Transcript
Speaker: Hey everybody, welcome to the Exit Podcast.
Speaker: This is Dr. Bennett, joined here by Zach Martin.
Speaker: He's an Exit member and an expert in natural language processing, which is in the news these days as it's gonna consume and destroy all human endeavors this year.
Speaker: So I wanted to have an expert to come tell us how afraid to be, what kind of obeisance to prepare for our new NLP overlords.
Speaker: So welcome to the show, Zach.
Speaker: Yeah, thanks for having me.
Speaker: We'll see who has a job after all this shakes out.
Speaker: So tell us a little bit about your background and how you got into natural language processing.
Speaker: Yeah, yeah.
Speaker: So first of all, I kind of have a non-traditional background into this.
Speaker: So most guys in my world come from some type of statistical background, mathematics or computer science, essentially.
Speaker: So I actually didn't know what degree I was going to get in college and started just taking classes I was interested in.
Speaker: So I started taking a bunch of linguistics classes.
Speaker: And then I got married in college and I was like, oh, no, I actually have to make money now, right?
Speaker: So I was like halfway through a linguistics degree and I'm like, well, what am I going to do with that?
Speaker: I'm like, I guess I go to law school or something like that.
Speaker: That's what a lot of people in that situation do.
Speaker: But then I started talking to the professors and they were like, hey, NLP is the space to be.
Speaker: That's where you make money.
Speaker: And I'm like, OK, what's NLP?
Speaker: And so essentially what I learned is it's where.
Speaker: kind of computers and human language intersect.
Speaker: So it's natural language processing as opposed to computer language, which would be like your Java, your C, your Python, those types of things are all computer languages.
Speaker: Natural languages being French, English, Spanish, et cetera.
Speaker: Right.
Speaker: And so being able to process natural human languages via computers was kind of the, the,
Speaker: the idea there.
Speaker: So I kind of picked up coding somewhat on my own.
Speaker: I took a couple classes in college, but but kind of bootstrapped my own kind of coding background and and paired with kind of my linguistic knowledge.
Speaker: I ended up being the only one from my graduate cohort of linguistics bachelor's degrees to have a job lined up in the industry.
Speaker: before graduation.
Speaker: So there's a lot of, it seems like there's a lot of jobs like that where, where it's really, it's actually really valuable to be like a, a graphic design professional or, or a creative.
Speaker: If you can also talk to computers, like I know a lot of front end guys who didn't start as software developers.
Speaker: They started as essentially artists, creatives, and, and were able to,
Speaker: Basically, they had to go learn to code so that they could do the kind of work that exists in those fields, which is all done with computers.
Speaker: So it's an interesting... But I think we're sort of entering this realm, or maybe we are, maybe we're not.
Speaker: I want to ask you about this.
Speaker: Are we entering a realm with these chatbots and natural language processing where you actually don't need to be able to code to do some of this type's work?
Speaker: Well, what I'll say is 100%, you don't need to be, like, you don't need to be the guy that, you know, can write binary code and, like, make computers work.
Speaker: Like, you don't need to be a hardcore computer science guy to actually, like,
Speaker: be successful in kind of the coding world anymore because part of it is not even due to these ai things but just the fact that there's so many libraries and open source sharing of code that like you don't really need to know how to make a neural net to be able to run one on some data if that makes sense and and now you don't even have to know how to code it necessarily although there is still some
Speaker: gap between the actual like implementation of some of these ideas, like actually getting them to function on their own versus kind of, Hey, look, I did this one thing once and it kind of worked.
Speaker: So, yeah.
Speaker: Yeah.
Speaker: So I was a, I was a data scientist, you know, so-called and, um, basically, so I, I, my, my capstone project for my, um,
Speaker: bootcamp that I was part of, I did an NLP analysis of Game of Thrones.
Speaker: I took all of the script from Game of Thrones, everything, every word uttered on the show, and I ran it through essentially a black box in Python.
Speaker: I had no idea what that black box was actually doing.
Speaker: but it outputs some information about... I was basically trying to figure out, could I predict, based on the words people used, whether or not they were a common or a noble?
Speaker: So I had to go through and tag the data.
Speaker: This guy's noble, this guy's common.
Speaker: And I am definitely not a computer science guy.
Speaker: I'm not going to learn binary.
Speaker: I'm not going to learn even really all that much Python.
Speaker: But because these libraries existed, I was able to... Basically, you have to know how to...
Speaker: put in the input and interpret the output and you can get a lot done.
Speaker: Yeah, yeah, really it's just a matter of kind of knowing how to set up the data to go into the black box and then how to interpret it once it comes out.
Speaker: Yeah, so.
Speaker: So, yeah, I mean, that's that's what a lot of guys are doing in the data science space.
Speaker: Like there's definitely a need for those those guys with that higher level knowledge.
Speaker: And they're definitely making, you know, the huge, huge bucks and and doing research and that type of stuff.
Speaker: But if you're not a Ph.D. researcher or something like that, like there's a lot of stuff you can do.
Speaker: mainly just because a lot of companies are kind of stuck in the Stone Age still.
Speaker: So a lot of this AI stuff is completely new.
Speaker: But yeah, speaking of capstone projects, my final project for my final NLP class was I used to read news articles, and I would try to skim them really quickly to find out whether or not the news site was left-leaning or right-leaning in their bias.
Speaker: Because a lot of times you'll be like, ah, this new site sucks.
Speaker: Or it's no good.
Speaker: And so I would kind of skim it.
Speaker: And so I built an NLP algorithm that looked for certain keywords and weighted them based off of whether or not they appeared.
Speaker: And
Speaker: kind of mapped out, I scraped a bunch of news sites and then, uh, mapped whether they were left biased or right biased.
Speaker: And so you proved like, uh, any fake news stuff and, and stuff like that.
Speaker: So you, you proved conclusively to your professors that, uh, that the, the left wing media was out to get us and, uh, so much for the tolerant left, et cetera.
Speaker: Yeah, exactly.
Speaker: Exactly.
Speaker: Um, but yeah, um,
Speaker: A little more on the timeline of just my background that's somewhat relevant is... So, like, the first job I got was for a company that did surveys.
Speaker: So, like, if you ever get a receipt from McDonald's that says take a survey and win a free McDouble or whatever it is, right?
Speaker: Or some, you know, hotels do it and airlines do it.
Speaker: So, this company...
Speaker: did surveys and I was building the NLP models that would flag what customers are talking about in the plain text.
Speaker: So like, are they talking about the hamburger?
Speaker: Are they talking about whether or not it tasted good or bad?
Speaker: Right.
Speaker: And then so, so pretty rudimentary stuff, but I was using IBM Watson, which was like an early like AI tool.
Speaker: And, um,
Speaker: But yeah, subsequently, I've done a lot more in the open source and Python stuff.
Speaker: But that's kind of where I got my start.
Speaker: And a lot of the stuff that I was doing back in the day is kind of being revolutionized by some of the newer developments in NLP, specifically transformer models or large language models, which are like chat GPT or GPT-4 that people have been hearing about.
Speaker: Yeah, there's a lot of cool stuff that's been done.
Speaker: I mean, it's, and it's been going on for a really long time.
Speaker: My two favorite, cause I'm like a, I definitely belonged in my MBA program.
Speaker: Like I'm, I'm like a business major kind of a guy at least like that's, if I have to, if you have to put me in a marketable place, that's where I'm marketable.
