00:14:14 · Kevin B. Boyd
[00:14:14] S1: Yeah, no, it's an interesting question. And I think there's actually a couple of answers. So one of the things to understand is that universities are not, at least our university in particular, is not very centralized. So there, you know, there's... We provide central IT and central infrastructure, But there's a lot that goes on in a very distributed fashion. Research labs, schools and divisions across the university. But what we do for our core central systems, generally don't do too much with startups. We do some, you know, if there are point solutions. But it's always this question of, okay, is it something that we can't get from anywhere else? And is the company still going to be there six months from now? And so, you know, think that's hard for us sometimes because what we're looking for is when we buy solutions, we want to make sure that they're still going to be here a year from now, two years from now, three years from now. Now, said that, there's a lot of innovative technology that gets integrated into our research all across the university. And I think there's a much more, much greater tolerance for things that, you know, it meets a need today. The need may be completely different a year from now.
00:15:35 · Grace Gong
[00:15:35] S0: So when, I guess like in the on the campus, is there any trends that in the AI space that you're really excited about whether it's coming out of like research or like, since like U Chicago has like a lot of like brilliant minds in terms of like, you know, I feel like a lot of students are probably innovating in the AI space as well. Is there any huge trends that you're personally seeing like a huge growth on campus?
00:16:00 · Kevin B. Boyd
[00:16:00] S1: You know, I guess the place that I'm the probably the most excited is it's what AI can really do for some of our research disciplines. And I'll give you a couple of examples. If you think about the research process, in multiple different disciplines, what you're often starting with is a problem. You have a problem that you're trying to solve. And it's a question of, all right, what's my hypothesis of how I'm actually going to be able to solve that? So in the biomedical space, which for us is very large, our biomedical sciences is about a third of the university, you have people who are researching how to address certain diseases. But if you think about the process for that, it's a lot of, okay, what are all the different drugs, all the different combinations of things that might have a positive effect on that same disease. In some ways the process is the same for the material sciences. So someone who's working on developing better batteries, is, you know, it's huge in the industry right now. So whether that is batteries that go into cell phones or batteries that go into cars. You know, there's a lot of research going on right now about, alright, how do we make a battery that holds more charge, charges faster, lasts longer, And much like in the biomedical sciences, you're trying to figure out, you know, what might have a positive effect on the disease in the same way in materials science, it's thinking about lots of different combinations of lithium and sodium and different chemicals that might produce a better battery. AI can be incredibly helpful in narrowing some of those things down, so that what you actually go and test in the lab are things that are more likely to be successful. So I think that's really exciting. Some of the other things though that have been just fascinating, where I think the start we might not have thought that AI would have a huge impact. So we have many individuals who research ancient languages. You think, well, what does AI have to do with ancient languages? Well, AI is really good at patterns and relationships. And in some of the ancient languages, you have certain languages that we think were a predecessor of certain other languages.
00:18:48 · Kevin B. Boyd, Grace Gong
AI can be incredibly helpful at looking through, you know, many of the photographs of old stone tablets and cartouches on a wall and say, okay, I think there's actually a relationship between this ancient language and these newer languages and finding some of that. That's really interesting and helps us understand the evolution of languages that are the precursors to languages that we have today. Speaking [00:19:21] S0: of, I feel like living, especially like I feel like building on top of campus, like I feel like there is a lot of question about like, you know, identity and cybersecurity. I feel like those are like some really huge concerns for, you know, like whenever people say, oh, like, like somewhere that like the data league, a lot of times is universities since, like, it is... Where, like, a lot of, like, the student datas are, like, sitting on top of. What what are things that you would do to decide on, like, you know, which kind of like, whether it's like cybersecurity product that you guys are like interested in or, you know, when we went to that you like the event or like the private thing in that pile, like, it's very, I would say, like a lot of it it's like, you know, on edge device and, you know, like, I guess like telecom related things. And I wonder, like, what are, how does the decision making process look like for you guys to think about like from the security perspective?
00:20:29 · Kevin B. Boyd
[00:20:29] S1: So security is incredibly important for us. And I think, you know, in an AI world, it's security, it's privacy, it's data governance, and all of those things together. When we look at security tools, you know, my CISO and I often talk about the fact that it's it's such a target rich environment. There are so many opportunities. Every single week, there are a dozen people who are trying to sell us a new security tool, a new security solution. It's really easy to get sort of caught up in what's the latest cool cybersecurity tool. And I think what, you know, the way we think about it is we've gotta step back and really focus on what are the requirements. What's what's the problem that we're trying to solve? And then from there, we usually take a pretty structured process. So we'll look at, you know, what are the tools that are available in the market in that particular space. Yeah. We'll compare the the features, the functions. We'll look at the stability of the company, all of that. But it is a space that's moving rapidly, and I think you know AI is really changing the game for cybersecurity. And it's both a positive and a negative. You know, the positives are that our security team has much better tools that allow us to analyze vulnerabilities, find problems faster. But the flip side in the security space is that the bad guys, the cyber attackers, also have AI and that's allowing them to move much faster than what we'd ever seen before.
00:22:14 · Grace Gong, Kevin B. Boyd
[00:22:14] S0: When I guess like when you were thinking about like, know, since like you hear about these pitches every day, is there like a particular product that like everybody's selling you to? Or what kind of attack like you feel like are things that's like the most frequently happening and like that... That's like kind of like on top of your mind when you are evaluating the different things that they're trying to pitch you? [00:22:44] S1: I'm not sure I understand the question. So are you asking what are we being pitched most often? Yeah. You know, don't know that I have a good answer for that. I think it runs the gamut. For whatever reason, it seems like in the last few weeks, it's been a lot of tools that supposedly help with AI governance. And AI, what is essentially data loss prevention. So making sure that data isn't going into places or going out of places that it shouldn't be. [00:23:27] S0: Speaking of that, so like if you're building like a AI assistant that searches like the university documents, how do you make sure it's only like retrieve the information that's particularly, you know, that the user is like allowed to see?