Rishal Hurbans is the author of Grokking Artificial Intelligence Algorithms. He walks us through how to learn different Machine Learning algorithms. He also then walks us through the different types of algorithms based on different natural systems and processes.

Hey, everybody, and welcome to another episode of Adventures in Machine Learning. I'm your host, Charles Max Wood, and we're talking to Rachael. Now you're in South Africa, which is a place I've always wanted to visit. I I think it's the videos of the sharks jumping out of the water and eating the seals or whatever that I've seen from down there. But you're the author of rocking let me see if I can get this right.

Grocking artificial intelligence algorithms for manning, which makes you an expert. Right? Because you wrote you literally wrote the book on it. But, yeah, I'm super excited to talk and just kind of figure out what's in this, in these algorithms, how to approach them, how to understand them and things like that. And and, yeah, just welcome to the show.

Great. Thanks for having me, Chuck. One thing around expert, actually personally kind of, really dislike that word. Ninja. Do you prefer ninja?

I dislike all those words. The reason being is I think, you know, it's just a personal thing. I think everyone, including myself, always has something to learn in whatever space you're working in. And when you say expert, people think you have every single answer to every everything. Yeah.

Just an aside. Yep. Yeah. It's funny how how people approach that, you know, for some people it's, you're an expert because you know a little bit more about a thing than somebody else. Right?

And then for others, it's yeah. It's this, Well, yeah. I can ask you a question. You just know off the top of your head. Boom.

Right? Anyway, so, yeah. In that case, maybe that's a case of of human artificial intelligence. Anyway, let's dive in and talk algorithms here for a minute. So we've had a lot of conversations with people over the last few months that we've been doing this where we've been discussing, like, specific use cases for artificial intelligence, or maybe we've talked in general about a particular algorithm or approach to solving a particular problem.

What's the focus of your book, though? Because it seems like it's a little bit different from that. Yeah. So it's quite interesting. I mean, I didn't actually put the idea to write this book.

It it happened from I'll be giving a few talks at some conferences introducing neural networks to people, and it was mainly developers. And it came from, I guess, something I saw happening around me and in my work and the community I'm I'm kind of part of, where people felt quite afraid to to experiment with with these techniques or technologies because it was really math heavy. Or a lot of the literature and material you'd find is really math heavy. So I think after doing a few of these, Manning approached me to, you know, see if I'm interested in in maybe writing a book with them. And this whole book is essentially a visual tutorial on various artificial intelligence algorithms.

Right? And AI, that's a artificial intelligence. I think everyone has a different definition for it. But basically, this book can yeah. This book contains not just kind of your typical machine learning and neural networks and reinforcement learning, but it starts at the beginning with kind of how do we solve problems in general?

So it starts with introducing the intuition of thinking about data and thinking about algorithms and computing and problem solving. And then it kind of goes into search, which is something we've solved a long time ago, but still something very powerful in a lot of applications these days and something that I'm really passionate about, which is nature inspired algorithms. So, you know, we've got genetic algorithms, which is based on the theory of evolution. There's ant colony optimization, which is based on how ants actually move around in the colonies, ask for help, collect food. And then there's also particle swarm optimization, which is is based on how birds and bees and and those kind of things actually flock.

So, essentially, in a nutshell, it's a kind of gentle introduction to these different algorithms that are where the teaching method is more focused on kind of visual learners, I would say, as well as, you know, kind of practical examples that you can work through rather than, you know, theoretical kind of math proofs that you that you would study. Right. That makes sense. I kinda wanna roll back to the initial approach that you had as far as just talking about how we solve problems and things like that and how that kinda leads into some of these algorithms. So so what does that look like?

We're kind of laying out the fundamentals here. What where do you start with people? Yes. I'd say chapter 1 is really focused around building the intuition about, and the mental model about these different words that you might come across when learning about AI or machine learning or whatever the case might be. So kind of creating the taxonomy and this model of how things fit together.

That's basically what chapter 1 is centered around. And that's important to to kind of understand the concepts further along in the book. And it's kind of written in in a way where each chapter kind of has concepts that build on another chapter. So I might've mentioned that, you know, it's not really math heavy. It's not to say that it doesn't contain any math.

