Unlocking Innovation: The Transformative Power of Generative AI in Higher Education

Generative AI is not just a buzzword; it's a game-changer in academia. Join us for a compelling conversation with Jules White from Vanderbilt University as we unpack the revolutionary impact of generative AI on higher education. Discover how this cutting-edge technology is unlocking new realms of innovation across academic disciplines, from nursing to interdisciplinary collaborations. Jules shares his pioneering efforts in integrating AI tools and infrastructure at Vanderbilt, enabling faculty, staff, and students to harness the power of leading technologies from industry giants like OpenAI and Anthropic.

The discussion takes a fascinating turn as we examine the concept of augmented intelligence in education and its broader societal implications. Generative AI, with its advanced capabilities and consumer-friendly applications such as ChatGPT, has captured the imagination of many. We explore the ethical considerations surrounding AI, particularly in addressing bias and ensuring diverse perspectives are included. By treating generative AI as a tool for augmented intelligence rather than a replacement for human decision-making, we promote a more thoughtful and responsible engagement with this technology, and we highlight emerging trends that academic institutions need to brace themselves for in a rapidly evolving landscape.

As we wrap up, we delve into the multifaceted applications of AI in both professional and everyday contexts. From AI-assisted meal planning to interdisciplinary collaborations in medicine and environmental science, AI is reshaping our approach to tasks across various fields. We tackle the misconception that generative AI can be seamlessly implemented without proper training, emphasizing the importance of mastering prompt engineering. Lastly, we discuss strategic challenges organizations face when integrating AI, underscoring the necessity of supporting leaders across disciplines to fully harness AI's transformative potential. Tune in to hear how embracing AI's creative power can unlock unprecedented possibilities in your world.

LinkedIn:  https://www.linkedin.com/in/jules-white-5717655/

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Chapters

• 0:00 Generative AI in Education Future
• 8:51 Augmented Intelligence in Education Future
• 20:09 Empowering Diversity in AI Implementation
• 28:17 Empowering Creativity With Generative AI

Transcript

Disclaimer/ Innovative Sim Solutions Ad/ Intro: 0:00

The views and opinions expressed in this program are those of the speakers and do not necessarily reflect the opinions or positions of anyone at Innovative Sim Solutions or our sponsors. This week's podcast is sponsored by Innovative Sim Solutions. Are you interested in the journey of simulation accreditation? Do you plan to design a new simulation center or expand your existing center? What about taking your program to the next level? Give Deb Tauber from Innovative Sim Solutions a call to support you in all your simulation needs. With years of experience, deb can coach your team to make your simulation dreams become reality. Learn more at www. innovativesimsolutions. com or just reach out to Deb Contact today. Welcome to The Sim Cafe, a podcast produced by the team at Innovative Sim Solutions, edited by Shelly House. Jo ou hos, D T r a Join our host, Deb Tauber, and co-host Jerrod Jeffries as they sit down with subject matter experts from across the globe to reimagine clinical education and the use of simulation. So pour yourself a cup of relaxation, sit back, tune in and learn something new from The Sim Cafe.

Deb Tauber: 1:31

Welcome to another episode of The T The Sim Cafe, and today we are here with Jules White from Vanderbilt and Jared is here with us. And welcome to the podcast. Thank you so much.

Jules White: 1:43

Thank you for having me.

Jerrod Jeffries: 1:44

It's great to connect.

Jules White: 1:45

So maybe first do you want to kick us off just a little of your overview, Jules, and tell our listeners what you do and a bit of your background, yeah so I'm a professor in computer science and then I'm the senior advisor to the chancellor on generative AI and enterprise and education, so a long title that basically means my job is to figure out all the different ways that we can incorporate generative AI into everything from the classroom to how we go about and do our normal operations within Vanderbilt.

Jules White: 2:14

So I have a group that we go and we've built the infrastructure, the generative AI infrastructure for Vanderbilt, so all faculty staff and students on campus have access to essentially unlimited use of all the models from OpenAI, all of the stuff from Anthropic stuff, from Mistral, you know Meta. And then we build all kinds of unique tools out of our research and deploy them across campus individual groups like the Department of Alumni Relations or Endowment or Faculty Affairs, and we figure out what are the ways that we can incorporate it to help do things I think of that we couldn't do before.

