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Now, onto the show.
Daniel: Welcome to another episode of the Practical AI podcast. This is Daniel Wightnack. I am CEO at Prediction Guard, and I'm really excited to follow-up today on on a topic that, of course, has come up on the show many times and even in our last episode came up, very specifically, which is how well our educational system is doing at kind of training students for this next generation of jobs, especially as it relates to data science, AI, and and kind of the latest wave of technologies. And, of course, I'm I'm very privileged because I'm live in a town where there's a great university, Purdue University. And so I'm I'm very privileged today to have with me two representatives from Purdue University and the data mine.
That's that's mine as in pickaxe, not not mined or mime. The data mine over at Purdue got Mark Daniel Ward, who is the executive director of the data mine, and also Katie Sander, who Katie Sanders, who's the chief operating officer. Welcome. Great to have you all.
Katie: Thanks for having us.
Daniel: Yeah. Great to great to have you here with us. Well, I kind of teed up a little bit of that in the sense that there's a lot of us in industry who are, of course, being bombarded by all sorts of changes to our jobs, the way we do our day to day tasks, whether that be coding or everything from HR to administrative things to even manufacturing jobs and all sorts of things. All of these are sort of being transformed by this latest wave of technologies. And I know, we'll talk more about what the data mine is here in a bit, but you all are kind of uniquely positioned to really think about this next wave of students that are coming into the workforce and and particularly how things like data science, AI, etcetera, are shaping maybe how they need to be trained or how they should be prepared for the workforce.
Could you give us a little bit of a sense of your viewpoint on that and maybe how people that aren't in the education space could think about you know, some of your perspective on what is changing or needs to change or what you're hoping to see happen.
Mark: You know, one thing we see is higher education is changing just the way industry is changing. The students, of course, still love coming to college and graduate school and all the fundamental learning that occurs in labs and courses, research centers on campus. But maybe more than ever, I don't know how you quantify that, but certainly there's an appetite among students as a generalization to see how the skills really translate into real world work. I think students and of course their parents and families are really interested in what they learn in college and then graduate school is going to get them and when they go into the workplace, how that's going to translate into careers more than just that initial job. What's the long run look like?
How are they building a foundation while they're here at Purdue for what they're gonna do in the rest of their career? So higher education is also wrestling with those changes as well.
Katie: And I think in previous years, you know, you were good if you had an internship, your junior year, your senior year, but now the way things have changed, that's just too late.
Daniel: Yeah. And I know that your focus at the data mine is related to, of course, data science, maybe the wider perspective of data engineering, AI machine learning, whatever it is. Why is that set of skills something that maybe students from across different departments or majors should have exposure to in a real world sense? You might think, Oh, well, computer science majors, certainly they need to know about AI, but why is this a concern that should be kind of interdisciplinary?
Mark: Data's just pervasive in industry, you know, aerospace engineers and people on manufacturing floors and out in the field and so on. Everybody's career is being remade because of the pervasiveness of data. So it's not just the computer scientists by any means. It's people wanna do predictions and build tools and have sensors and all kinds of automation and workflows that everybody's leveraging data driven tools and methodologies, you know, in their sector of industry.
Katie: One thing I learned, I'm not technical at all, so let's preface with that, is that data looks different to an engineer than it does a data scientist. So being able to have students that are able to look at data differently, then they can come up with the best outcome for whatever project they're working on or whatever work that they're doing.
Daniel: Yeah, and would you find, I guess you work with a lot of corporate partners. This is certainly something that I've seen in industry is often the team that you're working with, of course, is not just made up of developers or others, but there's people from a variety of backgrounds that hopefully are providing input to a problem. So is that part of what students need to be ready for is talking about maybe technical topics or data topics in an in a place where there's a diversity of of backgrounds? I don't know. Is is that part of the the goal there?
