Bradley Canham - The Standing Reserve That AI Is Ready to Release

The conversation this week is with Bradley Canham. Brad is the VP of Research for Transforma Insights, a UK based high tech analyst firm, corporate fellow and mentor at the University of St. Thomas Opus School of Business and Entrepreneur Program. He's also a member of the learning community at St. Thomas on generative AI and education. He's the founder and original editor of Eden Prairie Local News, a thriving news publication and example of social entrepreneurship.

If you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future AppliedAI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events!

• Emerging Technologies North
• AppliedAI Meetup

Resources and Topics Mentioned in this Episode

• Transforma Insights
• Eden Prairie Local News
• Natural language processing
• Customer relationship management
• GitHub Copilot
• Teaching What Can't Be Taught: The Shaman's Strategy by David Rigoni
• Carlos Castaneda
• Radical Uncertainty: Decision-Making Beyond the Numbers by John Kay and Mervyn King
• Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans and Avi Goldfarb

Enjoy!

Your host,
Justin Grammens

Transcript

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[00:00:00] Bradley Canham: I was in Africa a few years ago, I was in Ghana and there was a village on a river and it had been dammed, the river had been dammed, so the village had to move. So I think of AI in that kind of context right now because it is so new for a lot of people and a lot of markets. Also I would say the instrumental use, like are we going to use this for customer service or you know, analyzing something or chatbots or whatever it is, but also what it reveals about us.

Relative to having this capacity that it's, it's a standing reserve now of capacity. So what are we going to do with it? And then how am I, as an operator in a business, going to make sure of the audience, either in sales marketing or what have you, positioning context. It syncs up with that. That social side of growing a business.

[00:00:55] AI Voice: Welcome to the Conversations on Applied AI podcast, where Justin Grammens and the team at Emerging Technologies North talk with experts in the fields of artificial intelligence and deep learning. In each episode, we cut through the hype and dive into how these technologies are being applied to real world problems today.We hope that you find this episode educational and applicable to your industry and connect with us to learn more about our organization at AppliedAI. mn. Enjoy.

[00:01:26] Justin Grammens: Welcome everyone to the Conversations on Applied AI podcast. Today we have Brad Canham. Brad is the VP of Research for Transforma Insights, a UK based high tech analyst firm, corporate fellow and mentor at the University of St.

Thomas Opus School of Business and Entrepreneur Program. He's also a member of the learning community at St. Thomas on generative AI and education. He's the founder and original editor of Eden Prairie Local News, a thriving news publication and example of social entrepreneurship. Thank you, Brad, for being on the program today.

[00:01:55] Bradley Canham: All right. Thanks, Justin. Thanks for inviting me. I'm excited to be here.

[00:02:00] Justin Grammens: Awesome. Awesome. Well, I talked a little bit about, you know, where you're at today. I touched a little bit on Eden Prairie Local News with regards to maybe where you started as the original editor. Are there other things that maybe sort of got you to where you are in your work?

Career that you would want to maybe talk about to give the listeners a little bit of a background.

[00:02:16] Bradley Canham: Well, you know, yeah, I always like to give a shout out to Star Trek because… 

[00:02:20] Justin Grammens: Next Generation or like the original?

[00:02:22] Bradley Canham: The original Because yeah those programs at that time in the 70s when I was growing up were Inspirational right and kind of said hey, here's all these technology stories that are very exciting.

And so Star Trek. Star Trek fan. Gotcha. Then I had what I think I call kind of a ten year career after high school. I call that the Bohemian era. I did a lot of theater. I did a lot of writing. I traveled. The U. S. and overseas. And then my technology career really started in kind of the early 90s. I had a firm that provided demo services for companies like Microsoft and Acer and IBM and a bunch of software titles.

So if you saw people demoing Microsoft 95 at Best Buy in 1995 at the big launch they probably worked for me. Okay. Yeah. Interesting. And then I was at ADC Telecommunications here and Eden Prairie, so a bit of a telecom background, was with Allot Communications, Deep Packet Inspection Company, so a lot of network infrastructure organizations.

A lot, by the way, went to the NASDAQ, so that was kind of an early experience with kind of scaling up successfully. Oh, for sure. So we've done a lot of stuff since then, yeah. Yeah,

[00:03:42] Justin Grammens: yeah, cool, cool. It sounds like, you know, I guess I, I should know this about you, but I mean, what was your background in like a liberal arts education as you were talking about that?

I have a liberal arts education from Augsburg University. It was college here in the Twin Cities. Is that where you kind of thrived, I guess?

[00:03:57] Bradley Canham: Yep. Yep. I was an English major and, you know, I don't want to get too far off the track here, but you know, I traveled a lot during my undergraduate career. I actually went to seven different universities around the U S and overseas.

I was at the Shakespeare Institute actually for a while in Stratford on Avon. So a lot of stories there. We'll just kind of set it. Aside, but a nice woman at the University of Minnesota named Bev Atkinson kind of compiled all my crazy credits and said we're going to get you an English degree, Brad. And we'll never forget Bev.

She was an angel. Yeah. That's

[00:04:35] Justin Grammens: great having people like that in your life. Yeah. When you're young and innocent and don't really know how to navigate a lot of things. So that's, that's, that's awesome. That's great. Well, one of the things, I mean, we'll bring it back a little bit to AI and you mentioned, you know, we were talking about AI in education.

