WEBVTT NOTE This file was generated by Descript 00:00:27.046 --> 00:00:29.446 Eli Davis: Welcome to Artificial Intelligence, real Talk. 00:00:29.446 --> 00:00:41.466 This is Eli Davis, your host, and I have here Peter Swim who is a a conversational AI technologist living outside of Seattle, Washington. 00:00:41.676 --> 00:00:43.056 He founded. 00:00:43.536 --> 00:00:51.666 To Ville, LLC and specializes in conversational ai, early stage company planning and community building. 00:00:51.726 --> 00:01:01.883 He has helped companies grow towards successful exits, curate diverse and exclusive communities, and support other acts of human kindness. 00:01:02.483 --> 00:01:07.223 For fun, he also likes to produce and create strange music. 00:01:07.223 --> 00:01:08.183 How you doing, Peter? 00:01:08.588 --> 00:01:09.053 Peter Swimm: I am great. 00:01:09.053 --> 00:01:09.413 I'm great. 00:01:09.413 --> 00:01:09.983 Thanks for having me. 00:01:10.463 --> 00:01:20.903 Eli Davis: you know, one of the things that, I am interested in and I want my audience to know because you know, once you get into the artificial intelligence bubble, you start to think that everybody knows what you know, 00:01:21.503 --> 00:01:21.793 Peter Swimm: Yeah. 00:01:21.798 --> 00:01:21.878 Yeah. 00:01:22.973 --> 00:01:26.483 Eli Davis: so, so, so what we're gonna have to do is we're gonna have to ba basically break it down. 00:01:26.483 --> 00:01:27.753 Could you tell us what you do? 00:01:28.623 --> 00:01:29.133 Peter Swimm: Sure. 00:01:29.523 --> 00:01:36.813 So, for the last 10 years I've worked as a product owner in making tools for building conversational AI things. 00:01:37.083 --> 00:01:40.823 So think like chat bots you know, the robot voice on the end of the phone. 00:01:41.143 --> 00:01:45.703 You know, all kinds of things that are kind of like ways for people to talk naturally. 00:01:46.033 --> 00:01:46.663 IE not. 00:01:46.693 --> 00:01:54.473 You know, like enter commands into a command line, but just like through normal interactions cause the computers to do work. 00:01:54.983 --> 00:01:58.673 And that's like the simplest way to explain that industry, I think for lay people. 00:01:58.973 --> 00:02:10.103 And what I do is I help people design and build products that are, one, do what they set out to do and do it in a way that is beneficial for both businesses and the people who use them. 00:02:11.368 --> 00:02:14.638 Eli Davis: So, so, so from my research you use agents. 00:02:15.518 --> 00:02:21.008 Peter Swimm: Yeah, so the, they used to come 'em chatbots and they used to call 'em all kinds of things. 00:02:21.008 --> 00:02:24.158 And you know, it used to be press nine, but now it's agents. 00:02:24.458 --> 00:02:37.328 And I think the difference between the chatbots of your, and what they're calling agents today is the fact that these chatbots are able to make decisions on how the problem was solved independently of humans. 00:02:38.033 --> 00:02:39.173 And that's the pitch. 00:02:39.593 --> 00:02:40.883 But the reality is very different. 00:02:41.298 --> 00:02:44.483 Eli Davis: Okay, so, so, so that goes into one of the things I want to discuss. 00:02:44.483 --> 00:02:48.693 Us what is the importance of human first design and ai? 00:02:49.443 --> 00:02:54.558 Peter Swimm: So one computers work at our pleasure as humans, right? 00:02:55.053 --> 00:02:58.323 And they're, you know, if you have a computer that is. 00:02:58.878 --> 00:03:05.298 An agent with agency and it's making decisions who is responsible when the decision's wrong, right? 00:03:05.538 --> 00:03:08.688 And who's responsible when the decision's like harmful. 00:03:09.108 --> 00:03:24.558 And I think these are all like really important questions that kind of, that need and require human expertise to be available either as a form of like management of these agents or as a arbitrator of like decisions these agents make or whatever. 00:03:24.888 --> 00:03:25.908 So, of. 00:03:26.908 --> 00:03:33.758 There's this pressure, I think between like, people want agents because they think it costs too much to pay people, right? 00:03:33.938 --> 00:03:36.968 And they wanna get rid of people out of the whole thing so they can make more money. 