It does introduce the math to the reader, and you might find that something introduced in chapter 2 is useful in chapter 3, and so on and so forth. So that's the kinda kinda makeup of it. Right. So how how do you start thinking about about different algorithms? You know?

Because it seems like different algorithms are kind of so, yeah, they're focused on specific types of problems or specific, maybe solutions to a wide breadth of problems. So how do you start thinking about the different algorithms and whether or not you need to know them? Yeah. So there's this concept of old AI and new AI. I'm not too sure if you're you're familiar with that, but old AI is essentially when humans, you know, or programmers would actually figure out what the rules should be for a specific algorithm.

Examples of this are things like expert systems, where you have really complex rules, a really complex rule set, and the rule set might even be a little bit dynamic. But you always understand what the inputs are going to be and what the possible outputs would be given some input. Right? You know, so things like search algorithms that are spoken about in the book are really useful there, and that's useful for when the problem is is kind of well defined. So if you're trying to build a bot that plays chess, I know chess has been a a buzzword these days with The Queen's Gambit being really popular on Netflix.

So if you use that as an example, the rules of chess are well known. The the problem that we're solving is looking into the future of all the possibilities of moves. Right? Right. Whereas if we're looking at, say, Spotify recommendations, you know, you're listening to different types of music.

I don't know about you, but I have really diverse kind of taste in music. And I'm pretty sure that algorithm struggles a little bit, but it's still pretty good. You know? So the inputs there and outputs are, I guess, kind of well defined, but how you get there is a lot more complex. Does this person like the tempo of the music?

Do they like a specific artist? Do they like the genre? Do you know, there's a lot more unknowns in that case. Whereas when you compare it with chess, the rules of the game are quite well understood. So I think, you know, when you do have a fairly known problem space that you're working in, a classical algorithm like search might be useful.

Whereas things like neural networks and reinforcement learning, you know, have been really powerful when you have a lot of unstructured data, unknown kind of rule sets, and you kind of wanna find patterns in that data to inform future decisions. Right. So that makes sense. So then what algorithms are available for that kind of a thing where you're looking for those patterns and picking up the the parts that you're gonna use to make those recommendations? Yeah.

I mean, that's I guess there's so many options. Right? And that's, I guess, part of the point of the book is to kind of inform the the reader about what what would be useful to use in in what scenario. So, yeah, I've just pulled out at the back. There's a quick summary of it.

I don't know if you can read that. No, but if that's your book, people can go get it and we'll have a link in the show notes. Yeah. Yeah. So basically, it's it's a bit of a guideline on what to use in what, you know, scenario.

So, for example, I'm pretty sure you're familiar with linear regression. It's a very popular algorithm machine learning, and this is really useful to find correlations and data. So do you want to see if a bigger diamond means that it will sell for more money? Right? Right.

That seems pretty obvious. But what if you take into account this the the quality of the diamond, the clarity of it, the color of it, where it was sourced from? You know, the same algorithm, but adapted to to deal with multiple parameters would be useful in that case. So, yeah, I would say it's it's kind of based on how well you know the problem space and if you have data available to train. If you don't have data available, you should have rules to govern it.

And, yeah, the different approaches would be applicable in those different cases. I gotcha. So, yeah, you you've got kind of a cheat sheet there as far as, you know, here are the algorithms that you may wanna use, you know, under these specific circumstances or with this kind of data or things like that. How do you start to understand how they work? Because I've I've had it explained to me by some people that some of these algorithms feel a little bit like a black box.

Right? You put the data in. You know how it's set up, but you don't exactly know how it comes to the conclusions it comes to. Yeah. So a lot of that is explained in these chapters.

But one that I'm really passionate about, and maybe maybe it'll be really interesting for you is genetic algorithms. Don't know how much you know about that, but Not a lot, really. If you think about the theory of evolution from Charles Darwin, Basically, what he says is every organism, it's about survival of the fittest. So Mhmm. If if you're a, a fish, for example, if you can get more food, you will survive longer, which means you will reproduce more, which means you're right, means your genes would be carried on further.