I like to ask people how do they define AI when you tell somebody, well, I work in artificial intelligence, if you're on an elevator, like what's sort of your, your short pitch?

[00:04:57] Bradley Canham: That's a great question. So, but my short pitch is because I'm typically on the business side of these technology companies. And so I kind of preface my definition based on my point of view, which is typically my task, which is to grow a business.

Right, either as a founder or as someone on the marketing or business development context. So I like to take kind of a step back from the technical and instrumental uses of AI because that's typically not my. That's not my bag, so to speak. That's on the development side. Yeah, sure. Developing, you know, foundational model or a large language model or, you know, NLP and the acronyms that get thrown around.

Yes. What I'm looking at is how to make what is a new capacity, which is the kind of curation of human knowledge that then gets pointed at solving a problem as what I call the standing reserve of human knowledge. Kind of like I described this, you know, as as a dam, right? So you dam water, you dam human knowledge, and then you point it, right?

You either point it at the turbine or you point it at your I was in Africa a few years ago. I was in Ghana and there was a village That had been on a river and it had been dammed, the river had been dammed. So the village had to move, right? So I think of AI in that kind of context right now, because it is so new for a lot of people and a lot of markets and less about kind of the instrumental, not less, but also I would say the instrumental use, like we're going to use this for.

Customer service or, you know, analyzing something or chatbots or whatever it is, but also what it reveals about us relative to having this capacity that it's a standing reserve now of capacity. So what are we going to do with it? And then how am I as a operator in a business going to make sure the audience?

Either in a sales, marketing, or what have you, positioning context, it syncs up with that, that social side of growing a business. Yeah, yeah, sure. So, kind of a long way around it, but that's the way I kind of think it. What does it reveal about us? We'll think about it as a DAM, as kind of a standing reserve of capability.

And then also, how do we use it?

[00:07:20] Justin Grammens: Sure, sure. Yeah. And how do we use it kind of folds well into the whole reason I kind of started this group years and years ago was really around the applications, right? And this podcast is really around applied AI. I certainly, I have people that are on that love to talk about all the details and how, you know, neural networks work and all that sort of stuff.

But at its core, really, I'm really looking at interesting applications. And so, as you're talking with businesses and sort of in this field, what are some interesting things that you're, you're seeing businesses do, either publicly or privately or whatever you can share?

[00:07:52] Bradley Canham: Sure, that is core to my role. So, you know, I was, I was at your conference early in November.

I was in conference in Portland last week. I talked to people constantly. In fact, I talked to a company yesterday that has a, I don't know if I should mention the name or not, but. Anyhow, it has a AI powered aspect that layers into like a video meetings like we're having now, right? And it not only analyzes, then the AI is analyzing both the text for sentiment and kind of keywords and tracking what would be typically a sales situation or a meeting situation for enterprise deals.

It is also analyzing the video, right? So it's watching my eye tracking. It's watching my. You know, the corners of my mouth and stuff like that, gauging sentiment and engagement, right? And so, and then a real time feedback, it's got a graph that would be running for the other person that shows, oh, the sentiment, you know, is trending down, engagement is going up, and this kind of stuff.

And all that data then gets collated in the background and fed into a CRM, and so you can track Not only that deal, but kind of the whole ecosystem of enterprise sales projects that are going on, not just based on what I would call, you know, the salesperson's story of the deal, which is typically maybe kind of, sorta related to reality, but also this other, what is explicit data rather than just kind of the tacit, you know, review by a salesperson with.

You know, upper management, et cetera. So, to me, that's interesting as they go through. It's a combination of behavioral science and AI science, and they're trying to dial it in. And that is a space that I'm very, very interested in. It's part of the research that I've, you know, did in my academic career.

[00:09:51] Justin Grammens: Interesting. Yeah. Yeah. Yeah. And I know you worked around internet of things. I think we've talked about that in the past. So, you know, kind of this overlay of AI and IOT, which is basically where I came from. My, my background is in software engineering, software development. Built a number of different companies doing software services and products as well.

But, you know, I've been in IoT since before it was even called IoT. And I've been teaching at St. Thomas in Internet of Things, but the class has shifted now over the past couple of years to really around AI and machine learning around all these sensors. Where, where do you see that kind of headed in the future?

[00:10:27] Bradley Canham: Well, it's a great question. It's a reminder of one of the things that You know, I keep especially close tabs on, given my English degrees, the nature of the language that's associated with things like IoT, AI, SAS for that matter. You know, I mean, people are insiders. We kind of watch the jargon and we own and we invent a jargon as it goes along.

And so that's been part of my role, particularly in a sales and scale up capacity is understanding like, okay, this is the way. The jargon is being used now, and I can actually capture a piece of jargon here as a knowledge broker in this space. You know, I was at Casio Networks, which is a long range Bluetooth gateway company.

We did have an AI component that was based on pulling some of the sensor data that these gateways were pulling and there was a back end platform that then was part of the offering. to our clients. In general, the convergence of technologies is just speeding up in a feedback loop across the board, whether it's material science, IOT, AI.

