, like everybody listening to this, you know, everybody, we’re all busy, we’re running our own businesses and everybody’s trying to figure out like, how is a I gonna affect my business. And, and what I always say is it’s pretty simple in that we’ve had software and computers and technology now for decades. But um going back to your comment about algorithms, anything that those computers could do had to be programmed using an algorithm step by step for a human. And my go to example to explain why that was
Ron Green
00:20:56 – 00:21:39
uh a blocker is if you just think about um computer vision, you think about the human eyes and our ability to recognize images. If I show you a photo of an animal, you will instantly recognize what that animal is. And this is the part that blows people’s minds. You won’t be able to tell me why though. Like if I said, write down the steps, like write down the algorithm for why. You know, this is a photo of a cat, no matter what you come up with, you might say, well, you know, I look for the triangle shaped ears. Well, I can show you a photo of a cat where you can’t see the ears. And you’d say, well, you know, I’m kind of looking for a tail or I’m looking for this type of fur.
Ron Green
00:21:39 – 00:22:40
I can show you photos of a cat that don’t have either of those traits and we could go on and on and on, the reason is that your visual cortex, your mind, your brain has the ability to process and understand cats in this general level. In this general way, that is really, really, really deep and rich and we don’t have the ability to introspect our, our, our um our visual system at enough level to understand exactly how it works. And so what that meant was we couldn’t build sophisticated A I systems as recently as 1012 years ago, building a system that could tell you whether a photo was of a cat or a horse or a bird would have been science fiction. Now, with these deep learning A I systems, we’re able to um essentially use machine learning techniques.
Ron Green
00:22:40 – 00:23:34
We can show these models a bunch of examples and tell them what the right answer is and they can learn the more data we give them, the more they learn. But there is a catch, you know, so we can build these really, really powerful A I systems, but they’re a little bit like black boxes just like our own brain. We don’t really, at a deep level, understand how they make their decisions. Even though they’re completely deterministic, there’s no magic, it’s just software, it’s that the calculations, the math and the way the the information is stored is so spread out that it’s difficult um to really isolate and pin down a decision point. And I actually think that’s exciting because to me, it makes me feel like we’re on the right path because it’s very analogous to our own brains, right? Our own consciousness,
Ron Green
00:23:35 – 00:23:53
our, our own um way of reasoning is not necessarily accessible to us from an introspection perspective. And that’s OK. We’ve learned to live with that, right? We go through our whole lives, not necessarily understanding how our own brain works. And that’s not, that’s not a deal breaker.
Jeff Bullas
00:23:54 – 00:24:33
Yes. That’s the black box, the Brian, the black box of A I I, which uh it just does it. Now, the other question I have is: The other question I have is um, so working with companies, they come to you and do they come to you with a problem to solve or do they say, and they say, look, I need A I for this and you look at it and go, no, you don’t need a, I just, you can buy something off the shelf to do this. What’s um, what’s your thinking on that? And what’s your approach? And I think you turned away your first client. I think once that,
Ron Green
00:24:34 – 00:25:28
yeah, we actually did one of the first clients that approached us, wanted us to build a weapon system. Um And they were very sneaky about it and we, um, of course, we were able to suss that out eventually. But, um, you know, your question about what customers look like. Now, that’s a really good one because it’s changed quite a bit, I would say the first five years of Kung Fu A I, almost everybody we dealt with had a specific problem that they were hopeful that maybe A I could solve. And so, you know, people would come to us and they would say, hey, would it be possible to um you know, classify documents using A I or extract information um or identify um fraud or manufacturing defects, all these different types of problems that are
Ron Green
00:25:29 – 00:26:13
readily solvable by A I, but it’s not common knowledge or at least it wasn’t. Then um things really changed after chat G BT, then we started getting people coming to us saying, hey, I need some A I and we’d say, you know, like what do you need? And they would say, oh, it doesn’t really matter. I just need some A I fast because the board uh is breathing, the board’s breathing down my neck, you know, and that, that, that got a little bit weird that period only lasted about a year. And now I think we’re in the realm of where most people get what I can do. Um and they understand sort of the real
Ron Green
00:26:13 – 00:26:41
uh opportunities and values. But there are nuances that they need help with. And mostly it’s about uh determining the, the row I, the return of the investment. If I go solve this problem, how hard will it be and how much value will I get out of it compared to maybe solving these other sets of problems. And that’s the reason we added strategy as an offering a few years ago. And that’s been huge because now if a company comes to us and they say,
Ron Green
00:26:41 – 00:27:24