Katie: I would say yes. Definitely, giving students the opportunity to work with data no matter what major they're in. We have over 160 majors in our program. We have some, actually some marketing projects where marketing teams are working with data and they need assistance with that. So, you know, we can pull marketing students in on that as well as data science students or engineering students, whatever they're looking for in order to get that blend and to get all, you know, different insights as to what the solution could be.
Daniel: Yeah. I think that that is an excellent perspective. And I think it leads naturally into some discussion of the data mine itself, because I was very intrigued to learn, like I said, we've been talking about this on the show, but the sort of need for creative approaches to helping students wrestle with real world problems, but also wrestle with those in the context of AI and advanced technology, which is maybe outside of what you could do in kind of a standard classroom environment. And I was I was interested in in what you all are doing because it does seem like a very interesting creative approach. I see on your kind of description of the data mine, there's maybe a whole lot of words that we need to pick apart here, but there's, you say it's an interdisciplinary living learning community, again, open to students from every college.
But these students work alongside corporate partners and they solve real world challenges. So maybe if we just start with a little bit of picking apart that definition, We already talked a little bit about the interdisciplinary portion of this, but just to give us a sense of maybe growth and scale across the whole university, could you help us understand the scope of students that are involved in this? Katie, you mentioned even marketing and other things. Maybe, Mark, could you help us understand how this has grown and how pervasive or ubiquitous it is across the different departments at Purdue?
Mark: You know, DataMind grew out of a grant we had where we had 20 sophomore undergraduates a year living and working across the street here from where we're located in the Convergence Building. And those 20 sophomore undergraduates every year produced a ton of research outputs with faculty all over the campus. Well, one of those early career students at the time wanted to go work with the company. The other 99 worked with faculty. That really set something thinking in our mind about how we could also have research with external partners.
So when that grant was winding down that last year, our university administration was so supportive and said, well, what if we open this up to anybody on campus? How quickly would this grow? And we had 100 students that first year and then 600 and then 800, and no one's required to take this program, the state of mind that we offer. Students see a lot of value in it. We sense that the employers, the people hiring them for both internships and full time employment, see a lot of value in the skills the students are learning.
And just coming back to that interdisciplinary piece, each student kind of has their role in the larger team, an engineer, someone from Daniels School of Business, someone from a data science background or communications or liberal arts. The students kind of find their niche in a larger team within the larger corporate environment that they're working, and it just seems to be a model that's successful.
Daniel: And could you speak to maybe, Katie, the general structure of these teams? We're talking about, I think if I remember what you said as we were chatting before recording, that there's potentially over 2,000 students now that could participate in this, which is pretty incredible. Wide reaching. How are those students from different departments, interdisciplinary? How are they formed into teams?
And what does a team mean, I guess, is my question.
Katie: That's a loaded question. So we have a couple of different options, right? So we have our one credit seminar course where students are learning different skills, R, Python, SQL, Bash, depending on what level they choose. And then we have the three credit hour corporate partner program where students are working eight to 12 ish students per team are working on a data science project with a company. They are meeting weekly.
They have a fifty minute meeting with their mentor and then they have a little less than two hour lab that's led by a TA where they're working in agile methodology, you know, trying to solve whatever problem the company has presented.
Daniel: And is that structure either one of you could let me know. Have there been multiple iterations of that to learn? I'm very interested in kind of the a little bit of the backstory here because I'm sure there are others, maybe even at other universities, but other corporate maybe folks listening to the podcast that are wondering, you know, what is the work that's going on in universities to really figure out how to adapt to what students need to do. So was there iteration in that, challenges, things that you tried and then changed? Any thoughts on that and kind of the history of how that developed into what it is now?
Mark: I guess I could speak to that. You know, in the beginning we had undergraduate students, Katie already mentioned students from 160 some majors on campus now, but we didn't have graduate students involved. We often didn't have very much faculty involvement. Our relationships with many of these external partners were just starting to build. We have really deep partnerships now with some of our friends in industry and students will stay involved often throughout their undergrad study and sometimes even choose to stay at Purdue for the graduate study to be in DataMind because they recognize what an opportunity this is.