00:03:37.268 --> 00:03:41.048 And then there's people who want agents because humans have too much work. 00:03:41.438 --> 00:03:44.228 And there's some things that an agent could do better than a human. 00:03:44.828 --> 00:03:47.348 Same way that there's some things a human can do better than an agent. 00:03:47.768 --> 00:03:48.038 So. 00:03:49.038 --> 00:03:53.628 Eli Davis: So, the challenges in assuring AI serves as a tool rather than an replacement. 00:03:54.018 --> 00:04:02.368 So, you know, I teach at miles College and, you know, I offer I, I develop like a custom instruction chat vibe using open ai. 00:04:02.818 --> 00:04:07.948 And I I try to get them to use that to do the work. 00:04:08.408 --> 00:04:13.268 But what I find is they would rather have it replaced them. 00:04:13.988 --> 00:04:14.558 You know what I mean? 00:04:14.558 --> 00:04:14.858 So go. 00:04:14.888 --> 00:04:16.388 So lean into that for us. 00:04:17.513 --> 00:04:24.513 Peter Swimm: I mean, I. I, you know, on the bottom of my website I say no, the con content here was not made with gender of ai. 00:04:24.963 --> 00:04:35.068 'cause I think like, one I like writing, you know, I, you know, I was an English major and I like, you know, I was hooked in the idea of like writing ideas and working on them. 00:04:35.398 --> 00:04:41.128 That being said, I have a DHD, you know, it's hard for me to like, proofread and do my writing. 00:04:41.128 --> 00:04:42.268 So I'm using Grammarly. 00:04:43.168 --> 00:04:46.408 is like super effective for me because it, like, it helps me. 00:04:46.806 --> 00:04:49.386 I don't see, like, when I have a missing adjective, 00:04:49.441 --> 00:04:52.261 I just don't see it in my reviewing and it finds it and fixes it as awesome. 00:04:52.621 --> 00:05:00.981 But I think like you have to learn how to cook before you can push the instant food button, because then you'll never know if it's good or not. 00:05:01.221 --> 00:05:01.521 Right. 00:05:01.971 --> 00:05:06.281 And I think like, especially like Zoomers and stuff like that, they don't. 00:05:06.559 --> 00:05:08.659 And I think this also goes to the people who make the products. 00:05:08.659 --> 00:05:13.039 They don't understand, like the thing that comes out of it is plausibly content. 00:05:13.429 --> 00:05:18.199 It's plausibly, you know, things that make sense and are valuable, but is it good? 00:05:18.273 --> 00:05:24.458 and you probably, someone reads a lot of like content across the spectrum of quality to put it charitably, right? 00:05:24.458 --> 00:05:25.808 Like, and so like. 00:05:26.121 --> 00:05:38.669 I think like there's that bland valley in the middle that AI is good at that, you know, people can turn in and they didn't learn anything but it like serves the function of the paper, you know, the assignment. 00:05:38.999 --> 00:05:50.249 And I think like we are gonna have a reckoning moment as society where like there's a point of going to college or school, turning in assignments or to learn how to do a thing, you know? 00:05:50.789 --> 00:05:51.149 Right. 00:05:51.149 --> 00:05:53.639 And I think that's kinda like the challenge that we have right now. 00:05:53.639 --> 00:06:03.749 Like, you know, we have a lot of, like, I talk to a lot of people in industry whose like younger siblings are in college and they're just like, but Google says it, but Google is not reliable anymore. 00:06:04.139 --> 00:06:04.379 Right? 00:06:04.379 --> 00:06:13.129 Because it has the AI thing at the top that doesn't know who played for the 87 Mets and event baseball players that don't exist and stuff. 00:06:13.534 --> 00:06:18.694 There's no like difference in the presentation layer between bad quality and good quality, right? 00:06:19.204 --> 00:06:36.554 So, I think there's a lot of interesting questions that I tell my clients and my businesses seriously consider before you engage in it because if you lose your respect of your users as something that you know is unreliable, they'll stop using you, you know, so. 00:06:37.054 --> 00:06:38.629 Eli Davis: I think that is very important. 00:06:38.876 --> 00:06:39.161 All right. 00:06:39.161 --> 00:06:44.291 So, uh, so how does Toll Ville approach the balance between automation and human touch and business? 