Jerrod Jeffries: 2:54

I love that there's a lot of things that I want to go into already there, but I mean, is there a specific initiative that you want to take, so for Endowment or Naval Alumni Relations? One of them is there something that? One, when did you start with a lot of this? And two, or is there a goal set out initially, or is it more just experimentation?

Jules White: 3:15

Well, I think that the starting goal for for me is that I start all my talks If you've heard one of my talks, you've probably heard me say this, but I start all my talks with the same thing, which is like if you'd stopped me on the street November 1st of 2022, and you'd said this thing called ChatGPT is going to come out at the end of the month and here's what it's going to be able to do, I would have told you trust me, I'm a professor in computer science. I will not live to see that level of advance in computing. And then, a month later, something came out that I didn't believe would be possible in my lifetime. And so my starting point is helping people appreciate the importance of what's taking place, and that it's this huge opportunity I think particularly for higher education to because the technology is so generally applicable and does sort of certain foundational things that are going to impact every single discipline that this creates, this opportunity to go and reinvent every discipline, to think of things that we can now do that were impossible before, that the technology makes possible, and that creates an opportunity for higher education to go and figure out what are the ways that we're going to change the way that the world works in all these disciplines.

Jules White: 4:21

Now industry is going to do it simultaneously, but the problem in industry is they're often at capacity and there's not as much of the culture that there is in education to go and think from a research perspective of how do we reinvent things and then also measure does the reinvention work?

Jules White: 4:36

So I start from the perspective of like. Let's show capability and possibility and make sure that people are informed about what all the building blocks are and the different sort of ways of composing them and solving problems with them, and I built a lot of Coursera courses out of this idea. So I have about 500,000 students in my online classes on Coursera. I teach the building blocks and then we go in and work with the groups to understand what their problems are and which ones can realistically be tackled with these building blocks, and then often what we see is they have a better sense of like, which problems to go after, how they might use the building blocks, and our goal is to support sort of how they compose and put these things together and also to know when there's something that realistically, we're just not going to solve for them.

Jerrod Jeffries: 5:24

Okay, great, and then being I'm going back out a little bit, but being at Vanderbilt, are you working with one department, are you working with the overall school or university, or how does that work? How does your role work there?

Jules White: 5:35

Yeah, well, we work across all schools and departments. So we're actively trying to engage and work with anybody that's, you know, looking to engage with generative AI, and people all across campus are excited. So we have thousands of people across probably every school using the infrastructure that we've built, and then as a team, we go in and work with different groups or individual faculty. So we just did a project with a faculty member in nursing and she looks like becoming an expert on the prompt engineering. For how do you take a research study and use generative AI to assess if the research study has bias in its design? And so she was working on this. She came to us and said can I get help automating this and scaling it up with the infrastructure? So we helped her set up automation so she could go and scale up this idea and the analysis, and then we worked together on that. Or we go, and we worked with HR and they were interested in can we build a generative AI assistant? Or you can think of it as a chat bot or agent that can answer questions about someone's benefits so they can go and say well, I'm in this interesting situation, can I use this benefit, can I use it in this way, and so we worked with HR to build out that chatbot and it will eventually go out to everybody on campus.

Jules White: 6:54

So we're sort of like behind the scenes trying to provide the infrastructure, but then the broadly and we don't actually know everything that everybody's doing with it. I can guarantee you that it's like thousands and thousands of use cases, and whenever we've done things like we worked with the nursing school, patty Singstad and Regina Russell in the nursing school ran a big study within the nursing school to collect use cases, and when they collected the use cases, we started looking at them and we were really fascinated because there are lots of things that we hadn't seen before. We didn't know people were doing. There were lots of things that we suspected people were doing and we had done, and so we discovered. But then we go and work with specific departments on very targeted projects. So we have things that are happening on our infrastructure that we don't even know what it is, that it's probably creating efficiencies and all kinds of new innovations. And then we have things that are targeted, where we know exactly what the problems are that we're tackling, and those are usually focused on individual groups.

Jerrod Jeffries: 7:47

Wow.

Deb Tauber: 7:48

What steps is Vanderbilt University taking to address ethical concerns related to generative AI, such as bias, privacy and misinformation?