We've definitely evolved and continue to evolve. In addition to the way we've evolved working with companies, I'm sure we'll speak to this more as we get into the podcast, but we're trying to help other universities around the state and also outside of Indiana to adopt external facing types of models like the data mine in whatever sector of their state, whatever sector of the economy that they're interested to partner in.
Daniel: Interesting. And are there other examples of these sorts of external facing programs that are spinning up around the nation or even globally that you're aware of?
Katie: Yeah. We have, right now we have a partnership with, Youngstown State University. They are pretty much operating their own, you know, data mine instance. And then we have Data Mine of the Rockies, which is led by another faculty member here on campus who is working with students at Purdue and Colorado on different projects. We also have quite a few schools in Indiana that are working with us in different ways.
We have some that are folded into our seminars, some that are folded into projects and some that are working with faculty on research projects. We also have a grant that we're working with other institutions across The United States. And it's very similar in that some are working on projects, some are folded into our seminar course. But we are working with, I believe it's over 60 institutions, if not more.
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Daniel: Well, Mark, Katie, great introduction to kind of a little bit of the structure of this as it relates to kind of this interdisciplinary and and scale component. I'm interested in this next component of the description, this sort of living, learning community. Help me understand what that means and why it's important.
Mark: You know, early when we had our first grant before there was a data mine, I wasn't aware of how many resources the university puts towards students having a great first undergraduate year at Purdue. And then similarly, as students get ready to graduate the end of their junior year and into their senior year, wow, we have a ton of resources for students making that transition into the workforce. But there's this murky middle, especially one sophomore year where students are taking their hardest courses in their major, kind of finding their footing. You know, they've gotten through their 100 level courses and they're thinking about how their major is gonna transition into a career. And it's not just Purdue, but by no means.
It's well documented that that's a time when students start to question and struggle and really wrestle with what's their career going to turn out to be. So we love that many of our students choose to live together and they work together. While Ms. Sanders and I are at home, you know, with our respective families where all of the staff members close-up shop and we have dinner and go to sleep, the students are still working. You know, at 10:30 in the evening, they're bonding and they're learning from each other.
And that's when the real work in some sense happens. So by having many of the students live together in a residence hall and study together and faculty often have office hours there and we have our meetings there and the university has been so supportive, we're even building a second residence hall completely devoted to these students' experience. We think we've kind of been able to reshape some of those struggles students would have in an intensive science and engineering environment that is often corporate facing. And they end up feeling at home and finding their friends often who become lifelong friends and such. It really enriches the experience.
Daniel: Really interesting. Prior to this conversation, if you were to ask me, hey, what, all right, you're going to transform sort of data science and AI across a university campus. Probably the first thing that would come to my mind is not like build a residence hall. But it's interesting to hear like how this has actually kind of organically, it seems like somewhat organically come up. And maybe that's, is it part of the intentionality that you have of like, there's interdisciplinary students, maybe they're in their own buildings and different classes, but there's this kind of cross interdisciplinary space where they are, but you're also having, like you say, a lot of the meetings there and that sort of thing.
So have you found that the students associate that space, yes, with their living, but also kind of these projects and what they're a part of kind of in an impact sort of way. Like we're together, living together, working on these projects that have a wider impact. What is your, I don't know, any experience of how that sort of sense of place influences the students' view of the projects? Any thoughts?
Mark: I could say one thing that comes to mind, I think very fondly to our first year working with our friends at Cummins, because they would bring literally sometimes two van loads of Cummins employees all the way up from Columbus, Indiana to West Lafayette, a substantial drive of a couple hours. And they would meet with the students in a room in the residence hall where many of the students were living. And I could just imagine if I were a student, wow, I have people from one of the largest companies here in Indiana who are coming here to see me. Of course, it puts some pressure on them, but on the other hand, it shows that Cummins trust them and they see them as the future workforce at a major corporation like Cummins. And moreover, our Cummins friends are some of the nicest people that we work with.
