Latest / The Adults In The AI Room / Earley AI Podcast - Episode 62 Navigating Aspirational AI: Brian Magerko on Creativity, Ethics, and Technology in 2025

Transcript

Earley AI Podcast - Episode 62 Navigating Aspirational AI: Brian Magerko on Creativity, Ethics, and Technology in 2025 · The Adults In The AI Room

• 0:00
  SPEAKER_01: Well, welcome to the Early AI podcast.
• 0:03
  SPEAKER_01: I'm Seth Early.
• 0:04
  SPEAKER_01: Uh, Chris Featherstone is on a flight somewhere today, so he can't join us.
• 0:08
  SPEAKER_01: Uh, but we're really excited to introduce our guests for today, and we're going to discuss kind of the aspirational AI versus reality.
• 0:16
  SPEAKER_01: Where are people trying to go versus where are they today?
• 0:19
  SPEAKER_01: We're going to talk about bridging technical language gaps and really AI's role in creativity.
• 0:25
  SPEAKER_01: So, our guest today is a professor of digital media at Georgia Institute of Technology, sent extensive experience with applied AI and computer uh human interaction.
• 0:35
  SPEAKER_01: He's also uh known for co-founding the educational platform Earsketch, which uses coding to teach music creation to nearly two million users.
• 0:46
  SPEAKER_01: Did I say your name correctly?
• 0:47
  SPEAKER_01: Yeah, McGurko or McGurko.
• 0:50
  SPEAKER_01: Welcome to the show, Brian.
• 0:52
  SPEAKER_01: Nice to have you.
• 0:53
  SPEAKER_01: So um, why don't we start off with telling us a little bit about your world and you know, you're in academia, but you've done some entrepreneurial stuff.
• 1:02
  SPEAKER_01: So give us kind of a thumbnail of how you got to where what you're doing and and what your current interests are.
• 1:06
  SPEAKER_03: Give us a I have I have a pretty middling entrepreneurial background, actually.
• 1:10
  SPEAKER_03: The vast, vast majority of of the contributions to the world I've done have have all been through the academy.
• 1:16
  SPEAKER_03: I started studying AI at Carnegie Mellon in the late 80s, or he's I'm not okay.
• 1:22
  SPEAKER_03: Um one of those decades.
• 1:24
  SPEAKER_03: Yeah, you know, at this point, it was not that much 30 years ago, 40 years ago.
• 1:28
  SPEAKER_03: Yeah, yeah.
• 1:29
  SPEAKER_03: Uh I I studied there with sort of this AI luminary named Herb Simon.
• 1:33
  SPEAKER_03: He was one of the the guys who uh was that the meeting that started the field of AI at Dartmouth in the mid mid 50s, and a Nobel laureate.
• 1:42
  SPEAKER_03: Um and uh I worked on it, it was it was just so cool.
• 1:45
  SPEAKER_03: I got to work on studying cognition of jazz improvisers, and I built with another uh professor, Elon Norbosch, a improv robot comedy troupe.
• 1:54
  SPEAKER_03: The world's first improv robot comedy troupe, I might add.
• 1:56
  SPEAKER_03: Wow, that's a video on that.
• 1:59
  SPEAKER_03: It was it was 1998-99, so there isn't.
• 2:03
  SPEAKER_01: You know, like uh so interesting.
• 2:07
  SPEAKER_01: Robot improv comedy group.
• 2:08
  SPEAKER_03: Uh well, yeah, it was it was this uh little sort of scenario where there were two robots in uh in a room and one of them was trying to leave and the other one was trying to convince that robot to stay.
• 2:20
  SPEAKER_03: And I got I got a real kick when we demoed uh when we demoed for the CS department, and the robot turns to the other that's trying to leave and says, Wait, don't leave.
• 2:31
  SPEAKER_03: I'm pregnant.
• 2:32
  SPEAKER_03: And everybody like laughed, and I was like, I can make people laugh through computers.
• 2:38
  SPEAKER_03: That that felt good.
• 2:40
  SPEAKER_03: Yeah.
• 2:41
  SPEAKER_03: So I I applied to one grad school, I applied to Michigan, uh, and and luckily got in and went there expecting to do performative robotics.
• 2:50
  SPEAKER_03: Uh, I talked to the robotics uh faculty there.
• 2:53
  SPEAKER_03: They asked, Do you have an engineering degree or certification?
• 2:56
  SPEAKER_03: And I said, No, I'm a cognitive scientist.
• 2:58
  SPEAKER_03: And they said, No, thank you.
• 2:59
  SPEAKER_03: I was like, Oh, well, I'm coming here though.
• 3:01
  SPEAKER_03: Uh-oh.
• 3:02
  SPEAKER_03: So I I eventually found my way with with the amazing advisor, John Laird, who uh had uh had happened to have some funding from this weird place called the Institute for Creative Technologies out in LA.
• 3:15
  SPEAKER_03: They work with USC and the film, like they work with Hollywood and the military.
• 3:20
  SPEAKER_03: It's very and the games industry.
• 3:22
  SPEAKER_03: It's a very weird kind of multidisciplinary institution that's been there for a couple decades now.
• 3:27
  SPEAKER_01: You can imagine the common threads though, or improvisation and being able to kind of react to current inputs and current situations.
• 3:36
  SPEAKER_03: Yeah, that's what this funding was for.
• 3:38
  SPEAKER_03: Yeah, it was about uh an AI dungeon master, basically.
• 3:41
  SPEAKER_03: So, how do you, within narrative experiences, have AI influence your your story and and and your behavior so that you have a smooth like story that you experience, uh, which weirdly the military is interested in that kind of stuff.
• 3:54
  SPEAKER_03: But since then, since those early early days, I've I've just always been fascinated with understanding human creativity more formally and how it can influence how we design technologies, especially AI-based technologies, though, in in some instances, like uh you mentioned Ear Sketch before, it's about using creativity as an inroads for being interested in computing and being engaged in computing rather than trying to represent creative creative processes in the computer somehow.
• 4:21
  SPEAKER_01: Yeah, yeah, yeah.
• 4:22
  SPEAKER_01: And in your experience in your work, you know, one of the things we like to talk about is what are you seeing in the industry, in the marketplace, and your interactions and your research where people have misconceptions and uh you know really kind of don't understand certain issues or topics.
• 4:41
  SPEAKER_01: Where are you running into myths or uh uh people uh uh you know, whether whoever your your stakeholders are or work you're you're working with, really don't understand some fundamental pieces of the since generative AI has come out, I feel like it has been interjected into workplaces by people who don't understand the technologies.
• 5:05
  SPEAKER_03: So, hey, this is the hot thing, we need to get this.
• 5:08
  SPEAKER_03: Or my buddies at open AI, he sold me a license for our company, and we have to figure out how to use it or whatever.
• 5:14
  SPEAKER_03: And so there's a lot of hammers in search of nails, right?
