1000: Ten Years of the Super Data Science Podcast, with Jon, Kirill and Special Guests

14:46Jon Krohn:And so I guess the thing that I would love to be able to do with AI, and I think today, an individual like someone like you, Jepson, has probably already figured this out. You should just show us to the open source repo that you've already created that does this. Because I think what a lot of us need and what especially the people that are most easily taken advantage of by AI and by digital systems would benefit the most from some kind of agent that acts as a buffer between them and all of the information that they consume, the actions that they take, that's technologically possible today. And to basically allow somebody to wake up in the morning and instead of going to their Instagram feed or their LinkedIn feed or watching the news, it would be like, what would you, figuring out together, what would make you happiest today?

15:34Jon Krohn:Maybe it would be handwriting a letter to a relative or to an old friend and helping you do that and send it off or going for a walk in the woods without your phone. You know, like these kinds of activities that it's hard to do, you know, help us prioritize and actually live a life that makes us feel happy as opposed to just kind of getting sucked into a digital stream. Anyway, super long answer. What do you think about that, Jepson or Carol?

15:58Kirill Eremenko:Well, I was going to say on John's comment, there's a funny black mirror thread to pull on

16:03Jon Krohn:because if AI can set the perfect day, it can actually tell me everything to say to my wife and it can buy all of her presence on time.

16:12Kirill Eremenko:And so there's a balance there. But I agree generally with that. I'd love to hear, Carol, I'd love to hear your perspective on what would you fix? Oh, thanks for the question. It's good. I was thinking while Jones was speaking. I think there's a lot of really good causes out there in the world that help feed people, help with homelessness, help with education, like charities, basically charitable causes, non-for-profits that are operating all around the world. And I've noticed that the smaller ones where there's like one or two or three people like that are passionately traveling and actually doing this work on the ground, they seem to me trustworthy.

16:53Kirill Eremenko:And like, I see their updates, I see what they're doing. And it feels like the money that you give to them, it's being put into the right things. But the bigger ones, like, I don't know, maybe from UNESCO or from somewhere else, There's a lot of administration fees. So I'm always hesitant to donate money to a large organization because I don't know what percentage of that money is actually going to go towards all the bureaucracy and all the admin in there. So I think what would be really cool is if we could use AI to build not-for-profits or build charitable organizations that are scalable, but also with minimal overhead so that the money that goes into them actually goes to the purpose that it's supposed to go to.

17:38Kirill Eremenko:It's like at least, you know, maybe not 40 % or like, like right now, I think it's like 40 % goes to admin fees. Maybe we can cut down the admin fees down to like 5 % because we have this like AI just operating the whole thing. So I think that like we already have money funneling into these causes, but a lot of it gets wasted along the way. So if we can increase efficiency of that, I think that would be like a problem that can be solved with two, I don't know, two months, maybe a bit longer, but with$10 billion, I think, or a billion dollars, I think that would be enough.

18:09Jon Krohn:I like that. I guess both of your answers speak to efficiency.

18:13Kirill Eremenko:John talking about efficiency of time and attention and Kirill talking about efficiency of value to society.

18:22Jon Krohn:Agents are getting smarter every day, but even the smartest agents get stuck without the right context and the right tools. That's where Notion comes in. With the recent launch of custom agents, Notion became the collaborative AI workspace where teams and agents work side by side. And now, their new developer platform is turning that workspace into infrastructure developers can build on. What sets Notion apart is that the collaborative workspace and the platform you build on are the same thing, with permissions, context, and governance baked in from day one. Workers, for instance, are Notion-hosted sandboxes where I can run database syncs without standing up my own infrastructure.

18:55Jon Krohn:That means I can pull guest research, episode analytics, and my consulting client data from all the scattered systems where they live, then keep them synced in Notion databases automatically. My agents and my human team work from one single source of truth. Learn more about Notion's developer platform today at notion.com slash superdata. That's all lowercase letters, notion.com slash superdata to try Notion's developer platform today. And when you use our link, you're supporting our show, notion.com slash superdata. So Jepson, you just came on and asked that question, which is great, but you actually broke the rules by doing that because you were supposed to tell us who you are.

