844: In Case You Missed It in November 2024
38:08 Share

This is episode number 844, our In Case You Missed It in November episode. Welcome back to the Super Data Science Podcast. I am your host, John Krohn. This is an In Case You Missed It episode that highlights the best parts of conversations we had on the show over the past month.

In our first clip, I spoke to Dipali Vyas, who's Global Head of Data and AI at Korn Ferry. I wanted to know how executive search firms like Korn Ferry can deepen their relationships with clients and candidates while also keeping ahead of critical job data and doing what recruitment firms should be doing, finding the best candidates for the job. My own company, Nebula, also sits at the intersection of recruitment and AI, so I was extra keen to hear how the folks at Korn Ferry set out to resolve these questions.

In those 24 years, they've led you to today where you're global head of AI, data science, and financial technology, FinTech, at Korn Ferry. And people don't know Korn Ferry. It is one of the world's biggest recruitment firms. You probably know exactly the stats, but there's a few of these absolutely gigantic companies, and recruitment companies in Korn Ferry is one of them.

At Korn Ferry, you lead executive search and leadership consulting for strategic AI, data and analytics leaders across industries. Wow, that sounds relevant to our audience. You advise clients on talent management, succession planning, diversity and inclusion, and organizational design. You're an unbelievable person to have on the episode. Let me actually get to a question.

So as recruitment becomes increasingly technology-driven, particularly AI-driven in recent years, how should executive search firms evolve their relationship-building strategies with clients, with candidates, in order to maintain an authentic human element and fair evaluation?

while also trying to leverage data and AI to get those efficiencies. Yeah, it's a really great topic of conversation that's happening now in the job market. And honestly, there's...

so many different elements to this when it comes to recruiting in general. How are the firms handling it from an executive search or assessment or recruiting perspective? How are employers handling it in terms of what AI tools are they using to assess candidates? And how are candidates using these tools to get on top of the pile when they are searching for jobs? So

There's so many different things that are happening in the market. So I'll kind of break it down in a couple of different ways. I think every client that I talk to under the sun wants to understand AI. They want to use AI, whether it's to recruit talent, whether it's to increase productivity, whether it's to...

to optimize in some way, shape or form the way that AI is being embedded in their organization. Right. So, so AI is a topic I would say, you know, when you, when you look at the graph, right, like it's, it's on, on the vertical and it's on the horizontal, they want to use it everywhere and anywhere. What does that mean for candidates in the job market?

There was an interesting stat that had come across my desk recently. So, John, you know, there's almost a billion people on LinkedIn. Pre-AI tools for, and I'm talking about the candidate side, when a LinkedIn job post went up, roughly 250 applicants would apply to that job.

Post AI tools, because a lot of the candidates that are savvy, tech savvy, are now utilizing it to chat GPT their resume to really pass through these ATS systems. So that number went 10x, right? So now there's 2,500 people per application. Now, both sides are being burdened.

So coming back to sort of the origin of your question around what are companies going to look for and what do candidates need to do, I think the big question is,

How are candidates going to jump off of their black and white resume or their digital resume into a more authentic, you know, deliverable to actually showcase themselves in front of these clients for the jobs that they want?

My view and where I want to be very disruptive in this space, because I've been in it for 25 years or 24 years, is that I think the next gen is going to be video. I really believe that.

And I think that, you know, looking at platforms like LinkedIn, they're trying it and it hasn't been as successful for them. So they're doing it in a different way. They're trying to get the thought leadership on video. They're trying to do all these feet, the trying to embed it in the feed. So they're about the feed as opposed to the person.

And I think that's where a lot of, you know, people that are on the market and employers, leaders that are trying to attract talent are going to have to see talent differently and assess talent differently.

Yeah.

And so I've got a link to that one for you in the show notes. I've also got the Easy Apply Jobs Bot, which is another GitHub repo. It only has 500 stars, but could be worth checking out as an alternative. And then we've also got a couple of click and point tools for people out there who don't necessarily use GitHub and code. We've got Job Copilot and Lazy Apply. So you too can be contributing to that 10xing of applicants to jobs.