Jules White: 7:59

Yeah, well, I think the first thing is we have to realize that a lot of the way that we approach and think about ethics from AI is really based on the way AI used to work before generative AI came along, and so a lot of the way that we used to use AI was we used to use it to give us the answer or decide in place of the human. And with generative AI, really the way you want to use it, I would talk about it as augmented intelligence rather than artificial intelligence. You don you want to use it. I would talk about it as augmented intelligence rather than artificial intelligence. Like you don't want to use it to replace your intelligence, you want to give humans tools that help them to go and solve bigger and harder problems. So I describe it as like an exoskeleton.

Jerrod Jeffries: 8:36

Can I even interrupt you? Sorry to do this, but just for our listeners, can you even explain the difference between what AI is versus generative AI? For our listeners, thanks, and I want to get back to exactly what you're going on.

Jules White: 8:47

Yeah, yeah. So AI is like an umbrella term. Generative AI is a type of AI and generative AI is where all the excitement is right now. So I had been doing AI for a long time and I still didn't predict or believe that something like ChatGPT would exist, and what I say is that generative AI really took things to just this huge leap in advance. So generative AI is a type of AI, but it's where all of the excitement and the growth and the explosion and capability is happening. And you can take generative AI and you can pair it with traditional AI, but generative AI is like when you go into ChatsBT or you go into Anthropic Cloud or Microsoft Copilot or Meta AI. That's the technology that's powering all of those.

Jerrod Jeffries: 9:36

So maybe it would also be fair that when most people hear the term AI, or when they're seeing something on a consumer level, that would be mostly generative AI.

Jules White: 9:47

Well, it's not clear, because what we see is that, because generative AI is like this rocket that's taking off, everybody wants to say that it's AI that's taking off, because, if you've been doing AI, you want to claim that AI is the thing that's taking off because you want to be lifted by generative AI. In reality, when people say we're doing AI, you don't know what they're actually doing, and many, many of the software vendors, companies and things that are claiming that it's generative AI, while they're claiming AI, they may not actually be doing generative AI and probably most of them aren't doing all generative AI, or maybe they're not actually be doing generative AI and probably most of them aren't doing all generative AI, or maybe they're not really even doing something that unique, and so it's hard to differentiate, and this is part of the reason I like to talk about generative AI and not AI. Despite everybody wanting to talk about AI, I think we should really be differentiating and focused on generative AI.

Deb Tauber: 10:42

What about the ethical concern? How are you addressing that issue?

Jules White: 10:47

Well, I think this is an important part where there's a difference really between AI broadly and how we've done AI in the past. Which AI broadly and how we've done in the past is have it automate and do something in place of a human. And when you start doing it in place of a human, you run all kinds of risks like it has bias and it makes a bad decision. And the truth is is that all the bias comes from us right. As human beings, we produce the training data or the source of the training data, and our biases then get trained into the models. With generative AI, if you look at the way that we use it through these chat-based interfaces like ChatGPT, it doesn't have to work the way that we did before, where we use it to replace our own decision-making. We can use it as a tool where we augment our decision-making. So I talk about it in terms of augmented intelligence rather than artificial intelligence. So the goal is to work with the generative AI and have it support your decision-making, but not to replace it. And so it turns out you can do simple things with generative AI that you couldn't do with just AI before, to help eliminate bias. So one thing you can do is you can give it data and you can say give me three conflicting interpretations of this data and it can argue many different sides of the data and then suddenly, as a human being, you have to go and confront and decide which one is the right one and why. Because normally we go in and we have confirmation bias, we say this is what it is and you don't go and confront other perspectives. And so generative AI can go and give you many different perspectives on data, an issue, a situation, but only if you go and ask for it and only if you go in and push for that.

Jules White: 12:25

So one of the things we want to do and we try to do and I do this in all of my online courses is I teach like the concept of don't go for it and ask for an answer.

Jules White: 12:34

Go for it and ask for many different perspectives and then take all those perspectives back and use it to inform your decision making and think more deeply about the issue, form your decision-making and think more deeply about the issue. And so I think we combat it primarily by teaching human beings to use it in a thoughtful way. That isn't about replacing their own decision-making. And I tell students this I say look, if your sole job in life is to copy and paste some question from somebody else into generative AI and then copy and paste the answer back in, like it's replacing your intellect or thinking, it doesn't benefit you, and that's the one thing I can guarantee we can replace with AI is we can automate that piece. But what we can't automate is your sensibility, your aesthetics, your emotions, intuition, and that's what you want to put together with it, not replace with it.