And basically, you know, that's a kind of running theory of how we even evolved. And and generally, what happens is you have 2 parents. Okay? So you have one individual that might be really good. Let's say let's say this individual is really good at swimming.

Another individual is really good at climbing. Theoretically, if you mix the 2, you get a child that's good at swimming and climbing. Right? And that's essentially how genomic algorithms work. They randomly generate solutions given certain constraints.

So, you know, you might have a bit string that represents you know, in the book, it's a knapsack problem, but it could be you know, a toy example could be the stock market. Do you buy, hold, or sell? And you have a bit strength saying, b for buy, h for hold, s for sell. You have a bit strength for a certain time frame, and you say, cool. This is a solution.

Let's apply that to Apple stock and see, you know, if it's gonna make us money. And you'll generate thousands of those solutions, and you evaluate them with something called a fitness function. And in the case of that stock example I just gave, the fitness function would be, you know, after replaying that sequence of buying, selling, or holding, how much money did you make? Obviously, the more money, the more fit that individual is. Right.

And then what you do is you'll you'll kind of choose one of those good parents and another parent. You'll mix them, and you'll make a child. That's generally done by kind of taking the first half of of the one string and the second half of the other string. And that's the general intuition of how genetic algorithms work. And the reason I wanted to mention that kind of metaphorical example is because that's basically how every chapter starts.

It starts with this, something you can relate to. So in this case, there's a story about moss. You know, there's an actual actual proof of that way. There was a there was no population of black moths. There was a lot of white moths at a certain point in time.

And through industrialization, you know, things became black because of smoke. Right. The industrial revolution and the moss actually over generations adapted and they changed color to become black so that they're camouflaged with the environment of buildings and things. And that's an example of how kind of that concept's introduced. But what I really like to do as well is create flowcharts of how the algorithm works.

So rather rather than it being a black box or I'm pretty sure for a lot of people, a lot of the time, a math mathematical formula can look like a black box. You know, just looking at a complex formula, you know, a lot of the time you get kind of confused. I don't know what's happening. But what I like to use is flowcharts to show you what's happening at each stage. And there's also pseudo code.

So regardless of what kinda programmer you are, maybe whatever JavaScript, Python, c sharp, Java, it doesn't matter. You'd be able to kind of follow that flowchart and follow the pseudo code to actually implement the algorithms yourself. Yep. Yeah. That makes sense.

And the I I think that's really where things kind of broke down for me was just, you know, I go copy an algorithm or go copy some library somewhere. Right? And I pull it over into what I was working on, and I'd get the result I wanted, but it was magic in between. Right? And I like that idea of, yeah, just putting it into a flowchart and just walking through it.

I mean, even on paper. Right? And just kinda go on, okay. I'm I'm kinda getting the pieces here for what's, you know, what's going on, and I can see step by step how it's adapting. Right?

And so, yeah, it's it's got some waiting in there and some other things that it's doing, but as as it kind of moves through it, it's like, okay. You know, this is selecting for this, that's selecting for that. And at the end of the day, you know, you wind up getting the right answer, basically. Or, you know, you wind up getting a good answer in some cases. Right?

Like the recommendation engine. I don't know if there's a right answer, but there are better answers than others. And so, you know, I'm getting a good answer. I'm getting an answer that I'm looking for. And at the end of the day, I could see in stages how it got there.

Right? Yeah. Yeah. And I'm a very, very visual person. I really like, like, kind of representing things visually And related to what you're talking about, for example, in the neural networks chapter, you actually see each node in the neural network, each, I guess, hypothetical neuron change value.

Right? And you'll see exactly what numbers kind of got inputted and manipulated to change the value of that neuron. Whereas typically, when you're looking at a neural network, you give it some input, you give it an output, and the rest is magic. What I try to do there is, you know, kind of give the reader a window into what's happening with each and every element in that network. And let me be honest, most of the time when we're building solutions, you know, in the real world, we very rarely need to go and understand those calculations, what's happening at that detailed level.