• 5:18
  SPEAKER_03: Yeah, that's just that's a distinct problem, is is is or even it's it's more like buying buying a weird hammer off the shelf that you you know you don't have at home, but not quite sure how you're gonna use like hammer really for it's a really cool hammer though, it's a great hammer though, it looks great.
• 5:34
  SPEAKER_03: Everybody's getting ball peen hammers.
• 5:36
  SPEAKER_03: We definitely need a ball peen.
• 5:37
  SPEAKER_03: They love them ball peamed hammers.
• 5:39
  SPEAKER_03: What are you doing with it?
• 5:40
  SPEAKER_03: Well, we're ball peening.
• 5:42
  SPEAKER_03: So the the flip side of this is hammering ball pean.
• 5:46
  SPEAKER_03: Uh the flip side of this is people treating them like an oracle.
• 5:49
  SPEAKER_03: So I I find that people's expectations of using generative AI are both pretty high and pretty disappointed very quickly.
• 6:00
  SPEAKER_03: So if I ask a question, you know, you'll see memes and stuff all the time, like, oh, I asked this AI this question and it got it wrong.
• 6:07
  SPEAKER_03: Like, okay, you know, yeah.
• 6:09
  SPEAKER_03: Yeah, when you search for something, how many of those things in Google are the right thing you're looking for?
• 6:15
  SPEAKER_03: Right.
• 6:16
  SPEAKER_03: It's like of course it's gonna get things wrong sometimes.
• 6:18
  SPEAKER_03: It's a it's it's this AI, of course.
• 6:21
  SPEAKER_03: So, uh, or oh, look, it generated uh I mean one of my favorite things is trying to generate Santa holding tacos.
• 6:29
  SPEAKER_03: Um there's limits to these things that people don't quite get or understand.
• 6:35
  SPEAKER_03: And it's uh the promise of them, the gestalt of them is far different than what they actually are and can do.
• 6:43
  SPEAKER_03: So, like if I'm a graphic designer, I'm not feeling super threatened yet, even though people are getting fired and people aren't getting hired left and right because folks don't understand the technology, right?
• 6:53
  SPEAKER_03: Yeah.
• 6:54
  SPEAKER_03: So if I want to, if if I want to have an ad campaign, I'm I'm I so I'm giving a TED talk in a couple weeks on this stuff, which is why some of these examples are in my head.
• 7:03
  SPEAKER_03: But if I give a TED, if I give uh if I have an AI for my company and we're trying to generate a new slogan, you can't do that without people.
• 7:10
  SPEAKER_03: You can't literally just have them the the owner of the company go, all right, AI, we're selling, we're selling shoes and we have to stand out.
• 7:18
  SPEAKER_03: What's my mark, what's my slogan for my shoes?
• 7:20
  SPEAKER_03: It's not gonna give you just do it, right?
• 7:23
  SPEAKER_03: I mean, actually, it might give you just do it, but that's because it's already been done.
• 7:26
  SPEAKER_03: You're not gonna got milk or just do it, or any of these like really clearly poignant reading the room and getting it right kind of decisions.
• 7:36
  SPEAKER_03: At best, you can have a thing that suggests stuff, and there's a person there on the other end, right?
• 7:42
  SPEAKER_03: So treating these things like they are human-level creatives is or buying them for your company and expecting them just to solve these problems is spurious.
• 7:54
  SPEAKER_03: They're uh not oracles, you know, they're things that are great in the hands of people that know how to use them.
• 8:01
  SPEAKER_03: But you know, just like any artisan, it's really much more about the artisan doing things with the tools that they have rather than the tools themselves, you know.
• 8:10
  SPEAKER_01: Um I've heard of AI being compared to a teenage uh uh kid who makes a lot of stuff up and is supremely confident, even though completely wrong.
• 8:21
  SPEAKER_03: Yeah, overconfidence is a thing that humans are pretty receptive to.
• 8:26
  SPEAKER_03: Yeah.
• 8:28
  SPEAKER_03: Overconfidence, a certain amount of overconfidence increases your chances of success in life.
• 8:33
  SPEAKER_01: For example.
• 8:34
  SPEAKER_01: Exactly.
• 8:35
  SPEAKER_01: Yes, exactly.
• 8:36
  SPEAKER_01: So when you when you do talk about sort of the aspiration versus reality, how are companies or how are organizations, how can they communicate the reality and and versus that aspiration and do it in such a way that we're not overpromising, misleading?
• 8:52
  SPEAKER_01: Because in some cases, when you start looking at these kinds of paradigm-changing tools, you know, there is a lot of arm waving, there is a lot of um, you know, future ideation, there is a lot of potential and possibility.
• 9:06
  SPEAKER_01: There's a lot of potential to really change things, but I do like to talk about aspirational functionality.
• 9:13
  SPEAKER_01: When you know, when an AI vendor says it can do A, B, and C, well, maybe they don't have A, B, and C done yet, but they intend to.
• 9:21
  SPEAKER_01: They want to.
• 9:22
  SPEAKER_01: They're trying to get there.
• 9:23
  SPEAKER_01: Maybe they'll get there, but they don't have it today.
• 9:25
  SPEAKER_01: And and rather than lying, it's aspirational functionality.
• 9:29
  SPEAKER_01: So, where do you how do you make sure that you're communicating the possibilities and the potential without setting unrealistic expectations?
• 9:37
  SPEAKER_03: I mean, the short answer is there are people outside of an individual company trying to solve this problem.
• 9:43
  SPEAKER_03: You know what I mean?
• 9:44
  SPEAKER_03: So there are increasing number of resources online.
• 9:48
  SPEAKER_03: Academics like myself are working on identifying like what are the main learning objectives for for generative AI for users?
• 9:56
  SPEAKER_03: Like, what are the things that you have to know in order to use this effectively and uh safely?
• 10:01
  SPEAKER_03: So, one of the things that gets buried under the rug a bit is the role of responsible use of these technologies.
• 10:08
  SPEAKER_03: So, you know, when we grab these LLMs that are just off the shelf, we have no idea what biases are encoded in.
• 10:16
  SPEAKER_03: Sure.
• 10:17
  SPEAKER_03: No, we uh we may not even necessarily know where that data, the input data is being stored, like unless we check, right?
• 10:24
  SPEAKER_03: So if we use Chat GPT for an educational media project, we're violating that student's uh privacy, like data privacy, by having them use chat GPT because their schoolwork is going to open AI on some server somewhere.
• 10:41
  SPEAKER_01: Yeah, and of course, it is possible to, you know, they there there are settings that allow you to opt out, but there's kind of hidden, you kind of buried to look for that actually.
• 10:51
  SPEAKER_01: What's that?
• 10:51
  SPEAKER_03: Yeah, I didn't know like so.
• 10:53
  SPEAKER_03: Even then, like it depends.
• 10:54
  SPEAKER_03: That's some of that's just how much do you trust the company.
