Managing AI Risk and Return

Sarah J. Buszka: Welcome to the EDUCAUSE Rising Voices podcast, where we amplify the voices of young professionals in higher education. My name is Sarah, I'm your co-host and I'm also not joined today by Wes Johnson, who is our other co-host. He unfortunately couldn't be here, but I want to give him a huge shout out. Wes, you're here with us in spirit. We are members and friends of the edika Young Professionals Advisory Committee, also known as ypac. Today, I'm so excited to continue our conversation. I would call it part two of our AI conversations coming off of the Ikas Annual Conference in Nashville, where we talked about AI from a lens of one side of a coin, talking about how positive and exciting it is and the purpose of today's episode. It's not fully talk about how not positive or exciting AI is, but to talk about some of the challenges and some of the real concerns that we're all grappling with regarding AI and education.  So to set the stage today, we really are excited to kind of look into this issue more so from a research perspective. We have some wonderful resources coming out of EDUCAUSE, specifically the AI landscape study among many others. And for those of you who are familiar with that already might know who might be joining us on the show today, I'm excited to introduce two of our esteemed guests on the show who are going to help us really unpack this issue. The first is none other than the singular Jenay. Robert Jenay is the senior researcher at edika. Her areas of interest include the future of education and work, equitable and inclusive education and work, social and cultural influences on teaching and learning and ethical best practices. Recently, Jenay has been interested in how the recent proliferation of AI tools is impacting higher education, especially its impacts on digital transformation. Jenay holds a PhD in curriculum and instruction from the Pennsylvania State University. Welcome Jana.

Jenay Robert: Thank you, Sarah. That was an incredible introduction. No one's ever called me the singular Jenay Robert before, but that might be my new, maybe I can see if I can work that into my title here at Edge cost.

Sarah J. Buszka: Well, I think you should. I'm biased of course, but I feel like there's no other way to introduce you. The feeling is mutual. Thank you. Yes, thank you for being on the show with us today. And I am also extremely excited to introduce Jay James as well. Jay is also another singular member of our community here at Edge cause Jay James is the cybersecurity operation lead at Auburn University. Jay began his career as the IT specialist at Auburn University, the college of Osteopathic Medicine v om. It's a mouthful. In his current role he's developed in Implemented the first you heard it here, the first security operations center and cybersecurity student worker program for Auburn University. Outside of his cybersecurity role, Jay focuses on expanding leadership and mentorship opportunities for young professionals. And Jay has been involved in EDUCAUSE in several and many ways in including the ypac presenting at ED'S annual conference and serves as the ED cause ambassador for Auburn University and is also an award winner from Ed. Cause. Jay, thank you so much for being here. Welcome to the show.

Jay James: Thank you. And I'm excited to be here today. Sarah?

Sarah J. Buszka: Yes. Alright, well let's dive in. Before we start talking about AI though, I think for those who listen to the show know, we always like to ask our guests, what is your superpower? So Jenay, I'm going to start with you. What is your superpower?

Jenay Robert: Oh, wow. I think it probably changes from day to day depending on my mood. But as we're talking to the ed, cause community, I think together with my colleagues at Ed cause this ability to bring people together, to have meetings of the minds, to foster community. I love just being immersed in our community. And I guess, I don't know, maybe I'm not directly answering your question. I think it's a superpower we share together the ED cause community and yeah, it's one of my favorites.

Sarah J. Buszka: I love it. A convener. I agree. Thank you for sharing. Thank you. Of course. And Jay, what about you? What is your superpower?

Jay James: Oh wow. That is actually a really great question. I think on a day-to-day basis, I see myself as a dot connector and I'm really good at connecting dots. I'm really good at not only figuring out where are the opportunities in a situation of how we can actually tactically fix things and make them better amongst our technologies, but also with people figuring out how to connect the dots with people. Because sometimes in technology we forget the people part of it. So I think that's probably my superpower would say today.

Sarah J. Buszka: I love it. I'm nodding my head ferociously because I know you two both well enough to have seen you walk your talk and completely agree. So I'm excited to have a convener and a dot connector on the show today. And how apropos, I think for the topic that we have at hand as well, just because we're really talking about some of those challenges that come up around AI and thinking about it in the broader context and scope of higher education more broadly. So to your point, Jay, beyond just the technology piece and how are we connecting all of those dots and what does that connective tissue look like with respect to this technology? So my first question, I'll start with you Jenay on this and then go to uj. Most folks really think of AI from the perspective or I would say AI literacy from the perspective of skill boosting and building efficiency and reducing workload and some of those maybe more common applications when we think about AI more generally. But according to you Jay and also Jana, you've also been talking about it from a security perspective and knowing AI literacy being a security skill as well. So I'm curious to hear what is something the average organization maybe doesn't realize about the security value of AI literacy?