Jerrod Jeffries: 13:23

That's the first time I've heard that. I really like that. I mean different perspectives and it is, I mean, removing confirmation. Bias, of course, is huge for any, you know, student or faculty or above, and I really love that. You know, you can put it, because students of course want to take the shortcut to most things, or, I guess, humans, right. We try to create these mental shortcuts or whatever it is, and you know, trying to point blank of that. You know general AI can replace you. If you're just copying and pasting, you know, control, control C, control V, then that could be the case. But do you see, I mean, as you've spoken to, that, it's rapidly evolving. Do you anticipate anything within the field in the next five years, or what would universities or different type of academic institutions do for such a change in the landscape?

Jules White: 14:14

Yeah, Well, I think there's what's going to happen, I would say, in the next year or two. The big, I think, buzzword that we're going to see a lot of and rightly so, because it's going to be a tremendous change is this idea of agents or agentic AI. And if you go back to what these tools were built for, like it all goes back to this paper really came out of Google called the Transformer Model, and it had the title was Attention is All you Need. Came out of Google called the Transformer Model, and it had the title was Attention is All you Need, and the idea was they built this architecture that's fundamental to all these tools that we use for generative AI, and part of that what they were doing it for is they were testing it on translation, so translating human language from English into Spanish or something like that. But what it turns out is that, once these models get big enough is they can translate our thoughts and ideas and goals into computation, and this is kind of a crazy idea. But like you go in and you say like, okay, here's the data, I want a visualization of this and it can translate and control the computer to visualize the data in the way that you're requesting. It's not doing it itself, it's actually running code behind the scenes. It writes code and executes it like a software engineer to produce the visualization. Or you can go in and say take this and turn it into powerpoint for me, and it can write code to turn it into powerpoint. Or it can go and if you tell it like here are a couple systems like you can go and look things up over here and you can search this database, it can go and do it on your behalf. And so what agentic AI is about is like we go and we give it access to computing systems and then we describe what are the goals that we're trying to accomplish and it goes and runs all the computations and reacts and interacts and reasons about what's happening in order to achieve our goals. So you can imagine universities have all kinds of operations where that type of capability will help support what we do.

Jules White: 16:15

Because we struggle so much with all these software tools that don't exchange data. I'm sure this isn't a problem in the healthcare domain. We don't have any tools that don't exchange data, right, and we go into these tools and this tool doesn't have the button I need over here, but this one does. But now I gotta figure out how to get the data over there and they don't work together and it becomes this big problem. And this is gonna has the potential to eliminate a lot of the problems, because you go in and you say, okay, here's what I need to know or do, and then it figures out how to move the data around and how to perform the computations on your behalf, and then you get back what you need to do. The work and that's what agentic AI is going to be about is that type of automation to support, you know humans and getting work done without having to struggle through all these bad software tools that computer scientists like me, you know, write and give out to the world.

Jerrod Jeffries: 17:09

And can you use that interchangeably with agents, or is agents a different thing?

Jules White: 17:13

Yeah, agents, agentic AI is really about agents. So agents are like the and they sound really complicated, but it's actually looks very similar to behind the scenes, to what you do in ChatGPT. So if you go into ChatGPT and you say like, tell me step by step how to bake a chocolate cake, and now tell me the first step, it'll say go do this. And then you could come back to ChatGPT and say you know, here's what I just did. Or you could take a picture of what you just finished mixing up in the bowl and give it the picture and it would say okay, now go do this. And it would react through the conversation and, as you had a conversation telling it how you were baking the cake step-by-step, it would tell you what the next step was and the next step and you could give it you know feedback and text, or you could give it photographs or you could talk to it.

Jules White: 18:04

And the same thing is happening, except, rather than a human being going out and carrying out the stuff like, okay, I just mixed together the eggs and the flour, is telling a computer system go and do this. But whereas we can't directly talk to the computers because we don't understand the language directly it can, and so it can go and write and directly translate into the language of the computer and behind the scenes it's essentially having a conversation with the computer, and in the same way that it would have a conversation with you about step-by-step how to bake a cake and a recipe, and that's the foundation of it.

Deb Tauber: 18:40

Jules. What do you use it for on a day-to-day basis?