And do you think that that makes sense from the perspective of the candidate, Deepali? Do you think that people should be taking advantage of these tools or do you think it's just too much? It's just spray and pray. Do you think they should be more targeted? So as an executive search consultant and as sort of a de facto career coach, I

I'm going to lean towards no. And the reason why I say no is actually, I'll walk that back a bit. I probably have a one and one a answer to that. John, you've seen in the last 18 to 24 months within Silicon Valley and, you know, I probably even broadly a lot of layoffs.

I mean, we've been in the layoff game for quite a while now. In fact, my, you know, my TikTok went viral because I started talking about layoffs and I didn't want to just be the layoff reporting czar, but that's kind of where I, you know, got notoriety for, for whatever reason, but I'm bringing the real news of the job market. So yes, there have been a lot of really highly talented people that have been laid off and, and,

the stigma around layoffs, they start to panic and they need to apply to everything in order to land a job. So part one of that question is,

Can you use those tools to be more effective and productive in your job applications? Yes. The answer is yes there, right? If you want a 10 X you're because you know, you're, you're, you're at home and you need to apply to a certain amount of jobs and it's a numbers game and you need to get in front of these employers. Yes. Do it.

I think that the folks that have the ability to be more discerning about their job search need to think about how they're going to show up differently than those chat GPT wrapped resumes, right? Because they all start to look the same at some point.

And what's happening with these tools is you're able to download the job spec. You're able to upload your resume. You're able to match those keywords so you get past the ATS systems. That's effective if your resume wasn't getting picked up. More power to you. I totally agree with that. But then now you're going to have to jump through another hoop. And so there are things that you're going to have to do to show where those soft skills are more transferable.

And ATS is a term that you and I know very well. But if you're an executive or a recruiter, you would know it as well. But if you're a candidate, you might not. It stands for Applicant Tracking System. And it's a typically click and point tool that has these days lots of built-in AI bells and whistles with things for automatically based on keywords that show up in there, surfacing some of the applicants to the top.

Those were such interesting insights from Dipali, especially because I feel like the incumbent recruitment platforms are hitting a glass ceiling where finding and matching keywords in a candidate's application doesn't do enough to sort the people we need for a given role. At my company, Nebula, we've deployed much more rigorous approaches that use large language models to encode meaning in a job description and compare that with a candidate's profile.

This leads to a far fairer approach that is so much more nuanced than the way most existing data-driven recruitment methods profile candidates. You can hear our takeaways from this great conversation in full back in episode number 837.

Our next clip is from episode 839, and where we again touch on my own business ventures in AI, because in this one, I speak to Jess Ramos about developing courseware, which is something I do a lot of. Listeners to the show are eager learners, of course, and I've always had great feedback when we do shows where we get under the hood with course creators.

So I asked Jess about how she approaches making data science and AI topics accessible to people who may not have had so much exposure to the disciplines. When people are starting in a new career like data analytics or it could be data science, we've obviously talked about SQL as being an important skill. Do you think that that's the first skill that people should be learning or amongst the first skills? I guess kind of broadening from just SQL, you know,

What are the other things that people should be learning first? What should people be prioritizing? And yeah, is SQL like the first thing or one of the first things amongst that set? So if you're like brand new to data, like you truly don't have any foundation at all, I would say definitely start with like a little bit of data visualization, maybe play around in Power BI or Tableau or even Excel and just understand some very basics. So like columns,

and rows, different data types, how you can visualize data differently, maybe like bar charts, line graphs, a little bit of basic statistics like max, min, average, things like that. So if you're like brand new, I definitely start there. But

I actually tell people they should really start in SQL as long as you have a little bit of a data foundation, because I think SQL just broadens your data experience so well. You start to learn how data sets merge together. You get to learn a lot about data cleaning and how to reshape and transform data. So I think once you learn those skills, you can easily pick up anything else. So if you already know how to do it well in SQL, you can go into Power BI or

Tableau or whatever BI tool you want to use and apply those same concepts and transfer that knowledge over. So I do think SQL is probably one of the best investments because

That's a skill that all companies are going to use, like regardless of where you work. And it also gives you good knowledge that you can transfer over to whichever BI tool your companies work again. And then also, you know, Python or R later down the road too. Nice. So a lot of people, when they're starting in their careers, they would be thinking about...

educational programs that they could follow. So, you know, you just gave a great list there of the kinds of skills that people would want to be learning, but a lot of people, they want to learn amongst other people or alongside other people kind of, you know, having a teacher giving them guidance and mentorship, as we talked about earlier in this episode, the importance of mentorship. So there are lots of online courses and bootcamps out there on data analytics, business analytics, data science. You yourself are

have gone along what I guess we could call kind of the more traditional route of getting an undergraduate degree in math and then a graduate degree in business analytics. So

What do you think about those two different kinds of paths? I mean, I actually, I guess there's three. There's three to consider because there's, so there's what you were just talking about in your last answer. It actually is something that somebody could do completely unstructured where you're just, you know, you're chatting with something like ChatGPT even, you know, just kind of your own guided, self-guided path of education.