Jenay Robert: Well, first I just have to acknowledge that anything I know about this topic came from Jay and Jay's colleagues. Lemme just first say that y'all are doing amazing work and Jay was a, actually on our podcast, so the Shop Talk podcast that I help host at Ed cause where we've talked about some of these things and I always find that such an incredible learning opportunity to connect with this community. So just first shout out to the community are amazing, and the cybersecurity and Privacy Professionals conference might be my favorite. Don't tell anybody

Sarah J. Buszka: You just told everyone. Oh,

Jenay Robert: Shoot. Right, right. I forgot to say off direction. No, but yeah, so the one thing that really sinks in with me as someone who's not a privacy and security professional is the importance of security being cybersecurity, being every individual's responsibility at the institution. And it's something I've talked about on Shop Talk before I talk about this when I'm doing speaking engagements on the topic is that before I came to AGI cause and really connected with community members across the institution, I was just sort of tucked away at an institution as a researcher and thinking about my research on a day-to-day basis and not necessarily understanding how I am part of the perimeter of the institution when it comes to cybersecurity. And so that I think is when I really think about how cybersecurity is becoming complicated by ai, I think about each individual at the institution being part of that perimeter and really needing to understand AI literacy for their day-to-day job, but also for their personal data practices and also with that lens of what that means for the institution. So that's the biggest take home for me.

Sarah J. Buszka: I think that's really great. Jay, what would you add to that?

Jay James: Yeah, I'm just sitting here nodding my head because that was a great answer In terms of AI literacy, because you also mentioned too that AI has complicated cybersecurity in so different ways, but in higher ed environments, we focus in much on the end user and our community around security awareness and training. Because attackers know it's much harder to get through a lot of our technologies. We invested so much in, it's a lot easier to get through the individual. So when you're looking at social engineering, it's not the same as it used to be. It's not where you can look at a phishing email and notice misspellings or the basic things that we teach on a year to year basis. Now with AI evolve, we have to think about deep fakes and how there are videos of your team or leadership in your organization or you get a phone call from someone that you're really familiar with or the emails and how they are utilizing AI to cater just to you so it's not the same anymore.

And you have to become more literate in the AI space to know what these issues are and how you're using it because if not, you can catch yourself easily falling into any of these traps that attackers put out for you leveraging ai. So that is something that I'm trying to scream from the mountaintops that we have to start incorporating AI literacy into our security awareness and training programs because it's happening now. But also too, Jenay made another great point and data handling is so important. We've known that before, not to send personal information or operational information outside of our organization, but when we're having these AI tools, a lot is starting to get blurred where we bring in this new tool where we are not sure what it's integrated with. We don't know what other third party tools we're using in conjunction with how we're using things in our environment. So now we have to be much more aware of the data that we put into the generative AI tools or data that we store in certain places that has an AI integration. So being much more AI literate and aware is something that is going to make our jobs as security professionals a lot easier.

Jenay Robert: Absolutely. I want to add to that too about this idea that there's this sensitive data that you want to be careful about how you're feeding that to AI tools, but then there's also data that are not particularly sensitive or just sort of your digital footprint for the way we live our lives today that can be leveraged by AI tools in ways that can harm you, the institution and so forth. So the one example that I like to talk about a lot, because my voice is all over the internet and my face is all over the internet, is the DeepFakes that Jay mentioned. And this is something that in my personal life, I've actually had my mother-in-law sit down and have a conversation with chat GBT for example, to help her see how realistic those conversations can be and explain to her, imagine how realistic this would be if they used my voice in this interaction.

You would really think you're talking to me. And so I think it's also shifting our mindset a little bit about what that digital footprint looks like and what our digital risk can be. But this is not something that people necessarily talk about on a day-to-day basis in their regular lives. So I think the real concern on my end is that individuals at our institutions who perhaps aren't accessing sensitive information on a regular basis, maybe they think that AI literacy is not relevant to them, but they don't think about, well, is my voice available on the internet? Is my likeness available? Could somebody duplicate me pretty easily and access systems at the institution or access my personal life systems? So that's where, to me, there's a real blending of not just AI literacy but digital literacy both for work and for personal life.