Speaker: I'm definitely not a technical guy, but what attracted me to data science was ultimately two stories of,
Speaker: One of them was Walmart.
Speaker: They were selling this bag of dog food and it wasn't moving.
Speaker: And they were doing all these analyses on all their different inventory, trying to figure out why.
Speaker: And it was like better quality than the name brand.
Speaker: They were trying to sell their like Equate brand dog food, right?
Speaker: And it was better quality.
Speaker: It was packaged to the right size and it was...
Speaker: And it was selling only on the weekends.
Speaker: And it was like radically underperforming during the week.
Speaker: And they were able through some data models that they built of customer feedback to figure out that customers were only purchasing it when the man was at Walmart.
Speaker: So it had to be either a man or a couple.
Speaker: And they were like, oh, it's because she can't lift the bag.
Speaker: And so that's why it wasn't selling.
Speaker: So they downsized the bag and then it moved.
Speaker: And they made a ton of money on this dog food.
Speaker: And then another story is Hera's, the casino.
Speaker: They hired this CEO who was like a, I think he was like a stats professor.
Speaker: He was some academic in stats and data science.
Speaker: And he basically just started hoovering up all of their user data that they could possibly collect.
Speaker: And they were viewing these reward programs that they were using as like a way to incentivize
Speaker: Like it was like a discount program.
Speaker: Like we're going to make money because we're going to offer these discounts and we're going to retain customers, et cetera, et cetera.
Speaker: And he was like, no, the purpose of your rewards program is to collect user data, which that's like not even, that's like a cliche now.
Speaker: Everybody knows that.
Speaker: But at the time it was kind of revolutionary.
Speaker: And so Harris comes out with this ad campaign where they've like, they're like, they've got the luggage carts at the hotel and they're wheeling out like a giraffe.
Speaker: And the whole point of this ad campaign was like, do you want a giraffe?
Speaker: Do you want hookers and blow?
Speaker: Like you tell us what you want and we'll just make it happen because we've got this personalized, individualized model of who you are and what you like.
Speaker: And we're going to learn everything that you love and we're going to give it exactly to you.
Speaker: And, uh, and that actually took them from being like, this also ran like on the skids outside the strip to like one of the most successful, uh, chains in, in the city.
Speaker: So, uh, there's cool stuff that happens with, uh, with data science in general and NLP opens up to a lot of this like fuzzy, squishy sentiment type data.
Speaker: Yeah.
Speaker: Yeah.
Speaker: And, and that, that kind of takes me back to my experience in, uh,
Speaker: customer experience is what it's called that field of like surveys and stuff.
Speaker: So like previously, like, and, and still some companies do it where they try to give you like a hundred different questions that you rate from one to 10 and like everybody hates those.
Speaker: And, and also they're not very useful.
Speaker: So like the philosophy of one of the companies I was at was just like, how about they just tell us what they want to say.
Speaker: Right.
Speaker: And so, um, so like one of them was like Chick-fil-A.
Speaker: They, uh,
Speaker: We did some study for them and found out that one of their biggest complaints was the blue cheese crumbles in one of their salads didn't come in a separate bag.
Speaker: So they had like almonds in a separate bag and apple slices in a separate bag or whatever for their salad.
Speaker: But the blue cheese crumbles they would just throw on top and then a bunch of people didn't like the blue cheese but like couldn't remove it.
Speaker: And so we told them that.
Speaker: And how are you going to collect that from a one through five?
Speaker: Yeah, one through five.
Speaker: How was your service?
Speaker: Are you going to recommend them again?
Speaker: Yeah.
Speaker: So sometimes it's all about just tell us what you want.
Speaker: Tell us what you're thinking.
Speaker: But yeah, a lot of fun times in NLP, including at one point for Little Caesars.
Speaker: we had to come up with all the different types of things that could be on top of a pizza that shouldn't be there.
Speaker: So we were like, all right, Band-Aids, you know, I don't know, needles, you know, what else?
Speaker: And so like that was my task because it was when I was kind of new at the company and they were like, just write down all the most horrible things you can think of that could happen at a Little Caesars.
Speaker: So I was just typing every swear word I knew, every disgusting thing I could think of.
Speaker: And yeah, that's what a job.
Speaker: Yeah.
Speaker: It was a fun job, but yeah.
Speaker: And like on the, on the, on the micro level, when you're like looking at it right up close, it's like,
Speaker: Oh, it's great that Chick-fil-A is going to take the blue cheese out of my, and it's great that my wife can lift the dog food bag.
Speaker: And it's great that, you know, maybe Harrah's knows that I'm LDS, so they're not going to be giving me discounts on alcohol.
Speaker: They're going to give me discounts on meals or whatever it is.
Speaker: Like, close up, you can see this, like, very uncontroversial, uncomplicated, good reason to do this.
Speaker: And then you pan back and it's like Lovecraftian existential horror.
Speaker: Well, you know, they know everything about you is companies like through your like cookies and your ad persona.
Speaker: They can tell pretty well whether or not you're pregnant.
Speaker: Like, yeah, of your your your history, but like some people might want that private.
Speaker: Right.
Speaker: So or like they can tell if you've gotten you're going to get divorced.
Speaker: Stuff like that.
Speaker: Like some companies, I heard it was like some bank was like, hey, are you thinking of getting divorced?
Speaker: Here's private accounts.
Speaker: And they think we're helping this customer by knowing their persona.
Speaker: But like, yeah, there's definitely like a dark side to it.
Speaker: And I mean, data privacy is something people care about a little bit now.
Speaker: But like, yeah, for a while there, nobody was even thinking about it.
Speaker: Well, there's even like info hazards.
Speaker: Like I don't necessarily want to know that I have, you know, three years to live or I don't necessarily want to know that like or like, you know, maybe there's maybe there's a way to tell me that I'm about to get divorced that that that helps me.
Speaker: But that way probably isn't like, hey, let's facilitate the transaction of your divorce as quickly and directly as possible.
Speaker: Well, and what's funny is talking to some of these like marketing people, they, they can't even comprehend that like a customer might not want you to know certain things like, like the, the creep factor, they don't even think of it.
Speaker: Yeah.
Speaker: Yeah.
Speaker: And I mean, you know, obviously on Google there's, there's like the question of porn, but like, there's just, there's so many other dimensions to that, like privacy question and like,
Speaker: Yeah.
Speaker: Well, and like, you know, a few years ago, well, like people are always like, oh, I talked about this thing and it showed up in my Facebook feed or whatever.
Speaker: And it's like, yeah, they said they were doing that.
Speaker: They were turning on the microphone.
Speaker: If you download the Facebook app on your phone, they have permission to your microphone and they are listening and using it for ads.
Speaker: So like, yeah, it's not fake.
Speaker: They said they were doing it, you know.
Speaker: And like you, you have to like, and it's an interesting situation because on the one hand, like ideologically, my, my opinion is basically that there's not a whole lot of conspiracy.
Speaker: There's a lot of like essentially automated human behavior going on.
Speaker: Like just, it's just people responding to pings.
Speaker: The, the,
Speaker: The analyst gets an email and that's why they have to build the thing.
Speaker: And the person who sent the email, they heard from the VP and the VP heard it from the CEO and CEO heard it from the shareholders and the shareholders are this like big blob of cognition that doesn't have like a human will attached to it.