So one step more formal could be doing a boot camp or a collection of online courses that you curate for yourself. And then the most formal would be to get degrees, to go to a university and get a formal education, formal diploma, showing that you have, say, an analytic skill set or a machine learning or data science skill set. So what do you think about those various routes? And what would you recommend to different types of listeners?

Yeah, so I think that choice really does come down to the individual, as cheesy as that sounds. But I think everyone has different goals. Everyone has different financial means and different time restrictions. You know, if someone has a full-time job, they might not have as much flexibility with certain options.

But I would say that I'm glad I went to grad school. For me, it was a personal goal to go to grad school. I actually dreamed of that as a kid. Like I was like, I'm going to get a PhD or a master's degree when I grow up. So for me, it was very much like an educational goal that I had. And it was...

obviously a very structured way of learning data analytics. And by the time I graduated, I had all the skills I needed to go and apply for jobs, plus the added credibility of having an extra degree. So I think that was a huge plus, but I know realistically, financially, not everybody is going to be able to go to grad school. And a lot of people aren't going to be able to pause their life for a year or two and do a full-time grad program. So by no means am I saying that everybody should go to grad school, but

Um, I think when it comes down to learning on your own, I think the two kind of routes are a structured way, like a bootcamp, or there's kind of like

Your self-learning path where you can pick your own courses and stuff. I think if you have the discipline and the motivation, absolutely like curate your own learning path, like learn all the right skills, take like a few courses, maybe spend like a few hundred bucks on a few different courses that you know are going to be really good and set you up.

And that is totally enough to get a job in data. But it, of course, takes a lot of discipline, a lot of time. You have to build projects on your own and really practice and get those skills up to here so you can pass your interviews because you're not going to have the same credibility as putting a master's on your resume. But you're going to save a lot of money and probably a lot of time, too. I think the boot camp path is...

It depends on the bootcamp. I do not like seeing some bootcamps charging $10,000, $20,000. I mean, my grad program was $20,000. So I'm like, if you're going to spend 10 or 20,000 or take out loans, you might as well just get a master's because if you're going to make that kind of financial investment...

you should at least get a diploma for your wall and put some letters on your resume, you know? So I'm not a huge fan of predatory bootcamps that are very expensive for what they offer. But I do also think that there are very good

bootcamps out there. So shout out to Zach Wilson, his data engineering bootcamp. Like he's obviously very credible. His prices are very affordable. Like that's something that I would buy into versus one of these big corporations that's kind of preying on newbies. That is great guidance. Some more real talk from you in this episode. I appreciate your openness and, you know, uh, yeah, telling it like you see it, uh, you know, like

Like earlier in the episode, going into specific numbers on your salary. Now talking about these specific numbers and value that you get on boot camps. And I totally agree with you. There are definitely predatory kind of prices out there where, yeah, you could be getting a lot better value on either of the paths that you described.

Getting that graduate degree for the $10,000 or $20,000 or curating your own path. And as you said, you know, that requires some more motivation. Although it just occurred to me off the top of my head that potentially a way that you could very inexpensively, if you can somehow find even just a handful of other people

that are also interested in developing a career in data analytics, data science, machine learning. They could be people that you met online. You could literally post about it on LinkedIn and say, hey, I'm thinking about going into a career in data analytics. I come from this background. Here's some of the resources I was thinking of maybe learning or let's together come up with a course plan and hold each other accountable. You could, like you said, for hundreds of dollars...

You could develop all the same kinds of skills in a $10,000 or $20,000 boot camp or even a $10,000 or $20,000 master's potentially. And that independence, showing that independence, you're going to be developing a lot of skills there yourself that are...