19:33Jon Krohn:So I can do a little bit of an intro. So Jepson Taylor is the, I'm almost 100 % sure he's been on the show more than anyone else. For people who are confused because when you search super data science, Jepson Taylor, you don't find enough episodes. Also look for super data science, Ben Taylor. so this is some of the older ones especially when Kirill was hosting and Jepson is one of the most extraordinary people we love having him on the show he always brings great perspectives and actually on that note I actually think I know where you're calling in from presumably Utah Lehigh so Mike in the chat asks this question which I'm now showing up in the stream he's curious to hear your answer to the question that you asked as well Oh, okay.

20:22Jon Krohn:So I'll give a quick answer. So a month ago, an engineer that I look up to, he said that the bottom 90 % of AI developers will not be competitive with the top 10%. And I had never heard that before, but I agreed with him. And so what I would like to see is I would like that to not be true. So I would like if you are someone diving into the AI tools, you should be just as productive and be able to build just as amazing stuff as someone who says they're in the top 10 % or the top 5%.

20:56Kirill Eremenko:Because there's a long chasm between having Codex or Claude do something interesting and actually having it do something of real value. So that's what I get excited by is how do you bring the full power and capability down to everyone? Because I love the idea of miracles being built on a weekend by anyone, regardless of their background. That's exciting for me.

21:21Jon Krohn:I love that. Yeah, yeah. And it helps us understand your question better as well. Because when you said like the bottom 90%, I didn't know what you meant bottom of what. But yeah, you kind of meant like bottom of developers, AI engineers, that kind of thing. I was answering it kind of in a more general. I don't know what I was answering in terms of who's like, who is the top 10 % overall and the bottom 90%. I don't know exactly, but I just mean, you know, there's probably like some, yeah, anyway.

21:45Kirill Eremenko:Well, I like both of your answers and it's great to see you guys. I don't want to monopolize the time, but appreciate the show. And John is one of the best hosts in the industry. He is incredible. Yeah, he is the best. I always like cheating and trying to have him come to my NYU course because he does such a great job moderating so I'll let you guys jump back into the feed thanks Jepson, great seeing you again thanks man, catch you in a bit

22:16Jon Krohn:alright, so thanks everyone for making great use of the emoji in like all the people watching you're making great use of the emoji, I love that I hope that somehow that shows up in the final recording as opposed to just here in the live stream I guess we'll find out, it's our first time doing it um so carol how do you feel about me inviting on i've got my 96 year old grandmother uh she's here in the show yeah amazing and uh yeah so for listeners who don't already know her she has been in episode 900 episode 800 several others i can't remember if they're so nicely on the hundreds but it's great that she's also able to join us for 1000 here quickly let's do it And so, yeah, so I think my sister Stevie is with her.

23:02Kirill Eremenko:Let's answer a non-video question while your grandma joins. How about that, John? Perfect. Sounds great. Okay. We've got a question from Seema. What is the most surprising thing you learned while doing guest conversations? I'm going to adjust that question and I'm going to say, what is the most surprising thing? Because it's quite broad. What is the most surprising thing you learned about yourself while doing the guest conversations? John, question for you. Do you have something? Do you know? kirill about myself yeah oh it's been a while since i've uh hosted the shows um i guess that i was very shy at the start um and then it just kind of like that there's a ted talk about this uh i forgot it's it's not brene brown it's someone else but it's like fake it till you make it it's kind of like if you are feeling imposter syndrome about something or for myself like Like you just, just the best way to deal with it is just like, do your best and keep doing it until you yourself believe that you can do it.

24:02And then, you know, it will just come together.

24:05Kirill Eremenko:So I think that's, that's one thing I've learned about myself that I keep using in life still to this day. Like you'll never be ready enough. I will never be ready enough to do the thing, the next thing that I've planned to do. and I can spend months and years preparing for it or I can just start doing it and do my best and not be afraid to fall face down into the mud and get up and do it again. That's just the fastest way to getting the result that I'm after.

24:34Jon Krohn:Well, and you do an amazing job of it. Kirill has so many, you know, in addition to this podcast now going on 10 years, Kirill has lots of other ventures. Probably lots of people have experienced the millions, you know, he sold millions of copies of his courses in Udemy. You know, best-selling Udemy data science instructor of all time. So it's certainly working for you, Carol. All right. So we did, we figured it out here. My grandmother is joining the call. Here we go. Hello. Hi there. How's it going? Yeah. So we've got my sister, Stevie, who's actually never been on the show. She probably should have been.

25:08Jon Krohn:She's done lots of amazing things in her life. And my 96-year-old grandmother, who's, yeah, episode 800, 900, and some others. And one of the most beloved guests, I actually, I ran into someone, in San Francisco recently, who said that the only episodes that they listen to over and over again are the episodes with my grandmother. So yeah, it's, you guys had a question for us, right? You said - We did. And also I want to tell you where we're calling in from.