So it's this acceleration environment. And, you know, data is kind of the, I don't know what the good analogy is, again, I'll just kind of point back the DAM analogy. It's the water that's making all these devices valuable and, you know, usable in unique ways.

[00:11:56] Justin Grammens: Yeah. You know, as we were talking about all the acronyms and stuff, I just had to think back to people that are not, you're right, people that are not in our industry, right?

And think about, well, A, of course, our parents or whatnot, right? But then even just people that are not in tech. IoT doesn't mean anything, you know, and SaaS and, you know, Platform as a Service and all that sort of stuff. I mean, it doesn't mean anything. At the end of the day, they're just, they're looking to try and use the thing.

And in some ways, AI doesn't really mean anything to them either, right? I mean, it's a term that gets thrown around, but it gets thrown around in so many different aspects to it. I feel like it's actually confusing the situation. When I was at that Eden Prairie, you know, event that happened, that town hall event, You know, I, I do recall now of me saying you use AI every day and you have for many, many years.

You just didn't realize it,

[00:12:42] Bradley Canham: right? Yeah, I think that's an important topic, actually, when I go to events, I usually kind of, I do some writing about what is the theme of the event. And so at, at your event at the Scout Center several couple of weeks ago, I really thought the key to that event, the theme was the impact on the nature of work based on ideation and imagination.

Scott Bromander brought that up. The gentleman from Modern Climate brought that up Jason Tell. So I thought, okay, cause it's moving so fast. So I said, okay, the theme here is this is an early undertow that says we're moving from the industrial era into the imagination era, which is, you know. higher value of imagination, ideation, and kind of human interaction, as opposed to, you know, the kind of efficiency model of the industrial era of the past 270 years.

So that was the, the event at Eden Prairie. You know, this whole concept of democratizing AI, that term, you know, gets a lot of definitions created by companies who have a vested interest in pushing their technology everywhere, like Microsoft defines democratizing AI, etc. I thought that event, because it was a town hall sponsored by, you know, Eaton Prairie Local News, which, which I was You know, one of the founders of, and also the chamber that this is an actual example of democratizing AI.

Is this really for a lot of people who know nothing about AI, right? So there's less of that kind of enterprise corporate, you know, interest and pushing an agenda or, you know, all that kind of stuff. And people have no exposure to kind of go, Oh, okay. This is what AI is. And, and demystify it, and I thought it did a good job.

I thought you did a good job too. Yeah, thanks. Thank you again for that.

[00:14:41] Justin Grammens: Yeah. Yeah. Well, yeah, yeah. No, that, that's an interesting take on the theme. And you actually, in my head, I, I probably should be a little bit better. I mean, that, that was the third event that we've done and in that size of a conference, we'll be doing it again in the spring.

But this idea of actually having a general team, I've always been like, it kind of like like a soup, like, let's just put a bunch of things in the soup together and we'll make some sort of a goulash together, you know? And so that's kind of what I tried to do is just sort of grab speakers from all over different, different areas.

But you're right. As you said that it really was about the work. A lot of them sort of touched on that aspect, how it's going to affect their industry. We even had a woman that was there talking about. Human resources, right? And basically how they're using it, you know, internally within training the workforce and stuff.

So yeah, yeah. Fascinating way. I like that lens that you put on it. And you know, it's the beauty that I love of AI. That's kind of why we started this thing back in 2019 was really just sort of saw it as touching everything, right? It doesn't matter what industry you're in. Whereas I feel like, you know, a lot of these other technology groups, they're just, they're so vertically focused in, in some way.

You know, the internet of things group that I started, it was, you know, it's really around sensors and data, right? But if you're like in the financial markets or you're dealing with spreadsheets, yeah, the data finally gets to you But it's not really thought of an internet of thing application Whereas you come at it from the other way, which is intelligence, you can kind of apply that I mean literally look around anything can be become more intelligent in this

[00:16:03] Bradley Canham: time, right?

So that actually, you know, let me kind of do a wrap up around that it says, you know, part of my my intelligence my expertise is To be able to kind of conceive of patterns, right? So I'm a marketing guy, I've been a newspaper reporter and, you know, I've got a master's in business communication. So I've got kind of a language communications construct of.

you know, expertise. So that's my job, is to go to these events and be able to see, like, here's the pattern, this is what I see, and I can kind of put a flag in the ground, so to speak, to help people understand and demystify then, where are we? What are we talking about here? Because there's so much flux right now.

I call it the flux, right? The flow of all this data, all this stuff. Yeah. And, you know, intelligence is the use of knowledge. Right? So back to AI, like what is AI? So AI is intelligence that is drawing from knowledge. Right? And there's many kinds of knowledge. There's different types of knowledge. This gets a little esoteric, but, you know, I'm paying attention to what are the types of knowledge and expertise that are being displaced by AI that can be augmented by AI.

Jason Tell had a great presentation about how creating AI images, then you almost feel in the room how this guy, Jason, obviously is an expert, right? You could, you can almost feel there's a sense that this is a guy, extremely X, you know, has a high degree of expertise about how to kind of maneuver this, this new tool to create the results that he was looking for.

And there's definitely constraints. There are borders there, you know, parameters, and there's also some areas. Where you could see the use case kind of pulling forward. He's going, you know, stock images on one side. Don't get me the specificity and the consistency across images that I would use with an enterprise client.