hey, we want to get on this A I train, we think it’s gonna be important to remain competitive. But where do we start? They don’t have to know we can help them figure out, come up with the whole road map and then go, you know, execute on those individual A I um solutions one by one. And, but it’s very early, the good news for everybody listening to this I would say is most companies have barely started adopting A I if at all. And the good news is because of that, there’s a lot of low hanging fruit. You don’t have to spend a lot of money, you don’t have to do, you know, kind of Moonshot, complicated um projects um to get started. And,
Ron Green
00:27:25 – 00:27:50
you know, the other thing I would say as, as maybe uh words of encouragement which, which is start with something small, go get a win and you will see companies will get confidence that they can do this and it will build up internal support and internal momentum around A I don’t go try to solve, you know, uh one of the, you know, grand unsolved challenges as your first attempt right out of the gate,
Jeff Bullas
00:27:51 – 00:28:31
small steps first Mhm. Yeah. So the other thing that seems to be emerging over the last amongst many, but one is, uh, you have basically open A I where it scrapes a web, all the intelligence is put into, you know, a different genesis and there’s, you know, uh, chat GP T and the list goes on anthropic now. And so these are general open systems where you can, now, what’s happening is that I’m noticing that companies, discussions happening more around companies are actually going well. No, we’re not going to give away all our IP to the open A I,
Jeff Bullas
00:28:32 – 00:28:50
we’re going to keep it within our own boundaries. And because we want to be able to talk enough about what we do to get people excited, but we then don’t want to give away our secrets. So how is that unfolding for you? And is it a big thing?
Ron Green
00:28:51 – 00:29:49
Yeah, I think it is a big thing. Um Open A I showed the way with JG BT that there were really um impressive emergent capabilities once you got to a certain uh model size, once the, the parameter count was high enough and the data set was large enough. And of course, there were other things that they did around alignment that I think were very, very smart to make the systems actually useful to humans. But um meta of all companies really blows my mind, meta and uh you know, the parent company of Facebook they have released a series of models called Llama. And these are really powerful state of the art models that they’ve open sourced. And that is one of the reasons that um closed A I companies like ironically open A I
Ron Green
00:29:49 – 00:30:44
uh and anthropic have not been able to sort of corner the market on uh A I capabilities. And these large language models like Llama are open source. You can use them for training internal chat bots or knowledge systems. And what’s crazy about this is these are incredibly expensive systems to build. For example, right now, Lama four is being built. Um Jan Leon, the chief A I officer at Meta a couple of weeks ago said they’re, they’re training the Llama four right now and it’s being trained on 100,000 H 100 NVIDIA GP US. So that’s roughly about $3 billion in GP U S3 billion dollars in GP US. Um And then once they’re done, they’re gonna open source the model and so
Ron Green
00:30:45 – 00:31:30
that, you know, that is going to be a very, very powerful um capability that everybody will benefit from. And like we were talking about a little bit earlier, A I models now are powerful enough that you can use the previous generation model to help train the next generation. And so Llama four is being fine tuned and trained and approved by Llama three and only because Llama three was so capable, are we gonna have this really incredibly powerful Llama four. And that’s, you know, that’s to me, the positive feedback loop that is unlike all other technologies, sure, we’ve had um computer chips be used to design better computer chips for the next generation.
Ron Green
00:31:31 – 00:31:47
This is different. These are A I systems that are, that are training the next A I system. You know, we’re talking about a direct feedback loop, they’re bootstrapping their, their, their way up. Uh And it’s, that’s why I think the next few years are gonna just be phenomenally. Um amazing.
Jeff Bullas
00:31:48 – 00:31:59
So the Llama system is basically a model that allows you to train data internally in a company quickly and cheaply. Is that really what you’re saying?
Ron Green
00:32:00 – 00:32:50
Yeah, the Llama models, these are large uh multimodal language models. So they understand both text and images and they can both generate text and image uh images and you can use them and they’re open source. So you have access both to the weights and the source code uh and you can use them for commercial purposes. So like one really simple example that a lot of companies are doing is uh take one of these, take one of these llama models and fine tune it on your data. So uh you may fine tune it on all your help desk data. And now you could have a system that could just automatically answer help questions for you, your clients or maybe you could train it on all of your legal documents. And now you would have
Ron Green
00:32:51 – 00:33:16
um, a system that was closed. Nobody else could access it, you could keep it, you know, inside the firewall. But your legal team could now ask questions about, you know, uh service agreements or statements of work or other contracts that maybe the company has signed and have instant answers on because that model has private access to your data set and it’s a really competitive advantage. Yeah.