25:31Kirill Eremenko:We're calling in from about 10 feet above where John is right now.

25:40Kirill Eremenko:I love that. Everyone with a natural background. Yeah,

25:46Jon Krohn:exactly. There could be a beautiful wooded area behind me, but instead I went with a green screen. It's true.

25:52Kirill Eremenko:So our question is, with the benefit of a thousand episodes collectively under your belt, what piece of advice or insight do you wish you had Kirill before you launched and John before you took over as host? That's a great question. Piece of advice we had like before launching the podcast? I think, okay, I have one, I have one. I think the advice would be start a YouTube channel in parallel to just audio recordings of the podcast because the first, I think 500 episodes only had audio. And then it was John's idea like after a year of hosting or so to add video to the podcast. And now the YouTube channel is growing really big.

26:36Kirill Eremenko:And if somebody had, you know, like told me at the start, put in a little bit of extra effort, put the videos on YouTube as well. I think that would be great. I don't regret anything about it, but I think that would be useful advice. Thank you for the question.

26:51Jon Krohn:It's so crazy how we kind of learn these things slowly and they seem so easy in retrospect. I think that happens with a lot of decisions. But even though we started doing video around the time that I started hosting five years ago, we didn't focus on video production to any extent until less than a year ago. It's only been 10 months since we started thinking about video production first. So we, up until 10 months ago, this pod, we were, everything that we did about the show, the intro, every aspect of production and operations was geared to the audio only listeners experience and video was kind of an afterthought.

27:29Jon Krohn:But 10 months ago, after lots of study, we switched to being video podcast first. we still made sure that any decisions we made translated nicely to audio only. And so if you go to Spotify, if you go to Apple podcasts or wherever you listen to audio only podcasts today, you'll hear exactly the same thing as you see in the video episode. But now when you watch it on YouTube, there's lots of, there's visual things that show up on the screen that are nice. And we're planning on adding more and more of those. I know Mario, who was here earlier, a video editor is excited about that. And YouTube is huge.

28:02Jon Krohn:I don't know if people, if regular listeners know this, but YouTube is the world's biggest podcasting platform. No, I didn't know. And it's been huge for us. So since doing those changes 10 months ago, we went from 20 ,000 followers or subscribers on YouTube to now over 250 ,000. And so, yeah, it's pretty interesting to think that if we'd done that not 10 months ago, but 10 years ago, where the show would be in terms of subscribers. So yeah, really cool thing. I don't know if I have a great answer to that question. Kirill, it seems like you kind of inhaled to say something else.

28:34Kirill Eremenko:No, no, that was all my answer. I do want to ask your grandmother a question.

28:41Jon Krohn:Oh, wow.

28:42Kirill Eremenko:Yes. I would love to know, thank you for coming on the show and thank you for coming on the show many times. I would love to know, how has John changed since he started hosting the podcast five years ago? He loves me more.

29:01Kirill Eremenko:He really cares more.

29:04Jon Krohn:I love it. I love it. I was always mean to her before I started hosting the show. I said, I would always say to her, I deserve a podcast. Why does nobody just hand me a popular podcast? And I was always angry. So now I'm nicer to people. I love everyone more.

29:21Kirill Eremenko:No, you're nice because, you know, you have to keep your grandmother coming on every year. And she has to be nice to you on those episodes. Thank you. That was a lovely answer. Thank you. That was going to be Baba's question. Like what? When are you going back? When I'm going back on the show. Oh, when you're going to be back on the show? As soon as Jonathan needs me.

29:46Jon Krohn:Nice. Soon. Hopefully soon. I'll just really quickly answer the same question that you asked, is that one piece of advice that I wish I had, and it's actually advice I got from Kirill, maybe six months or a year into hosting, was I was pretty rigid about following like the questions that I had prepared and the podcast wasn't very conversational. And I think now many years later, I'm still trying to get better all the time and listeners definitely provide me with tips on ways that I could be making the show even better. But I think focusing on making things conversational has hopefully made a big positive difference to everyone on the show.

30:23Jon Krohn:It certainly makes the experience more enjoyable for me and not taking notes up until very recently. I was always taking detailed notes of everything everyone was saying. And Kirill Riley was like, it looks like you're distracted. It looks like you're not paying attention. And so now I just rely on AI tools to do the transcript and take the notes, which works really well. Fantastic. Yeah.