00:06:44.801 --> 00:06:44.891 Peter Swimm: Yeah. 00:06:46.296 --> 00:06:46.586 Yeah. 00:06:46.691 --> 00:06:50.521 So, there's like two big problems that people come to for conversational ai. 00:06:51.101 --> 00:06:51.971 One is, 00:06:52.021 --> 00:06:56.701 Eli Davis: and lemme just inject real quick because that's what Toll V does. 00:06:56.751 --> 00:06:57.201 Peter Swimm: That's right. 00:06:57.426 --> 00:06:58.746 Eli Davis: ational ai. 00:06:58.776 --> 00:06:59.046 Go ahead. 00:06:59.151 --> 00:06:59.331 Peter Swimm: Yeah. 00:06:59.331 --> 00:07:00.501 So that's our bread and butter. 00:07:00.501 --> 00:07:02.031 And we get two kinds of customers. 00:07:02.131 --> 00:07:04.321 you know, our big client is like externally facing. 00:07:04.321 --> 00:07:16.051 So like, say for example, you're flying and your flight gets canceled and you get a text message from a agent that offers you a rebook, then you go into whole process. 00:07:16.456 --> 00:07:20.686 And that's probably an agent, you know, that's probably not a person texting every single person at flight cancel that 00:07:20.851 --> 00:07:21.781 An automated process. 00:07:22.171 --> 00:07:24.411 And then the other kind of things are like internal scenarios. 00:07:24.411 --> 00:07:33.391 Like, I worked for a client that is the largest employer in the United States and their healthcare is bigger than the Medicaid in 13 American states. 00:07:33.811 --> 00:07:38.401 So, and they have all the problems for those people answered by 200 people in a call center. 00:07:38.776 --> 00:07:43.696 In the south, and like when COVID happened, the call center had to shut down. 00:07:43.696 --> 00:07:45.016 And so no one's picking up the phone. 00:07:45.946 --> 00:07:47.716 So they had in a hurry. 00:07:47.716 --> 00:07:52.546 They had to develop a system that at least would tell people, there's only 10 people on the phone right now. 00:07:52.546 --> 00:07:53.866 We're working on getting more people up. 00:07:54.286 --> 00:07:58.396 Here's the top answers that we can answer without even knowing who you are. 00:07:58.666 --> 00:07:58.936 You know? 00:07:59.436 --> 00:08:01.236 And those are kind of things that people are trying to build. 00:08:01.236 --> 00:08:02.256 They're looking for like. 00:08:03.256 --> 00:08:05.446 You know, there's people that wanna automate everything. 00:08:05.656 --> 00:08:13.966 You know, they want, like I've had a person from that same company who was like, I wanna have a completely automated contact center in 45 days, or whatever. 00:08:14.056 --> 00:08:15.766 This was half a decade ago. 00:08:15.826 --> 00:08:17.146 It was even less plausible then. 00:08:17.411 --> 00:08:20.581 But but the other thing is like, there's hundreds of the scenarios that are like. 00:08:21.581 --> 00:08:24.461 Super trivial scenarios that take up a lot of time. 00:08:24.881 --> 00:08:29.261 Like we did an analysis of the contact center and the average call length is two minutes, right? 00:08:29.261 --> 00:08:33.131 Two and a half minutes and a minute of that was verification. 00:08:33.551 --> 00:08:37.241 So like you just saying who you are and where you're calling from and your account number. 00:08:37.706 --> 00:08:38.036 Right. 00:08:38.396 --> 00:08:46.286 And so we found out that, okay, so we can do twice as amount of work if we put the bot on the front hand of the conversation and the human on the other half. 00:08:46.586 --> 00:08:55.046 So when the human gets the call, little baseball card pops up with all the data they collected, and then you just go right to the helping versus the investigation. 00:08:55.286 --> 00:08:55.586 You know? 00:08:55.916 --> 00:08:56.546 So there's like. 00:08:56.626 --> 00:09:08.656 If there's a spectrum of things that you can do to like, increase the performance of people without increasing the workload, and I think that's like when it's like a really, it's cooking, you know, that's like a good scenario. 00:09:08.656 --> 00:09:11.476 I think that I try to encourage our clients to like look at. 00:09:12.956 --> 00:09:20.726 Eli Davis: So, I just tried to I just applied for this fellowship and it's using, it is Fusion, artificial intelligence and AI together. 