Jay James: Wow. I think you added some more. You have my brain going right now because I think something that really sparked in me is that it's not just an individualism more of a community thing of how you're having these conversations because I sat down with my family too and then my parents and we have a safe word where if there's certain situations where they get calls from us from unknown numbers, and it sounds like us, we have safe words that we use because we know this technology exists. So I'm even thinking about in our environments, what conversations are we having at the university of if situations like this happen, how do we respond to them and what do we do to make sure that we are being secure and we are aware of what potentially could be going on?

Sarah J. Buszka: Absolutely. I love this conversation too because we're really thinking about things and that are really going to be impacting us long-term, not only at home, but of course at work. And it's becoming a more challenging environment with so many of these escalating threat actors and security concerns with this new technology, and it's tough to keep up with it. All right, I'm seeing nods. Yeah, so I'm kind of curious, evolving this conversation a little bit more, maybe flipping it a little bit on its head because I can imagine, and this may be a question for you to start with Jay, running a SOC is a really difficult thing to do, especially with how you've done it, just launching something basically entirely from scratch and bringing student staff in. There's obviously a training component there. It's hard to scale and launch a brand new operation. And so I would imagine technology like this, especially AI, could potentially be helpful in training or in standing up an operation center like that too.

And that gets me to thinking a little bit about ROI, and I think many of us keep hearing return on investment if we're bringing in AI into our workplace. Many of us see the values, we also see challenges and we see bringing AI into our organizations to help us keep up with those increased threat actors and challenges. So how do we justify that, right? AI investment can be expensive and we want to make sure we're getting a return on investment for bringing this technology in if we are bringing into our organizations. So how do we define ROI using ai? And maybe my question to you to start with you James, is what metrics should we be using to measure ROI on AI investment?

Jay James: Definitely. I think well first start where you are in the metrics that you're currently tracking and seeing how AI can improve those metrics. I think sometimes we think that AI is just the answer, and then we get and implement AI technologies and then we just expect things just to be better. So we have to take a step back and think about, okay, based off of the current metrics that we are currently using to determine our performance, if we're moving in the right direction, how would AI have an impact on those? Right now we're doing things like our meantime to discovery and meantime to resolution for incidents. That's gone down tremendously. Being able to say that. You mentioned about the training, right? We utilize one of our tools to help train the students because it said, Hey, I'm a new analyst in the field. Can you help me learn about how these incidents work? Can you teach me what are some new topics I need to learn? And our student success rate in terms of graduating, getting internships, jobs, we can tie it to that as well. It's really looking at all of those different opportunities because it can evolve over time as well. There's metrics that we planned for, but they were outcomes that we didn't realize that would be beneficial because of the tools that we've implemented. So you just want to be mindful to keep out for those little nuggets of things that are being beneficial as well.

Sarah J. Buszka: Well, that's wonderful. Thank you for sharing. Janay. What would you add to that?

Jenay Robert: Yeah, first I want to say that from our research, what we found over the last, I don't know, six months or so, is that measuring ROI of AI tools is really a nascent area for institutions. We ask people, are you measuring ROI? The majority of our respondents either don't know if their institutions are measuring this or how they're doing it. So I think this is a major area of opportunity for us as a community to have conversations like what you're opening the door for here. The other thing I'll add, and Jay made great points about measuring some of these things that you're already looking at and thinking about how this evolves over time. I would add to think very robustly about ROI and what kind of value you're hoping to get from these tools. They're not necessarily always things that we can measure quantitatively, and that's okay.

That is, however, something that makes this a very challenging task when we're trying to measure value of something that is, for lack of a better word, kind of squishy, but measuring things like user satisfaction or in the case of let's say a teaching and learning tool, student engagement, things that simply make people's jobs easier and they feel a little happier when they show up to work that day more interesting. These are all things that are worthwhile but not necessarily easy to measure quantitatively. So yeah, in addition to all the things Jay said, I would say think robustly about what ROI means, think robustly about the value you're hoping to get from a tool and be willing to perhaps step outside the box when it comes to measurement approaches.

Sarah J. Buszka: I love that. I think what I'm hearing Jay say is start where you are. Don't overthink it. If you're already tracking something, start there and think about how you might improve even one metric. And then Jenay, you're kind of bringing it home by saying, don't be afraid to rethink it all right? Maybe you need to change your definition of metrics and how you are measuring whatever output or outcome you're looking for. So I think that's really good advice. I'm excited to keep this conversation going as a community moving forward too, because I think this is going to evolve over time too as these tools change. I think this is not the last time we'll be talking about it, but I think in my own experience too, it keeps coming up and I think a lot of folks have different definitions of what ROI even means.