Either employable skills showing that you're able to organize a group or independently as an individual be able to curate the right resources to succeed. I mean, that is a highly employable skill. But simultaneously, those are the same kinds of skills that allow you to be a great entrepreneur and to be making money on your own. Totally. I don't know. So a number of different ideas there for people to sink their teeth into. Yeah. I hope I didn't talk too badly about boot camps.

I mean, there's, I mean, and you know, there's also, there is probably also, uh, you know, while I would think carefully about it, there probably also are scenarios where you think, okay, you know what? Um, I have this career break right now. I've got three months or six months cause I'm on gardening leave from, uh, you know, I was, I was working at a big bank and they've given me gardening leave for six months. Um,

money's not a problem for this person because they've just left an investment bank or whatever. They left a software developer job at an investment bank and they're like, I want to be a data scientist. I want to get into machine learning. And you don't want to take that year or two years to get a master's, especially if you'd be pursuing it part-time. So you think, okay, well this bootcamp, even though it's a bit more expensive, I can get immersed in this right away. And often with that kind of price, they do put a lot of effort into partnerships with industry companies

which is something that you that's kind of a big part of I think what you're buying with that price tag. Yeah, I agree. I think the right boot camp can be really good for somebody, especially because some people do want that structure. They want to be told exactly what to learn, how to learn it and when to learn it. So I think that's great along with the industry connections. But I think once you get into like the 10,000, $20,000 range, that's when I'm a little like,

Is it really that much value? I don't know, but that's just my take. I wouldn't spend that much unless it were for a master's. Jess's opinions about the different pathways resonated with me because while I've taken a more traditional path by getting a doctorate focused on machine learning, I've met a ton of people in the field who have taken other routes into data science and AI.

I feel that we have a lot to learn from each other because all our life, educational and professional experiences combined are what make collaborative projects so much better informed and long-lasting and allow the AI systems we're developing to be so much more impactful. All right, my next clip is taken from episode 835 with Brian McCann, who is CTO of You.com, Y-O-U.com. Yet again, this clip is on a topic that I personally love in it,

Brian and I discussed the potential for AI models to help generate proteins especially designed for tasks that today only the natural proteins in our bodies could carry out. In addition to generating text, you've repurposed language models for protein generation, which makes sense to me as, you know, I have a biology background, a neuroscience background.

And so, you know, I'm aware, maybe not all of our listeners are aware that the proteins in your body that do all the functional stuff for you, that all every imaginable thing that your body can do happens because of, well, except for some small, some relatively small exceptions, but generally speaking, proteins are doing all the work and the, and proteins are a, a sequence just like they're a one dimensional sequence, just like a character string, right?

You know, made up of these things called amino acids and each amino acid has slightly different properties, but you basically, you create this chain of amino acids. You could think of them as like letters of the alphabet and they, yeah, they allow you to create the vast, the incredible amount of functional capability that our bodies have, you know, this ability.

the proteins that allow your eye to see versus your liver to detoxify alcohol, your skin to do all of the things your skin does. You know, I could obviously talk, you could go on with examples for hours. There's a lot of biology out there. There's a lot of things that our body does. And all of this just encoded by these one-dimensional sequences of a relatively small number of amino acids, 20 something in humans. And

So something that's interesting, you have this connection to MoonHub that we talked about earlier, and this is just a stab in the dark. I don't know what your answer is going to be here, but about a year ago, I went to Berlin and I interviewed Ingmar Schuster, who has a startup that does, they're in the business of doing this. They're in the business of creating proteins. Axozyme?

Yeah, exactly, Exosign. And when I was there with Inkmar, he mentioned your co-founder, Richard Socher, having recently been there at the Mirantics AI campus in Berlin. So I don't know, there just seems like a connection there in some way, which could be spurious. Yeah, I'll have to meet him one day. My connection to the protein world has evolved tremendously

primarily through co-author on the Progen paper from the Salesforce days. As you said, we took the control model, trained it for protein generation, called it Progen. We were thinking exactly the way you were thinking. There's way more sequences of proteins than secondary and tertiary structures, which are expensive to generate. What if we could make a model that just depends on the sequences? That spun out into a startup called ProFluent.