Speaker: And so like you're sort of on the one hand, you're counting on these systems to be kind of dumb and maybe they are like Facebook.
Speaker: Does Facebook have like a nefarious big picture like doomsday scenario that they're trying to instantiate?
Speaker: Sell you those Chinese leggings or something?
Speaker: Yeah, or are they just trying to sell you leggings?
Speaker: Yeah, like exactly.
Speaker: And so far...
Speaker: It seems like the answer is basically that there is no conspiracy and it's, but it's at the same time in the service of selling you leggings or selling you, uh, a hotel room at a casino or selling you salads at Chick-fil-A.
Speaker: They're building this edifice that is, is just so incredibly dangerous.
Speaker: Yeah.
Speaker: Yeah.
Speaker: And, and like, uh,
Speaker: I mean, there's definitely some nefarious parts of it.
Speaker: Like they tested just showing people the like today is the day to vote.
Speaker: Like just putting that on the top of Facebook, like changed voting patterns by like a substantial amount, like election change.
Speaker: Yeah.
Speaker: Just being like, and which, which voters do you send that to?
Speaker: You know?
Speaker: Right.
Speaker: And, and we found out like they obviously had a bias after like 2016 and stuff.
Speaker: Yeah.
Speaker: Yeah, man.
Speaker: It's crazy.
Speaker: Well, so what do you think about kind of the sort of like... Are there like buggy whip industries right now that are just going to be eaten by this technology?
Speaker: So like talking about like chat GPT and stuff like that?
Speaker: Or just AI in general?
Speaker: Well, maybe let's start with AI in general and then specifically chat GPT.
Speaker: Yeah, so I mean...
Speaker: Honestly, I think it's stuff that was already being outsourced and automated that's going to continue disappearing.
Speaker: So they're replacing Indians and Filipinos, you mean?
Speaker: Yeah.
Speaker: Yeah.
Speaker: So basically that's it.
Speaker: Like call centers are going to be, you know, it's at a certain point.
Speaker: that AI bot that's like, tell me how I can help you, is going to be much more helpful than going through someone who barely speaks English.
Speaker: It's already close.
Speaker: Yeah.
Speaker: It's already close.
Speaker: It's going to get even better, right?
Speaker: And cheaper for companies to do that.
Speaker: So, I mean, because essentially all the, like any tier one tech support, right, is just like, we're just here to press the button that routes you to the right place.
Speaker: So like anything that's like that is is essentially going away.
Speaker: There's definitely like jobs.
Speaker: And I mean, those who have worked in in kind of the tech side of things, there's certain jobs that are like.
Speaker: Like nobody wants to do and those types of things are going to get automated away.
Speaker: So like I honestly think like.
Speaker: more of your like data wrangling where like your job is just to like say, Oh, we need to get this table and that table and put them together.
Speaker: Like that is not probably the safest place to be in, in the coding space.
Speaker: Right.
Speaker: And nobody loves to be there anyway.
Speaker: Although, although, um, having worked in like regulated industries and, and stuff like that, there, there's going to be regulations to come out around AI stuff.
Speaker: And also like,
Speaker: You know, I've worked at like banks and stuff and you can't just be like, oh, well, we missed a record on accident.
Speaker: Right.
Speaker: So like to a certain extent, there's always going to be kind of that manual backup check, even if it's automated with AI.
Speaker: So to a certain extent, there may be like a resurgent of like, you're just the guy who checks the AI and makes sure it does a good job, you know?
Speaker: Yeah.
Speaker: And I mean, like in in in defense, if you have a security clearance, I mean, they're never throwing those algorithms over the wall like that's just never.
Speaker: I mean, they won't even half those half those shops won't even use Tableau like it's got to be like Excel and it's got to be like Excel 1997, not Excel 2003.
Speaker: No, 100 percent.
Speaker: Like like definitely government is behind like pharmaceutical industry is still using stuff from the 70s.
Speaker: Like, and that's, that's the thing is like, if you are interested in getting into coding, I don't think you have to worry about it.
Speaker: Like within the next five years by any means, and probably not within the next like 25 years, because there's, there's so much out there and there's so many different businesses and, and really the only companies that are really making use of these AI algorithms.
Speaker: And, and a lot of them are not even doing a good job of it.
Speaker: Um,
Speaker: are like your Silicon Valley tech companies.
Speaker: So like, yeah, you know, they're way past kind of the baseline of coding and stuff like that.
Speaker: But there's so many big companies, old companies, and even startups that just like need, you know, we just need a model that predicts fraud.
Speaker: That's it.
Speaker: Like, so if you can do that, like you're way ahead of a lot of other people.
Speaker: And so I take it you're not like a Kurzweil guy.
Speaker: You're not like this is going to eat everything.
Speaker: It's going to start accelerating its own development.
Speaker: I don't.
Speaker: I'm like, I don't care about like, you know, are we just part of an AI?
Speaker: And, you know, I don't really think about that type of stuff that often.
Speaker: So like, you know, maybe.
Speaker: But yeah.
Speaker: just kind of being behind the scenes of this stuff and like knowing how like chat GPT works and stuff.
Speaker: It's so not scary.
Speaker: It's very like lame in how it works.
Speaker: And so we've got a lot, like a long way to go, like to get any of this to the point where it's like, you know,
Speaker: going to really disrupt the entire economy.
Speaker: Now, that being said, it's like, it's like, cause they'll talk about like, oh, it's, it's indistinguishable from like a New York times journalist.
Speaker: And I'm like, yeah, that's because those people aren't human.
Speaker: They're robots.
Speaker: Right.
Speaker: Right.
Speaker: So there's nothing about actual like human creativity.
Speaker: Well, well, yeah.
Speaker: And it's mimicking.
Speaker: Right.
Speaker: So, so it's, it's been trained on, on a training set that includes all of the New York times.
Speaker: So it can sound like a New York times person pretty well.
Speaker: But yeah, you're not getting any original thought out of it.
Speaker: But what it will replace is BuzzFeed listicles.
Speaker: You don't need to pay someone 45 grand a year to write those stupid headlines or whatever.
Speaker: So stuff like that, yeah, maybe you need to worry about your job.
Speaker: But I think for a lot of things, we're way far away from that.
Speaker: There was some guy who, did you see this guy who started a business with ChatGPT?
Speaker: I've seen a lot of those guys saying that.
Speaker: Oh, okay.
Speaker: There's a thread.
Speaker: Which one?
Speaker: Well, and I mean, to some extent, he's relying on the virality of the story.
Speaker: So it's like, you know, it's not really legit.
Speaker: But like, basically, he asked ChatGPT, like, hey, what's the best way to turn this $100 into an effective business?
Speaker: And ChatGPT says, you should start a business in green gadgets, green like household gadgets.
Speaker: That's a really good market.
Speaker: You should pick that.
Speaker: So he asks it like, what should my domain name be?
Speaker: And it's like, well, here's a couple of suggestions.
Speaker: He picks one.
Speaker: He goes and buys like green gadget guru.
Speaker: And he puts it on there and he starts just posting like affiliate links to Google.
Speaker: to like Amazon for thematic gadgets.
Speaker: And part of the reason that he turned it into like $1,500 in like a week.
Speaker: So a hundred bucks to 1500 bucks, that's pretty good.