So I'm curious to see how it evolves, maybe continuing this, I'm thinking about a lot of organizations that I work with that I'm sure you've been ad hearing from Jena, from your perspective, obviously serving the whole community and then JE U too directly being responsible for a SOC and for students and for bringing in some of this technology. I'm curious to hear from Jenay first from the kind of member perspective of Ed cause and then Jay, from that individual institution perspective, what would you say is the biggest roadblock for institutions to adopt AI that's outside of cost?

Jenay Robert: Yeah, it's a great question. I think first it really depends on the institution from my perspective of working with so many EDUCAUSE members that each institution really has their own unique set of challenges. And that's really important to recognize, especially if you're in a leadership position trying to think about how to move forward with some initiative. You need to be collecting data and information from your local community, from your institution, and being really critical about collecting that data carefully disaggregating that data, getting a robust understanding locally. Now, having said that, we do see some common challenges across institutions, and we've talked about this in our AI landscape studies and also in the most recent of these larger studies that we do in ai, which is the impact AI is having on work. So that published January 12th and this way, what we're seeing is that really the social aspects of ai, so people who are strongly resistant, people who are strongly trying to adopt AI for all things, there's kind of a clash in some of these communities.

It's important to recognize that at most of our institutions, the majority of people are not on either end of that spectrum and they're in the middle. And this is something that we try to talk about a lot in our research because we're living in a time, and you can see this in our horizon research, you can see this just in the day-to-day conversations we're having at work where there are a lot of divisive factors on our campuses, social, political, economic, environmental, technological, you name it, there are plenty of things to divide us. And so I think in that kind of social climate, we want to be really careful about yet another thing that can serve to divide our communities. And so how do we approach this? I think circling back to that idea of AI literacy, really having a more robust understanding of what AI tools are and what they can accomplish, having really hard conversations about what our purposes are at our institutions, what is it that our goals are, and how do these new technologies align with our goals, can help forge that path a little bit. But I think it all starts with awareness of where your people are and how resistant or excited or in between they might be.

Sarah J. Buszka: That's really great that even recognizing that there's a spectrum of folks, right? There's extremes and then there's folks in the middle kind of following that bell curve and just I think being aware of that, what I'm hearing sounds like a great place to start thinking about this. Jay, what would you add from your perspective on those biggest roadblocks for institutions to adopt AI outside of the cost?

Jay James: Definitely. Well, going back to the spectrum of where you are and how you feel. I'm just thinking about the diversity of individuals, especially in institutions like Auburn, where you have so many different colleges within the university that have a lot of different wants and needs where engineering and their investment into research in relations to ai, to other units. We're more focused in on how we can leverage AI operationally to improve our processes. Where you have the student experience and how it's being impacted by ai. You have all of these different competing factors and thoughts and vendors reaching out to so many different individuals about so many different things. Just the governance around all of it. I think that there is a lot of hesitation and sometimes anxiety when there's not good governance around ai, something that's fairly new and how everything integrates together and how AI might not nicely fit to the current governance model that you have and how you need to enhance it and potentially have a governance committee around AI specifically. And I think that because everyone's worried about who's accountable, who's responsible for what is the acceptable use and who is responsible for implementing the acceptable use of it in our environment. And everyone wants to use it though. So where you have everyone purchasing the new tools and implementing it and now you're looking at it, taking a step back and realizing, oh wow, this is a lot, and how are we going to properly govern all of it, which causes some anxiety around should we have anything else yet? Should we push for this new tool Yet, getting more clarity around that, building out those policy standards and procedures around it. Even just general guidelines if policy might be a little bit too stringent right now of exactly what you're going to do, but building that clarity I think helps out a lot and reduces some of that anxiety around it where you can implement more of these tools

Sarah J. Buszka: And playing that out too, Jay, what I'm hearing is we have to figure out where we can agree as an institution on where to use AI or not to use ai, and we can't boil the ocean either. We have to start somewhere and typically start small. And I really love that you're bringing that point up because I think at least for me too personally, in my experience as well, that has been a huge sticking point. And honestly, I think one of the biggest roadblocks to actually even getting anything done with ai, and then back to our previous question, ROI, you can't hit ROI if you can't agree as an institution on how we're going to deal with this or not. So I think that's kind of the key. And those are tough conversations. Probably going to have a whole different conversation and a whole different episode on that, and maybe we will actually, maybe I'm sending us up for that because I think it would probably be helpful for our community and for me certainly to hear how we're actually having those conversations. So maybe I'm signing us up for that if you're open to it.