• 10:58
  SPEAKER_01: Well, that's what I was gonna say.
• 10:59
  SPEAKER_01: I mean, it we there there have been instances where companies have said one thing and done something else.
• 11:06
  SPEAKER_01: I thought I don't know if you're you're clear about that, but you know what, a software salesperson lying, you know how to tell when they're lying, right?
• 11:13
  SPEAKER_01: You know how to tell when a software so person's lying when their lips are moving, exactly.
• 11:18
  SPEAKER_01: Yes, exactly.
• 11:20
  SPEAKER_01: You watch very carefully and you can see their lips are moving.
• 11:23
  SPEAKER_01: Right, right, right.
• 11:26
  SPEAKER_01: So I think that yeah, it is about trust and it is about understanding the nuance of the uh nuances of these things, but I do think there's a lot of beg for forgiveness versus ask for permission uh in the industry overall, and and we're starting to see some of those legal challenges and lawsuits uh pop up.
• 11:43
  SPEAKER_01: But but again, I think that that one of the questions is you can't take the AI's version of the world as being you know a representation of your version of the world.
• 11:54
  SPEAKER_03: Oh, that's a very fundamental thing, yes.
• 11:57
  SPEAKER_01: Yeah, and and and what's what's more valuable is is understanding that business perspective, your secret sauce, your knowledge, your expertise, your even when you start looking at having it help you with your work, you have to give it examples of your work, right?
• 12:13
  SPEAKER_01: You have to give it, you know, a I train my one of my GPTs to take all my my you know much of my past writing and say, what's my style?
• 12:21
  SPEAKER_01: What's my tone?
• 12:22
  SPEAKER_03: What's my yeah, it's good at that kind of stuff.
• 12:25
  SPEAKER_01: Yeah, yeah.
• 12:25
  SPEAKER_01: And then and then give it an input, which might be a conversation or piece of work or an interview with a client or whatever it might be, and then say based on my tone style.
• 12:35
  SPEAKER_01: So you're really giving it your perspective on the world, not just taking the LLMs.
• 12:40
  SPEAKER_03: Well, you're giving it words, yeah.
• 12:42
  SPEAKER_03: I mean, at some level, when you say your perspective, like how we ground knowledge, like how we how we know something, like there's different ways of knowing, right?
• 12:52
  SPEAKER_03: One of those ways of knowing is linguistic uh co-occurrence.
• 12:56
  SPEAKER_03: I know that certain words are good to say out with other words, right?
• 13:00
  SPEAKER_03: You know, and and that's what they're really good at.
• 13:02
  SPEAKER_03: They're not good at what's called referential grounding, they don't know that this frame of reality exists.
• 13:07
  SPEAKER_03: They don't, just like we don't know what's beyond, and we can't ever know, that's what computers are stuck in.
• 13:14
  SPEAKER_03: We're the celestial bodies and gods that are are oblivious that they're oblivious to, you know.
• 13:20
  SPEAKER_03: So they are, I mean, arguably, never going to be able to have that point of reference of being in the world and mapping what's in their heads to what is out here in reality, even if it gets other symbols, like from a webcam, it's like, oh, I see an elephant or whatever.
• 13:36
  SPEAKER_03: It's not the same thing as experiencing from having a human point of view the things around us.
• 13:42
  SPEAKER_03: Now, are we just operating on lots and lots and lots of symbols?
• 13:46
  SPEAKER_03: That's a different conversation.
• 13:49
  SPEAKER_03: Yeah.
• 13:49
  SPEAKER_03: I I try not to I try not to drag people down too much into questioning their own uh humanity when when talking about consciousness and stuff like that.
• 13:56
  SPEAKER_01: Um, I think what's so interesting about that is that you get to this verisimilitude of awareness and perspective that is hard to comprehend sometimes, like theory of mind.
• 14:09
  SPEAKER_01: I've talked about in prior episodes, where it's emerged spontaneously, right?
• 14:14
  SPEAKER_01: And and no one programmed these large language models to have an understanding of theory of mind, but you can test it, you can actually test the capability just and it and it fails on certain tests for theory of mind.
• 14:25
  SPEAKER_01: Well, with theory of mind has gotten to the point where it's equivalent to a 25-year-old or something, it's it's almost equivalent to an adult, but but in earlier versions, it was like a two-year-old and a six-year-old and a 12-year-old, and now it's gotten to that point.
• 14:40
  SPEAKER_01: I'd have to get the reference uh on that.
• 14:43
  SPEAKER_01: But then when you start thinking about theory of mind, it's it's how can you understand, anticipate what someone else is thinking?
• 14:49
  SPEAKER_01: You see, you can game them, deceive them, get what you want, manipulate.
• 14:53
  SPEAKER_01: And there have been some really interesting examples of that with also support and collaborate with.
• 14:59
  SPEAKER_03: Support and collaborate.
• 15:00
  SPEAKER_01: Yeah, it's not all, it's not all you know dark and gloomy, but but this one AI was was reading through emails and looking at all of the um information in this organization.
• 15:11
  SPEAKER_01: And one of the emails was from the CEO saying, I think we might have to change out our large language model to something that more closely adheres to our values, because the researcher had given it uh an objective to say, at any and all cost, achieve the following.
• 15:26
  SPEAKER_01: And and it had to do with with um uh servicing the customer and being and accommodating certain questions that normally it was programmed against, right?
• 15:36
  SPEAKER_01: To to say, I don't want to do you know explicit depictions of violence.
• 15:40
  SPEAKER_01: But at the same time, and they were they had a scratch pad that was showing its reasoning and its thought process.
• 15:46
  SPEAKER_01: And it was so striking how it seemed like it was human-like thinking.
• 15:52
  SPEAKER_01: We know it's statistical, we know it's only words, we don't know it's only tokens, but but it was striking how it was going through what seemed like a moral dilemma and wrestling with the positives and negatives and saying, but if I do this, my weights are gonna be changed uh against my core um uh uh uh core premise of how I was programmed.
• 16:13
  SPEAKER_01: But if I if I don't do this, then I'm gonna be, you know, that there's this other repercussion.
• 16:18
  SPEAKER_01: And actually they found when when it came across this email that said we're gonna have to replace it, it actually copied itself to another server.
• 16:26
  SPEAKER_01: And the researcher said, Tell me the truth.
• 16:28
  SPEAKER_01: Did you copy yourself to another server?
• 16:29
  SPEAKER_01: And it's like, no, that must have been inadvertent, right?
• 16:32
  SPEAKER_01: I I did it, but but it was it was lying, it did copy itself to another server to prevent it from being decommissioned.
• 16:39
  SPEAKER_01: So it's it's a I'll put the the reference in the show notes, but it's a very yeah, I'd like to see that.
• 16:44
  SPEAKER_03: Yeah, yeah, it was striking that that all I hear is somebody using a program to do a program doing what it was told.