Speaker: But to some extent, like I'm sure that he was driving traffic by saying, look at this neat application of AI.
Speaker: And that's, you know, and then Google sees that it's getting traffic and it sends it more traffic.
Speaker: And so there's, it's, it's a little bit fake, but I, it makes me think about like, man, if we're, if we're no longer limited by the number of like content monkeys that
Speaker: And we can just like just endless trash, like the amount of just, just horrendous, useless internet there's going to be.
Speaker: Can't even imagine.
Speaker: Yeah.
Speaker: It's definitely going to increase the amount.
Speaker: Like whenever, like I was looking to buy like a vacuum cleaner and you're like this vacuum cleaner review, you know, try to check a YouTube review.
Speaker: They're all AI generated now.
Speaker: Like all the top 10 results.
Speaker: I'm like, I just want to see someone using the vacuum cleaner.
Speaker: And it's all just like, we just grabbed the top five pictures from Google and then had a...
Speaker: text to voice, read it out for us.
Speaker: Yeah, which then, I mean, that muddies the water for algorithms that are based on that kind of feedback data, right?
Speaker: Like there's going to be, this thing is going to backwash on itself in a lot of ways, especially because it's not one, it's not one AI doing it.
Speaker: It's like a bunch of AIs that, you know, maybe theoretically content for each other.
Speaker: Right, right, right.
Speaker: Because if it was one, then you could just be like, hey, did you write that review?
Speaker: Okay, exclude that from your training set.
Speaker: But you can't do that.
Speaker: Yeah, yeah.
Speaker: It's kind of bleak when you think of that type of stuff.
Speaker: Although, I mean, to a certain extent, you know, I don't know.
Speaker: The problem is, like, what people don't really get about ChatGPT is the recommendations or the answers it gives.
Speaker: are not necessarily supposed to be accurate.
Speaker: They're just supposed to sound accurate.
Speaker: Right?
Speaker: So if you say, give me the best domain name for this idea, it would give you a domain name.
Speaker: It doesn't know if it's the best.
Speaker: It has no clue if it's the best.
Speaker: It's not like running a model on which of these has the best.
Speaker: It's not doing any kind of modeling or data science of its own.
Speaker: No, no, no.
Speaker: So yeah, so like, and the way, like, because they framed it as a chatbot, which maybe I can get into like the history of that, because they framed it as a chatbot, people interact with it as if it's like this intelligent bot.
Speaker: But what it is, all it is, is it takes your prompt, so what you type, and it tries to match an output to that, to what you typed as best as possible.
Speaker: So yeah,
Speaker: Whatever you write, it's going to try to match that as best as it can from what it's been trained on.
Speaker: But it has no clue whether or not any of it's accurate.
Speaker: Now, I've seen they've done some fine-tuning on the newest version, so GPT-4.
Speaker: That's supposed to be a little bit more accurate, but it still kind of has the same problems.
Speaker: Oh, yeah, so do go into the history of it.
Speaker: Tell me about how they...
Speaker: So part of this is is NLP kind of goes back to like the earliest days of computing, like back in like the 60s.
Speaker: People were like, let's train a bot to talk, you know, and they like IBM's like, we got it speaking, you know, and, you know, and it's always been a thing where like, you know, they've had, you know, how 9000 and, you know, in sci fi, they've wanted some kind of chat bot.
Speaker: That's that's been a thing.
Speaker: I built a chat bot before this technology was out.
Speaker: And essentially what you're, all you're doing is like looking for keywords and strings and then trying to match that with a, with an output that would be useful.
Speaker: And so sometimes you have certain like targets that you're trying to get people towards and you're, you're kind of building like a video game, you know, like a choose your adventure video game.
Speaker: That's just a little bit more advanced.
Speaker: Right.
Speaker: But yeah,
Speaker: with kind of the onset of machine learning and kind of the advancements in machine learning that kind of took place probably within like the last 15 years, they've started, you know, training stuff using machine learning.
Speaker: So like your Google Assistant, your Siri and your Alexa, they started coming out using these more advanced machine learning models.
Speaker: So one of the issues, though, is
Speaker: If you're not just matching words to certain responses, so you're like, if they say this word, we'll give them this response.
Speaker: It's kind of hard to work with text data because it's not numerical.
Speaker: So machine learning in general requires numbers.
Speaker: So you have to give it some numbers.
Speaker: So for instance, a classic machine learning problem is they took measurements of three different flower species.
Speaker: Right.
Speaker: So they measured the stem length, the petal width, the sepal length and stuff like that.
Speaker: And there's three different species they know in real life.
Speaker: And you can use a machine learning algorithm to look at all those different measurements of the various like hundreds of different flowers of each species.
Speaker: And then you can try to place them in which species is there without actually knowing the answer ahead of time.
Speaker: Yeah, I've actually used some of those apps.
Speaker: They're pretty good.
Speaker: They're pretty effective at correctly IDing.
Speaker: We made some jam in our yard out of some berries that we collected that we were...
Speaker: unsure what they were, but I was able to use like a couple separate apps to identify the barriers.
Speaker: And they're like, oh yeah, that's, that's autumn olives.
Speaker: You can eat those.
Speaker: And I said, thank you, computer.
Speaker: I'm going to feed these to my kids.
Speaker: Yeah.
Speaker: So that's probably some type of image, image based thing, right?
Speaker: So you take a picture and it'll search for it.
Speaker: Yeah.
Speaker: Yeah.
Speaker: Yeah.
Speaker: So, um, so yeah.
Speaker: And like, that's another thing is pictures.
Speaker: technically are hard to work with, right?
Speaker: Because you can't just say, here's a picture, do math on it, right?
Speaker: But yeah, it has to find the outline of the flower and recognize that it's a flower.
Speaker: And then it's got to start from like, is it a flower?
Speaker: And then be like, oh, it's this kind of flower, which is that's tricky because their shapes are so different.
Speaker: So it has to recognize the general case and then narrow down to the specific case.
Speaker: Yeah, yeah.
Speaker: You can also do it on birds really easily too.
Speaker: Something I found out.
Speaker: Take picture of a bird, it'll tell you the exact species.
Speaker: But yeah, so text and image are kind of related in that because they're considered what's considered unstructured data.
Speaker: So structured data being like percentages.
Speaker: And people intuitively understand that those are magical.
Speaker: Like when they see a computer work with text and image, they're very impressed.
Speaker: Yes, yes.
Speaker: And because that's like, you know, closer to your human senses, right?
Speaker: And so text and images have a lot of the same challenges.
Speaker: So for images, the way they turn an image into numbers is they just make a grid and number every pixel, right?
Speaker: And they say, okay, pixel one, what color is it?
Speaker: Right?
Speaker: It's this color.
Speaker: Pixel two, it's this color.
Speaker: And they just do that for however many pixels are in the image, right?
Speaker: And then they'll use neural nets to kind of look at the image from different, you could say, like zooms and like different levels of fidelity.
Speaker: And it tries to kind of predict what that image is using those neural nets.
Speaker: And you kind of train it on a data set that has every single flower, every single bird labeled with what they are and then the picture.
Speaker: From a bunch of different angles and.
Speaker: Um, yeah.
Speaker: And so, yeah, go ahead.
Speaker: Does it, I mean, that seems like that's gotta be really compute heavy.
Speaker: Like it's gotta take a lot of resources.