But I think that by understanding it to a certain degree, you have a level of confidence and also ability to kind of debug and investigate and and figure out what's happening in there if things do go wrong or if you don't get the results you need. And you know, something that's becoming quite, I guess, popular, prominent as we move forward is the kind of, I guess, legal and ethical concerns around machine learning and AI. Right. You know, some, I guess, regulations or proposed regulations want companies to be able to tell the customer or some authority, you know, what what did that algorithm do? What did that calculation do to come up with a decision?

You can't just say, well, it learned from data. They they want you to be able to kind of reason about why it's making certain choices. Example of that is, I think there was a story about an organization that seemed to have unfair loan grants. You know, it was granting some people loans and other people it wasn't, But they were in, you know, the parameters are quite similar. They couldn't kind of explain why that was happening.

And, you know, that's Fox concern. If you don't know why it's happening, how can you make the decision? And I think if, you know, a lot of the time as as developers or engineers or kind of people that are implementing tech, we often forget that we are, in some shape or form, also responsible, even partly for Mhmm. Those outcomes. So the better we can prepare ourselves, the better we can understand what we're doing, the better we can make those decisions for whatever the outcome would be.

Yeah. That makes sense. I mean, for me, if if it is or isn't a good you know, for the loan, for example. Right? If it's gonna be a loan that's gonna be paid back on time, blah blah blah, you know, I get that you want some kind of qualification.

But, yeah, the computer told me no isn't necessarily a good reason to give somebody. The other thing is is that I think we all have this innate idea around fairness, like, what's fair and what's not. And so we always wonder a little bit, oh, well, is it because I'm from this place or because I look this way or because I, you know, I I want to do something that's unconventional? And that's why it's telling me no. Right?

And so in those cases, it may not be fair. Right? Because somebody else who has slightly different circumstances than me that may or may not actually impact their ability to pay it back any better than I can may actually get that loan, and that's what people worry about there too. And so by having it this opaque system, it's hard to know that it's fair, and and that's what I think we see a lot of people kinda get hung up on. Where in reality, it probably is measuring 95% of the stuff it's measuring is legitimately plays into whether or not, you know, historically, other people have paid back their loans.

And so it's this interesting balancing act, I think, just from the standpoint of yeah. You know? Is this is this the right thing? Is it doing the right thing? And these algorithms don't have a sense of morality.

They don't have a sense of fairness. They don't have a sense of whatever. Right? They work off of, you know, how you set them up and what you train them with. Yeah.

I agree with that 100%. I think, you know, especially with kind of enterprise at the moment, there's a little bit of an obsession with automation. Mhmm. You know, and we we look at maybe some of the role models, you know, the giants like Netflix and Spotify and Google and Facebook and all those companies. And, you know, they automate a lot of things.

And I think there's a place for automation. But I think, you know, for the example you just mentioned, you know, shouldn't that just come down to human judgment? Like, let the computer give you an output. Okay? Maybe not as as, you know, black and white as yes or no.

Maybe, you know, give you, you know, an indication or. Yeah. Yeah. And let let a person go and look at the details, go and look at the application, go, you know, do what they need to do to make an informed decision. I mean, when you need a show recommended to you on Netflix, the consequence of the recommendation being bad is is very low.

You know, maybe you waste 15 minutes watching something that you didn't want to watch. But, you know, there's much more severe consequences in in other areas, like finance or health care. And I think, you know, people gotta be careful, especially decision makers in in these organizations. Like, there is this obsession to automate things because when the streamline things, we're gonna be more efficient, but that doesn't mean you're gonna be more effective. You know, you like you said, you can't simulate morality in a machine.

Yep. I think beyond morality, it's, you know, I think we're pretty amazing. You know, as humans, we're able to put together these abstract concepts and string together these relationships. And we can't explain what's going on in our heads, but we can reason about it that way. Whereas, you know, the computer is going to do what you ask the computer to do.