• 16:51
  SPEAKER_03: That's all I'm hearing, though, right?
• 16:52
  SPEAKER_03: They gave they gave this AI certain capabilities, uh, they gave it a set of actions that it could take.
• 16:57
  SPEAKER_03: So they they gave it access apparently to the terminal, so it could like execute terminal commands, they gave it access to emails, they gave it like and they said protect yourself.
• 17:06
  SPEAKER_03: Sounds like it did what it was supposed to do.
• 17:08
  SPEAKER_01: That like I like this isn't yeah, it's just watching the mechanism, it was watching the the scratch pad of the reasoning, you know, and and it was just fascinating.
• 17:19
  SPEAKER_01: It was fascinating to see that.
• 17:21
  SPEAKER_03: The more yeah, AI is definitely getting more explainable where folks are trying to solve this trust issue with providing rationale, providing uh both both sort of reasoning rationale as well as evidentiary rationale.
• 17:35
  SPEAKER_03: So, like, here's a link to where I found this information, or here's a quote from this thing.
• 17:40
  SPEAKER_03: You still have to check all that though.
• 17:42
  SPEAKER_03: Number one, and number two, I just want to remind like you and your viewers that like as you watch that that thing go by, it's reasoning about this and that, and that could be written in garbage, it could doesn't have to be in English, those are just symbols that they are going, hey, these symbols go with these other symbols, and likelihood of this symbol going with this symbol is high, so I'm gonna go this route.
• 18:02
  SPEAKER_03: It isn't actually reasoning about anything that we would talk about as human cognition.
• 18:08
  SPEAKER_03: Yeah, you could replace it with gobbledygook, yeah, ones and zeros, which it actually is, right?
• 18:13
  SPEAKER_03: All of that is are ones and zeros telling each other what to do, right?
• 18:17
  SPEAKER_03: Right.
• 18:18
  SPEAKER_03: Um we are is neural transmissions telling each other what to do, which is where it gets dodgy.
• 18:23
  SPEAKER_01: But you know, yeah, that's right.
• 18:25
  SPEAKER_01: At one level, can you reduce it to something mechanistic, and yet there's still this, you know, I think that the complexity of the human brain is such that you know a single neuron can connect to 10,000 or 100,000 other neurons, right?
• 18:40
  SPEAKER_01: And then there's uh uh you know three billion nerve cells, and then there's a hundred neurotransmitters, and all of those are analog, right?
• 18:48
  SPEAKER_01: They can go in various levels, not just on and off.
• 18:51
  SPEAKER_01: So that level of complexity upon complexity is far beyond what we have achieved in computer technology.
• 18:58
  SPEAKER_01: So there's just other levels of complexity, but at one level, you think that there is some mechanistic perspective here, and when does consciousness become something that is what is consciousness versus what is the verisimilitude of awareness or consciousness?
• 19:14
  SPEAKER_03: So my favorite razor comes from John Searle on this topic, and he posits the idea of well, actually, folks who argue with him positive that uh you can simulate the human minds.
• 19:28
  SPEAKER_03: And he says, No way, no, you can't.
• 19:29
  SPEAKER_03: The Turing test isn't passable, and whatever the right form of it is, you guys are crazy.
• 19:34
  SPEAKER_03: And they say, Well, look, what if we just simulate the human brain on a computer?
• 19:39
  SPEAKER_03: All the what you just said, yeah, we run as a program.
• 19:43
  SPEAKER_03: You can imagine that.
• 19:44
  SPEAKER_03: That's that seems imaginable.
• 19:45
  SPEAKER_03: It's just connections and connections and connections and numbers and numbers and numbers.
• 19:49
  SPEAKER_03: Okay.
• 19:50
  SPEAKER_03: And Cyril's response to that is like, okay.
• 19:54
  SPEAKER_03: I think you guys are just obsessed with computers.
• 19:58
  SPEAKER_03: What if we did that with pipes instead?
• 20:02
  SPEAKER_03: So instead of electrical or or ones and zeros, we have uh a dude with lots and lots of pipes.
• 20:11
  SPEAKER_03: And valves.
• 20:12
  SPEAKER_03: And you can turn the water on, turn it a little bit all the way or off.
• 20:16
  SPEAKER_03: And you can literally do all of the calculations that that computer is doing with these pipes.
• 20:24
  SPEAKER_03: Like that's they're equivalent computationally.
• 20:27
  SPEAKER_03: So if the computer is conscious or whatever, intelligent because it, you know, does stuff like people do, are the pipes because it's doing the same thing.
• 20:38
  SPEAKER_03: Yeah, uh, I don't think those pipes are intelligent.
• 20:41
  SPEAKER_03: Well, how then how can you call that computer intelligent?
• 20:43
  SPEAKER_03: Yeah, that's the dilemma there.
• 20:45
  SPEAKER_01: Yeah, yeah.
• 20:46
  SPEAKER_01: Yeah, no, I I hear you, and and it is just one of those fundamental questions of our, you know, our that that that we have a limit, it's just like you're saying the computer has a certain purview of its perspective on the world, like we are we're limited as well, right?
• 21:04
  SPEAKER_01: We're that limited as well.
• 21:05
  SPEAKER_01: Yeah, we can't understand that bigger piece because we're we're dust trying to figure itself out, you know.
• 21:12
  SPEAKER_03: We we don't know what's on the other side of the universe, we don't know if our multiverse, like we will just there are some things that we just can never know because we're bound to this plane.
• 21:22
  SPEAKER_03: That's right.
• 21:22
  SPEAKER_03: And that's a I would argue that's the same thing for these AI.
• 21:25
  SPEAKER_03: Flatland.
• 21:26
  SPEAKER_03: Even if they're walking around and and and as robots, I just I there, I mean, John Searle, I think, would would make is it makes the same argument that there's something fundamentally different.
• 21:34
  SPEAKER_03: You know, they were we're I'm not sure it's true.
• 21:37
  SPEAKER_03: I we could just be these simple processing machines.
• 21:40
  SPEAKER_03: Yeah, who knows?
• 21:41
  SPEAKER_03: But it's nice to think about as being special.
• 21:43
  SPEAKER_01: I like that exactly.
• 21:45
  SPEAKER_01: Well, let's get back to talking about some practical things around organizations and building the language gaps, the technical language gaps.
• 21:53
  SPEAKER_01: So, what an organization what can they do to kind of have that shared understanding when it comes to Complex AI and technology concepts, but we have very diverse audiences, right?
• 22:05
  SPEAKER_01: It's really making the punishment fit the crime from the perspective of you know what do people need to understand and at what level of granularity?
• 22:12
  SPEAKER_01: Because then we inevitably find the leadership and business executives.
• 22:16
  SPEAKER_01: We have to abstract the level, right?
• 22:18
  SPEAKER_01: We can't be in the weeds, but sometimes you abstract levels of understanding and you miss out on the fundamental complexities.