Jules White: 18:44

Yeah, so I mean really all kinds of things. I mean, one of the things that I think is so valuable to anybody is just using it to. We often rush off into I'm gonna solve the problem this way, or I need a solution that looks like this, and so I've tried to get in the habit of, before I rush off to do something, to take a step back and say am I doing the right thing in the first place? You know what are other ways that I could solve this problem? Or is my strategy just fundamentally off and I take time to do that exploration, more that thought partnering is what people call it, and so if I have a problem, I'll often be biased towards the solution that I've used in the past and I'll go in and I'll say give me five solutions to how I could solve this problem, and then that helps me to stop and think is this the right way? And that helps inform my thinking. Or if I have a really important email, my goal is not to have it write the email for me. Like just this morning, I was writing an email and I was trying to think about the best way to communicate what I wanted to convey to the other side and I said let's talk about the psychology of the different ways that I could convey this idea.

Jules White: 20:09

No-transcript, I use it as a software engineer to write code all the time. I use it to do data analysis. I use it to do extraction of data so you can give it like. A great example is you could give it a photograph of a hospital room and you could say has anybody fallen in this picture? Is a nurse in the room? Is there an infusion pump in the room? Is the family in the room? Is the nurse at the workstation? And you can take one photograph and you can essentially keep extracting information and data from it. You can give it a screenshot.

Jules White: 20:43

One of my favorites is I just did a class on meal planning and the challenge for me with meal planning is that I have these grand plans and then I come home at the end of the day and I'm tired and I talked to my wife and she's like it's your day to cook according to the meal plan and I'm like great, let's order out and in, like it when it hits my actual schedule, like the reality of my schedule doesn't work with the meal plan.

Jules White: 21:06

So I take a screenshot of my calendar. I give it to chat GBT and I say design a meal plan around my calendar. Look at my calendar and read it. Design a meal plan around my calendar so that easy meals or leftovers are available on days where I've got a long schedule and like that's, like the ability to go and take something two things that I can't fuse together easily otherwise, or to adapt a plan on the fly based on you know what's going on like. Those types of things are really valuable to me in simply even eating healthier or saving money on eating out or not wasting food.

Deb Tauber: 21:44

Right, I used it just the other day. I'm having a five and six year old little boy's birthday party on Saturday, so I went ahead and typed what are good activities to do for a five and six year old birthday party and he came up with a treasure hunt and pinatas and you know some of the stuff I I hadn't thought of in a long time.

Jerrod Jeffries: 22:06

So it really helped guide me to, I think, which is going to be a great five and six year old birthday party yeah absolutely, and even thinking back to yours, jules, it's like you just take a picture of what's in your fridge and tell them to make your recipe with what's in there. Absolutely, absolutely, and I guess that's where we're seeing all these different types of wrappers, right when it's someone's just saying, ok, have, have recipe AI, and then they use something with within. Yeah, and then, as a professor of community science, how do you approach this interdisciplinary collaboration towards fields like medicine or environment science, or maybe even humanities, so on?

Jules White: 22:44

well, you know, I I look at it as like I can provide building blocks and capabilities to support, but I'm never going to be knowledgeable enough in the in the domain to be able to really have an impact. So my goal is to like, inform of the art of the possible and get my examples close enough that there can be some of those early adopters in that department or discipline who can see what I'm showing them and then know how to take it and apply it in the impactful way within their department. So, like you know, whenever you get those early adopters, what they do is I really like to work with examples, concrete examples, because I feel like a lot of the discussion of AI is so nebulous, like I don't even know what it is Like I listen to some of these talks and I'm like I have no idea what they're talking about. And I'm in computer science. You know it's so nebulous. What does this mean? But I like concrete examples mean, but I like concrete examples. And so I think when you show concrete examples and then you get an early adopter who can then go and build concrete examples for nursing or any other discipline, then that becomes something really powerful. And if you show somebody look, I can take a photograph and I can do this. They can say I can take a photograph of a hospital room and I can do this other thing that's really important.

Jules White: 23:59

Or simulation's a great example where I learned something that I'd never thought of when I was at the GNSH conference, where somebody showed me that they'd done a simulation and they turned on chat, gpt voice mode and they put it on the basically in the room and they just had it listen to the simulation. And then at the end they said now act as the expert trainer that's evaluating the simulation. And then at the end they said now act as the expert trainer that's evaluating the simulation and using this framework and I apologize because I'm not expert enough to know what the framework was Give the team feedback on how they did in the simulation. And it gave a fantastic analysis of how they performed, what they'd done really well, what they were a little slow on and what they could maybe practice to improve on in the future.