30:45Kirill Eremenko:Well, congratulations, guys. And just some information from upstairs. It smells like we have a pretty good dinner waiting for us. I love it. Thank you very much. Thank you. Bye. Thank you. Bye-bye. That was awesome. All right.

31:02Jon Krohn:Nice. Do you want to pick the next guest, Kirill? I don't know if there's maybe some questions that you saw that you'd like.

31:07Kirill Eremenko:I'd love Josh Jansen to come onto the video. He's one of our instructors and one of our bootcamp alumni. He was actually doing a workshop yesterday on, what is it called? Spec-driven development. Josh, jump on. Josh is one of your big fans. Also, he's been listening to every episode of the podcast for the past two and a half years. Yeah. Josh, if you're there, jump on and give us your question. Josh, what's going on?

31:36Jon Krohn:Good to meet you. I guess Kirill already knows you well.

31:39Kirill Eremenko:Yeah. Yeah. It's been awesome for my career to been listening now for two and a half years. It's really helped me in my career, helped me dig in. The bootcamp was awesome last year as well. The question I have for both John and Kirill. In this, you hear the term SaaSpocalypse, right? And I work in corporate America, and we have the ability to make really good internally generative AI applications. At the same time, we have vendors that our company has been working with for years, if not longer. And of course, every one of these companies has an agentic tool of some sort that they're trying to bolt on and it's a constant um um kind of challenge internally to talk about what makes sense for us to you know buy and add on or what makes sense for us to build now that we have the ability to deploy use agents and really move fast and nimble so is there a framework that you would recommend that we explore that you know could help evaluate these different opportunities as we're looking at, you know, whether to buy versus build.

32:53Yeah.

32:53Jon Krohn:So there was a guest on the show last autumn or late summer. Her name is Larissa Schneider and her episode number. Let me look that up for you quickly. So Larissa Schneider was on episode nine, three, two in October of last year. And Larissa Schneider is the co-founder and COO of a San Francisco based firm called Unframe that raised 50 million, they announced to raise a$50 million just a couple of weeks before she came on the show. And what their business does is they, they have kind of off the shelf software solutions that all require some kind of customization within the enterprise in order to be successful.

33:41Jon Krohn:And so they have tons of experience with dozens, if not hundreds of enterprise deployments. And from across those, Larissa, if you ever get a chance to see her speak, maybe like people should follow her on LinkedIn or whatever to get a sense of the great insights that she has on how to have successful deployments and how to decide on when to buy versus build. But essentially, this is kind of a bit of a difficult thing to describe when it needs to be audio only, but it's very easy to see visually, if only I could do that. It's this two by two matrix where, um, along the bottom of the matrix, it's like, how, how much does this product differentiate your business?

34:23Jon Krohn:And then the vertical axis is how much does this new feature or product, uh, how, how much time or cost complexity is there in creating it? And so something that is both that doesn't differentiate you and that also is high cost. That is the, you should just avoid those projects entirely. Projects that differentiate you a lot, but are slow to build, those are some things that you should build. Those are things that you should do internally. So your employees, your AI engineers, your software developers, data scientists should work on those kinds of projects. You should buy when it's the diagonally opposite quadrant, which is something that is fast to build, but not very differentiating.

35:13Jon Krohn:And then something that could be both fast to build and highly differentiating, that's where you should partner with a firm like her company on frame, or dare I say, like my own consulting firm, Y Carrot, where you can quickly get this differentiable capability by partnering with a consultant or something like that.

35:34Kirill Eremenko:That's interesting. Because if you think about at corporate America, they like quick wins, right? Like something that can be turned around in weeks versus years, right? Or months. Um, and both of the instances you mentioned there with quick wins was actually partnering with an outside company to get it done. Is that correct? Based on the quadrant plot, I probably should wrote down the quadrant plot, but speed is actually for both of those instances would be partnering with a third party or a consultant to get it done. Is that right? Yeah.

36:08Jon Krohn:If it doesn't differentiate you and it can be done quickly, you might as well just buy that. Like there's no, you know, that is a place it's, it's something that capability is going to be table stakes for everyone in your industry in no time anyway. So why spend time on it? But if there's something like, if you think about, you know, the biggest tech companies in the world, your Nvidia, Google meta, they made bets on particular kinds of functionality where it would take a long time or would cost a lot of money to build up that data set or build up that moat, create some kind of network effect, and it's paid off.