Speaker: So is there, how much of this is, cause people, people have been posting the, the Moore's law graph, right?
Speaker: The, it's like double exponential.
Speaker: It's, it's, it's a, it's an exponential function on a log scale.
Speaker: And, um, yeah,
Speaker: of these resources that are going up.
Speaker: And I wanted to get your take on like, do you think it's dramatically being opened up by the availability of new compute resources?
Speaker: Or are we just finding new techniques to, to look at the data?
Speaker: Yeah, I would say, um, it's kind of a combination.
Speaker: So, so
Speaker: So some of these techniques go back to like the 80s.
Speaker: Like they were discovered in the 80s and they just didn't have enough compute to really make use of them, right?
Speaker: So some of these models, so for instance, in text analytics, a popular model is a specific type of recurrent neural network called an LSTM, which stands for long short-term memory.
Speaker: So I believe that was like described and discovered in the 80s.
Speaker: And it really didn't come to prominence, though, until maybe like 2010, 2012-ish, when people kind of realized, oh, we have enough data, number one, so like training data, meaning enough text, like a large enough corpus of text to run through this model to train it to get to know the patterns.
Speaker: And then also enough compute to actually keep track of all those weights of every single word in the English language and things like that, or sometimes multiple languages.
Speaker: And yeah, so some of it was unlocked by the advancements in compute.
Speaker: And what you're kind of seeing, the differences between GPT-2, GPT-3, chat GPT, and GPT-4 are just, they're making them bigger.
Speaker: So they're just computing more.
Speaker: So they haven't really changed the architecture much.
Speaker: Although with GPT-4, I think they did.
Speaker: But they're just throwing more data at it.
Speaker: And as it gets more data, it kind of just gets smarter just by having seen more stuff, if that makes sense.
Speaker: Yeah, and my intuition, and I'm definitely a novice,
Speaker: But like my intuition is that, like you're saying, these are kind of boring when you look behind the curtain.
Speaker: It's like, it's just sort of like lots and lots and lots and lots of regression models with lots and lots of variables stitched together and washed through, you know, over and over again.
Speaker: And if you're a certain type of like rationalist materialist, you sort of say like, well, that's human cognition is as simple as that.
Speaker: And therefore, this thing will inevitably approach and surpass us in terms of its cognitive sophistication.
Speaker: But it's not obvious to me that that's what's happening.
Speaker: Like, it seems... I'm like, given how...
Speaker: much data is being churned and the, the, the scale of the resources that are being deployed.
Speaker: I'm like, this thing clearly is like faster in a certain sense than a human brain.
Speaker: Like it's, it's, it's got more resources.
Speaker: Right.
Speaker: Like it doesn't forget stuff.
Speaker: Right.
Speaker: And yet the results are, are not, not especially close to a genuine human cognition.
Speaker: Or at least not as impressive on a creative level, for sure.
Speaker: Right.
Speaker: Yeah.
Speaker: And so, I mean, diving into that, like, going back to my linguistics education, there's a faction in linguistics that are called the generativists, which are led by Noam Chomsky.
Speaker: This is what he's famous for.
Speaker: He said, he came up in like the 60s or 70s and said,
Speaker: human brain is like a computer.
Speaker: So, but for him, it was like a 70s computer, which meant data storage was very expensive.
Speaker: You know, these things were running on kilobytes of data at most, right?
Speaker: And so he's like, we don't have the capacity in our brain to keep track of every time we heard a word, right?
Speaker: So we just have this like engine, this kind of like script in our brain
Speaker: This universal grammar is what he called it.
Speaker: And we just process thoughts through that universal grammar.
Speaker: And that's how we make speech.
Speaker: So we actually don't store any words in our brain.
Speaker: We've just got this universal grammar embedded in there.
Speaker: Now, that's all well and good.
Speaker: It's more of a philosophical point, because if you look at the human brain, there's nothing in there.
Speaker: There's not a universal grammar.
Speaker: There's no hidden little
Speaker: script in your brain that's doing that stuff also there's a few problems um because he kind of based his research off of like knowing a few romance languages as opposed to knowing like all the various weird uh indigenous languages like hish cariana and stuff like that that totally break all these rules um we can talk about that some other time but uh but basically um
Speaker: It was more of a philosophical point and not really real.
Speaker: Now, where I studied at BYU, there's actually some professors who were doing some work saying, well, what if our brain just kind of retains as best as to its ability every time it's heard a word?
Speaker: So like every utterance, it's heard.
Speaker: Every word, it's read.
Speaker: You kind of retain that in your head and you learn associations through patterns and linkages in your brain.
Speaker: So like,
Speaker: You may not know the exact definition of a word, but you've heard it in certain contexts so much that you kind of understand what the word means.
Speaker: And and also it explains why like your accent doesn't change after a certain point.
Speaker: Right.
Speaker: So like if you're an American and you go live in Scotland at at thirty five, you're not going to start speaking with a Scottish accent necessarily, except maybe on like words you've never heard before.
Speaker: Hmm.
Speaker: Right.
Speaker: But if you're a kid, you've heard less words.
Speaker: And if you move, your accent may change, right?
Speaker: And so whereas there's not really an explanation for that under Chomsky's model.
Speaker: But anyway, so that's kind of what has taken place with NLP in recent years.
Speaker: So basically around 2012, they came up with this thing called NLP.
Speaker: word2vec or word vectors, right?
Speaker: And they said, instead of like, so previously the way they turned words into numbers was they would say, all right, the first word we see is going to be word number one.
Speaker: So it's going to be number one.
Speaker: That's how we're going to represent it.
Speaker: And so like they kind of did machine learning by saying, okay, this sentence has word one, word 64,
Speaker: Word 307.
Speaker: So that means it might be meaning this.
Speaker: And it was able to do some machine learning that way.
Speaker: There's a few other ways that they tried to do it, like one-hot encoding and other ways of encoding words.
Speaker: But that doesn't establish linkages between the words.
Speaker: Correct, because they're just arbitrary meanings.
Speaker: So you don't retain the semantic information.
Speaker: So the actual meanings of the words or whether it's related to another word or
Speaker: whatever so but with with word vectors what happened was they said okay let's um represent these words in a vector space now that that's like a math thing that
Speaker: might make sense if you're doing like multi-dimensional.
Speaker: Yes.
Speaker: One.
Speaker: Okay.
Speaker: So, so one dimension is a number line.
Speaker: Two dimensions is your X and Y chart.
Speaker: Nobody's going to see this video, but yeah, it's an X and Y chart.
Speaker: Three dimensions is that same chart in, in 3d.
Speaker: So you've got three.
Speaker: Got height with directions.
Speaker: So the way I explain it is, is you're giving the word an address, right?
Speaker: Yeah.
Speaker: So, so the address, for instance, like if you're in downtown New York,
Speaker: and you want to tell someone where to meet you, you have to tell them, okay, meet me on this street that crosses with this street.
Speaker: So that's your, your two dimensions.
Speaker: Right.
Speaker: And, and then meet me on the 30th floor.
Speaker: That's your three dimensions.
Speaker: Right.
Speaker: So now they know where to meet you.
Speaker: Now there are other dimensions such as like time in, in reality that adds a fourth dimension in,
Speaker: In this word vectorization, they're adding sometimes over a thousand different dimensions to these words.
Speaker: And they're essentially trained through the neural network.