Jules White: 24:43

And now I learned something that I'm going to go and take back to you, because we do similar types of things in, you know, simulations and cybersecurity. They're called cybersecurity tabletops. We do this in classes. You know you have role playing, all of these types of things. So you know the goal is to really, I think, exchange examples that can inspire. You know that we can be each and be inspired by.

Deb Tauber: 25:05

Essentially cross-pollination of ideas and thoughts. Yeah, to move the science further.

Jules White: 25:11

Absolutely.

Deb Tauber: 25:14

Now in your role as an advisor. What challenges have you encountered in bridging the gap between administrative strategy and the technical complexities of generative AI?

Jules White: 25:25

Yeah, well, I think that one of the challenges that you see so I do a lot of working with industry and a lot of the challenge you see in industry is that basically they go and they give generative AI out without training, and there I believe it's a huge misconception that you can just give this out to people without training and expect them to use it effectively. And there's all this denigration of prompt engineering. That's just messing around with little words, and I respectfully disagree. I think it's much more about learning to solve and think about how to use generative AI to solve problems and understanding the capabilities and the building blocks. And what I see as a problem is people go and just unleash it without training to support it, and when you do that, what you have is people that go in and they use it in this very surface way, and then you hear all of the discussion of well, what's the return on investment? Right, what is it really buying me? And like that, 11 minutes in the day or whatever this quoted is the real benefit, and that's not the benefit of it. That may be the benefit, if all you can do is use it in a very surface, sort of superficial way is you can get 11 minutes, and so that's the biggest challenge is so many places rush to give it out without real supportive training.

Jules White: 26:38

And I think the second piece that's a big problem is that a lot of places don't give sufficient time.

Jules White: 26:44

Because you need to give training but then you need to give people time to go and practice and experiment and work with it and many, many places don't do that.

Jules White: 26:52

And then they come back and they say what's the return on investment? And the return on investment is actually an interesting challenge because many of the things that you can now do with it, we don't even have a baseline to know what to compare against, you know. So those are the challenges. People expect these big instant like monetary, quantitative results that we don't even know what the baseline is and there wasn't the proper training and time and everything given to do it proper training and time and everything given to do it. And then also a lot of people go off and focus on, like the things that replacing things that we already do and trying to do it more effective. But the thing that I'm interested in is like if a CEO goes and uses it, a thought partner, and they make one better decision for the company that pays for it, probably for everybody, but those ROIs aren't captured, and so I think that's a bit a lot of the challenge too.

Jerrod Jeffries: 27:42

I love your analogy on your foundational building blocks for every use case, right You're. You're not ever saying do this or do that, you're just saying this is the way it has capabilities and it's. I guess maybe the saying is you lead a horse to water, right and then, but what it does is up to them. This has been fascinating, and so I think one of our last questions so in this again goes back to the, because you were advisor to the senior advisor to the chancellor. That's correct too, right, vanderbilt? Yes, yeah, those are some big shoes to fill. So again, appreciate the time here. Jules, do you see a lot of complexities around administrative strategy and this comes from your CEO question as well around administrative strategy and how to overcome those? Can you just give me a little more color within those or how you're kind of filling your role?

Jules White: 28:36

I think one of the most important things from a strategy perspective is one you really have to focus on realizing that it may not be the people that are doing AI or computer science, data science, in your organization. They may not be the leaders in this Some of the leaders may come from there but there may be leaders all over who are innovating within your organization. So one of the sort of key things is figuring out how do you find those people and how do you support them and support them in going in building out and inspiring the next wave of innovators, and that's sort of. You have to sort of come up with a strategy that creates those ripple effects of innovation, and it may not be as easy, like a lot of times, because of the structures and foundations and organizations. Certain groups want to say we own this, we are the leaders, that's it, we do AI, it has to go through us, and that I don't think works well in this space because it's so interdisciplinary.

Jules White: 29:33

I think budget is another challenge, because people want to think of like pay for X out of here, but when the impact is so broad, nobody knows who should pay for it and how, and so that creates issues on figuring out who needs to pay for this. Leaders really need to understand it themselves and understand it themselves by using it, and actually being a leader is not actively engaged and does not know how to use it themselves. I don't think this is a technology that they can effectively design or strategize with, and so what we've seen one of the things that's kind of been amazing at Vanderbilt is a lot of our leadership has gone and trained themselves on it and they use it, but then you go into organizations that say, oh, I know about AI, I've learned all about it and they've watched all these slide decks that have it at a high level and they can't really effectively build strategy around it. So I think those are some of the important sort of things that have to be worked out when you're dealing with it.