36:48Kirill Eremenko:Oh, that makes sense. It makes sense. So appreciate that.

36:53Jon Krohn:Nice. Thanks, Josh. Thank you. All right. So let's see. are there any questions that you saw Kirill that you were looking to have answered?

37:01Kirill Eremenko:Yeah I like these two questions that are linked. We've got a question from Vpin.

37:07Jon Krohn:Oh wait sorry Geert is here.

37:09Kirill Eremenko:So put a pin in that.

37:11Jon Krohn:Sorry here we go Geert. Hey Geert what's up? Hi how are you doing? Or it's probably more of a is it like a ha sound? Yeah it's one of those sounds very difficult to pronounce anywhere other than the Netherlands. Exactly I always say when we have Dutch guests on they are the hardest names to say for sure yeah i have three of those sounds in my in my in my name nice so so hear it from the

37:33Kirill Eremenko:netherlands what's your question for us yeah i was a professor of uh human ai collaboration

37:39Jon Krohn:at technical university um and one of the big questions that i've always had was how can we use the educational systems to teach students of all ages in a safe way to learn how to use

37:55Kirill Eremenko:AI rather than having them use it and then not being able to figure out when it's appropriate or when it's hallucinating? What can we do?

38:06Jon Krohn:Yeah. So in terms of younger, so I realize you're kind of asking for people of all ages, but a really interesting episode that we had recently was episode 983 with Tracy Walker Griffith, who is the principal of a school in Boston. You're nodding your head here. So it sounds like you might've listened to that one. Yeah. So she has great tips for you know there's specific things that they've learned like i think i can't remember this cut off exactly but it was something like kids under the age of 10 they shouldn't be using ai tools directly because they can't grasp that this isn't like a conscious being that's speaking to them um but for kids that young ai tools can still be really helpful for their teachers to be developing curricula.

38:48Jon Krohn:I really love that episode, but it was all for the juniors. Right, right.

38:56Kirill Eremenko:But now it's a really big decrease in the amount of positions for engineers

39:04Jon Krohn:or data scientists. And the universities and the high schools are not keeping up with teaching those experts actually to be the experts in the new age. For sure. Yeah, I think a lot of programs out there aren't going to be helpful. So I think it's kind of, if you're looking at university programs or some kind of paid program for data science or AI engineering or software development, you've got to be really careful to make sure you're picking one that is actually helping you succeed. And so, for example, we had Kyungyoung Cho on this podcast recently in, that was episode, it was a recent one, 977, Kyungung Cho was on the show.

39:51Jon Krohn:He's one of the most prolific AI researchers in the world right now in terms of number of citations. And he teaches at New York University, NYU, and he did a first year course, or sorry, not a first year course. I think it was a second year course for computer science students on machine learning And the previous time that you taught that course several years ago, you learned, you know, from kind of out of a textbook and in the traditional way of learning. But now he has the kids do everything with Gen.ai. Like you have to use, I think, I think NYU has like Gemini subscriptions for everyone or something like that.

40:30So everyone had to be using Gemini to be, you know, automatically creating solutions.

40:38Jon Krohn:and so you know I think that's the kind of skill that we need in workplaces and also you're saying it was interesting that you said that it's difficult for engineers and data scientists to get jobs it seems like we did an episode on this very recently episode 994 it's an episode on is AI putting new grads out of work and it seems like part there's like a number of factors but AI might not be not be the cause of less hiring it can be that it was like a golden ticket for a long time. If you got a programming degree, it was very good job security, very high salary for a long time. And now that big benefit has kind of come down and computer science degrees are now more in line with other careers in terms of their employment levels.

41:26Jon Krohn:And part of that is also probably not just because of AI, but because of overhiring in the post-pandemic boom that tech companies did. So there was like this overhiring that happened and now they're kind of right-sizing. So I think that there's still a really bright future for people who are doing technical things, but you need to be learning the modern skills. You need to be using Gen.AI. Platforms like Udemy, courses like Curels, courses like Ed Donner's, the superdatascience.com platform itself, these are great ways for people to be learning the modern skills that actually matter. and if you master those skills, you have the ability to have a bigger impact than ever before.

42:05Jon Krohn:And so you're great value for a company to hire you because you can use these tools effectively and do so much more, have so much more impact than ever before. Really long answer. Kirill, I don't know if you have any thoughts.