Jeff Bullas
00:33:17 – 00:33:46
Yeah. And the interesting thing that you mentioned, the law, you know, legal firms and so on, I had an interview with a phd who was a lawyer who was actually interested in A I and we had a chat about and my partner actually, she’s a lawyer, a corporate lawyer. It does big contracts and he said that the big challenge in law with A I is no matter what you do, the review, the review process is actually the big bottleneck. How do you see that? And will that be solved?
Ron Green
00:33:47 – 00:34:30
You know, I don’t know if I would ever say it will be solved because um a lot of, a lot of almost any complicated process involves nuances. And um you know, I think you could argue that a law, you know, is obviously a human construct in that, you know, not necessarily two humans would agree on what is fair or what is right. But those nuances aside, it will absolutely help with things like doing uh, a citation review, finding legal precedents. It will help with, um, going through, can imagine going through,
Ron Green
00:34:30 – 00:35:25
you know, thousands of historical contracts and looking for anomalies. That would, that’s a trivial thing to do now where, you know, even search engines couldn’t have helped you with that before because they could have found words or, you know, keyword phrases, but they wouldn’t have understood the documents at a deep semantic level. And of course, of course, with generative A I, now you can actually generate, you know, uh documents or generate um statements of work or generate uh responses to um legal cases. And so, um pretty much every domain that was blocked by software because it required perceptual capabilities, whether it’s vision, speech, um auditorial, whatever it was, um you know, touch with robotics, all of those are fine
Ron Green
00:35:25 – 00:35:42
getting unblocked because of modern AI. And we’re gonna, we’re gonna be able to build software systems that are much, much more capable of humanlike behaviors because of these new capabilities within an A I that we just literally couldn’t do until very recently.
Jeff Bullas
00:35:42 – 00:35:50
Yeah. So what you’re saying is that A I is gonna continue to evolve and grow more and more, but we’re still gonna need human intervention and oversight.
Ron Green
00:35:51 – 00:36:37
Absolutely. I mean, there’s not, there’s not a question, I mean, there will come a point where A, these A I systems are vastly more intelligent than us. Um That doesn’t necessarily mean, you know, we’re talking about a terminator scenario, but it does mean that we will have a i assistance that will have to dumb things down for us. Right. They will give us advice and they will have to talk to us in a way that we will understand and, and, you know, break steps down, um, it’ll be childlike for them but it would be really powerful for us because it’ll, it’ll, it will be the equivalent of having a super intelligence looking out for us. Uh just, you know, 24 7 make, you know, giving us good advice and guiding us.
Jeff Bullas
00:36:37 – 00:36:41
Yeah. So what you’re really saying is a I is the amplification of humanity, then
Ron Green
00:36:42 – 00:37:12
it’s an amplification of humanity. And I think it’s an extension, meaning we will be able to um not only do things like explore scientific boundaries more quickly, but I think it’s gonna uh expand on the artistic front as well, you know, if you think about music, I’m a, you know, huge music lover and, and, you know, like Bach and Beethoven and Chopin, some of my favorite, favorite composers. Um
Ron Green
00:37:13 – 00:37:40
the idea that these A I systems might be able to compose music, mimicking them. I find that intriguing the fact that it might be able to degenerate music that supersedes them and goes beyond their capabilities, which I, you know, I would use these as examples of the highest quality music ever composed, you know, examples of that, but we might be able to exceed those bounds. I mean, I’m thrilled by that idea. I love it.
Jeff Bullas
00:37:41 – 00:38:40
Yeah. And, and this is the, that segues nicely into the other question I have. I’d like to talk about, which is we didn’t think that I was actually going to be creative. We thought it’d just do maths, research data, all that stuff that is a bit boring but necessary. So where do we, where do you see the role of the machine helping us as humans to be more creative? And you’ve maybe touched on already because you started a company which actually creates A I for architectural creativity. So let’s have a little chat about A I and creativity and the threat it poses to us as humans in terms of if because that raises a big question. This is the thing I’m really loving with the intersection of human and the machine
Jeff Bullas
00:38:40 – 00:39:27
It raises really big questions about humanity. In other words, if the machine can actually create this beautiful building, why as an architect, do I bother if the machine can create a piece of artwork? That looks fantastic. Why should I bother as an artist if I can write an incredible article? Why should I bother writing? I’m interested because for me, part of the discussion in my mind and also with others is that one of the things I love about A I and chat G BT is that I always felt there’s much more trap within me that’s hidden because I’ve been trapped within culture, religion, society. And a way of thinking that puts me in a box that I find hard to break open.