And if it has if it sees something new and it doesn't have data, let's say we're doing, you know, deep learning or something, it sees something new, but it doesn't have a story data that can inform what that means. It's not going to know what to do, whereas, you know, generally, we do. Yep. So I really like your approach to kind of breaking down these algorithms and then thinking about how they come into play and, you know, some of the ethics and morality around this stuff. But I wanna kinda switch gears back to the algorithms themselves.

So we've talked about evolutionary algorithms, right, where it's, okay, we take, you know, kind of half of this approach and half of this other approach or half of this information and half of this other information and and, you know, kind of play it it through the system and see how it works. And I'm pretty sure we've talked a lot about neural networks. That's something that it seems like a lot of people are are working on right now. But you've got this swarm intelligence. You've got ants and particles, and you mentioned them at the beginning of the show and they're in your book.

So how do those work? Like, how were those different from the evolutionary or neural networks? Cool. Yeah. Let's talk about the ants, ant colony optimization.

And what's really cool about that one is it's almost identically mapped to how ants operate in the wild. So if you've ever seen a trail of ants walking in your kind of garden or on your patio or something like that, you might notice that there was keep this kind of single file trail that they're following. Right. And why they do that is something called pheromones. So ants drop pheromones and they have a variety of different pheromones.

And pheromones are just you can see it as perfumes. They each have a different kind of scent, and an ant will drop a pheromone and another ant will will pick up on that pheromone. So the reason you see this trail of ants, not to say that no ants walked any anywhere else. The reason they they keep on that trail is because the pheromone intensity is the strongest on that trail. Right?

Right. And what what we found through studies on ants is that ants actually find the shortest path between a food source and, you know, their their home or their colony. So and they use this concept of pheromones. So imagine a few ants take 2 different routes to get you a a food source and back. The ants on the shortest path actually end up leaving behind stronger pheromones.

And, obviously, the more ants on that trail, the more intense that that trail of pheromones becomes, and that becomes the shortest path between the food source and the colony. Yeah. And, essentially, the the ant colony optimization algorithm is really great at solving shortest path problems. I'm pretty sure you've you've you might have heard of the traveling salesperson problem, where, you know, a salesperson has to travel to a 100 different cities, but they have to minimize the the kind of total distance traveled. Yep.

This algorithm is perfect for solving solving those kind of, problems. It's also been used to solve, telecom network routing problems, believe it or not. Mhmm. And basically, the algorithm works, in a way where you simulate these ants. You basically deploy and starting at different or going to different locations.

And then they move to to another location based on an element of randomness and the pheromone intensity on that path. And the emergent behavior is that the shortest path between all of those different nodes, will become apparent after a certain number of iterations of the ants, moving around. So that's quite a fascinating algorithm that solves, you know, important problems in in computer science and in, you know, real world application. But it's really cool that it's inspired almost directly from watching real life ants work. Yep.

That that's fascinating. It it kind of I I have Google Maps in my head from that. Right? But, yeah. Yeah.

There are there are other uses besides I I guess you talked about, the network. Right? And that matters because you can get data back faster, right, if you go the most direct route. Other places where this algorithm is used? Yeah.

I think it's generally most useful when you're trying to find the shortest path, whether that be, you know, networks or or other, other problems. Maybe you're a logistics company that needs to route your trucks, you know, to optimize fuel, optimize deliveries. You know, the the algorithm can be useful there. It's also been used in strange ways for, for example, edge detection of images. You know, you can manipulate an image based on the pixel intensity, the, and the, the pheromones along the edges of, of a picture might get, I guess, highlighted.

And people have used that algorithm to to, yeah, do edge detection in images. So yeah. I get but that's I guess that's a bit of an outlier. There's there's much better ways to do edge detection or let's say more efficient ways. Makes sense.

What's the particles algorithm? Yeah. Particle swarm optimization is based on, you could say, the flocking and behavior of birds or bees. So, essentially, when you have a very large solution space, right? So imagine that.

Well, the example I use there is you've got a drone. You're trying to make a drone and you're trying to find the correct ratio of plastic and aluminum to use such that the drone can take off as quickly as possible, but it can also be resistant to the wind. Right? That means it can't be too light, but it also can't be too heavy. So there's this kind of interesting balance in the materials that you're using.

