00:41:50 · Grace Gong, Kevin B. Boyd
[00:41:50] S0: One of the audience is asking, what's your perspective on going cloud versus on prem with your technology around campus? [00:42:01] S1: Yeah. No, it's a great question. So our strategy over the last, let's say seven years has been very much with our applications, moving them to the cloud. So in the vast majority of cases, that's software as a service. So, you know, when you look at our core systems, it's Workday for HR, it's Oracle Fusion for our financial applications, Our admission systems are Slate, which is a SaaS based. Canvas is the learning management system, which is SaaS. Our advancement systems or alumni relations systems are built on the Salesforce platform. So that's been a very clear direction for us, and I think that served us well. It's given us a lot of really useful capabilities. We, you know, our very our intentional strategy is to move off of on premises for administrative applications. And we're trying to do that as rapidly as we possibly can. But I make the distinction with research because that's that's not so much the direction with with research. Research, you know, as I said, because of the nature of the compute requirements, it often tips the scale to actually doing things on premises where you buy the hardware and then you have that hardware available to you for, you know, for the life of the the equipment.
00:42:01 · Kevin B. Boyd, Grace Gong
[00:42:01] S1: Yeah. No, it's a great question. So our strategy over the last, let's say seven years has been very much with our applications, moving them to the cloud. So in the vast majority of cases, that's software as a service. So, you know, when you look at our core systems, it's Workday for HR, it's Oracle Fusion for our financial applications, Our admission systems are Slate, which is a SaaS based. Canvas is the learning management system, which is SaaS. Our advancement systems or alumni relations systems are built on the Salesforce platform. So that's been a very clear direction for us, and I think that served us well. It's given us a lot of really useful capabilities. We, you know, our very our intentional strategy is to move off of on premises for administrative applications. And we're trying to do that as rapidly as we possibly can. But I make the distinction with research because that's that's not so much the direction with with research. Research, you know, as I said, because of the nature of the compute requirements, it often tips the scale to actually doing things on premises where you buy the hardware and then you have that hardware available to you for, you know, for the life of the the equipment. [00:43:31] S0: What's your thought on... No problem. And then also, hi, Samuel. I wanna say hi to the audience who comments. So when you're thinking about, like, your current AI stack for your software, for your team, what does that look like?
00:43:31 · Grace Gong, Kevin B. Boyd
[00:43:31] S0: What's your thought on... No problem. And then also, hi, Samuel. I wanna say hi to the audience who comments. So when you're thinking about, like, your current AI stack for your software, for your team, what does that look like? [00:43:52] S1: Does that look like? So... Yeah. Wait. So are you talking about, like, when we're building agents or you're talking about other things? [00:44:03] S0: When you are working internally. And then the separate question would be like, you know, what's your thought on AI agents? So... Because right now, currently, personally, I'm using, like, Instinct and then you mentioned about, like, cloud co work. Right? So, like, does every faculty is building out their own agent? And like I also had a question and before I like, know, I feel like I have like a very limited memory, but when you're thinking about like, I feel like nowadays like everyone's using let's call it like Claude or try GPT to ask questions or get answers. How does school actually prevent people from like, I guess nowadays, like everybody is using it anyway. So like, do people still do homework that's like without AI or like people just default that this person will use AI, but we'll just grade people based off the AI answers.
00:44:53 · Kevin B. Boyd
[00:44:53] S1: Okay, so you got a lot of questions in there. So let me try and tackle them sort of one by one. So if you think about AI more broadly across the university, like I can answer, you know, what we're doing with our central systems. But you know, as I say, we're very distributed. So there's a lot of other technology that available within, in particular research, teaching as well. So, you know, as I've said, like, we've gone big with the Anthropic, making that available to everyone. Previously, as you mentioned, we had built our own own AI tool. It was called Phoenix AI. It was using the OpenAI models, but we put our own front end on it to, at the time, provide the level of security, privacy, and digital accessibility that we wanted. But the reality is that we also have every one of the frontier models is in use in different ways, whether that's Google's Gemini, OpenAI's ChatGPT, the Meta models, and then open source models as well, or open weight models. It really depends on the application. And there are some things where it's high sensitivity. Like, I'll use my information security team as an example. They tend to use more of the open weight models running on prem because they really don't want the highly sensitive security data going to the cloud. There are other things where, in some cases, faculty may just be experimenting with different models. In other cases, are particular models that are better for particular disciplines. And I think that's something we'll increasingly see in the future, where, you know, if you think of the frontier models, many of them are... The corpus of information is very broad. But what's really useful in, let's say, biomedical sciences and the legal field and some of these other fields are models that have been... Where the corpus of information has been more carefully curated. The legal field is a great example of this. You don't necessarily want a model providing answers to an attorney that has, I don't know, you know, some random information from the internet that... Because the corpus of knowledge came from Wikipedia or something like that. So I think that's true. Now, you had a question about students and homework. You know, that's a challenging question. And we, in so many ways, we don't view that as a technology problem. We view that as a people challenge.
00:47:48 · Kevin B. Boyd, Grace Gong
You know, faculty are being encouraged to be really clear with students about what the expectation is for how they can use AI in the class. What's appropriate for them to use and what's not. The other thing I'll say is that universities are being really forced to rethink how they do evaluation. So, you know, prior to AI, many courses used papers. You'd go home, you'd write a paper, you know, you'd turn in a 25 page paper. In the world of AI, it doesn't work so well. Because as much as you might say, please don't use AI to write the paper, you know that some number of people are going to do that. And so what we're seeing is a shift to more in class evaluation, whether that's tests or quizzes, and doing it in a way that AI isn't available to individuals. So you actually determine, have you mastered the subject? [00:48:51] S0: What's your thought on open weight model? Since nowadays people are kind of fear of having their data to be put into some of these like large labs and like everybody's like doing like open weight model these days. And do you see the same trends apply to the university or it's like not necessarily the biggest concern? [00:49:15] S1: I think it's not necessarily the biggest concern. I think there is a level of concern about data security about what the source of the open weight models are. And, you know, I think that is a consideration, but there's no question that the open weight models have place to play in research.
00:49:15 · Kevin B. Boyd, Grace Gong
[00:49:15] S1: I think it's not necessarily the biggest concern. I think there is a level of concern about data security about what the source of the open weight models are. And, you know, I think that is a consideration, but there's no question that the open weight models have place to play in research. [00:49:38] S0: Joly Betty is asking like, one of our audience is asking like, what's the biggest security challenge you face as more research teams started using AI?