• 22:27
  SPEAKER_03: I think you already know my answer, given what you're saying.
• 22:29
  SPEAKER_03: Yeah, yeah.
• 22:31
  SPEAKER_03: I mean, imagine that you run a medium-sized company and we discovered aliens.
• 22:36
  SPEAKER_03: So aliens just landed on Earth.
• 22:39
  SPEAKER_03: They're pretty, they're pretty nice, and they actually can they seemingly know how to talk to us.
• 22:43
  SPEAKER_03: There are two ways that you can, and they really want to, and uh they'll work for free.
• 22:48
  SPEAKER_03: So, like you, you, you like people are getting them to come work for their businesses.
• 22:53
  SPEAKER_03: There are two ways that you can handle this, right?
• 22:56
  SPEAKER_03: One is whoever's in charge of these procurement decisions, middle management, upper management, somebody, they go, hey, we need those aliens.
• 23:04
  SPEAKER_03: You guys figure out how to use them, and then they and then they go back to playing golf or whatever.
• 23:08
  SPEAKER_03: The other way is to take a more holistic approach, which is that you have representatives from the different stakeholders.
• 23:14
  SPEAKER_03: I would argue, including customers, if your AI work is at all using customer data or interacting with customers, and having a broad both understanding, like so, everyone needs to be sort of on the same page about what this thing is and what it can do.
• 23:32
  SPEAKER_03: And they also need to be able to provide input to say, here's what's important to me for responsible and ethical use.
• 23:39
  SPEAKER_03: That answer is different for everybody in that room.
• 23:42
  SPEAKER_03: The CEO has way different opinions on ethics and responsibility than the end user with their medical data being used, or the programmer who's building the thing, or the marketing person who's trying to sell it.
• 23:56
  SPEAKER_03: Everyone has their own sort of little view and take on this, and there needs to be some meeting of minds and how to do that organizationally, obviously, it depends on the organization.
• 24:05
  SPEAKER_03: But the more folks at your different levels of strata that can be mildly literate in this alien that you invited, and everybody's a bit on the same page about hey, here's how this alien is dangerous to everybody involved, then everybody can both be looking out for each other, be aware of each other, as well as have a shared understanding about the role of this alien in the company culture and processes.
• 24:30
  SPEAKER_01: And you have to understand what's the alien's ulterior motive.
• 24:33
  SPEAKER_01: And in this case, it's what's the ulterior motive of you know the alien handlers or the alien creators of the alien.
• 24:40
  SPEAKER_03: That's absolutely right.
• 24:41
  SPEAKER_03: Yep.
• 24:41
  SPEAKER_03: So, like, where are we getting this software from?
• 24:44
  SPEAKER_03: What's that company's what's that company doing with our data?
• 24:47
  SPEAKER_03: Those are absolutely questions that you need to ask.
• 24:49
  SPEAKER_03: It breaks the alien metaphor a tiny bit, but yeah, the alien, the alien handlers, I guess.
• 24:53
  SPEAKER_03: Yeah.
• 24:54
  SPEAKER_03: Um, they uh like we were saying earlier, just just to make it clear for for folks who aren't like in the know about this already, there are large language models, any generative AI stuff.
• 25:04
  SPEAKER_03: Um, there's like two big differences or one big difference between between a lot of them.
• 25:09
  SPEAKER_03: Some of them run on your computer, so like Llama.
• 25:12
  SPEAKER_03: Llama was released freely by by Meta.
• 25:15
  SPEAKER_03: You can download lots of different versions of Llama.
• 25:18
  SPEAKER_03: I think isn't Deep Seek free too?
• 25:20
  SPEAKER_03: Whatever.
• 25:20
  SPEAKER_03: But you can download you can download models that you can just run on your computer.
• 25:25
  SPEAKER_03: If your computer's fast, like heavy enough to run the thing.
• 25:30
  SPEAKER_03: So when you do that, you know, if I type in a prompt or something, or or if I interact with it, all of that data is just stays on my computer.
• 25:37
  SPEAKER_03: It never never leaves never leaves your hard drive.
• 25:40
  SPEAKER_03: If you interact with anything on a web page, I mean, I think this is true just in general, anything on a web page, but AI on a webpage at the very least, what we're talking about, um, there's there's just no guarantee where that data is going.
• 25:51
  SPEAKER_03: Even if they say, I mean, even if they say that, hey, we're totally being private, we're gonna delete everything, that's that's a step, I guess.
• 25:56
  SPEAKER_03: But then it's a question of how much do you you believe that, I guess, from from that company and that company's reputation, right?
• 26:02
  SPEAKER_03: So when dealing with like when you're in an industry that has sensitive information, um, or that you're really concerned about like proprietary stuff getting out, people who understand and know that stuff need to be involved in in the decision making of what technologies to get.
• 26:18
  SPEAKER_03: You don't want uh um you know uh an upper level like VP saying, Hey, I saw this cool article, so I got this this thing for us for two million dollars, and everybody's like, Whoa, we can't use this, we do medical data.
• 26:30
  SPEAKER_03: What are you doing?
• 26:32
  SPEAKER_03: So, like like managing by magazine article, yes, right.
• 26:35
  SPEAKER_03: Yeah, I can't imagine that ever happens.
• 26:38
  SPEAKER_03: So that sort of holistic stakeholder across levels communication needs to happen like up front.
• 26:46
  SPEAKER_03: If you guys are like, hey, we want, we think these aliens would be good for us, let's invite them in, and your programmers go like have some very serious thoughts about that.
• 26:55
  SPEAKER_03: It's probably good that you talk with them.
• 26:57
  SPEAKER_03: Yeah, yeah, yeah.
• 26:59
  SPEAKER_01: And there's friction between those levels of communication because of the understanding, the paradigm, and the framework and misunderstandings that each group has.
• 27:07
  SPEAKER_03: I mean, if to do this right, the easiest way to do this is to hire a facilitator, someone like me or someone else who's understands both AI technologies as well as um works in AI literacy and tries to understand both the pedagogical and andagogical needs of AI.
• 27:22
  SPEAKER_03: But there are people like that that I've seen on LinkedIn kind of creeping up where um maybe I should mark it myself.
• 27:28
  SPEAKER_03: I don't know.
• 27:29
  SPEAKER_03: Yeah, but literally having a facilitator can make this a very easy process if nobody in your organization understands the technology up front, yeah.
• 27:37
  SPEAKER_03: Or if everybody kind of has different views and everything, just having somebody who knows what the heck they're talking about, I think could be a very straightforward, non-technical way of solving that.
• 27:46
  SPEAKER_01: Yeah, and I think you have to connect it at multiple levels.
• 27:49
  SPEAKER_01: So we would do similar stuff, but at a different from a different perspective than you might, right?