42:20Kirill Eremenko:Off the top of the head, of my head, the answer would be just time. Like give it a few more years and the models will be good enough that like already, like what, you know, compared to two years ago, the hallucinations are down like tenfold probably. So I wouldn't stress about it. Like by the time, if I was like an entrepreneur looking at this problem, I wouldn't do this problem because by the time I come up with a solution, uh, the AI models would be so good that it's not a problem anymore. It's kind of like, remember at the start when, uh, AI came out and people at universities were using it to write essays and everybody was like oh that's not fair and blah blah blah uh it's it's not ethical and then there was i really clearly remember there was a startup which said like we will build we're building a tool to recognize when text was ai written versus when it's human written we know how to do that like good luck with that like where are they i would love to know where that startup is now like it's it's just like you're fighting you your your solution or your kind of like intention of startup is fighting against the tide you never want to build solutions that are fighting against the tide of time and of technological evolution and this is one of those that i i'm seeing it's it's in that direction like you want to be like a rising tide floats old boats you want to find things that your solution will become more valuable with time rather than less valuable it's like a short window of time where a solution like that will be needed before hallucinations are you know negligible yeah so what we saw in the university was when that first boom happened that regulations were saying ban everything and they're still recovering from actually the first ban was done within weeks and now i'm trying

44:07Jon Krohn:to gradually adopt it taking so long especially also because the people that make the regulations that have no idea what AI can actually do.

44:17Kirill Eremenko:Yeah, yeah, it's sad. It's sad. But like, it's normal. The pace of, there's two, like I talk about two speeds of AI. One speed of AI is the research speed and that's the speed we hear about and the speed that is the fear, the basis for fear mongering. Oh, you know, Claude can do this or Mythos came out or Codex can do this and blah, blah, blah. Like every week something new and like you get really anxious and lost in, why do I even need these skills? Like, what can I do? Where is my future going? So that's the speed of research. But then there's the speed of adoption. The speed of adoption is like, like speed of research is like the bullet train flying through the countryside, whereas speed of adoption is like a traffic jam on the highway.

45:03Kirill Eremenko:Like, it's not going as fast, you know, whether it's universities or enterprises or government. like I don't think that AI adoption is going to just magically happen very quickly. Like there's legacy systems, there's employee change management, resistance, pushback. Like as you said, people don't even know what AI can do. There's so many roadblocks to adoption that whenever people tell me that, hey, I'm worried about AI, I'm stressed, I'm like lost and fearful, fall, I just tell them, don't worry. Just look at the speed of adoption. You have to beat the speed of adoption, not the speed of research.

45:45Kirill Eremenko:Don't try to keep up with the speed of research. Just be faster than the speed of adoption. And it's as simple as it has always been, whether it was machine learning five years ago, data science 10 years ago. It's not as hard to be ahead of speed of adoption. That's all you have to do.

46:03Jon Krohn:Thank you so much for the answer.

46:05Kirill Eremenko:Thanks, Hirt. Thank you very much.

46:08Jon Krohn:Nice. All right. So there are, I think we should probably maybe just have like one or two more. I don't know how you're feeling, Kirill. Sounds good. Unfortunately, there's lots of great questions and lots of people, but we just can't have the episode go on forever. I guess this shows we'll have to do this again. So Adrian asked, episode 2000, what do you think the data ML AI landscape will look like by then? and that's like I don't know I think R will have a comeback yeah exactly oh yeah things are moving so fast I mean there's the METR check out their charts on how quickly AI capabilities are progressing and it's pretty mind blowing you know the task length that it would take a human to do is doubling kind of every few months now.

47:07Jon Krohn:And yeah, the task length that, yeah. So if it's a task that took humans eight hours a couple of months ago, already the cutting edge systems could handle it. And now it's handling 16 hour tasks. It's really crazy.

47:21Kirill Eremenko:But at the same time, it's like some things are, hey, Adrian, hey. Can you hear me? Oh, Adrian is from Romania, from Bucharest.