And to do a field shoot is going to be extremely costly. And then there are all these legal parameters and copyright issues right now around AI. So how am I navigating my organization to use AI? And I thought that description to me, I'm like. Okay, he's talking about using it as the core of it was the ideation capabilities that it made Extremely easier to kind of pursue these weird little angles, right?

Right, right So that's what you know, it's a piece of what I try to pay attention to is not words like knowledge expertise intelligence efficiency Like, what is all that automation? What does that look like in a marketplace? What does it look like for a company and how do you kind of make it work and grow the organization?

Cause it gets, you know, I've been in scale up startups. You have to, it gets very confusing. It gets, you know, it's a bit chaotic. It's my job to kind of make it clear, like this. Is what we're talking about here. This is what this market will respond to.

[00:19:15] Justin Grammens: Yeah. Yeah. Well, I mean, the thing you touched on was using the tool, I think, right.

I mean, that was the thing that I think I heard was, was one of it was here's somebody that's really expert in the field. They've been doing this for decades. But yet here comes along this new tool, and instead of shying away from it and being like, I'm not going to use that thing, you know, I know what I'm doing, he has kind of embraced it.

Kind of a general theme that I think I've seen most of these people that are presenting at these conferences and that are, you know, kind of trying to get the more awareness out is, is look, you, you really have to start using this because. It really will help you, and I mean, I'll give a case in point, you know, I'm starting to, well, I have been, but I'm using them more and more and more, is basically, you know, Copilot, so coding assistants that are sitting next to me while I'm writing code.

And at first, and this was back at least six months, at first I was like, oh, that's interesting. This is GitHub? Yeah, so I'm using GitHub Copilot. I plugged it into a couple different IDEs, but yeah. And what's so cool is, is that it gives me little hints along the way about how to even use the tool even better, right?

And so when I first started using it, it's, again, like I say, it was pretty mundane because I think I was pretty mundane too. I would basically be like, well, you know, why don't you just generate me a function that does X, Y, or Z, and then we'll go ahead and spit it out. I'm like, well, that's nothing impressive.

Like I could have written code, you know, but now, and again, it's just going to get better and better, but. Now, you know, I basically start to write a comment, and it comments all of it for me. You know, like it knows what the code does, and it says, here's what, here's, here's a nice comment that you should write.

And then it even now starts bringing in other prompts. Would you like to create an additional class to do X, Y, or Z? And I'm like, well, yes, why? Or no. And so, The tools are becoming better and better. And so I actually came into work today. I talked to one of my engineers and I'm like, have you started using this?

You know, and some people on the team have some people on the team haven't, but I'm like, you gotta get on this. I mean, anyone who has it just needs to just start exploring it and using it. And I can see people across the gamut. I've been writing code for 20 years now, right? But, you know, there are even hints of things that it does, and I'm like, that's pretty cool.

You know, I like that. So, again, I'm not saying that I'm using it even to the best of its ability right now, but we all need to be, regardless of your skill level. And I even petition that people that are younger programmers that are just coming out of school, they're going to get the most bang for the buck, because They can literally start writing code, a five to ten year person tomorrow, right?

And, you know, I, I hear some hesitation from some people saying, well, no, but I want to understand how the code works. And I'm like, I get that, but this solves in my mind, the blank sheet problem, right? The, the idea that generative AI can actually just generate something for you. That's, this was all my perspective on it.

I'd love to hear yours on just a lot of stuff wrapped up in that, around the value of generative AI. The value of people using it, regardless of their skills. Where do you see that going? Or where do you see that today?

[00:22:01] Bradley Canham: Well, it's funny you should mention the blank sheet problem. I think that's actually in the title of the article that I wrote.

I think we're going to publish it in a day or two. About the conference over at, by Fort Snelling. It solves the blank sheet problem. Because everybody kind of knows, you know, that term, right? Scott Bromander talked about kind of the rule of thumb when he was talking about building his zoo kiosk for coders being kind of this 10 percent ideation, 90 percent coding and work and that shifting based on, as you mentioned, this kind of co pilot concept and, you know, actual maybe tool and GitHub about.

To a 4060, right? So I do use it similarly because I, I mean, I tend to, I write a ton, right? So I'm constantly cranking out stuff and it's, it is helpful. It does help generate again, ideas and kind of angles on things. And sometimes when I've got some really complex kind of. You know, stuff where I'm taking all, what about knowledge and intelligence and efficiency and blah, blah, blah.

And I throw some of that, you know, at chat GPT and say, hey, give me a, give me your two cents on how these things work together in this particular area or space and why somebody should care. You know, it'll spit something out quickly. So it helps me think faster in a way. Yes.

[00:23:20] Justin Grammens: Yes. Yeah. And so, and again, this is a big question.

Do you think overall it's a, it's a net positive tool for our society or a net negative or a net neutral? What do you think the, the history is going to show with regards to just generative AI and these large language? It's like, there's a lot of controversy these days. Yeah.

[00:23:39] Bradley Canham: I mean, I'm going to pull it back to kind of the discussion around standing reserves and what it reveals about us and think about like, you know, what you could do with a damned.