Jeff Bullas
00:39:28 – 00:39:46
And what I love about chatting with others is you actually ask it a prompt and you ask it maybe multiple prompts, but then it almost explodes your fixed thinking by giving you other options you hadn’t thought of. I’d be interested in what you think of the role of A I in human creativity.
Ron Green
00:39:47 – 00:40:42
Uh This is an area that I’m really excited about and, you know, I’ll preface this by saying that it really, it, it, it surprises me and um confuses me when I see people say things like, oh a I will never have, you know, human level creativity because, um you know, unless, unless you believe that, you know, a miracle is occurring inside our brains, you know, and that, you know, physics and chemistry and biology don’t apply and there’s sort of, you know, some really, um uh you know, interdimensional play going on. Um I think we can absolutely figure out ways to extend humans, creative capabilities and imagining capabilities. And so, for example,
Ron Green
00:40:43 – 00:41:31
You know, when the um uh camera was invented, a lot of people thought, oh, well, that will be the end of paintings, right? Why would anybody ever want the painting again? Because now we can just take a photograph. Well, that clearly didn’t happen, right? Um It became um it opened up different pathways and, and artists responded in, in different ways to capture reality in, in more creative ways. And we’re gonna see the exact same thing with I, right? Once we have the ability for these A I systems to, in the beginning, it would be just mostly merging, like you’ll say, well, what if I, what if I, you know, combine these two disparate concepts or approaches and I synthesize them and that alone will be, you know, really creative and interesting. But
Ron Green
00:41:32 – 00:42:11
um long term, I think it’s actually all but impossible to imagine the ways creativity will be unlocked. This is, this is the analogy I always use. We have a as uh humankind, we’ve been locked in one room for all of our existence and that room was all of the things that our own brains were capable of creating, right? We did not have the ability to see beyond what our own uh cognitive creative abilities could conceive and A I is opening this door. And now we’re going to be able to look out and we’re going to be able to not just
Ron Green
00:42:12 – 00:42:56
um for example, hear the best music that humans could, can create, we’re gonna be able to hear music that may be beyond humans, creative capabilities and then we’ll be able to play and merge and collaborate in ways that are frankly I think a noble at this point and everything that I said around art and creativity also applies to science and research and discovery, which is just the other side of that coin. And so I think that we’re really a Daisy Row and we can barely understand or appreciate how um positive, positively disruptive this A I revolution is gonna be.
Jeff Bullas
00:42:57 – 00:43:45
Yeah, I do find it fascinating and I’m reflecting on your comment about the artists’ thought that photography was going to destroy their business. And if you go back to, you know, I’ve been to Europe a lot of times and I wander through art galleries and museums and I see pictures of dead people that’s made by amazing artists and I maybe just not the right sort of arse talk about this. But the reality is that uh they were just capturing the physical view of a person. And what happened is when photography showed up, that job’s sort of done right in a way, except that you get artists that use big, you know, pastel and do capture the essence of someone within an image.
Jeff Bullas
00:43:45 – 00:44:15
And when I started my blog, for example, my partner said to me, she said, why don’t you get a caricature of yourself? And said, well, I’m gonna look gorgeous, handsome and young and beautiful. But so, you know, I said, you want to paint me with a big head and look like a dickhead. And um so what happened was this art friend of ours did a character of me sitting on a pile of books, slapped up in my hand penciled behind my ear, reading a book, whatever it was, I can’t, it’s, it’s there so long. And what was fantastic was he captured me
Jeff Bullas
00:44:17 – 00:44:37
the artist. And I think the other thing we’re moving beyond is just capturing what’s in front of us, which is a physicality which has got boundaries. And once you set something, then it’s actually hard to think outside the box, you become more fixed in your thinking. So what we have is we had us up at Cao said, fuck that. We’re actually gonna do abstract shit, right? So
Ron Green
00:44:38 – 00:45:23
That’s exactly right. It’s exactly right. It, it, it, it took art and direction that it had never gone before. You could have painted abstractly for centuries and millennia. It took the camera to really force artists to think differently differently and explore differently. In fact, I’ll, I’ll give you one more example that I just love. There’s, there’s the game going. This um board game from China goes back millennia in 20. Uh I think it was tw uh 2016 when uh alpha go beat the then world champion, it made a move and everybody at the time thought the move was, was a mistake and that it was
Ron Green
00:45:23 – 00:46:00
um an error on the A A I that move is famously called move 37 turned out to be critical to its victory later. And that move was so radical that it is now taught to all go players. And the game ago was essentially sort of just exploring part of the space it could have been played in. And now that move has opened human players’ eyes and there are entirely new different approaches to the game that were always there, but we just hadn’t thought to explore them until I showed us the way.