The CEO there's name is Ali Madani. Great guy. They've been doing great work because we had shown that you could generate these proteins and you could synthesize them in a wet lab. You could get proteins that did not appear in nature but had better fitness.

lower energy. So they were better overall, better at the tasks that they were designed for. And that became pro-fluent. So I'm still connected to that world, not through Ingmar directly, but I love to talk to him. Maybe we've met and we could re-meet. But I think to generalize a little bit, continuing to push a lot of these

of deep learning versus machine learning. For me, that move is getting out of the way of the algorithms as much as possible. Instead of designing features, don't. Just make them parameters. Then we've got out of the way of transformers. Instead of having this recurrence and our conceptual biases, let's just have an architecture that

more or less just does matrix multiplications and then allows for sharing of information in context. Context, context, context, keep adding context, larger context windows, context vectors, whatever it is. Unify as much as possible because whether it's vision and language or just different parts of language like code, the fact that code helps with logical tasks in language models, right? Helps you do better on LSAT questions. It's kind of interesting.

The fact that literally taking control, which was a model trained on English, and then using that to train on proteins was a much more stable training curve and a faster learning curve than training from scratch. That's odd. What does English have to do with the sequence of amino acids? Well, there's something general enough about learning how to do alignment and do sequence generation or something going on there. Similarity, just at the core of it all.

And I think we need to keep pushing all of this into the natural sciences, like more and more. So biology, chemistry, physics, I, you know, if there was, I don't know if I've said this before, or at least like publicly or in recording, but I think the same way I felt in 2013 about

about the deep learning transition being good, but our imposition of conceptual tasks and such on how we were doing AI being bad. And so we need to move towards more unified stuff. I feel the same way about science. There's something about the way that we've been doing science that's a little bit constrained by our perceptions and our projections

onto the world that perhaps AI, broadly speaking, some sort of computational algorithmic approach could unlock for us. And it might feel very similar. It might feel at first that it's less explainable. People always go back and say, well, the move from machine learning to deep learning was less explainable. Mixing all the data, oh, it's less explainable. Oh, we can't explain what's really in a word vector anymore.

I think there's an opportunity to go after something really, really fundamental about our understanding of the universe by getting out of the way and just giving as much context to these systems as possible. I keep a topology book with me and a couple different branches. I feel like there's something missing that we probably can't figure out, but maybe AI can for us. We might not explain it in our current terms,

but it'll be a much better predictor of how things work and we'll find use cases for that. It makes perfect sense to me. I think you're spot on, 100%. To try to make this maybe a little bit more concrete or explain it in a slightly different way, when we go to university, you studied philosophy and computer science in separate departments and those separate departments

cover a standard curriculum that has evolved over time. This is what's important in philosophy. This is what's important in computer science. You're going to learn algorithms and data structures. Everyone's going to do it over here. But those constraints of saying, philosophy stuff belongs over here in this building with these people and computer science belongs over here. Some people like you

Do a study of philosophy and computer science, and in some way your mind might be able to then make connections between them and have interesting ideas about semantic meaning and how natural language models could work or unified models could work. But we can only get exposed to so many different things, humans. But an AI system can scale anything

way, way, way, way more than us. And it can not just be learning philosophy and computer science, but it can be learning every subject and putting all subjects into a high-dimensional vector representation. Or a context window, yeah. Right. And somehow, yeah, in ways that we might not be able to understand, it'll be able to make predictions or assimilate ideas across all knowledge in a way that a human never could. I think so. And there's...

Yeah, so I'm looking forward to the next few decades of science as we learn how to incorporate these tools more and more.

Maybe our fundamental understanding of the universe will change and we won't necessarily run into some of the problems we have with it now. I love Brian's approach to using AI in his thinking about how it could solve so many problems that the human brain is simply not equipped to handle. His episode gave me a lot of pause for thought, and I know a lot of listeners who have been loving it too. And my final clip from November is taken from episode number 833 with Dr. Martin Goodson.

Martin is chief scientist and CEO at Evolution AI, though he has had many other roles in learned societies like the Royal Statistical Society, which he credits as opening a whole bunch of doors in his career.

With this in mind, as well as his work with the European Commission, I asked Martin what he makes of the apparent disconnect between who we might call the celebrities of AI, like Elon Musk and Bill Gates, and the actual deeper knowledge of the discipline that so many actual AI experts have.