Jay James: And I've learned too, even having conversations with faculty, for example, where they want to be very aggressive into how they leverage AI within their research, for example, because of what they can do with it, but also the same faculty might have some resistance in how we implement it in terms of teaching and how students leverage it for their coursework. So it's definitely something that's pretty complex, but having those conversations definitely help.

Sarah J. Buszka: Absolutely. Yeah, complex I think is such a simple word to describe such a tough, crunchy issue. And complex by definition is not a simple word, but you mentioned students, so I kind of want to bring the conversation to talk a little bit about students and also just young professionals, early career professionals as well because, and I'm sure you're seeing this too, Jay and Jena, I'm sure you're hearing this in research. I'm hearing this in my own personal life as a mentor of many undergraduate students across the country right now, many of those students are computer science or related majors. They are also worried about AI making their future job prospects irrelevant and having those opportunities evaporating. I have many cases right now where I have some of my students coming to me saying, I have no opportunities. There is nothing open. And so I really want to address this because this is happening. We are seeing it. We're seeing changes in majors. I've been seeing some of my own students change majors because they don't think they're going to have a career in computer science coming out of an undergraduate degree. So I'm curious from both of your lenses, especially if you have firsthand experience with some of these conversations, Jay, I'm especially looking at you, but what would be one step that you would recommend for young professionals or those undergraduate students to take to ensure that they stay competitive in the workforce?

Jay James: That is a great question because I'm dealing with that right now where students are worried, especially when you have some vendors saying hire an agent where you're like, oh, it's changing how we are speaking about the technology. We're humanizing it. So you're competing against the technologies. So I think, well, something that I do with my students at sel, them, you first and foremost, you still need to be the subject matter expert in what you're doing. You can learn all. It's important for you to understand artificial intelligence to be able to build those skills. But what makes you stand out is the person that's going to be able to leverage AI with the subject matter expertise. So when you're looking at opportunities, whether it's a student worker role or an internship, find those opportunities where you can do work that it's not easy for AI to just replace our students. I still make them do things manually before they leverage ai so they understand because they need to know when the output is incorrect. Just because it comes back cleanly doesn't mean that it's correct. And being able to tell those stories about how you leverage AI and be able to make those call outs, those are things that you can benefit from. I feel very fortunate that I was able to develop my skillset before this because I think that I'm able to catch things and notice things and be able to correct things while working with these AI systems that others that if they haven't been able to do these things before, artificial intelligence, the implementation of those tools, it'll be much harder for them to do that. So I really tell the students to still become that subject matter expertise, keep your pulse of the industry and learn about those that are leveraging it and how they're utilizing it in their day-to-day. And that's what's really going to help you out. I can resonate, and I'm being honest where I say, yes, it's changing. Things are changing, but you have to find a way to gain those skills and be that subject matter expert. Those are going to really help you elevate and make you stand out.

Sarah J. Buszka: Yeah, what I'm hearing is no shortcuts still. Even though AI can be perceived as a shortcut, you still have to do the foundations in order to know what is actually going on. Yeah. Yeah. Jenay, what would you add?

Jenay Robert: Yeah, my response might sound overly optimistic. So I want to first acknowledge that there are a lot of people who will be listening or watching this who are in a tough spot with job search or trying to prepare for what careers look like in the world of ai. So I just want to acknowledge that first, that some folks are going through really tough job searches or really stuck in a hard spot and nothing I'm about to say should take away from that experience at all. And in those cases, I think some of the things Jay talked about are really helpful, reaching out to trusted mentors, trying to get an understanding of the lay of the land and where those careers are going so you can try to get a step ahead of all of this change, which is so much easier said than done. Now, having said that, my optimistic take on this, and this is when I talk to young people in my life, my own kids, my youngest is 24 years old and looking at where careers are changing, and I talk to her boyfriend, they're both in the medical field.

This is a career path, very much impacted by ai. My friends' kids are all in that age group. And when I talk to folks who are kind of just entering the workforce, what I say to them is that this is an incredible time for them to think about a completely new future for their field. They're not steeped in some of the traditions that some of us are. Those of us who have been around for years or more. I'm not going to give away anything about my age here, but those of us who have been around for a couple of decades,

Sarah J. Buszka: You're in a safe space, Jenay.