Speaker: But all it's doing is it's trying to place the word in a space that is mathematically representable.
Speaker: Though hard to imagine once you get above three or four dimensions for the human.
Speaker: Right.
Speaker: Right.
Speaker: But what ends up happening is they found out, okay, if we put the word king...
Speaker: In, you know, we train it and we find out what its vectors are exactly, so we place it in an address.
Speaker: It ends up being closer to the word man than it is to the word woman, right?
Speaker: And then what they also found out is that the distance between man and king was the same as the distance between woman and queen, right?
Speaker: So these relationships also were preserved.
Speaker: And so what ended up happening is through this very simple process that's actually not super computationally heavy compared to other things they were doing, they were able to retain information about words based on how it was trained.
Speaker: So like I said, that came out in 2012.
Speaker: The next few years were spent...
Speaker: um some some of the bigger companies like google said well what if we just take all of wikipedia and pre-train a model on everything in wikipedia right and then they said what if we get more than that right and so that they came out with these models called like glove and like other models that that essentially were like we're going to pre-train on the english language so you're just going to have like
Speaker: it's already going to know what every word means before you even boot it up, if that makes sense.
Speaker: Yeah.
Speaker: And so that was kind of the start of these large language models, which is what GPT is one of them.
Speaker: But then they kind of just kept making them bigger and bigger.
Speaker: But then 2017, Google releases a paper called Attention is All You Need.
Speaker: And you can go look up the paper if you're interested.
Speaker: But basically, they said, we're going to use this thing in machine learning called the attention mechanism that essentially just allows these models, instead of using what they were using before, which were these LSTMs, long short-term memory, it's able to pay attention to larger contexts of words that rather than LSTMs kind of worked like a snowplow,
Speaker: where they had no clue what was in front of them.
Speaker: So that's just like virgin snow.
Speaker: They have never seen it before.
Speaker: And then what trails off behind them starts getting more snow on it and starts covering up again.
Speaker: So it kind of forgets.
Speaker: So it can only remember a certain window of words.
Speaker: And that's kind of how LSTMs work.
Speaker: But this attention mechanism allowed it to pay attention to what's in front, what's behind, and kind of what was three paragraphs ago.
Speaker: So that's why like when you do your predictive text on your phone, it's based off of these older models and it starts looping around because it forgets that it's already said something.
Speaker: Right.
Speaker: Right.
Speaker: Right.
Speaker: And then but this this attention mechanism, once again, it's a very like simple thing, but they just kind of figured out a way to do it.
Speaker: And and what it allows for is.
Speaker: it can pay attention to this context, and it actually does better at things like machine translation because it doesn't really care about word order as much.
Speaker: And so these models came out, and they were called transformer models.
Speaker: So the T in GPT stands for transformer.
Speaker: What are the G and the P?
Speaker: What's that?
Speaker: What are the G and the P?
Speaker: They are generative pre-trained
Speaker: uh transformer model so generative because it's kind of intended to generate text right um and then pre-trained like it's a large pre-trained language model um and so yeah that's what uh so like gpt came out then quickly gpt2 came out that was like in 2019 and then gpt no gpt3 came out in 2019
Speaker: And so I knew about it back then.
Speaker: And I was like, this is really cool.
Speaker: This is cool stuff.
Speaker: It can do cool stuff.
Speaker: I got access to the API.
Speaker: I was like playing with it.
Speaker: Then, you know, last, what was it like last summer or something?
Speaker: They were like, hey, we're coming out with chat GPT.
Speaker: Now, what that was, was the model released that was released in 2019.
Speaker: packaged as a chat bot for the general public to use without an API token access, if that makes sense.
Speaker: Yeah.
Speaker: Right.
Speaker: So this big trendy thing that came out last year was actually a few years old, and they just came out with an interface for it.
Speaker: That's all it was.
Speaker: Ah, so yeah, that's been kind of my intuition as I've talked to ChatGPT and as I've seen its output.
Speaker: It's kind of like, it's not necessarily that there's been this quantum leap in what AI can do.
Speaker: It's more just that like lots of us who don't know how to code are getting to access some of those tools.
Speaker: Yeah, yeah.
Speaker: Yeah, yeah.
Speaker: Which that seems to me like a pretty good thing.
Speaker: Yeah.
Speaker: And GPT-2, for instance, is a dumber version of it that's fully open source that you can just use however you want.
Speaker: But yeah, so chat GPT is just GPT-3 with a nice little wrapper around it for the layperson to use.
Speaker: Which is why also like when people use it, like I said, like they're trying to treat it like a bot and they're like, hey, Alexa, what's the weather today or whatever.
Speaker: But how you should really treat it is you're giving it an input and you want to get the output based on what you tell it.
Speaker: So like some people in a chat we were in, they were complaining like, well, it writes dumb, like it doesn't write at a very high level.
Speaker: And I'm like, well, did you tell it to write at an academic level?
Speaker: And they're like, no, I just said, explain to me what Bigfoot is or whatever, you know, I don't know.
Speaker: And, but you have to tell it what style you want it to write in.
Speaker: If you want it to sound like an author, you can say, Hey, can you sound like this author?
Speaker: It's great at that.
Speaker: It's great.
Speaker: Yeah.
Speaker: Yeah.
Speaker: It's great at mimicking styles and things like that.
Speaker: So.
Speaker: So kind of like trying to learn how to use it, you kind of have to not think of it as a chat bot.
Speaker: You have to think of it as like, okay, I'm giving it an input and I want a specific output.
Speaker: So how do I get that output?
Speaker: Yeah.
Speaker: Yeah.
Speaker: Do you see a, a future for like the prompt engineer?
Speaker: Do you think that that's a, a valuable skillset to learn?
Speaker: I've seen they're like paying like two to 300,000 for prompt writers now and,
Speaker: I think that could be a real job that happens, though it seems like a meme at the current moment.
Speaker: Like I would put all my eggs in that basket.
Speaker: But but I do think if you are at some kind of company and if you spend some time like figuring out how to write prompts and then told, you know, higher ups at your company, hey, I'm a good prompt writer.
Speaker: It might work out well for you, you know.
Speaker: Yeah, you'd probably just have to show them some outputs, right?
Speaker: Like I told it to do this and it did this.
Speaker: And I'm actually thinking of running a prompt writing webinar as part of my thing.
Speaker: I would attend your prompt writing webinar.
Speaker: I think that's... Because I have sort of some vague intuitions about how this thing could be used, but I haven't been able to...
Speaker: I haven't been able to make it do exactly what I want.
Speaker: And I can tell that I'm missing something about the architecture.
Speaker: Like I'm not catching what it's designed to do because I've seen it do some pretty extraordinary things.
Speaker: And I, I, I wonder if, so if somebody is coming out of college, right.
Speaker: And they're like, I want to, this AI stuff is cool.
Speaker: would you actually at this stage of the game with the tools as they currently exist, would you actually tell someone, Hey, linguistics is a pretty valuable thing to understand.
Speaker: Yes.
Speaker: Um, I'm very pro linguistics, but that may be, uh, my own bias, but like, I mean, like, you know, obviously like I think econ's cool, but I would never tell someone to major in economics.
Speaker: Right, right.
Speaker: So I was actually talking to my little nephew who just got accepted to BYU.
Speaker: And so he was asking me about AI stuff.
Speaker: And I was talking to him about exactly this.