Jeff Bullas
00:46:01 – 00:46:52
Well, this is the thing about it. It actually explodes fixed thinking. It challenges fixed thinking because it challenges our purpose. Uh Nick Bostrom, the polymath out of Oxford University, who’d written two books and I talk about him regularly. I interviewed him earlier this year, which I’m really grateful for when I interviewed a polymath from Oxford University. Gee I needed, I did a little bit of preparation. I think so. But what’s interesting is that um he even thinks that humans, the A I could help humans create artificial purpose. Now, I’m not going to go down that rabbit hole because that’s another topic of conversation that um is fascinating but interesting. Um But the other thing I attended a conference event this last Friday, actually in Sydney,
Jeff Bullas
00:46:52 – 00:47:20
ah where Deepak chopra is very much about Eastern philosophy and wholeness and Oneness and Eastern Practice meets modern science. Um He’s written a book called Digital Dharma which is basically the role of A I in helping us discover and enhance our completeness and oneness with the world, with the universe. Um And it was fascinating to go and hear him speak. So here we are. We’ve got Eastern Philosophy meets A I
Jeff Bullas
00:47:23 – 00:47:25
and it’s looking fantastic
Jeff Bullas
00:47:28 – 00:48:03
and this is where we’re going, we’re going into the unknown. The box has exploded. And uh and when I saw a chat with you for the first time, two years ago, I went, this is going to change the world. And my editor said to me, she says, I’m going to lose my job. I said, no, we might have less writers, but we still need human senior editors. So, but yeah, for me, this is just mind boggling. I couldn’t agree
Ron Green
00:48:03 – 00:48:08
more. I couldn’t have been more. And I share all of your uh all of your optimism. I really do.
Jeff Bullas
00:48:09 – 00:48:15
It’s just um I’m so grateful to be alive in this time, the spot I
Ron Green
00:48:15 – 00:48:50
I am, too. You know, you’re talking to somebody who uh you know, if you’d asked me in the nineties when I was dying and it, but it was my passion. I’m not sure I would have bet on this resurgence. Um And I feel unbelievably grateful that I got this opportunity that the field did resuscitate and lo and behold, all the things I was passionate about. You know, all those years ago are exactly the techniques that are the engine behind this modern A I revolution. So I’m, I’m, I feel unbelievably fortunate.
Jeff Bullas
00:48:50 – 00:49:34
Yeah. So we are both out and it’s just a fantastic time to be living. The other interesting thing is just reflecting on a IA I wouldn’t happen unless there was an intersection of technologies. OK. It would not exist without the web because you actually can’t get enough data. That’s right. You couldn’t get it without GP U or CPU technologies that exist that came out of gaming. That’s exactly right. So what else, uh What other technologies have intersected to create the opportunity? I think neuroscience is another one. In fact, I don’t know how many there are, but that’d be an interesting article actually, what technologies have made A I today possible,
Ron Green
00:49:35 – 00:50:29
you know, I think that there are, I typically say there are six major things and you already listed some of them, one of them. Um And, and this is maybe arguably the most critical is massive digital data sets up without the internet and everything being available online that doesn’t happen giant jump and compute that GP US gave us, you know, back in the nineties we thought, oh, if our computers were 10 times 100 times faster, we’d be in good shape. Now, we were, you know, millions and millions and billions of times off. But the other ones that are really important are there are some algorithmic advances, like really simple things like R and badge norms. I’m not even including attention mechanisms, but that’s clearly important too. And then the other three don’t get as much
Ron Green
00:50:30 – 00:51:29
attention. But I think they’re equally important open source open source machine learning frameworks, um uh especially the ability to do auto differentiation, which means we can now create radically new experimental architectures. And we don’t have to worry about calculating the gradient for back propagation. All of that math is done for us automatically for free. Um And then the other two are um transfer learning, the ability to train models to do some specific task and then leverage those models for some other unrelated task that they can actually do pretty well because they were pretty trained on this other one. It saves an enormous amount of time and money and is a part of the revolution. And then lastly, I would say this technique is called self supervised learning. The ability, the ability to train um a large
Jeff Bullas
00:51:29 – 00:51:32
is that spelled. So the acronym for that is self, self,
Ron Green
00:51:33 – 00:52:24
self like self supervised learning. And you know, the big models out there like Claude from Anthropic and Chat G BT from MO A I, you know, it, it, it’s, it’s not, it’s it’s slight exaggeration to say they were trained on everything on the internet, but it’s to a close approximation. That’s true. Well, how in the world would we label all the data on the internet? Because you know, these models have to be told what the right answer is. Well, using these techniques of self supervised learning where you do uh you do sort of, you know, sentence completion or masking of these other tasks, you actually can train on these enormous data sets. And that uh that’s what has enabled these emergent behaviors at really, really large scales. So
Ron Green
00:52:25 – 00:52:35
um I, I could go and there’s, there’s more, but I, I think those half dozen are the key, the, the, the six that I think are most responsible for the current A I revolution.