I spent a large portion of the morning, now six years later, talking to my friends at Cummins about what we're gonna do next year. That model where we are fortunate to meet very good mentors in industry who care a ton about our student success has tended to provide another layer that maybe a regular faculty member might not have in the research team. Students are getting feedback from people who aren't faculty, and it's really well informed because it's informed by the industry experience. So I think that's a really key part of why the model's been successful is not something Katie and I or our team is doing, but rather we are surrounded by good friends who seem to believe in this model and even more believe in our students and their potential to contribute and make a difference.
Katie: I would likely say, I would say two things. One being the students get more sense of a community, right? When you're seeing the same people every day, you're walking down the hall and you're thinking, oh, that's so and so, or you like walk down campus. And I know there's thousands of people on campus, but it's sometimes nice to see a familiar face. And I think that sense of community really helps with the projects because they're able to dive deeper into those personal relationships.
That being said, one thing is we have our Indianapolis, Data Mine of Indianapolis, and the mentors are meeting with students on-site because of the close proximity to many of the companies, which has been like transformational, like down there, like it's been transformative. The students love it. The companies are getting that one on one time. We can't always do that here in West Lafayette because of the location, because of location. So that's been really great.
And I forgot to mention that earlier that Indianapolis, you know, we started that January '24 with seminar and then kicked off the corporate partners program this last fall. And it was very successful and a great experience for students, especially students that are coming in their freshman year, they get that experience firsthand.
Daniel: Yeah, it's been really interesting to hear, you know, just anecdotally, similar sort of things on the industry side when we've been engaging customers and when we're able to meet them on-site, to their conference room, right? It's like that sort of interaction has such a meaningful contribution to building that relationship and rapport and the trust that can be built there that is really, really difficult in a virtual environment. And I know many people are good at that and sometimes there's not the chance to be in the same room. But I definitely think that instilling that in students' minds of the kind of continued importance of some face to face interaction and building that relational component is really key. That's very encouraging to me.
This kind of, I guess, naturally leads into the discussion of this next bit of the definition of the data mine that I've been using, which is this corporate partnership. We've talked about the interdisciplinary student teams. We've talked about even how those students are living and working together in these interesting, you know, living, learning communities, that that are sounds like are expanding. What about the the corporate partnership side of things? Maybe if you could just help us understand the mechanics of how that works.
I know some capstone projects at different universities or student projects at some universities, I've heard from colleagues in industry, sometimes they can be viewed as, It's good that we can help these students, but there's no benefit to the company, but maybe it's good that we can help the students. But this is definitely a different view, or at least what I've seen from what's going on at the data mine, a different view of what student projects could be. So maybe in a general sense, like how do you think that universities and corporate partners can actually engage together in a way that is mutually beneficial, yes, to the students and their future career, but also to the corporate partners?
Katie: I would say that a lot of the projects that we get, they're not mission critical projects. The experiential learning piece is not mission critical, but they are projects that, you you'd know, really like to get to, but you don't have the time to get to, you don't have the manpower, things that have been on the back burner, things that, you know, need revived a little bit, or something that you've always wanted to do, but just, you know, haven't had the time to do. Those are the type of projects that these students work on. And the students are so smart and innovative. Like it's crazy to me, honestly, because when I was that age, I was not doing that.
Like, so it's just been, you know, every company has a different reason why they are coming to the data mine. Some of it's a talent pipeline. Some of it is like, look, I just wanna hire some interns and this is a great way to get connected with students. And you're, you already know their work ethic because you're working with them for the nine months prior to maybe them starting an internship. Some people, you know, some companies are, well, we have this project, let's just try this and see if we get it, see if it works out.
And it's awesome when it does work out, right? So there are different reasons why companies will join and, you know, our retention's pretty good as well. So I think that speaks to the work that the students do.