My last question for you is related to the public's perception of AI, which seems to be influenced a lot by high-profile tech personalities. So I, for example, at the time of recording, I had been watching Bill Gates' Netflix special called What's Next, which is at least the first episode is all about AI. And it became, I have been laughing to myself about

Because, I mean, with apologies to Bill Gates, who is a very impressive individual and quite learned, but it became quite obvious, at least at the time of this show being filmed, which looks like it was about a year ago, it looks like it was 2023, based on kind of like the chat GPT related things that they're talking about. And it's pretty clear that Bill Gates does not have an understanding of AI that I expect the vast majority of the listeners to this podcast have.

And so that was a really interesting experience for me because I would think that he is the kind of person that would understand these things well. But in the first episode, the funniest part for me so far is Bill Gates has this yellow notepad and he has like the words like train in a box and then like

like written off of the box or something. And the people who are filming it, as he's explaining a bit about these same kinds of things that we were talking about earlier, supervised learning, reinforcement learning, they made the directorial decision to use footage zoomed in of his notebook on this thing that is like, it's like some important, oh, Bill Gates notebook. Look at this great schematic that he drew. And I'm just like, what? What?

The word train in a box. Um,

And so, yeah, kind of my expectations of at least 2023 Bill Gates expectation, my expectation of Bill Gates, 2023 knowledge of AI, uh, was much less than I would have anticipated from him. And so you have a quote, uh, from another podcast that you did where you said, it's true. I don't really know anyone in the field of AI who thinks of Elon Musk as an expert on AI and, you know,

That is the kind of person that I would expect to be more off the mark than Bill Gates, I guess, on their understanding of AI. But

Yeah, we have this problem where the public's perception is being influenced by these kinds of high-profile tech personalities. It doesn't seem like people like Fei-Fei Li or Jeff Hinton, who really know what's going on, share the same kind of reach as these other kinds of people who the public seems to think, oh, Elon Musk, Bill Gates, these are AI experts. Yeah. What was the question? Right. I didn't really ask a question, did I? No.

Well, I guess I just evoked your point. You asked the questions, right? I don't think I can ask a question. I can ask a question. What can we do about this? Yes, yes, yes. That's it. What can we do about this? So you mentioned before that I got this machine learning meetup that I run, you know, part of the organizing committee, let's say.

We have lots of academics who come on and give talks, some of them give really great talks. It's really amazing actually, I really love it and the talks are absolutely amazing. But I have to say that we quite often, sometimes let's say, sometimes we get academics on who give talks and they're really overhyping stuff.

And it's very easy if you're outside the field, like some of the people that you're talking about, to read some papers. But you could become an expert, like a self-proclaimed expert quite easily by reading stuff on archive. You know, you could sort of read those papers and stuff and get to become a self-proclaimed expert quite easily. The problem is, one of the problems is, there are many problems, but I'm just going to highlight one. One is that...

the academics are publishing stuff and they're over claiming. The titles of the papers are just wildly, they don't have the evidence to claim what they're claiming. I won't mention names because it's unfair, but we do have people who come to the meetup and they give a talk and they just make up stuff that's just very overhyped claims. Once you put them under scrutiny, it just falls apart. They don't have the evidence.

Both you and I, we met in a world-class research institute in genetics. It was world-class. So we learned at first hand what it means to be really rigorous and what the scientific method is.

at the highest level. I'm not saying that I was the highest level, working at the highest level, but we definitely worked around people who were working at the highest level. And we took on board a lot of lessons then. And I just feel like, and I actually sometimes get quite annoyed with some of our speakers, I have to say. We have someone who recently came, they gave a talk and they said,

Oh, you know, I'm not going to talk about any of the technical stuff here because I don't think you're going to be interested. We don't have time to talk about the technical stuff. You know, you're in a technical meetup. You should be under scrutiny. And I think we all need to do better in terms of

raising the bar of the scientific culture within machine learning. And I think that if we did that, we would do much better. Like this would go towards some way to solving the problem that you're talking about. Back in our day, working in genetics, people used to write papers in your university and then you'd have a PR department who would like make up these massively over-claimed headlines that would go into the newspapers.

But now just people skip the PR team and they just do it themselves. The academics do it directly. They just cut the PR people out of the job. And I just don't think that's a positive. So I guess we should, yeah. What should we do about it? We should stop doing that.

All right, that's it for today's In Case You Missed It episode. To be sure not to miss any of our exciting upcoming episodes, be sure to 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.