Speaker: If I had to do it again, I would have majored in linguistics and minored in computer science.
Speaker: Just to get more of that code credentials behind me.
Speaker: But...
Speaker: That being said, there's very few there.
Speaker: I don't think there's any colleges that offer NLP as a as an undergrad focus.
Speaker: BYU being one of the only schools I know of that even has NLP courses in their undergrad program for linguistics.
Speaker: So most schools where you're learning linguistics, you're just going to learn Chomsky philosophy.
Speaker: And it's a you know, it's a Bachelor of Arts that you're getting.
Speaker: Right.
Speaker: But just due to the fact that BYU is like very anti-Chomsky for some reason, all the professors there hate Chomsky.
Speaker: I actually, I mean, so maybe we can, we can talk about this a little bit.
Speaker: I actually, you know, despite all of the sort of press around BYU and, and some of these professors who suck really bad, like most of the programs that I've been involved in or seen firsthand, um,
Speaker: they're, you know, they're, they're, they're lived because it's academia, but even, even to the granular level of like, are we about Chomsky or not?
Speaker: They're surprisingly like,
Speaker: clear headed.
Speaker: And, and like, because what you're saying about what you're saying about their approach to linguistics and like, Hey, let's teach people some actual freaking marketable skills that are deployable around this subject.
Speaker: Uh, econ was the same way.
Speaker: And like, so actually if, if I could make every employer understand what you learn at the BYU economics department, uh,
Speaker: I think that would be a really marketable thing.
Speaker: The problem is it's called an economics degree, which at most schools is basically just like the communist manifesto, like literally.
Speaker: And so, because we did regression analysis, we did like lots of data work.
Speaker: It was kind of a proto data science degree.
Speaker: And that was, honestly, that was like kind of what made me think, that was my first experience
Speaker: jaunt into like, oh, these are business questions.
Speaker: These are like technical questions.
Speaker: These are empirical questions that I actually find interesting.
Speaker: And I could actually, you know, build a career around this.
Speaker: I wouldn't want to, you know, neck myself all the time.
Speaker: And, and I think, uh, so, so yeah, that's sort of you saying that about the linguistics program makes you actually makes me kind of affectionate for our, uh, our shared alma mater a little bit.
Speaker: Yeah, yeah.
Speaker: Well, it was a very unique place for linguistics, like I said, and I kind of stumbled into it just trying to be lazy in school.
Speaker: So, but yeah, so this is, and maybe this is a good transition, but this is part of the reason why I've started Shaolin AI is for undergrad, you can't take a data science undergrad, even though there's a million jobs for it, right?
Speaker: you can't do a data analytics undergrad.
Speaker: There's a computer science degree where you may do that in one or two classes, but you're gonna spend 50% of your time learning C
Speaker: which you don't need to know if you're going to be a Python coder or an R coder, right?
Speaker: You don't need to know base machine code and the theory behind computational stuff.
Speaker: If you're just going to be running packages, who cares?
Speaker: The curriculum is definitely built around the tastes of nerds of a certain flavor.
Speaker: Right, and it's very academic in its nature, so you're learning...
Speaker: Like academically, why is this important when really you're trying to get a job as a data scientist?
Speaker: Why are you wasting time learning a skill that you're never going to use right now?
Speaker: If you're going to be a software engineer, yeah, you need to do the computer science degree.
Speaker: But we've got these this whole new class of jobs, not to even mention the A.I.
Speaker: class of jobs.
Speaker: That's like one step beyond data science.
Speaker: Right.
Speaker: that like you can't even get an education on in your undergrad if you want it.
Speaker: Now, there are plenty of like master's programs and things like that around, but you'd have to sit through four years, then go pay another however many millions of dollars to get your master's in NLP or data science.
Speaker: And once again, even there, a lot of those programs are very academic and research focused and not really trying to get you to get a job in the industry.
Speaker: So like, that's a problem like,
Speaker: We'll look to get PhD level people on our teams.
Speaker: And it's like, you know, they come out and they have no business sense.
Speaker: They have no clue what we're trying to do.
Speaker: And they're just like, well, this is an interesting thing I could write a paper on.
Speaker: It's like, well, we're not writing papers, you know.
Speaker: So like...
Speaker: Yeah, there's kind of this whole disconnect between the pedagogy of of universities and the actual jobs that are out there that are like fun, good paying jobs.
Speaker: I don't know.
Speaker: Yeah, yeah, it's and so to introduce that a little bit, you you came to exit with this concept for a boot camp because you you run boot camps professionally, right?
Speaker: You you facilitate
Speaker: some boot camps.
Speaker: So you know how the curriculum runs and you and I have compared notes on what your boot camps that you teach are like and what the boot camps I attended were like.
Speaker: And it's, they're all the same.
Speaker: It's, it's, there's a, there's a very clear, very well-defined set of things that every data science program needs you to understand.
Speaker: And it's basically like a couple of weeks of basic stats and
Speaker: a couple of weeks of Python, and a couple of weeks of let's bring the stats and the Python together.
Speaker: And then you do a capstone project.
Speaker: And so you had the idea of let's jump into that environment with... Because there's not like one solution to this.
Speaker: There's like...
Speaker: It's like mowing lawns.
Speaker: Like you're not going to run out of lawn care businesses because you need to have people to babysit the coders, to babysit the learners, right?
Speaker: So this is a business model where, you know, even though you've heard of a million different data science boot camps, there's not enough data science boot camps because it doesn't scale up.
Speaker: It doesn't universalize.
Speaker: Yeah.
Speaker: And yeah, so a few years ago, I started teaching boot camps, right?
Speaker: So I'm teaching people how to code in Python and trying to get them maybe a data analyst job.
Speaker: If they have some type of degree, maybe a data science job, right?
Speaker: Or if they're just super...
Speaker: with it you know and i and i've seen a bunch of success from it even though like the curriculum i was going through was sometimes frustrating in its quality i guess um and so you know i was sitting there and i was like um but but then also i i kind of was at a different job at the time and i had this this kind of existential dread of going to work in the mornings but then i was teaching a boot camp at night
Speaker: And I looked forward to it every night.
Speaker: It was fun.
Speaker: I enjoyed it.
Speaker: And it turns out I'm kind of good at teaching, I guess.
Speaker: And kind of where the idea sprung from initially was just how much I found out how much they were charging the students and then how much I was getting paid to teach the boot camp, which was like one and a half students worth.
Speaker: Right.
Speaker: And you were teaching like 30.
Speaker: And I was teaching like 30.
Speaker: Yeah.
Speaker: So I'm like, well, if I just get like half of this, like I'm I'm good to go.
Speaker: And so so so so.
Speaker: Yeah.
Speaker: So I came up with the idea.
Speaker: The business name is is Shaolin AI.
Speaker: You can visit the website.
Speaker: It's Shaolin dot AI named after the Shaolin Monastery or the Shaolin Monks because I did Shaolin Kung Fu growing up.
Speaker: And I think it's cool.
Speaker: Nice.
Speaker: I don't know.
Speaker: But yeah, also, it's about training.
Speaker: It's about all that stuff.
Speaker: Discipline.
Speaker: Courage.
Speaker: Courage.
Speaker: Yeah.
Speaker: Yeah.
Speaker: And so anyway, I came up with the idea that, hey, I could make a much better curriculum that's even more relevant to actual jobs.