47:30Jon Krohn:I know that yeah welcome so yeah you've asked a lot of questions in the chat actually I was thinking that one that might be a good one to answer is your one on if you had to recommend just one thing to someone trying to break into the field today what would it be and I guess you mean kind of the data science field

47:48Kirill Eremenko:well data science

47:50Jon Krohn:AI machine learning there was someone recently on the show where they kind of had five key steps for getting hired. Oh, wait. No, actually, that was a five-minute Friday that I did. That was me. That was me, John Krohn. That was me. That was in the same episode. We were talking about it not long ago. Episode 994, how is AI putting new grads out of work? And I think we were just talking about that with Heart a few minutes ago, that same episode. At the end of that episode, I had five tips for what people could be doing. but there's basically like off the top of my head it was things like making sure you have a great github repo with projects that you've actually done yourself that's a critical thing and then another critical thing is leveraging your network it's so much more valuable so you know going going to in-person meetups career events meeting people in person is so much more valuable than clicking that linkedin apply button one more time i think you know the vast majority of the time that just goes into like a filter.

48:55Jon Krohn:And increasingly, you know, companies, my previous startup where I was co-founder was called Nebula and we were making tools that made it easier for hiring firms to be able to sort through lots of applications. And the reason why they're doing that is because, I mean, it's, it's helpful anyway, but it's become even more and more and more important because now there are so many tools that allow you to create cover letters and resumes that are specific to a specific job. And so people can be sending out thousands of applications a day. It just, yeah, it creates, there's just so much noise in the traditional application process.

49:29Jon Krohn:So if you want to break into the field today, I think it's in person. Okay. Thank you very much. Yeah. Thanks, Adrian. I really appreciate it.

49:37Kirill Eremenko:Thanks, Adrian.

49:38Jon Krohn:I've got a really quick question here I can answer. So Mohamed asked, John, do you have any plans to release a revised edition of your book with simpler, more reader-friendly language? I think the book is Deep Learning Illustrated. I have no plans to do that, Mohamed, but the good news is you can now do that with LLMs. You can just take whatever, you know, any part of that book from the very beginning. You could just, you know, get an electronic version of the book and then throw it into LLMs and say, you know, starting with page one, you know, make this into simpler language. LLMs could do that amazingly.

50:13Jon Krohn:And it could even factor in your particular background, Mohamed, whatever education background you have, whatever work you've done in the past, you could let the LLM know about that and you can get more reader-friendly language bespoke exactly for you. And even better, you could have it in the style of Snoop Dogg rapping it.

50:34Kirill Eremenko:And I recommend Notebook LLM for that. you can turn the book into a podcast even using something like notebook.

50:42Jon Krohn:Sheila asked, she asked me what inspired me to found YCaret, why the name? And then Kirill, she has a question for you, which you can kind of get prepared for there. So YCaret is my AI software consulting firm. And there's a huge amount of demand, Sheila and listeners, for people who can go quickly from concept to working AI product. And so that is what we do at YCaret. It's been, you know, it has not been hard to get traction. I'm sure having this podcast doesn't hurt. And when we mentioned it from time to time. Yeah. And then you asked about the name and the YCaret comes from YHAT. So every machine learning model, statistical model, when it makes a prediction, it is the symbol that we use, the mathematical symbol, is Y with this little hat on top.

51:37Jon Krohn:And that little hat in computer science is called a carrot. Somebody named Adam mentioned that in the chat. And that carrot, it's on a US English keyboard. It's above the six. They spell it C-A-R-E-T, but that's not very fun. So we call the company Y carrot, like the vegetable. And that allows us to use the carrot emoji liberally on social media and even in emails. And then, yeah, so there's a bit of, maybe Kirill, it's a good time to explain to Sheila. So she says, why did you join the Super Data Science site? So maybe you could tell us a little bit about the history of superdatascience.com and how it relates to the podcast.

52:16Kirill Eremenko:Not much to tell, really. Like I was teaching data science courses on Udemy and I thought back in 2015, and I thought, oh, it would be really cool to have a brand for this site. And this URL was available and I took it. That's it. end of story.

52:34Jon Krohn:All right. Well, Carol, if you don't mind, we have, uh, my dad is actually just knocked on the door to come in here. Maybe we can give us a final, he's, he's interested to know how we met. And, uh, so we could fill listeners in on that. Oh, hello. Here's my dad. Hey dad. Where are you calling in from dad? Can you hear me? Yes, we can loud and clear.

52:56Kirill Eremenko:Yeah. I'm calling from Canada, from Stratford, Ontario. And, uh, Obviously, I'm biased when it comes to cheerleading. And I feel a great pride that compelled me to sort of insert myself here. I only periodically look in, given my background. Things tend to sort of go over my head. I mean, it's not my expertise. but the thousands show, you know, stemming from your incredible inceptional work, Carol, and John's sort of following in your footsteps immaculately, just, you know, leads me to wish you guys the most heartfelt congratulations to you, John, of course, and Carol. Very nice to see you here.