Jeff Bullas
00:52:36 – 00:53:26
And then outside those six, for example, there are supporting technologies which have been around for a long time. But an evolution of those technologies such as energy production, electricity. In other words, we wouldn’t be able to produce the vast amount of energy required to actually keep this moving forward, which is nuclear energy which becomes a bit of this. But and then on top of that, we then have renewables which then are also. And in Australia, we’ve got shitloads of desert here and lots of sun and we’re maybe in a position to be that. But so yay, yeah, like you said, there’s many more intersection technology that allows A I to thrive and grow and at the velocity it is.
Jeff Bullas
00:53:26 – 00:54:27
So I’m fascinated by those. So I’ll just sum them up everyone, datasets, huge data sets, uh computing processes, especially you know, the uh CG US or Sorry. Yeah, GP U GP U sorry uh computing processes uh algorithmic um development, open source machine learning frameworks, transfer learning and self supervised learning. Um That’s so, that’s it. And I was intrigued because I was just thinking about today, what technologies have allowed A I to thrive and explode and you’ve summed them up into six. And this may be primary and then there would be secondaries as well. But the mobile phone and the smartphone and the Apple phone, they were the intersection of technologies that turned up and made things different. There’s a whole range of things you won’t even go there. But the other thing I want to touch on too is
Jeff Bullas
00:54:28 – 00:55:15
the latest phase of A I amongst a lot. But there’s one I think that’s standing out a bit at the moment is the rise of A I agents. In other words, not only can you come up with an idea, but I can act on the idea by plugging into it. In other words, you’ve got no idea how I want to start a business. So I want to create a website. I want to create a sales funnel. I want to, I want to source stuff from Amazon. I want a machine to go out and find them for you. So, and an example of that, I think chat has already put that in place. I think Genesis has just put it in place, but also sales forces in place just announced a what they call agent force.
Jeff Bullas
00:55:15 – 00:55:47
So, what’s great about A I? It allows you to create ideas, create content, and media information. But the challenge with being human is that quite often we just sit on our ass and go. That’s a nice idea. But actually don’t do anything where the A I agent is the one that can kick your ass and go and do it for you? OK. So will we have a one person billion dollar company that the A I agents?
Ron Green
00:55:48 – 00:56:06
Yeah, I’ve, you know, I’ve heard of this, you know, this idea of like use A, I use generative A I to help you think of a new business, come up with a business plan and then use it to sort of make your business decisions, right? You, you tell me, tell me how to allocate my money. Tell me how to prioritize my future
Jeff Bullas
00:56:07 – 00:56:08
idea of this product.
Ron Green
00:56:09 – 00:56:55
Exactly. Yeah, exactly. You know, obviously it’s early days for that. But um, if we are on the path that I think we are too super intelligent, you know, a GIS then, yeah. And like I said earlier, you know, it may be the case that it’s gonna have to dumb it down for us and say like, all right, don’t mess up this business plan I’ve created for us. You know, you need to execute, uh you need to execute on it uh the way I’ve specified. So I think that’s totally possible. But in the short term, what’s really interesting is this agentic A I that you mentioned, the idea that these um large language models are more and more capable of generating like fully functional websites or generating applications just from the description.
Ron Green
00:56:56 – 00:57:46
And then uh Anthropic a few weeks ago announced um that they’re enabling uh uh sort of control now. So you can essentially interact with Claude and have it do things um that are sandboxed. So you might have, you might give it access to a spreadsheet and ask it to manipulate data within that spreadsheet. Go search the internet, come back, pull together some, you know, maybe some interim report and create analysis and present it to you, right? And so actually manipulating web browsers and doing search and clicking and clicking on buttons and things like that, those are things that all of these models are going to be capable of in 2025.