River, right? You can use it to irrigate your crops or you can fill it up and flood the enemy. You know, the same thing too, is like, you know, a hydrogen atom, right? It was just floating around and then, Oh, we learned how to fission that. And we can do nuclear reactors to create energy, or we can drop bombs.

I do think AI. is of a similar class of technology, broadly speaking, and that it has similar capabilities in that regard. It is moving much faster and the means to regulate it and that kind of stuff is, you know, at this point, at least much, much looser than these other technologies, dams, fission, et cetera.

We track. By the way, you know, the regulatory environment around AI, so that's part of what we do as an organization here. So, it's interesting to see what's out there right now, but, you know, I think it remains to be seen. You know, if you're thinking critically, it's like, good, bad, I don't know. Like, honestly, I don't know.

But it is extremely powerful, so it's, it is a source of anxiety and a source of, you know, is this going to liberate the human race?

[00:25:06] Justin Grammens: Yeah, yeah, yeah, sure. You and I both teach at the University of St. Thomas. You're in the School of Entrepreneurship, is that right?

[00:25:13] Bradley Canham: Right, yeah. So I'm a corporate fellow, which is, I don't teach per se, is what I assist and, you know, help instructors with real world examples of You know, whatever they're teaching, if it's, you know, innovation or business models, you know, I basically provide, like, oh, here's what this looks like in a real, you know, the real world, right, kind of thing.

So that's nice. I don't have to grade. I don't have to do anything. Yeah.

[00:25:38] Justin Grammens: Do you get a chance to come in and like guest lecture at all? Do you do much of that?

[00:25:42] Bradley Canham: I do. Yeah. So I do get to, you know, come in and there's certain, certain classes, et cetera, where. Like my doctorate is in leadership from St. Thomas.

So if there's a leadership class, I'm probably an expert compared to the professor running the class on entrepreneurship who wants to talk about leadership. All right. Well, I can talk for the whole class. I can give you like real life examples of here's what leadership looks like, particularly ethical leadership, I might add.

In a startup environment, in a scale up environment, why it's important and those kinds of things. Excellent. Cool. Yeah, it's fun. I love it. I love it. I do it mostly because, you know, I just love it. I want to see those bright, shiny faces sometimes.

[00:26:27] Justin Grammens: So how do you, how do you see artificial intelligence then sort of seeping in?

Are you guys having a lot of conversations around AI leadership? Or even just AI in education, I guess, just how it's working its way into the classroom.

[00:26:38] Bradley Canham: Yeah, I think, I think there's, you know, around kind of the use case model. And so on the one hand, I'll back up a little bit, students are going to, you know, start using AI to finish their assignments if they haven't already, right?

It's there, it's a tool, they're going to get their hands on it. So one of the things I've been thinking about is for the students to engage with, say, a chat GPT session, say around historical character, and have conversations with that character. Yeah. Based on what they are, you know, the information and the questions that they're providing and that be part of an assignment and also then have chat GPT or some other, you know, cloud or, you know, whatever, do an analysis of what type of critical thinking is evident in the conversation, because ultimately that's Part of the, you know, model around a liberal arts education is learning how to think regardless of what we're thinking about, if it's history, if it's coding, if it's English, if it's entrepreneurship, demonstrate that you're able to kind of have a critical view.

Of, you know, the information before you, and I think chat GPT and other AI tools could provide that kind of analysis and feedback is, you know, okay, you did a comparison based on a historical reference or what have you here, or you're listing different segments of an argument that can be applied that, you know, are supported by evidence and that kind of thing.

And so it's an engagement around the depth of education itself. Yeah.

[00:28:19] Justin Grammens: Interesting. Yeah. Yeah. No, I see that. I mean, I just see a broad situation in a lot of ways where people are like, yeah, this thing's going ahead and writing stuff for me. But what's most interesting, I think, around generative AI is that then you can start asking it to help you pinpoint or solve certain problems.

So like. I know a guy who worked for Optum, and they have a lot of data that they could load into these GPTs. Here's the cost of running a health center in some third world country. And here's some inhibitors as to why people can't afford it or can't get to it. And here's Some issues around the government, you know, thing.

And then they had just loaded it in with a lot of data and it was a very finely tuned model. But the beauty of it then, it's like, how do we solve this problem? You know? And so then you can actually start asking it to generate you some of the solutions that you wanted to get. And that's what I think is just so fascinating about how I feel like we've turned the corner on this.

It's not just, I'm going to Google and research and get results. And that's kind of what I talked about in this town hall. It's like, no, this is a different paradigm. Like my thing, I talked about it, having the librarian actually have read all of the possible books out there, and then you can have a conversation and ask questions about it, right?

Librarian pointing you to aisle five, section C, row five, row six, and read that book, right? So the idea is that students could kind of actually have a deeper conversation around this conversationally. I think ultimately. Will produce a better. Hopefully, again, who knows, but a more richer experience that the student maybe wouldn't have been able to have, especially if they're in a classroom of 300 other students, right?

Like a lecture hall, like how are they supposed to get in a lot of these details with a professor or, or some sort of TA.

[00:30:00] Bradley Canham: Right? Yeah, no, I think you're, you're, you're onto something, you know, if the choices between, Hey, go learn this information and then regurgitate an answer and Hey, Start having a ongoing conversation and start asking questions, start applying.