Jenay Robert: But I think some of us are kind of, we are already steeped in some of these norms. And so the youngest and the newest people, it's not always age. Sometimes it's just career change, midlife, whatever that looks like. You are not going to be held back by some of these same mental roadblocks. Some of us have to get over and really navigate. And that's where I think some of the most exciting and incredible change can happen is when you can think completely differently about a problem. I'll tell a little story from my past, which is when I was an undergraduate. So my undergraduate degree is actually in chemistry, fun fact. And I was working in a research lab doing a synthesis of organic compounds, and my advisor gave me this project to create this compound. And it took me a while, but I did it.

And when I did it and he had the data in front of him to show that I did it, he was shocked. And he said, I didn't think this was possible. I was like, well, then why did you give me this project that was not possible? And he said, because that is what made it possible. When someone doesn't know it's an impossible job, they will figure it out and they will make it happen. And so he routinely gives undergraduates the most impossible projects because they're the ones who can figure it out. And I think we're in a similar space now with ai. It's a technology that could be massively transformative, also extremely risky. And I think the people who can figure all of that out the best are perhaps the people who don't know yet what's impossible.

Sarah J. Buszka: I love that. No, thank you. I appreciate having the optimistic perspective as well. And frankly, when I'm talking with my students, that's what I'm trying to say to them too, is yes, what you thought it would look like is different. It is changing, and we just have to accept that it's a tough reality. To Jay's point, things are changing. We have to acknowledge that. And instead of thinking about all the things we might be moving away from, what are we moving towards? What can we dream up and we don't know yet? And that's some of the exciting part. And to your point, also Jenay some of the scary parts as well. It's a balance. Sometimes I feel like we're going like this every day, right? But overall, I think we're heading in an exciting space and there are more opportunities even if others are changing. This leads me to maybe my last question for you both and probably one of my favorite questions because we love doing myth busting on this show. Actually, our first episode we ever did was all about myth busting. So it's one of our favorite things. So I wanted to ask both of you, I'll start with Jay first in one minute or less. If you could debunk one myth about ai, what would it be?

Jay James: AI isn't the silver bullet that's going to fix everything. We definitely get that a lot with working with vendors. We're talking to others, and just like with any other emergent technology we've dealt with, where we have to think about not only the technology but the people and the process. So understanding that those individuals in your organization need to understand how to use the tools properly and responsibly as well as you have to have the policy standards and procedures in order for you to effectively use them. So that's what I would say is that it's not that silver bullet. You have to think about all of the pieces in order to make this thing successful.

Sarah J. Buszka: I love it. I'm going to scream that from the rooftops Jenay. What is one myth that you would like to bust about AI on this show?

Jenay Robert: I love this. My myth is AI equals chat. GPT. If I could bust that myth, that would make me so happy, but I want to expand on that just a little. I have a minute, right?

Give a researcher a microphone. It's a problem. We don't like to stop talking. So there's a couple of challenges with that. One is that it's just incredibly limiting. We fail to see the wide, wide range of applications of generative AI technologies. We also leave out all of the other types of AI technologies that have been developed for decades that are useful and safe and important to the way that our lives run. So that's one reason. The other reason is that it's also quite dangerous to speak to the theme of this episode and thinking about some of the risks and all of the things that we've discussed so far, is that if you think AI is just chop GPT, you're going to leave a lot of stuff out in your personal life. I quoted some statistic about, and I don't remember it, but I posted on LinkedIn a couple months ago about the number of medical patents related to AI tools that have kind of ballooned over the last year or two. So there are personal things. For example, your medical care that AI is going to be relevant to, that's not chat GPT. There are going to be other scientific applications. There are going to be social applications. So if you're using social media, I love it when someone I'm friends with on Facebook says how much they hate AI and they refuse to use ai, and they're posting this on Facebook.

So it's incredibly dangerous to not know the wide range of applications of AI technologies. So that's the myth I would bust.

Sarah J. Buszka: I love it. Chat, GPT is not AI folks. You heard it here and it's also not a silver bullet. Mic drops, double mic drops. We love doing on the show. Thank you both for sharing. Well, with that, I appreciate both of you joining us on the show today for this myth busting, for kind of talking about some of the challenges around ai. I think it's a great compliment for our very AI episode before this. So for our listeners, if you haven't heard that episode, I please encourage you to tune into that. But with that, thank you all. Thank you for listening and see you all next time.