Jeff Bullas
00:57:46 – 00:58:15
Yeah, it just blows me away where it’s all going and um and I’m just so grateful to have the opportunity to talk to smart people around the world that especially and we’re leaning into the whole A I topic and how it impacts humanity, business and the world. Um One of the interviews we’ve recently is um a company that’s doing created a financial app to help people make financial decisions um by using A I to actually make sense of the confusion and complexity of investments um
Jeff Bullas
00:58:16 – 00:59:05
and also to prompt them as well. So it’s called Muller dot A Imool A, so that was fascinating. We have d A chara going into Eastern Science, Eastern Philosophy meets Western science and then how can we blend them to actually make ourselves bigger and better people and humans, hopefully, and a better world, of course. Um So yeah, we’re just, we’re moving into the future so fast and the trouble is as humans, we’re actually not wired for that fast change. So our DNA and genes I think are gonna still struggle to keep up with the evolution of the machine because most people, most people hate change. And um
Jeff Bullas
00:59:06 – 00:59:29
and yeah, and that’s the sort of thing that one of the big questions I have is how do you help humans manage change and create change? Um I’ve been faced with many changes in life and someone, a friend of mine uh was speaking to another friend of mine said, what does Jeff do? And he says, well, I don’t know exactly what he does, but he just keeps evolving and changing. And I went, I quite like that. Um
Ron Green
00:59:29 – 00:59:59
I love that and I think that’s a great philosophy. Um I, I get it, a lot of people do um resist change but I, you know, for what it’s worth, I love it. I wanna keep growing. I wanna keep evolving. I wanna keep exploring and um I think that’s part of the reason I love the tech business so much because men talk about something that doesn’t stop evolving. And then, uh A I is just, you know, that cranked up to 11. So it’s unbelievable.
Jeff Bullas
00:59:59 – 01:00:47
Yeah, I started my career as a high school teacher and I did about five or six years and I looked at fellow teachers that were very old. They were in their forties and, and they looked tired and worn out and the curriculum never seemed to change. And I said they’re teaching stuff to students that the students are always asking, why are we learning this? And that was a very good question. And because you have to, it’s actually not a very good answer. So for me, I felt that we were being the curriculum and what we should be learning as humans was being imposed from on high by the professors who had never left school. Um, telling us what is important in life. And I’m going, uh you guys have no fucking idea. Um You never left school. There’s a problem straight away. Oh, a
Ron Green
01:00:48 – 01:00:50
100%.
Jeff Bullas
01:00:50 – 01:01:51
But the reality for me is that, um I, I know I, during my school holidays as in teacher, school holidays, I discovered I quite like selling. So I checked out three different industries, life insurance, real estate, and tech sales. And I just looked at the other two and it just didn’t feel right for a variety of reasons. And tech just felt interesting because it looked like it was the future. And I watched the moon landing in 1967. I think it was 6969. Was it? Ok. So, and I went, wow, so deep within me. And we don’t know what’s within ourselves. Right. We’re sort of like this because we’re given a path, parents society trying to break those bonds happens sometimes, ah, either you choose to blow up your life or someone chooses to blow it up for you.
Ron Green
01:01:52 – 01:01:55
Right. That’s so true.
Jeff Bullas
01:01:56 – 01:02:00
So, for me, I chose technology in 1984
Ron Green
01:02:01 – 01:02:14
and you made a good choice. I mean, that is, uh, that is definitely a, a domain that has, um, um, uh, expanded and evolved quite a bit more than insurance. I think
Jeff Bullas
01:02:14 – 01:02:46
It’s 6%. Well, the other thing I love about it too, amongst many is that I now am surrounded not by old, 40 year olds and 50 year olds and 60 year olds who look like they got 1 ft in the grave and, and have been frozen inside. Not, and this is not anything against old people because guess what, I’m, I’m one of those. But the reality is that I’m surrounded by 2030, something that just, I’m just blown away by their curiosity and drive, which I had when I was 2030 still had. But, yeah,
Ron Green
01:02:47 – 01:03:11
Oh, I’m so lucky at Kung Fu A I I literally work with some of the smartest people on planet Earth. I mean, just brilliant people with phd S. Um And it is no exaggeration to say that one of the reasons I am so excited to get out of bed is to work with these young dynamic, incredibly smart, kind people every single day.
