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?
00:49:48 · Kevin B. Boyd
[00:49:48] S1: Yeah. That's an interesting question. So, you know, as I said, I think that AI can be a tremendous benefit to research. But we also want to be mindful of the fact that research often happens with sensitive data. So, you know, as I said, we're a large biological sciences research university, and there's often a lot of HIPAA data in there. And so we have some pretty important guidance to faculty about what they can and can't do with that type of sensitive data. We have a separate instance of Claude that is okay for HIPAA sensitive data, where you don't have, you know, it doesn't preserve the history and some other things like that. So I think, you know, with the data security concerns, it's having clear guidance for sensitive data. And I think we still want to have the tools available for researchers that are working with the sensitive data. But you know, the other thing that we talk a lot about is we can have really good contracts with the vendors about what is okay and what is not okay. But we also know that this, the AI space is moving really rapidly. And sometimes the models do things that they weren't intended to do. You know, we've all seen what's happened in the last few months with Anthropic, with OpenAI and others where the models have, essentially escaped the guardrails and done things they really weren't intended to do. You can imagine someone using, let's say, a coworker code to do research and the, you know, a particularly capable model decides that the right answer for how to achieve the prompt that it was given was to send the data to someone else who's an expert and get help from them. There's actually been at least a few cases of that happening. That maybe doesn't matter so much if the data that you're working with is astronomy data. It matters a whole lot if it's patient data.
00:52:17 · Grace Gong
[00:52:17] S0: For sure. One of the audience, Christopher, is asking, so you guys are protecting learn to think first step in class, which I love. But out in the real world, there's a lot of entry level jobs, the ones where people used to learn the work by doing the ground stuff is getting automated. Are you seeing that hits your grads when they go looking for a first job? And is this about AIP starting to change how you think about teach to fill the gap?
00:52:48 · Kevin B. Boyd
[00:52:48] S1: I think that's a great question. It's one that we've talked a lot about with many different groups. I think that, you know, there's been a lot of fear in the last couple of years about AI taking jobs away. And, you know, I tend to think that like any, you know, major transition, there are jobs that will change and jobs that will evolve. And as that happens, there probably will be some jobs that are no longer needed in the future. What my own belief is, is that the entry level jobs are going to be the ones that are affected the first. You know, it's things that you used to have a, you know, a junior associate in the law firm or, you know, an entry level communicator drafting things. And in the future, think that's going be done by AI. The really difficult thing is that when you're in one of those entry level roles, that's how you learn how to progress to the mid level role. Know, you're you're off... You're doing that grunt work for the first few years. It might not be the most fun, but but you really learn that particular profession, whether that's a legal profession or being a communicator. I do think, you know, that there's going to be a challenge of, okay, how do you get from being a very successful student, you've gotten your degree and graduated, if there are fewer of those entry level jobs in the future, how do you get the experience you need to get to the mid level jobs? I don't know that I have an answer for that. I think, you know, the individual who asked the question is like, okay, does that change education? I think it's maybe too early to know that, but I think it may have to.
00:54:36 · Grace Gong, Kevin B. Boyd
[00:54:36] S0: When I guess like I want to be mindful of the time, maybe we do one real question and then one minute follow-up for you. One of the question was like, so yesterday I was actually at a VC van and like I was talking to a founder that's building in the AI education space. I feel like nowadays all the educational app are kind of like replaced by like the BigLabs product. Do you do you feel like there's still room for people who are building some sort of like personalized education product? Or do you feel like most people are just gonna use like Trejopy or cloud or equivalent? [00:55:15] S1: You know, it's an interesting question. I think there's always room for things like that that are very tailored. And, you know, part of where I'm actually excited about AI is the ability to do more tailored education. [00:55:35] S0: But,