What you're learning here, let's say you're learning about Winston Churchill, and you say, okay, take Winston Churchill, let's apply it to you know, I'll reference kind of being in Ghana again, I was in Ghana about five years ago, how would Winston Churchill go about solving a problem of a well that was falling apart in this Zungo, this Muslim village along the river.

The river, as well as the way I would look at it based on the input that I've given. So you start getting this like, Oh, okay, Winston Churchill would look at it this way. Yes. Well, that is super interesting, right? You might learn something about leadership there. You might learn something about, I don't know, motivating a village and or people on, you know, over here in the U.

S. to be part of the solution.

[00:31:06] Justin Grammens: Absolutely. Absolutely. Yeah. Yeah. Yeah. And I guess what the other thing I was thinking when you were saying that is like, right now then you can actually, you know, the student could actually send a link to the chat that they had with this, right? So the teacher, professor, whoever it is, could actually go through and see all the questions that were asked and see all the ones that were answered and sort of like be able to see what the student did.

Yes. You know, they didn't have to read, you know, a hundred pages of this book. They actually interfaced with this thing. But it's like, Yeah. You can do that today. You can literally send your ChatshippeeT link up to somebody and they can see the whole thread. So, you know, you can, you can see the thought process.

There was some work that was being done there. And then of course they can summarize it at the end, right? And then put in their own nuances, right? So yeah, I, I just, I feel like, you know, I'm not teaching a class that has a lot of writing, teaching a lot of class, I'm teaching a class that has a lot of technology and using APIs and collecting data and building large language models.

Even small language models and even no language models. We do a lot of stuff with computer vision. But I do ask students every week to basically find a current article that they have found and present in front of the class what that's about. And they can definitely use ChatGPT to just summarize the article.

They don't need to read the whole article. They just, I'm sure 99 percent of them are using ChatGPT to summarize it. But they still need a regurgitated voice, right? So for me, I think you're right. Students are using it and I, you know, there's no sense in having them not use the tool that's out there, but I think there's some interesting and unique ways that I think all of us as educators can kind of like leverage it to make it a little more, I guess, to, to bring out the power of the tool rather than kind of pooh poohing it and saying, well, all they're doing is just putting a prompt in.

I think there's a lot more.

[00:32:41] Bradley Canham: Right. Well, I mean, partly what may be, I don't know, around it, I tend to think is that what requires educators then to also get familiar with the capacity to kind of use AI as a tool alongside your, your role as a teacher, right? So that means getting into it, understanding what it can do.

So I can, you know, I take, let's say a written paper or even code in some way, and then find prompts that show. Like, okay, well, the structure of what was written, say, for example, as a text could have been reduced by 25%. It could have been enlivened by, you know using some less adjectives, more verbs, things like that.

I mean, that's a fair use in, I think, an education model to use AI to help kind of manage the process. But I think, you know, to your point, to actually have somebody Voice something to put it in their own words is also part of the education process. Again, I'll pull it back to like, because we are social creatures, we do need to hear our own voice, right?

And part of education is empowering students to feel that they have their own voice and coming to, you know, a relationship between what they're learning and who they are. Right? Yes. So I think that piece, you know, there's a path to make it work together. Yeah. Right? Rather than one being, you know, students being dependent on AI or feeling, you know, mystified by it or disempowered and that kind of thing.

Yeah.

[00:34:23] Justin Grammens: As you were talking, you know, I was thinking about, well, you know, I, I'm not an English major, I'm not a very good writer, but I would go to the writing lab, right, and I would talk to somebody who was an English major and they'd help me rewrite the thing. I know what I'm doing, what, that's what students can do today in their dorm room.

ChatGPT has helped them rewrite it just like they would have had to deal with human at the, at the English writing lab. So we're making it easier for them to do it. Of course, they're not, they're losing that human touch, which I think there's a huge piece of that.

[00:34:51] Bradley Canham: I mean, I can give you a story, you know, a mentor of mine at, at St.

Thomas professor Rigoni wrote a book called teaching what can't be taught. And it was a study of a tech lab at a university and how the individuals in that tech lab effectively learned not so much just by, say, writing code or, you know, swapping out circuit boards or what have you, but it was also about being at the tech lab.

So what he found in his research is those individuals who kind of invested themselves in the social environment of the tech lab at a university. the university after a while, they just got stuff, right? They just knew stuff. It isn't that they were taught directly, right? It was part of this collective social environment.

Honestly, just kind of think what, what you bring to the community here. And again, I'm a big fan of that is it's a community structure that, you know, it's got its own dynamic, very hard to put into words, you know. But it has exceptional value in a technical space, right? I call it, and I did this at Eden Prairie Local News, I call it the story of us.

You have the story of how you all met the first time you met, just like couples do when they first meet other couples. How'd you meet? That's a social construct. You have gifts that are given, right? This is in fact kind of a gift that you give to the community, that I give to the community, is kind of these conversations.

And there's certain, certain rituals that you have put in place, right? Certain cadence, in my case, in Prairie Local News, we do a print edition, you know, every quarter ish, something like that. There's a cadence to things, there's a rhythm to it. So those things, the story of us, rituals, and gifts, they create this environment, back to Rigoni's thing, you know, that is a part of any technology space, right?