Mark: I was just gonna say, feel like we're growing with some of our companies, you know, like before I came into the podcast, I went down the hall, the office suite for Beck's Hybrids, which is just on the catty corner from our office. And, you know, when we first started talking with I think they had on the order of 300 employees and they just hired their one thousandth employee this year. We're just a very, very small part, but we feel part of that growth. We really feel that in our team and they always make us feel like we're part of the family at Beck's. When you were first broaching this question, kind of what hole are we basically filling with some of these partners?
You know, yesterday I was in Chicago and not naming any names, but I met with the company for the very first time who has 75 employees. It's not a huge company, but the challenge, the gap is they don't have any data scientists yet. They have one intern and the intern's just amazing. But if they had full time folks on staff who could leverage AI and ML and do some of the predictive analytics and so on, they recognize that they could go from being one of the players in that industry to becoming a leader. And if they take a chance on us at DataMine, they can try the students on for size and see what kind of research and development they can do.
And it's not just they're being nice to the students or helping them with the capstone like you said, Daniel. It's more like, okay, we actually sense there's a real opportunity to advance our business here with these folks at DataMine. So when we're able to make impacts like that and the students' work is really valued by the company, I think that's when we've hit our sweet spot.
Daniel: Yeah, that's great. And just to give a sense, I mean, I know there's probably some corporate partners that can't be mentioned for confidentiality reasons or other things, but either of you, I don't know if you can give a sense of kind of how many corporate partners you either are or have worked with and kind of the range of sizes and just a sense of industries maybe, because it is interesting that similar to what you said, Katie, I remember being an undergrad and doing projects. I was not exposed to working with actual real companies outside of if I did pursue an internship or something like that formally. But as part of the actual university side, I wasn't exposed to that. So I really, just from a personal standpoint, had no idea how a lot of industry even operated or worked or that sort of thing.
So I don't know, could either of you give us a sense of that, types of companies and numbers, that sort of thing?
Katie: Last year, we had 88 projects with about 60 different companies. And then I don't know, Mark, if you wanna maybe take the rest of that. I know that part. I know the numbers part.
Daniel: That's that's you know the data at the data money. Yeah.
Katie: Yeah. I don't know how to work with it, but I know the numbers.
Mark: We try to be really affordable. I mean, we, you know, we have not doubled our number of companies in the last year or two, but what we've done is we've gone a lot deeper in our relationships with the companies. And many companies where we started out just doing a pilot or, you know, a free project, no cost or whatever have turned into two and three and four and five projects with the companies, all of which are paid or frequently sponsored research, which is all proprietary and not disclosed. I think that's been the biggest change in terms of our business model is the depth of work that we've done with some of these companies. You asked about some
Katie: of the
Mark: domains of application of these projects. Of course, it's hard to categorize, but you know, we think about aerospace, defense. I mentioned agriculture earlier, our friends at BAX is an example of that. Talked We a little bit about manufacturing some. We haven't really alluded to pharmaceutical science, computational drug discovery.
Every vertical companies need these students, you know, so.
Katie: I will say too, our previous projects are listed on our website as well. If anybody wanted to go check out last year's symposium, there's posters with videos from the students. That option is there.
Mark: Yeah, it helps when you go to meet a company for the first time, you don't have to do a hard sell anymore. I never bring a PowerPoint to a meeting anymore. We simply ahead of time, we send that link Katie mentioned. Here's what we did last year. Here's who our friends in industry are.
Let the work speak for itself. How can we help you next?
Daniel: Yeah, thank you all for kind of sharing some of that about the symposium, the link, the projects, the corporate partners. Just so our listeners know, we will include a link to the DataMine, website in our show notes. So go ahead and scroll down there to click through, take a look. I'm just scrolling through all of the corporate partners, are all, you know, just in incredibly interesting from Allison Transmission to Dow Chemical, Johnson and Johnson, Lockheed Martin, etcetera. Just very, you know, names that people will recognize, but also some names that maybe, are are smaller to to your point, but still have really interesting interesting work and and you can explore all those things.