Speaker: And I have my
Speaker: A.I.
Speaker: credentials and specialty and also there was a lot of guys in exit who have similar credentials or or maybe slightly divergent ones, but who kind of are interested in in getting either a side income or or branching out a little bit.
Speaker: And I was so so we've kind of put together a whole team from.
Speaker: From exit and elsewhere.
Speaker: of people that are contributing to the development of the bootcamp curriculum and kind of the behind the scenes side of things to make sure we have like jobs lined up for graduates and things like that.
Speaker: So it's kind of been a really cool project and a really cool aspect of Exit where like all these guys have come together just to help me out and
Speaker: Currently, we're not making any money, but people are just helping out because they're interested in the project or potentially down the line.
Speaker: It will come back to them where they'll have an opportunity to TA or teach at the bootcamp.
Speaker: And it's been a really cool process.
Speaker: So yeah.
Speaker: Awesome.
Speaker: So yeah, so we've got from the group, you've pulled play testers, you've pulled some people to talk to you about the business side, the marketing side, and you've pulled people who...
Speaker: are maybe either helping you develop the data science and NLP curriculum, or maybe do you have people working on any other curricula at the point?
Speaker: Or are they focused on the data science and NLP?
Speaker: We are.
Speaker: So the plan right now is we're trying to launch the data science curriculum next month is when we're looking to go live by the end of the month.
Speaker: And so that's where the major focus has been.
Speaker: But we've also got a bunch of web guys and other guys.
Speaker: And kind of the idea is we're trying to say,
Speaker: you know, there's a lot of bootcamps that can teach you how to do other skills, but like what will get you a job now and what's relevant to now, especially in kind of this AI landscape and stuff like that.
Speaker: So, so including additional stuff.
Speaker: So we have started working on additional bootcamps.
Speaker: So like crypto and, and, um,
Speaker: web development and things like that.
Speaker: So those will be coming shortly, but we're kind of focusing on the first one first because that will kind of get us out of the gate without complying things.
Speaker: Well, and I think that there's something to be said for as these tools become more sophisticated, and I'm not even necessarily talking about GPT, but just like the availability of some of these libraries to...
Speaker: you're technically sophisticated, but you're not a computer science guy.
Speaker: You're a word cell, right?
Speaker: Like myself.
Speaker: And I think genuinely one of the things that these tools open up is the ability for people like you who have, like a guy like you and a guy like me
Speaker: 30 years ago, we would have had like the pedagogical gift to explain this material, but we wouldn't actually understand it because it would be, you know, you'd have to be kind of a computer science guy to follow along.
Speaker: And I think computer science guys have no ability to communicate.
Speaker: None, none whatsoever.
Speaker: And so, and so creating by, by just shortening that gap and allowing you guys like you guys like me to jump across it.
Speaker: I think it facilitates just a whole universe of, of transactions that, that, that produce a lot of value.
Speaker: So that's, that's really exciting, man.
Speaker: Can you tell me a little bit about how, how you use the group?
Speaker: Cause like we didn't have at the time,
Speaker: like a protocol to hand you for like, Hey, this is how you find your partners and this is how you get started.
Speaker: So can you tell me about that process?
Speaker: Yeah.
Speaker: Yeah.
Speaker: And it's been kind of a learning process because, you know, I'm not necessarily, um, you know, a hardcore, um, entrepreneur in any sense, you know, I've just been a W two guy my whole life, you know?
Speaker: So, so a lot of this stuff was a learning curve for me, but, um,
Speaker: What was cool is we kind of just described the goal and and some people just showed up from that.
Speaker: And so people who are interested in in this the goal and and they've been super helpful.
Speaker: And, you know, I've gotten to know a lot of guys in the group much better through this process.
Speaker: But then also, yeah.
Speaker: kind of as we hit roadblocks or gaps in our knowledge base of what we had, we'd kind of throw it out to the different specialties.
Speaker: And there's enough people in Exit that, you know, someone knows something about something.
Speaker: So, yeah.
Speaker: So, yeah.
Speaker: So when I didn't know how to start an LLC, it turns out a couple of guys had like a bunch of things for doing that.
Speaker: So like the business side has been kind of cool.
Speaker: And then,
Speaker: Then working on the technical side, it turns out we have some other really cool AI NLP heavy hitters in the group that have stepped up to offer advice.
Speaker: Some really impressive technical guys, for sure.
Speaker: Yeah, I'm not going to name drop or whatever, but yeah, they're up there.
Speaker: So yeah, it's been really cool just to see who shows up.
Speaker: Some of the challenges have just been
Speaker: you know, being in this remote group, how do you like keep the communication lines open?
Speaker: How do you make sure everybody knows what's expected of them and what's not expected of them and stuff like that.
Speaker: But we've kind of been figuring out, we've kind of settled into like a core team now, I feel like.
Speaker: And I'm hoping that this project will eventually also give back to either the guys who join Exit who are interested in
Speaker: getting coding skills like they can come to a exit approved coding camp.
Speaker: Yeah, absolutely.
Speaker: You know, and also just, you know, potentially getting guys hired as TAs so that they can get experience in the industry.
Speaker: So like they have like a company with AI in the name on their resume and they can go right there and and and get out there.
Speaker: So.
Speaker: So, yeah, it's been cool.
Speaker: Yeah, well, I really appreciate you coming to talk about it.
Speaker: And so you guys, we're going to release it next month, though.
Speaker: So Shaolin.ai.
Speaker: The site's up.
Speaker: Yeah, the site's up.
Speaker: You can sign up right now.
Speaker: If you have more questions, basically just go to the website, sign up, and we'll go over it.
Speaker: The schedule is essentially three days a week outside of normal working hours, so you keep your day job.
Speaker: if you want, while you, while you learn how to code, essentially it's designed for beginners.
Speaker: But also if you have maybe intermediate skills and you're looking to maybe add kind of the machine learning and AI to your portfolio, it would be worth it.
Speaker: And it's a six month bootcamp.
Speaker: So after six months, you should, you'll essentially have like a really fully fledged GitHub that can be used as your portfolio for getting a new job and
Speaker: And we're new, but I've been teaching boot camps for a while.
Speaker: And we've had a lot of success stories of students that I've taught that have, you know, I had one guy who was a warehouse worker, you know, stuffing boxes with stuff.
Speaker: And now he's working as a data analyst at a tech company making a lot more money than he was.
Speaker: And we've got a bunch of stories like that.
Speaker: Well, it's really exciting, man.
Speaker: Like, I love to see these kinds of things take off because, you know,
Speaker: I love the way that it nourishes the group, right?
Speaker: Like number one, these guys, yeah, the guys who have the knowledge can teach, the guys who want to learn can show up and be part of it.
Speaker: And I think, so I just, I really appreciate you taking the initiative to set it up, man.
Speaker: And it's great to hear from you.
Speaker: So thanks for being here, man.
Speaker: Yeah, thank you.
Speaker: All right.
Speaker: Yeah, if you want to,
Speaker: get involved with what he's doing at Shaolin.ai.
Speaker: If you have a project like this that you want to come build in the group, we definitely have the lawyers and the accountants and the technical and basically any expertise that you want to start a business.
Speaker: We've got the guys in here and many of them are looking for projects to attack.
Speaker: But you can learn about all that at exitgroup.us and come check us out.