That's how learning takes place. social construct as much as it is a technical

[00:37:02] Justin Grammens: environment. Oh, I love that. I love it. Is there, is that actually like publicly accessible, what he

[00:37:07] Bradley Canham: wrote? Yeah. Yeah. Rigoni's, Rigoni's book is Rigoni's, I just love him to death. Get a little into some of the, you know, maybe nuts and bolts of it, but he has kind of a, what's called a phantasmagoric sense of knowledge.

So he uses Carlos Castaneda's kind of if you're familiar with those books, kind of this. guru ish type transfer of knowledge that happens, that you cannot learn something just by having somebody teach you. You have to experience things, right? That's how true experts arise. That it's like. 10, 000 hours.

They see patterns. Yeah. The patterns, and patterns, and patterns, and patterns, and patterns, and after a while, you just know stuff. You just

[00:37:49] Justin Grammens: do. Yeah. Well, I want to make sure that I get that link, or at least a link to him, after this podcast because, yeah, I have liner notes that go out with the podcast and all the stuff that we talk about.

I will make sure that they're listed in the transcript, and so people can get some of this stuff. You mentioned, we talked about his work are, are there any other books that come to mind that you would want to share, either

[00:38:07] Bradley Canham: AI or non AI? Oh, gosh, you know, I, I think the, the note they sent you, I mean, I read constantly, so I'm kind of a true book lover.

If you see behind me here, it's like, that's my, my lovely wife, you know, accommodates my bookshelves, but yeah, since I'm coming at it from kind of the, you know, the more social side, kind of business growth on the business side kind of thing Gosh, I'm reading a a book right now called Radical Uncertainty by a couple of economists.

Oh, yeah. Yeah, that's a good one. Because we're in such an environment of uncertainty, when he talks about uncertainty, it's like, okay, well, you know, I always take a pause when I hear a word being used a million times, like uncertainty. Okay, well, what are we talking about? Let's define that. And then I'll go read a book.

Like, okay, let's read a book on uncertainty, because I want to understand what this is and why everybody's talking about it. So that's. I think that's a good one. Again, I tend to read kind of fundamental, sociological, and organizational theory. Yeah, yeah. Because I think of my role in the business context is kind of applying those theories in a practical sense.

I don't like getting caught up in the latest, greatest. You know, business trends stuff, because I've seen trends come and go. I think about like, you know, back in the bad old marketing days when Google Analytics first came out and they would list IPs and you could kind of pull an IP and go, Oh, that's so and so, right?

You would link somebody's IP to their name. And we thought, oh, sales and marketing problem is solved, right? We know who's coming to our website. Gosh, you know, this is easy money. And, well, of course, that didn't turn out to be true. And there's still a lot of that kind of thing that shows up, right? It's like, oh, now AI is going to solve all our, you know, sales and marketing problems and coding problems, supply chain problems.

Well, it turns out there's all this. You know, sociology that's happening. There's a lot of leadership issues. There's a lot of You know, other things that I think, to me, some of the fundamental writers on those topics have got, are much better dialed in than some people trying to grab headlines and sell their coaching services and that kind of stuff,

[00:40:21] Justin Grammens: so.

Yeah, you mentioned radical uncertainty, I will, I will put a link to that. Yeah. And you mentioned, I think you said it was, you mentioned economists, right? And that was the backbone, or some of the people behind it.

[00:40:31] Bradley Canham: Yeah, one of them was the executor of the check for the, for the UK. Okay. I mean, these are, these are not fringe economists.

These, these are very, very well known economists. Yeah,

[00:40:42] Justin Grammens: there was another one that I read. This goes back some years, but it's called Prediction Machines. And it was, it was basically, you know, these economists sort of broke down what AI really means from a prediction standpoint, you know, and how at the end of the day and, and again, it kind of falls into large language models in some ways, right?

It's just kind of predicting what the next, what the next word is. This was, their book was at least five years old or even more now, but really, really highly educated people in economics, talking about AI. And like you say, they're talking about it from a very, very simplistic point of view about how this can actually is being applied.

And so I love it when somebody takes, they're just an expert in an industry and they're talking about. The best ways to do stuff. And it just so happens that they're using AI, the AI technology. They don't come at it from the other way. Like, kind of like what you're saying, like, I'm just going to bash AI into everything that I use.

It's more of a, I understand my industry really, really well. I've been doing this for a long time and here's how we do it. And it just so happens that we're using AI or the techniques around official intelligence really to enhance

[00:41:43] Bradley Canham: it. Yeah. To me, what you're, you know, alluding to is kind of maybe back to the beginning of the conversation.

It's like, all right, well, let's start with use cases. Right. Let's start with how can this actually be used? You know, I want to know the truth that a professional believes in a particular space. I don't want to just show up and say, Hey, pain point feature benefit, right? I'm going to flood back to the dam analogy.

It's like, you know, as a marketer salesperson, you want to flood the castle with. So that the people in the castle go to the towers and say, Oh, here's where the features and benefits are. Gosh, I should buy you. Right. Right. You see what I'm saying? It's like we first flood the environment with pain and then we say, Oh, okay.

But people in professions are very particularly technical professions are like not fooled by that kind of stuff anymore. Right? Yeah. I could use example of what's it core wireless and they. They've bought part of Twilio's offering, Microvisor. So they're going to market with a message that is unbreakable firmware updates, right?