53:56Kirill Eremenko:And, of course, to all involved, you know, Natalie, Mario, and many others that make this such an informative and successful podcast. I mean, episode 1000. Wow. Bravo. Bravo, gentlemen and team. And, yeah. So my hat was on to you. Super J to science podcast hat. And I wish you ongoing success. I know you'll have it. And yeah, I was just curious because I didn't know how you met. And so that's maybe a social, it's not a technical question, but that was my curious moment.

54:41Jon Krohn:Nice. Kirill, do you want to do this or do you want me to do it?

54:43Kirill Eremenko:Sounds good. I'm happy to do it. Thank you, sir. It's very lovely to meet you. And thank you for John. You raised him really well. the way we met is John had just published his book, Deep Learning Illustrated. It was making the rounds on LinkedIn. And I noticed I invited him, John, to the podcast. And then we had a great episode recording. And I remember back then, John was kind of getting his feet wet with podcasting with this show called um artificial neural network news network a4n where him jepson jepson taylor and a few others were creating this um kind of like talk show discussion and they were on episode four and i thought um i think yeah i thought like uh this is so interesting um and i can see that john really wants to host a show and i can look at thought sat in the back of my mind And then towards the end of the year, I felt for myself that I'm like, I think I'm done with this podcast.

55:46Kirill Eremenko:Like I just felt I had this intuition. I think I've given it everything I could give. And I thought, okay, who can replace me? And the first thing that came to mind is like, John Crone. I don't even know why. Like I met him once or twice and like in video. And I was like, I don't know why where this came from. I'm just going to trust my intuition. So I pulled him up, made him the offer and John just accepted. And that's pretty much it. And we only met in person last year, actually. So we had been working together for four years before we met in person. Well, you're bringing the world together.

56:19Kirill Eremenko:And what a fantastic thing.

56:22Jon Krohn:Kudos to you guys. Cheers, Dad. Thanks for coming on. Really appreciate it. You're very welcome. I'll see you soon. For dinner. Yeah, I actually, I looked up while you and my dad were chatting there. My dad's name is Willingham, by the way. I don't think we mentioned that on air. And so the very first message, you sent me a message in April 2020 that said, hey, John, I've heard about your work and your book. Would you like to join me on the Super Data Science Podcast where we could promote it to 10 ,000 plus weekly listeners? Kind regards, Kirill Aramanco. Oh, nice. Not AI generated, for sure.

56:56Jon Krohn:No, if you had, it would have been pretty bad at that time. Yeah. Nice. All right. Well, this has been a really fun experiment. tons of tons of questions i hope we mostly touched on the ones you know that would be most interesting to the audience and there was a lot of overlap between questions so hopefully um you know if we didn't exactly answer yours or if you didn't get a chance to be on the show this was really fun i enjoyed doing it so kira we should probably do it again and hopefully folks who didn't get to come on last time they get to come on next time it looks like uh we actually there's even family members of people cal aldubay who's been on the show a couple of times i think his mom, Kathleen, she's in the chat.

57:38Jon Krohn:And she had really nice things to say in the chat and also some great comments and questions about Cal's content as well as ours. So yeah, great to have all this. Kirill, I don't know if you have any parting thoughts. Sounds good.

57:50Kirill Eremenko:Yeah, no, loved it. Loved it. It's fun. Thank you everybody for joining. Yeah, I don't think you'll have to wait a thousand episodes for us to do it again. Hopefully, John and I will have a chat about it and see if we can plan and add all of these soon.

58:04Jon Krohn:Thanks very much, everyone. Wow, a thousand episodes. Can't wait to see what the next thousand bring for the show and for our whole industry. In episode 1000, Kirill, myself, and a range of regular listeners from my grandmother to rock star AI entrepreneur Jepson Taylor discussed what we do with$10 billion of AI investment, rules of thumb for build versus buy, tips for breaking into the AI industry, and Kirill's advice for anyone anxious about the pace of AI to focus on beating the speed of adoption rather than the speed of research. I hope you enjoyed this landmark episode to be sure not to miss any of our exciting upcoming episodes, thousands to come, I'm sure.

58:46Jon Krohn:Subscribe to this podcast if you haven't already, but most importantly, I hope you'll just keep on listening. Until next time, keep on rocking it out there and I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon. Thank you.