I'm wondering, all have been exposed to a lot of amazing projects that have gone on within the data mine. Are there any that come to mind that, you know, like you said, Mark, when you're talking to corporate partner or Katie, you're, engaging either on the student or the corporate side that typically come to mind like, Oh, if I get to mention this one, always a really inspiring one or an interesting one where students overcame a challenge or something like that. Any particular ones that you'd like to highlight?
Mark: What do you think, Katie? Who are we gonna name drop here?
Katie: Well, I don't if I wanna name drop, but one of my favorite ones was, you know, chatbots are like, everybody wants one now. Everybody wants to just be able to say, Hey, where's this and this and find it for me. And so one of the projects last year, the students worked really hard to create a chatbot. And then I love to see when the company like is able to implement that to make their processes easier. So that was one of the ones that came to mind for me.
It also, I think, depends on the company, right? Like certain companies might not have an interest in like what other companies in a different domain are doing. So I think it kind of depends on who you're talking with. And just to mention too, from the student side, one of my favorite stories is we had a student who was in computer science and there, you know, a lot of students are looking at the bigger companies, right? We know the big companies, but they ended up doing an internship with a smaller company.
And, you know, their feedback was, I would have never known that they were collecting data this way. And, you know, I could work for a company like this. I didn't know they existed without my experience in the data mine. So that's just like one of my favorite things is just exposing students to different opportunities that they might not have thought were there.
Mark: I could pile on in a similar direction there. Last night, as I was waiting for my ride home from Chicago, I'm writing letters of recommendation. And wow, I write a lot of letters. I might write letters for 40 or 50 or 60 people every year. And I had three I was putting together last night and I was tired.
And as I'm writing the letters, you know, I'm checking my email and a student writes, Doctor. Ward, I just wanted to write and let you know I got into this grad school. I can't believe this research mentor I'm going to get to work with. I think it's really putting me on track for after I finish my master's or their doctorate. Don't remember, you know, the kind of career they're going to have ahead.
And they came back over and over to Datamind made that possible for me. That's priceless, you know, I mean, like I'm sitting there working on my letters and I'm exhausted, but that's what renews you, you know, that's what really kind of gives you hope for the future at these companies. If we think about an employer like Eli Lilly in Indiana, they're just transformative to the economy here. And we love working with Eli Lilly. They're just that they're good beyond measure to Purdue, but we also feel lucky to work with AstraZeneca and AbbVie and our friends at Merck and so on.
And when students are able to kind of see the full breadth of what's going on in an industry and have a lot of choices, we feel it's good for everybody.
Daniel: That's awesome. I'm just, you know, as we speak, scrolling through projects and reading amazing things that students have done, I see everything from detecting digital fraud in healthcare with, L events. There's, you know, there's forecasting, soy yields. There's, you know, reliability for NLP machine learning models, with with Ford, just a a lot of really interesting things. And that only scratches the surface.
I really recommend people to go and scroll through the website. And there's a lot of interesting videos and posters to take a look at. And, our listeners will be interested to know that the Practical AI podcast has submitted a project for this upcoming year, and there'll be a team of students working with us. And, I will not there is some really cool stuff that they're going to be doing. I won't reveal the full extent of that, but, I think it will be something that our listeners will be able to to interact with, coming out of the coming out of the project and also, you know, something that that I think will be quite fun.
So I'll tease that a little bit, but we're excited to work with these students and really understand from our own experiential level for myself and Chris, our cohost, and our listeners actually pulling them into this type of new creative way that students are learning data science and AI. At this podcast, we're always talking about making AI and data science and these methodologies accessible and practical for everyone. Certainly a part of that is workforce development and education and this kind of academic industry partnership. Really So excited to actually see that materialize. So thank you both for making that happen.
This is really exciting. I'm wondering so number one is we should mention, explicitly on show that the data mine is still looking for corporate partners and there are many students that are there's a whole group of amazing students that still are looking for projects. If you're listening to this in August, you know, July or August of of of twenty twenty five, go ahead and reach out to the data mine. There's still the opportunity for those projects, or maybe you're listening to this sometime in the future. I'm I'm sure they would still love to, love to hear from you.