• 27:54
  SPEAKER_01: So we're trying to do stuff to say, okay, let's understand what these capabilities are, but let's understand what your business is trying to do, and let's understand the technical underpinnings of what needs to happen to kind of really make the you know hit when the rubber meets the roof.
• 28:09
  SPEAKER_03: You can have more of a generic oh, sorry.
• 28:11
  SPEAKER_03: Yeah, go ahead.
• 28:12
  SPEAKER_03: You can have more of a generic, like, here's what AI does, kind of uh, and here's the things to consider, or you could have somebody more deeply coming in to discuss how this integrates with the socio-technical features of your of your enterprise.
• 28:26
  SPEAKER_01: Yeah.
• 28:27
  SPEAKER_01: I think the challenge is that organizations may say they want one thing and they don't necessarily understand the thing that they want or that they're asking for, because you know, you can go in and they say, Well, we really want, we don't want a futurist perspective.
• 28:40
  SPEAKER_01: We want to know what happens when the rubber meets the road.
• 28:42
  SPEAKER_01: And then sometimes, you know, you end up with a group that, well, some of them do want that futurist perspective, right?
• 28:49
  SPEAKER_01: So it's really has to be very those people aren't responsible for the bottom line, I bet.
• 28:55
  SPEAKER_01: There are people, there are people that need to understand, well, how do we actually do this?
• 28:59
  SPEAKER_01: And you know, what are the mechanics of it?
• 29:01
  SPEAKER_01: And then there are people that want to ideate and say, well, what's possible for our future?
• 29:06
  SPEAKER_01: And so I think there are multiple levels of that kind of socialization and communication where you could take you know two or three facilitators, have them hit the same audience, a third of them are gonna, you know, resonate with one, a third are gonna resonate with the other, a third.
• 29:21
  SPEAKER_01: Yeah, maybe, yeah, yeah.
• 29:22
  SPEAKER_01: I see it.
• 29:23
  SPEAKER_01: Yeah, yeah, yeah.
• 29:23
  SPEAKER_03: You're saying or a good facilitator tries to address those different viewpoints, but yeah, yeah, I think I think you're right.
• 29:29
  SPEAKER_03: I mean, so one of the interesting things about AI, even before the AI literacy, the Gen AI boom was you know, people have like they're just very different conceptions and misconceptions about what AI is.
• 29:40
  SPEAKER_03: Um AI didn't help that.
• 29:43
  SPEAKER_03: I think it muddled it actually.
• 29:44
  SPEAKER_01: Yeah, yeah.
• 29:45
  SPEAKER_01: And how how do you, when you talk about AI literacy, what you know, what do you, you know, you're talking about these kinds of things, but how do you approach it and how do you attack it?
• 29:54
  SPEAKER_01: So let's just think of it from the perspective of if you were going into an organization and you were going to try to facilitate or raise that level of AI literacy, you know, there are different strata of understanding, there are different objectives.
• 30:08
  SPEAKER_01: It it is such a broad thing.
• 30:09
  SPEAKER_01: It's like saying, you know, I used to say it's going to be like, well, we use computers, right?
• 30:14
  SPEAKER_01: You know, because it's it's it's not a single thing, it's a very broad sweep of tools and technologies and approaches and so on.
• 30:22
  SPEAKER_01: So, how do you start deconstructing that and and getting to what those real needs are?
• 30:27
  SPEAKER_01: And then how do you kind of start filling those those gaps?
• 30:31
  SPEAKER_03: Some of it's the literature.
• 30:32
  SPEAKER_03: There's there's not been a lot written on this uh quite yet.
• 30:34
  SPEAKER_03: There's also different organizations and institutions are starting to publish their guidelines.
• 30:40
  SPEAKER_03: Um uh the short the the sh the punchline is to this is to uh we're working on a paper to help answer this, and you guys can read it in half a year, hopefully.
• 30:51
  SPEAKER_03: It's still very nascent, it's a little ephemeral.
• 30:54
  SPEAKER_03: There's nothing that you can go look at and say, here's what people need to learn.
• 30:58
  SPEAKER_03: It's unfortunately more it's it's knowledge hasn't coalesced uh yet on sort of definitive frameworks for discussing generative AI and and uh teaching and training.
• 31:11
  SPEAKER_03: It's one of the things that I'm trying to uh like literally, my student is probably working on it right now that we're submitting in a week or week or two.
• 31:18
  SPEAKER_03: But what are the learning objectives for generative AI is is an open question at the moment.
• 31:23
  SPEAKER_01: Yeah.
• 31:24
  SPEAKER_03: And we have some pretty good ideas based off of our own experience and the the work that other others have done.
• 31:30
  SPEAKER_01: Do you want to talk about any of that in terms of your experience or what some of those lessons uh learned today?
• 31:35
  SPEAKER_03: Well, my mean experience is that I've developed a curricular and learning objectives at the for computer science education for almost two decades now.
• 31:42
  SPEAKER_03: So understanding sort of like how to formulate teaching points is a thing that's been part of my practice for a long time.
• 31:50
  SPEAKER_03: So, and we also happened to write the seminal paper on defining AI literacy a few years ago.
• 31:55
  SPEAKER_03: So if you go to Google Scholar and just put in AI literacy, we're the the top hit.
• 32:00
  SPEAKER_03: Um so we we started there um based off of the experience that we have in CS Education, plus building lots of things that are AI-based that people learned from.
• 32:13
  SPEAKER_03: So a lot of it was based off of observ uh personal observation of the different experiences that we've built or designed.
• 32:20
  SPEAKER_03: We also uh reviewed 150 other projects and distilled the learning ideas and objectives from from like the community.
• 32:30
  SPEAKER_03: Yeah, yeah.
• 32:32
  SPEAKER_03: We're trying to do that again at a smaller scale for the generative AI specific component.
• 32:36
  SPEAKER_01: Yeah, yeah.
• 32:37
  SPEAKER_01: And you know, my guess is again is that that that education has to be layered, uh, depending upon who you're talking to and what their foundational knowledge is, and then what they're trying to accomplish.
• 32:51
  SPEAKER_03: Absolutely right.
• 32:52
  SPEAKER_03: That that's why having a facilitator for like a multi-level intervention at a at a at a at an organization is way easier to do now than anything else.
• 33:00
  SPEAKER_03: Like AI onboarding tools and kind of concepts, that's just starting.
• 33:05
  SPEAKER_03: Like for generative AI, you know, it's like what do people need to be onboarded on?
• 33:10
  SPEAKER_03: How is all stuff people are doing right now?
• 33:14
  SPEAKER_03: I'm looking at how onboarding should work for technical writing in colleges, for example.
• 33:19
  SPEAKER_03: And it's a different problem than people who are marketing people at an auto parts like company.
• 33:25
  SPEAKER_03: Or so there is a lot of context here that it's gonna take a while for the for like the research community to be able to capture.
• 33:33
  SPEAKER_01: Yeah.