And obviously, in this very simple example, we're discounting, you know, the aerodynamics of the design and such. So if you have that problem and you want to find these this ratio, I guess the simplest way to do it is to brute force, right? You try every possible ratio between the 2. But when that is a real number and you're dealing with, you know, 0.1 plastic and 0.35 aluminum. Right?

You know, those levels. You're gonna have to brute force that, and that's gonna probably take a computer months or years, depending on the constraints you have. Right? So what a particle swarm optimization algorithm does is it puts these different particles at different outputs, okay, so at different points in that solution space. So one particle might be at 0.10 0 point 5.

So 0.1 plastic, 0.5 aluminum. Another one might be at 1.2 plastic and 2.5 aluminum. Right. We get randomized in this space. And basically, how it works is similar to how birds actually flock is each particle tries to move itself to the center of the swarm.

So if you imagine this 3 d space with these particles everywhere, right, and it would be a 3 d space with these two variables and whatever the output would be, that each particle tries to get to the center of the swarm, but it also determines if its fitness is better than the swarm's fitness. Right? So every particle takes into consideration the result from the swarm. So if I have a friend all the way down there, and, you know, he has way better fitness to me, I'll move towards that particle. But at the same time, I want to move towards the center of the entire swarm.

And just based on those principles, the emergent behavior is that after many iterations, you find that the swarm finds a global optima. Right? Mhmm. I mean, it does have this this risk of falling into a local optima. So we talk a little about local optima and global optima.

You know, there might be a good solution at one one part of the search space, but a much better solution elsewhere. And that's where you kind of tweak these parameters over, you know, how much should I favor my own fitness? How much should I favor the the the fitness of the swarm? And, you know, how much should I favor kind of trying to get to the center of this one? But, yeah, that one's also quite quite a fascinating algorithm.

It's used for optimization problems. So, like, if you're, I don't know, a factory that's trying to produce a new kind of biodegradable bottle, you know, and you wanna experiment with different materials, that algorithm would be really useful. The the critical thing about that algorithm is you need similar to the ants, the genetic algorithm, you need a good fitness function. So if you could simulate the chemistry of different kind of compounds, and there are programs that can do that, you can use something like a PSO to design the perfect kind of biodegradable model that will also lost somebody, you know, a decent amount of time just as a hypothetical. Right.

Yeah. That's that's actually how birds when you see those those patterns of bird flocks, that's actually how they they work as well in principle. Interesting. That's really interesting. Well, I think we're kinda getting toward the end of our time.

If people want to learn more, I guess they can go pick up your book from Manning, and we'll put a link in the show notes. I also have a link for Manning for, I think, 35% off. So or a coupon. Sorry. So we'll put that in the show notes as well.

And then I think they actually sent me some codes I can give away. So if you go to devchat.tv/grok ml, then I will or grok a I because it's AI in the title. Grok a I, you can actually enter your email address for the giveaway, and we'll give away 5 copies of the book. Rochelle, are are there other places that you like to send people though to kinda level up on this stuff? Yeah.

I'd say, not just Siri places, but what I'd say is if you're interested in getting into, you know, this kind of topic, I know it's hot and trending at the moment. There's a lot of kind of courses and degrees and all sorts of things out there. But if you feel like it's kind of out of reach or it's not for you, I'd I'd urge you to take a take a shot. Like, doesn't have to be through my book. Just go, you know, try something simple.

Go on to Kaggle. Kaggle has a lot of cool resources to to get you kind of up to speed, at least with the basic concepts through some sample datasets. But I guess the the core message here is if you think you can't do it, maybe challenge yourself and try at least. Because something I've learned is that, you know, when you let this kind of fictional story you make up in your head that something's way too difficult for you, then it's definitely not gonna happen. But if you at least just try, you know, you can make it happen.

Yep. Absolutely. Alright. Well, the last segment of the show is picks, and picks are things that we just shout out about on the show. So, for example, a lot of people will pick, like, books or TV shows.