And of course, we'll include that link below. What what's maybe just from y'all's perspective, for corporate partners that are coming in and and wanting to sponsor projects, you mentioned a couple of things of how different corporate partners, view this, but what's the value proposition that someone listening to the podcast could bring to their coworkers or supervisors at their company to kind of tell about this interesting thing that's happening at Purdue around data science and AI? What would be the elevator pitch that you could help them give?
Katie: We always joke that Mark could sell a used plastic bag, so so go ahead.
Daniel: There you go. I need to hire you into into my company as a salesman.
Mark: Yeah. Would you like to buy this coffee scoop? I could keep it affordable. Think of all the things you could do with this. It's it's not just a regular coffee.
Daniel: It's multi purpose.
Mark: Yeah. Yeah. Yeah. Yeah. But, you know, I mean, it's it's so affordable affordable for these companies.
We we sign these five year agreements with companies so they don't have to go back to their lawyers. Purdue doesn't have to renegotiate anything. Everything's just sort of laid out so that a manager who's got an idea can rubber meets the road, go go work with these students. And then once that happens, once one company takes a chance on one team, invariably all of their siblings in that company, all their buddies, all people in different parts of the org, when they hear about it, they want it for themselves too. It's an easy story to tell.
Katie: And I think we helped through that process as well. I mean, Mark was writing project descriptions last week, so he has an eye and an ear and a brain for it. He can definitely find where there might be holes or suggestions or things like that. And we'll with you, him and our corporate partners and data science team as well.
Daniel: That's awesome. And as we kind of get closer to the end of our conversation here, maybe just taking a step back and as you all of course, have the immediate things that you're part of leading into this next semester of the data mine and students engaging with partners and that sort of thing. But as you, whatever it is, lie on your bed and think about the future of what's happening, what's changing, especially over even the last couple of years, just so much has changed, what excites you or what are you looking forward to that may be possible for the type of program that you're running or for just this type of education in higher ed more generally? What are you looking forward to and what most excites you about the future in that regard?
Mark: Can I say world domination? I meet it in the most The data mine everywhere. The data mine everywhere. It's not just about something we're doing in Purdue, it's become a model for engagement. And I really believe it's silly when we mentioned some of these companies that they're going to invest time and money and effort to go to 10 or 20 campuses around the Midwest or maybe around the country to do their recruiting.
And then they're going to hire in onesies and twosies, people who they've often never worked with before and take a chance and give them a full time job. My sense is that it's so much more fun to build things together and whoever it resonates with, both on the student side or on the company side, just have that natural matchmaking. Learn by doing all the R and D and the AI and ML, all the value creation. So my hope is that this model continues to be adopted by many different kinds of institutions, not just here in Indiana or in the Midwest, but all over the country. And those of us in higher ed need to stick together.
I mean, we're all wrestling with the same changes together, just that industry is as well. And it's a big tent. Let's continue to work together on initiatives we can all just help each other.
Daniel: That's great. Any thoughts from your end, Katie?
Katie: I would say similar and that, you know, it's exciting to see what the workforce is gonna look like with students getting engaged with companies earlier in their lives versus later. So anything's possible.
Daniel: That's great. That's a good thought to end on. Well, thank you both for the work that you're doing, and the creativity that you're putting into this type of program and the inspiration that it is for both corporate partners who are finding new ways to engage and recruit, but also other universities and educators in general who might be at a loss for how to, you know, find a model that works in the the changing ecosystem that we're a part of. So thank you both for your work, and and looking forward to, rolling out and revealing some of what we're working on with with Practical AI as as the year unfolds. So, thank you both for taking time.
Really appreciate it.
Katie: Thank you so much.
Mark: Yeah. Thank you.
Jerod: Alright, that's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner Prediction Guard for providing operational support for the show. Check them out at predictionguard.com.
Also, to Breakmaster Cylinder for the Beats and to you for listening. That's all for now, but you'll hear from us again next week.