Jerrod Jeffries: 30:37

Well, and into that I mean one. I love that. But that's also not just within academic institutions, right? That should be across every single organization in the world. But I think, if you're fine with it, we should also put a link up to your Coursera so all our listeners can touch on that too. For the we'll put that in the show notes, but I think like sorry.

Jules White: 30:57

Yeah, yeah, yeah, that would be great. Appreciate it.

Jerrod Jeffries: 31:00

Yeah. Well, yeah, I mean half a million people taking the course. I mean there has to be something, something, something's good there.

Jules White: 31:06

Yeah, the biggest one has about 340 ish thousand. I just put the link into it and then I have another 15 or so courses on this that make up the rest of the 150,000.

Deb Tauber: 31:18

What did you think when you started to use the generative AI? Because you're a forward thinker with your field, it had to be kind of overwhelming.

Jules White: 31:29

Well, you know, actually it's interesting because I had a graduate student who I owe a huge debt for telling me to go and pay attention. And so, before ChatGPT came out, he was looking at the earlier GPT versions and he said this is amazing. And I was like yeah, yeah, yeah, and I wasn't paying much attention. And then ChatGPT came out and he immediately was texting me and saying you have to go, look at this, you have to look at it. And I went and looked at it and I was like, oh, that's interesting. And I started off kind of like you know, playing around, you know, write a poem about this. Ha ha ha, that's humorous.

Jules White: 32:04

And then my father was a professor of creative writing, so I called him up. I'm like this is fascinating. And so we were on the phone I remember it like we were talking on the phone and coming up with things to say to it. And the one that really got me to where wait a minute, this is different was we said imagine the world that didn't have odd numbers. What are all the things that would have to change? And it gave this incredible answer to what happens if odd numbers go away what is that and how does that impact the world and what do we have to do differently and like it's? It's that's not a question a computer should be able to answer, and it could, and it answered it really effectively. And then we started digging deeper and so then it was just like the the most exciting, fun thing that I've done, you know, for the last, for my entire career, basically, is exploring the depth of capability of this spaceship that landed Wow, thank you.

Deb Tauber: 33:03

Is there anything you'd like to leave our listeners with?

Jules White: 33:07

I would say that realize that whatever you've heard about generative AI and what's happening, it's completely underselling how important this is going to be in our lifetimes. And as one concrete, simple example, I can take out some leftover sushi in my fridge. I can take a picture of it and I can say tell me how to make it and give me a complete list of ingredients. And it can tell you how to make it.

Jules White: 33:34

And if you had gotten together like 100 PhDs and given them all the supercomputing in the world and you had said now make a system that you can take a picture of an arbitrary thing and it will tell you how to make it, it wouldn't have been possible before this came out.

Jules White: 33:48

And now you can do it on your phone with two sentences, and that is one of like an infinite number of things that it can do, and it's just really limited by your creativity and critical thinking about how you use it and innovate with it. So the future is out there and it's not going to just be computer scientists driving it. It's going to be everybody and every discipline and their creativity and what they can figure out to do differently or make possible because of this. And so I encourage people to go and engage with it, really take a deeper look at it and think not about using it like internet search, like ask it a question and get an answer, but thinking about solving problems in conversation. And how could I do something very differently than anything I've ever imagined with this technology?

Deb Tauber: 34:39

Thank you. You've inspired me to ask some different questions.

Jules White: 34:43

Yeah, of course you should check out my course.

Deb Tauber: 34:45

Okay, and it's available on that link that you just sent. Okay, perfect, thank you so much for the time.

Jerrod Jeffries: 34:53

This has been fascinating, Jules. I mean it's, it's really appreciated. Yeah, thank you so much. We appreciate this has been fascinating.

Deb Tauber: 34:58

Jules, I mean, it's really appreciated. Yeah, thank you so much.

Jules White: 34:59

We appreciate you, we appreciate what you're doing and I will absolutely take that course, okay, and you can be reached on where LinkedIn's a great place to find me, or just search Jules White, prompt Engineering for ChatGPT, and you'll find me on Coursera.

Deb Tauber: 35:16

Perfect, all right.

Disclaimer/ Innovative Sim Solutions Ad/ Intro: 35:18

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