So the truth is, if you are a firmware engineer, you are very familiar with this very jargony word, bricks, right? Something gets bricked. You've turned a noun, a brick, into a verb, right? Something got bricked. Yes. And then Core Wireless has then made that an adjective. So it's a quality of the service that they're trying to push to the market.

So it's in a very jargony way, but it's also a truth that firmware engineers just kind of go, well, if I'm going to update a thousand devices, I know I'm going to have bricks. Now, these guys are approaching that environment and saying, I'm going to create this, you know, granted it's a marketing campaign, but it's.

It addresses a fundamental truth. Firmware engineers are socially invested in being experts. And they're going to hear this, they're going to see this thing that says unbrickable firmware updates. And they're going to, you know, throw up the BS flag. But they can't avoid looking at it because it's a credible claim coming from a credible company.

And it's not filled with pain. It's not like pain, feature, benefit. It's more like, if you're an expert in this space, you should pay attention. Right? Right. And they have, and they will. And that campaign, it's as geeky and kind of technical as you can get, but it's been wildly successful. I know the guy who, who started it over at Core Wireless.

And so kind of swinging back to AI use cases. is like Jason Tell's description of AI images. So if you're going to connect AI to creating beautiful images for a branding agency, if you understand the truth of Jason Tell's environment, which is field photos are going to cost tens of thousands, maybe hundreds of thousands of dollars.

Stock photos are not consistent. AI kind of gets you there, but there's this legal issue. If you can't show up in the market and say, I'm selling to Jason a AI tool, I'm going to say, we have solved the copyright issue around AI images, right? And he is a expert in that space. He's going to look at my AI offering.

Absolutely. Yep. So it's, it's less kind of baloney ish. It's more like focusing on the truth that technical people want to see, right? Yeah,

[00:45:21] Justin Grammens: sure, sure.

[00:45:23] Bradley Canham: Does that

[00:45:23] Justin Grammens: make sense? Well, yeah, it sounds to me, it feels like we're targeting to them, to their pain point, because in a different industry, that wouldn't mean anything to anybody, right?

Right. You said solving your, your photography legal issues. Like if you're, if you're in healthcare, you're like, well, care, that's not my important, my, I, I'm concerned about security, right? I'm concerned about. About personal information, you know, PII, there's certain things that I care about. So your message to them would be different.

But however you're layering the technology in there to solve the problem, they're going to glom on to.

[00:45:51] Bradley Canham: Yeah. Yeah. I kind of like the idea of like making it, you know, speaking to the truth of professionals who hold, I'd say, social capital in their space. They vet claims that come at them, right? And so they're, they're going, yeah, this is a pain, but it's also like, okay, this is what I conceive of to be true.

And if you're saying that just adjacent to what I believe to be true is this other option, I need to look at that.

[00:46:18] Justin Grammens: Sure. Sure. Well, it's been great. This has been great, Brad. Question for you. How do people get ahold of you?

[00:46:24] Bradley Canham: Well, brad. canham. C A N H A M at transformuhinsights. com. That's, that's a good option.

I'm also on, on LinkedIn and where else can, I mean, is, is that good

[00:46:36] Justin Grammens: enough? That's perfect. That's perfect. And I'll put them in the show notes as well. You going to get any, any interesting shows or conferences coming up at all off the top of your head or

[00:46:46] Bradley Canham: kind of we'll be at CES. We're just looking at a list.

I've got like the list, we've got a kind of master list of like every technology show on the planet. And so, yeah, we're going to be, we're going to be a number of shows in the UK. I know I I I O T world. Hymns we'll be at all kinds of stuff. Yeah, exciting. Yeah, the website, I mean, we've got a list of the events that we'll be at.

They'll also list our names, because that's kind of a constant part of the job here. And it's actually part of the fun, too.

[00:47:16] Justin Grammens: Yeah, for sure. Well, that's great. Glad you can get out and do that. I wish I could spend a little more time doing that. I certainly Certainly will be doing more when I'm not teaching.

So I teach in the fall and then usually in the spring, I've been doing independent studies with, with students. So, which has been really fun. We've been able to go deep into a, into a specific use case, a specific problem. Like last spring, I helped a student work on American sign language. So they're actually able to use a camera.

And it recognized American sign language and then translated that to English. So it was kind of fun. And so when I work on those, that gives me more time to actually travel, get a chance to get out there, speak at conferences or attend some. So yeah, I, like you, feels like kind of thrive in the community.

I'm out interacting with other humans. So it's really exciting, but yeah, looking forward to having you back on the program in the future. And you can tell us a little bit more about what you've seen and what you guys are up to, but. Appreciate the time today, Brad. It's been great. Great conversation. A lot of fun.

Yeah.

[00:48:12] Bradley Canham: Yeah. Good to see you again, Justin. Really appreciate it. Thanks so much.

[00:48:17] AI Voice: You've listened to another episode of the Conversations on Applied AI podcast. We hope you are eager to learn more about applying artificial intelligence and deep learning within your organization. You can visit us at applied ai mn to keep up to date on our events and connect with our amazing community.

Please don't hesitate to reach out to Justin at Applied AI if you are interested in participating in a future episode. Thank you for listening.