Jeff Bullas
01:03:13 – 01:03:31
I think that’s actually a nice segue to our final question, which I asked you at the beginning. I think I have a hint of maybe some elements of it that you are going to say, but I’m not going to say that. Um I’m gonna let you tell me and our audience if you had all the money in the world, Ron
Jeff Bullas
01:03:34 – 01:03:56
and you didn’t need to work again. Slash work is a suitcase where that means many things work, national purpose, they all work. Um If you had all the money in the world, what would you do every day? That would bring you deep joy and it can be a singular singularity or it could be a multi, multiple answer. Ok? I’d look forward to what you say.
Ron Green
01:03:57 – 01:04:58
I’m, I’m extremely lucky and I kind of hinted that hinted at this earlier, the fact that all, you know, all I wanted to do my whole, you know, adult life was work in artificial intelligence and I’m really close to doing exactly what I would want to do. Probably the only thing I would tweak is now that artificial intelligence has achieved human level capabilities on many fronts and superhuman level capabilities on other fronts. Um I am increasingly interested in leveraging its predictive capabilities for science specifically within biotech and healthcare and, you know, to be a little bit more specific. Um I’ve got, I’ve got a background in genomics and um
Ron Green
01:04:59 – 01:05:33
the ability for these large language models to take in enormous context windows of uh DNA or amino acids um or whatever it may, may be and do protein fold prediction or, you know, genetic analysis. I think that we’re right on the cusp of that being sort of a breakthrough domain. I say that because of the capabilities of alpha fold which essentially solved one of the grand, you know, challenges of biology recently
Jeff Bullas
01:05:33 – 01:05:34
and Nobel Prize
Ron Green
01:05:35 – 01:06:32
and, and, and rightly so, in my opinion, one Osim and jumped the Nobel Prize in chemistry for the work there. Um I believe that we, we have some, we have some wood to chop on some of the technical blocking issues there. And I wanna be a part of that and I want to um um provide A I capabilities. So we’re gonna uh essentially give us a generational jump in the treatments we’re gonna have for uh diseases and genetic therapies and things like that. And I can hardly think of a better way to spend my time because it would be leveraging my professional expertise and uh at the intersection of my personal passion and then also making the world a better place. So that is not only what I would do, but it is probably what I’m gonna do.
Jeff Bullas
01:06:32 – 01:06:36
Ok? It sounds like a start up bubbling away inside your brain.
Ron Green
01:06:37 – 01:06:38
I think it is.
Jeff Bullas
01:06:40 – 01:07:05
Yeah. And, and thank you very much for sharing that. And um I think making a difference and um, almost everyone I interview will chat to because it’s not an interview. Uh, you don’t have to pass anything. We just do fireside chats, um, telling stories, um, is, uh, you know, that most of the entrepreneurs and people I talk to are already doing what they love to do.
Ron Green
01:07:05 – 01:07:07
I know. It is kind of funny.
Jeff Bullas
01:07:07 – 01:07:41
Yeah. And it’s sort of hats off to you and all those that have come before, um, that, uh, have leaned in to do what they love doing rather than wait till they have 1 ft in the grave. And, um, so a lot of people go to their grave with their song unsung and that’s very sad. Um, but that’s the way society is made up of them as well. But yeah, for a I, for me watching A I and is that, you know, humans are pattern recognition machines, but A I has taken that pattern recognition to a whole new level and we are
Jeff Bullas
01:07:41 – 01:08:14
see more truth and signal in the noise of the huge data sets that we have today that the machine helps us find the magic. And some of the conversation I had with my writing buddy who’s 22 years younger than me. He said I just need to live long enough so I can live for a lot longer. And, um, so, um, don’t know how that’s gonna look. But, um, yeah, yeah, I think the other thing that I sense with you is just this burning curiosity, which is just fabulous to see.
Ron Green
01:08:14 – 01:08:23
No, thank you. Thank you. It was just a pleasure to chat with you today. I had to, I had a complete blast.
Jeff Bullas
01:08:23 – 01:08:41
Yeah, so did I, the blast was the word I was gonna use because that’s the same for me and I use a very overused water word it’s called Awesome. Um That’s also what it’s been for me today. Really? It is. Well, you know, I’m maybe an eighties child because I escaped in the eighties. So there you go. There you
Ron Green
01:08:41 – 01:08:42
go. There you go.
Jeff Bullas
01:08:43 – 01:08:50
Thank you, Ron. It’s been an absolute pleasure and a joy and um I, I’ll do this for free for a long time.
Ron Green
01:08:51 – 01:08:56
Thank you, Jeff. I had a tremendous time. I really, really appreciate you having me on. Thanks
Jeff Bullas
01:08:56 – 01:08:57
John.