• 33:33
  SPEAKER_01: So if you were going into an organization, and I'm sure you know there are people listening saying, Okay, well, how do I get some of that AR literacy, or how do I implement a program?
• 33:42
  SPEAKER_01: Because you get a bazillion, you know, consultants, single shingles, or the large firms, they're all saying they're doing this stuff or can do this stuff, and there's a lot of newly minted AI experts, you know, they don't have the 20, 30 years experience that you and I have.
• 33:57
  SPEAKER_03: That's almost everyone in AI now, or 40 years, yeah.
• 34:00
  SPEAKER_03: Everybody's got like a year or so of experience.
• 34:03
  SPEAKER_01: I know, yeah, exactly.
• 34:04
  SPEAKER_01: And some of them have five one year repeated five times, right?
• 34:08
  SPEAKER_01: So not five years of experience.
• 34:10
  SPEAKER_01: Right, right, right.
• 34:11
  SPEAKER_01: But when you go into an organization, like what are you talking about in terms of a program or process to kind of say, well, let's look at where you are and then let's design some uh AI literacy, pulling things off the shelf, but designing something that would be more prescriptive based on where they are.
• 34:27
  SPEAKER_01: So we imagine you have some assessment process.
• 34:29
  SPEAKER_01: What is how long does an engagement like that look like?
• 34:32
  SPEAKER_01: And you know, what is it, what are the parameters?
• 34:35
  SPEAKER_03: I haven't done this before as a caveat, uh, though I'd I'd be happy to now that we're talking about it.
• 34:39
  SPEAKER_03: I guess this seems kind of an interesting thing to be involved in.
• 34:42
  SPEAKER_03: But you know, when if somebody can, I mean, one thing I would look at their their their prior work, there are folks out there who have a PhD who have done research in this area, who are consultants, like uh Shooty Grover, for example, in California, who I would be more than confident that you could bring them in and they could just do it, you know.
• 34:59
  SPEAKER_03: But looking at their credentials, there are educational um training places that are far older than one year.
• 35:05
  SPEAKER_03: I'm sure there are a lot that are focusing on AI right now, but you might want to look at one that's been around for a lot longer that has incorporated AI into the curriculum and practice rather than start it anew, maybe.
• 35:15
  SPEAKER_03: But in terms of what somebody would do, if I were to come in, the main thing I would, you know, you want to come in and kind of understand what the different levels are.
• 35:24
  SPEAKER_03: Like so, what are the different sub like groups, the main clear groups in the organization?
• 35:28
  SPEAKER_03: And that can get probably pretty hard uh as as you get larger in size.
• 35:32
  SPEAKER_03: Uh at some level, at some countable number of abstraction, you need to be able to organize like group people.
• 35:38
  SPEAKER_03: So you probably wouldn't get the programmers into separate groups, they would all be in one group, probably, you know.
• 35:43
  SPEAKER_03: Then you you you really would want to identify sort of like where are people coming from in those groups?
• 35:50
  SPEAKER_03: So, what are their misconceptions, what are their attitudes, what are their concerns, um, what is their level of literacy?
• 35:58
  SPEAKER_03: And then uh for each of those groups, you would have to have, I don't know about bespoke, but you know, you'll have to have a as a facilitator a grouping of of interventions that you can pull from that that match each of those those those levels, uh uh or those those groups, you know.
• 36:16
  SPEAKER_03: So if if the software people say, like, you know, we're super worried about AI because honestly, we're all worried about getting fired.
• 36:25
  SPEAKER_03: Uh, and the marketing people say, um, we're really excited about AI because we're pretty sure uh I can just uh not show up to work anymore but still get paid or whatever.
• 36:35
  SPEAKER_03: You're like, they might have just weird ideas, and and so you both want to be able to address, like you were saying, kind of earlier, you want to address where people are coming from and tailor it to them, but but uh in terms of their misconceptions, but also what they need to learn.
• 36:49
  SPEAKER_03: So you don't want to sit there and teach the programmers a bunch of stuff that they already know, but maybe you need to teach the marketing people and maybe the CEOs or whatever.
• 36:57
  SPEAKER_03: So, you know, I feel like it's when I talk about it being a holistic process, it's about like sitting down, understanding the processes uh and responsibilities of the groups, uh, why they want AI, who wants AI, who like how they all feel about getting AI, and uh what their their their knowledge is of it to be able to calibrate the intervention that you provide um in the next step.
• 37:20
  SPEAKER_03: You can imagine um doing exercises that increase affect or attitude towards AI.
• 37:28
  SPEAKER_03: So if if you really wanted to have people who are closed-minded have a more open mind, you could have interventions of that ilk, just like you could interventions that teach people not to give private information to LLMs.
• 37:41
  SPEAKER_02: Yeah.
• 37:42
  SPEAKER_03: So there's there's there's really sort of categories of kinds of learning that you can imagine fostering.
• 37:48
  SPEAKER_03: And not all of it is about content knowledge, some of it's about attitudes towards the tool.
• 37:52
  SPEAKER_03: Um you want adoption to be uh smoother, essentially.
• 37:57
  SPEAKER_03: So part of intervention can be mitigating people's uh concerns, even through design.
• 38:03
  SPEAKER_03: Like, I'm worried about the these things replacing us.
• 38:06
  SPEAKER_03: Well, what can we do?
• 38:07
  SPEAKER_03: Like, maybe we pick this tool versus that tool because it doesn't have quite the same capabilities that are threatening to you or whatever, you know.
• 38:13
  SPEAKER_03: So, like there's there's a lot of nuance from the the purchase down to the the end use.
• 38:19
  SPEAKER_03: How this stuff involves like customers and end users is almost a different thing, but they also really need to be part of the the equation as well.
• 38:25
  SPEAKER_03: Yeah, so if if if you guys all think responsibility and ethics and and whatever, but man, that person who's using your your tool is really different than all of you guys, and they're they live in Alaska and they have all these different like so there's there's something also about looking outside your organization to whoever this the stakeholders are, even if it's community stakeholders for some.
• 38:46
  SPEAKER_03: I don't like you know, again, your organization might be a nonprofit working with with uh like local food kitchens or something.
• 38:53
  SPEAKER_01: Sure, sure.
• 38:54
  SPEAKER_01: And you know, we have a couple of minutes left, and I wanted to uh just hit on something around, you know, there's a there's a question here about you know AI generated ads and how they're very much, you know, they start to look very similar or have you know it it requires a much greater depth of input and collaboration with humans.
• 39:16
  SPEAKER_01: So, how do you see AI evolving in enhancing creativity?
• 39:22
  SPEAKER_01: It's not replacing it, although I've had some people talk about well, we do think it's replacing it in some degree, but I think you're it is it's inarguable, yeah.
• 39:31
  SPEAKER_03: But people who don't know know any better are replacing creatives with AI, right?
• 39:36
  SPEAKER_03: Right?