They'll pick a particular thing that they learned. Some generally, it's things that are making your life better. Right? So I'll go ahead and throw out some pics just to kind of give you an idea of of what we're talking about here. So the first pick I'm gonna throw out is a book.

It's a nonfiction book. Most of the books that I read anymore are, but this is one that I've been listening to on Audible. It's called The Hero with a Thousand Faces by Joseph Campbell, and it is a 70 year old book. But what he does is he walks through basically kind of the epic journeys of the heroes in religious, mythological, and cultural iconology, and he kinda gives you the formula for creating those same kinds of stories. And what's funny is is, like, the current state of the art sort of for movies and TV shows, they all follow this same story arc.

And it's funny because I've started like, even in a TV episode, I I can almost pick out, okay, that guy's the culprit. Right? You know, if it's a mystery. Right? That that's the murderer.

That you know? And and it's just kinda funny. You know? This is this is the breakthrough that the the protagonist is gonna have. You know, just listening to this book, I've kinda picked up some of that, and it's been kind of funny because, yeah, a lot of these, movies and TV shows are somewhat formulaic that way.

Right? You can kinda pick out the major elements. You may not be able to call, like, exactly how the hero is going to break through, but you can usually pick out they're going to break through in somewhat this way involving this person or that person, blah blah blah. Right? So you kinda get the idea.

And some some of these movies too, like, they try and turn it on their heads. One one example is the Tennant movie that came out in September, I wanna say. We went and saw it. Right? And so you anyway, they they turned some of the tropes around, but at the end of the day, it still followed the formula.

So, anyway, fascinating, fascinating book. So I'm I'm gonna pick that. And then the other thing I'm gonna pick is I have a smoker. It looks like a mini fridge sitting out on my porch, but I have a smoker and I just I love putting meat in it and just oh, man. So good.

I'm looking forward to smoking a couple of pork butts this weekend, and I just I freaking love the thing. So I'm gonna pick a smoker. I'll put my particular model in or something that looks very much like it if I can't find the exact one on Amazon because I think we got it at Walmart. But I'll put a link to it in the show notes as well just so that if you're if you're thinking, oh, well, that'd be nice. I think the one I got was a $100 on a Black Friday deal.

They're usually, like, 2 or $300. So I'll see if I can find a similar deal for you folks and and let you know where you get it. But, yeah, I just, you know, put a rub on the meat, throw it in there, and after, you know I think the the ones that I have here, the pork butts that I'm gonna be running through are, like, £8. So I have to run them for, like, 10 hours. And then yeah.

That's so good. Anyway, the so that's my pick. Rochelle, what are your picks? Yes. While you're talking, your storytelling book reminds me of another book with a very different purpose.

It's more towards kind of marketing and I guess communicating your brand or product or service. It's called building a story brand. And Oh, so good. Donald Miller. Yep.

Yep. It's actually exactly what you described, but using those elements to, you know, engage with an audience, engage with with people. So I guess that would be one pick related to your one. So, so good. I think, yeah, the thing that's made my life better recently is I challenged myself to kind of do something in October.

You might know inktober. It's a art kind of challenge that gives you prompts every day to draw something around those prompts. So they might say hope, and you think of something to draw about hope. And what I did is I researched a different topic about the world, not necessarily tech related, but maybe CO2 emissions, maybe how's the fishing industry kind of evolved over time? And for 31 days, I basically just researched a completely different topic that I wouldn't normally go and learn about.

And I kind of just wrote some doodle notes about it. So you can actually check it out on my website, aurobinds.com. But what I would say is, you know, if you could learn something new every day, it's it's quite an ask or maybe even just practice something new every day or something different every day. Yeah. That's added a lot of value to me personally.

So I don't know if that qualifies as a pick, but that would be that would be one of mine as well. Awesome. Alright. Well, if people want to, connect with you online, where do they find you? Yeah.

I think it's probably best places. My website's just rherbens.com or Twitter or LinkedIn. I'm pretty sure if you type my name in, you'll find me. I haven't I haven't found another person with my name and surname, so should be pretty easy. Nice.

Alright. Well, let's go ahead and wrap this up. Thanks everyone for listening, and Max out.