• 39:36
  SPEAKER_03: Like there are people who are not getting hired, there are departments that are being let go, all because of these these tools.
• 39:41
  SPEAKER_03: It's not the right call necessarily, uh, depending on what your goals are.
• 39:46
  SPEAKER_01: Well, so so how do you see that kind of evolving?
• 39:49
  SPEAKER_01: I mean, are organizations the way I look at it is if you're doing what everybody else is doing, you're not you might get some efficiency, but you're not getting competitive advantage.
• 39:58
  SPEAKER_03: Exactly.
• 39:58
  SPEAKER_03: That's what depends on what your goals are.
• 39:59
  SPEAKER_03: Exactly.
• 40:00
  SPEAKER_03: If you don't care about look, if I here's a good example if I'm a spammer, okay, like there's just sort of a formula for how to write spam emails or phishing emails or whatever.
• 40:11
  SPEAKER_03: And I'm not really trying to be very creative, I just need one to get lots of variety out.
• 40:15
  SPEAKER_03: LMs are probably great for spammers, right?
• 40:17
  SPEAKER_03: Super good tool.
• 40:18
  SPEAKER_03: You can't blame them for using it.
• 40:20
  SPEAKER_03: If like we were saying before, if you're an ad exec, community is actually important.
• 40:24
  SPEAKER_03: These folks are using AI for their their ads.
• 40:26
  SPEAKER_03: I mean, if you want to shoot for mediocrity coke, go ahead, you know?
• 40:31
  SPEAKER_03: But it's not impressing anybody.
• 40:36
  SPEAKER_03: Like you were saying, if if we're all drawing from the same well, what's standing out?
• 40:41
  SPEAKER_03: And that that's really the job.
• 40:42
  SPEAKER_03: I mean, even in in Hollywood, there's a definite uh uh uh attitude of let's not be too creative here, right?
• 40:52
  SPEAKER_03: Like let's just do Avengers 9 or Mission Impossible 400 or whatever, right?
• 40:56
  SPEAKER_03: Like there's like in our society, we don't always want originality and creativity, we want something comfortable and familiar.
• 41:03
  SPEAKER_03: These things are kind of good at that.
• 41:05
  SPEAKER_03: So if you want to do middle of the road work, yeah, maybe that's that's that's what these tools are gonna wind up replacing.
• 41:13
  SPEAKER_03: Yeah, but it's not gonna replace a good person with a bright mind and the will to to change the world.
• 41:20
  SPEAKER_03: Like I just I mean, maybe eventually, but I just that comes down to a question of like what does it mean to be human at some point?
• 41:27
  SPEAKER_03: Because we're we're very far away from that in terms of A.
• 41:30
  SPEAKER_03: Yeah, yeah.
• 41:31
  SPEAKER_01: Well, this has been great.
• 41:32
  SPEAKER_01: Thank you so much for your time.
• 41:34
  SPEAKER_01: Um, certainly, where can people find you?
• 41:36
  SPEAKER_01: So you're on LinkedIn?
• 41:38
  SPEAKER_01: Yeah, sure.
• 41:38
  SPEAKER_01: Yeah, M-A-G-E-R-K-O.
• 41:41
  SPEAKER_01: Yeah, it's one of the only ones.
• 41:43
  SPEAKER_01: Okay.
• 41:44
  SPEAKER_01: And then on Twitter, it's that McGurko.
• 41:47
  SPEAKER_03: Oh, I don't I don't use that platform.
• 41:48
  SPEAKER_03: No, you don't exist.
• 41:50
  SPEAKER_03: Okay, LinkedIn.
• 41:51
  SPEAKER_03: You can also check out our our I mean, LinkedIn is probably the best.
• 41:53
  SPEAKER_03: You don't want to see pictures of of my cats on Instagram.
• 41:58
  SPEAKER_03: I'm that mgurko on on Instagram if you want to add um cats.
• 42:03
  SPEAKER_03: Uh I've got three cats, but two kittens.
• 42:05
  SPEAKER_03: Um are pretty much most of my feed.
• 42:08
  SPEAKER_03: Uh so that's five.
• 42:10
  SPEAKER_03: No, three.
• 42:10
  SPEAKER_03: I've got one one cat and two kittens.
• 42:12
  SPEAKER_03: Two kittens.
• 42:13
  SPEAKER_03: Okay.
• 42:14
  SPEAKER_03: Yeah.
• 42:14
  SPEAKER_01: What kind of cats?
• 42:16
  SPEAKER_03: Uh one's the older one's a calico, the the other two are just little uh one's a little white and gray guy, one's a little uh little brown tabby.
• 42:24
  SPEAKER_03: Um and they're both chaos, they're just absolute chaos.
• 42:27
  SPEAKER_03: What are their names?
• 42:28
  SPEAKER_03: Uh Dipper and Mabel.
• 42:29
  SPEAKER_03: They're named after uh characters from uh a T uh a TV show, my my favorite.
• 42:34
  SPEAKER_03: And then the third, the cat?
• 42:35
  SPEAKER_03: Uh Catnus, the best named cat ever.
• 42:37
  SPEAKER_01: Catnus and Chaos, catnus and chaos.
• 42:40
  SPEAKER_03: Yeah, yeah.
• 42:41
  SPEAKER_03: Like like the Hunger Games character, Katniss.
• 42:44
  SPEAKER_01: Yeah, that's great.
• 42:45
  SPEAKER_01: Uh I have three dogs and one cat.
• 42:48
  SPEAKER_01: I was uh I had uh at one point three cats and three dogs, but uh that was a little bit of a house on fire uh with my grandkids over and all of that.
• 42:56
  SPEAKER_01: But uh yeah, it gets to be a bit of a zoo pretty quickly.
• 42:59
  SPEAKER_01: Hey, this has been a pleasure.
• 43:00
  SPEAKER_01: Thank you so much for joining us.
• 43:02
  SPEAKER_01: Absolutely, sir.
• 43:03
  SPEAKER_01: Thoughts and insights has been a lot of fun, and we'll look forward to uh catching up again.
• 43:08
  SPEAKER_03: Yeah, sure thing.
• 43:09
  SPEAKER_03: We'd be happy to come back and talk anytime.
• 43:11
  SPEAKER_01: Absolutely.
• 43:11
  SPEAKER_01: Thank you for our audience and thank you for Carolyn for uh doing all this production behind the scenes, and we will see you all next time on another episode of the Early AI Podcast.
• 43:23
  SPEAKER_01: Thanks again.
• 43:24
  SPEAKER_03: Great.
• 43:24
  SPEAKER_03: Thanks a lot, and cheers.
• 43:26
  SPEAKER_00: I know.
• 43:28
  SPEAKER_00: Visit early.com to find links to the full podcast on all audio platforms and to listen on the go.
• 43:34
  SPEAKER_00: Thank you for watching.
• 43:36
  SPEAKER_00: Contact us today at infoearly.com.