00:09:21.186 --> 00:09:27.926 What I noticed when I was applying was that it wanted you to make sure that you use artificial intelligence. 00:09:28.226 --> 00:09:47.866 My and you were just talking about the productivity I. What that made me to think of is, will the workload become more, because productivity will increase, will we be required to do more, to produce more and also higher quality at the same time. 00:09:49.306 --> 00:09:53.536 Peter Swimm: You know, I think, I mean, that is a very good question to ask, and I think that's. 00:09:54.316 --> 00:10:02.776 Anyone who's working in an organization that is adopting AI should be pushing for solutioning and arbitration. 00:10:02.776 --> 00:10:10.006 Like, like if you think about it, like if a company is using automation to answer questions, the data has to come from somewhere. 00:10:10.066 --> 00:10:12.206 So someone has to write the reference document. 00:10:12.206 --> 00:10:14.036 So the employee manual has to exist. 00:10:14.246 --> 00:10:14.516 Right. 00:10:14.636 --> 00:10:15.356 And you can't. 00:10:16.356 --> 00:10:26.311 At some point, there's a writer and sometimes, and at some point there's a programmer and there's no, like, there's no manner from heaven without that initial spark of human ingenuity. 00:10:27.676 --> 00:10:39.036 So if I work for a company and companies have this like, you know, like the retail companies of the world, they have their call centers and all their email and they've kept copy of every single email they've gotten for 20 years. 00:10:39.486 --> 00:10:50.526 So they have the data to create a map of the most likely scenarios and do things if I as an employee are responsible for 10% of that. 00:10:51.126 --> 00:10:53.316 I'm not gonna get 10% of the revenue they save. 00:10:53.796 --> 00:10:54.156 You know? 00:10:54.696 --> 00:11:01.276 And I think like there's gonna be a greater awareness of the work that people do and the input that goes into it. 00:11:01.366 --> 00:11:07.846 And there has to be some sort of reckoning of paying people, like, like open AI stole books. 00:11:08.791 --> 00:11:09.421 End of story. 00:11:09.611 --> 00:11:13.031 They went to the li, you know, they went to all the library systems and they downloaded books. 00:11:13.511 --> 00:11:17.591 And they're arguing in court right now that like one book doesn't amount to anything. 00:11:17.591 --> 00:11:19.361 It's like the corpus of the world. 00:11:19.361 --> 00:11:20.171 That's the value. 00:11:20.651 --> 00:11:22.631 And therefore they don't owe any money to anyone. 00:11:23.321 --> 00:11:28.211 But the books wouldn't exist without someone to publish the books without paying the offers to write the books. 00:11:28.211 --> 00:11:28.511 Right. 00:11:28.871 --> 00:11:33.421 And so if you cut off that industry. 00:11:34.516 --> 00:11:36.256 The models are not gonna approve anymore. 00:11:36.346 --> 00:11:39.646 They're just gonna be where they are today and then stagnate. 00:11:40.186 --> 00:11:46.966 And it kind of goes to like your students, like they're like, they don't learn how to write, then they can't write without open ai. 00:11:47.206 --> 00:11:51.766 And so open AI now has people hooked on the $20 a month plan forever, right? 00:11:52.036 --> 00:11:54.646 Because they can't do work without it. 00:11:54.796 --> 00:11:55.846 And I think that's their. 00:11:56.716 --> 00:11:57.136 Eli Davis: Plan. 00:11:57.196 --> 00:11:58.486 Peter Swimm: their, that's the plan. 00:11:58.986 --> 00:12:06.376 That's their ex. You don't, you, you don't burn 2 billion, you don't burn $2 billion a month without a revenue strategy. 00:12:06.436 --> 00:12:06.736 Right. 00:12:07.066 --> 00:12:11.546 And so, so we use these tools and, you know, for different goals. 00:12:11.546 --> 00:12:19.731 And I think like there, there's two things that I have my I caution my clients is we increased in two years. 00:12:19.731 --> 00:12:20.121 We went. 00:12:20.646 --> 00:12:24.786 Super deep in quality, but it looks like that quality's plateaued. 00:12:25.266 --> 00:12:27.136 And we're not at a hundred percent coverage yet. 00:12:27.166 --> 00:12:29.596 We're at like 65% coverage. 00:12:29.656 --> 00:12:29.926 Right. 00:12:30.466 --> 00:12:34.396 And so you're gonna spend a lot of money being the Guinea pig for that last 30%. 00:12:34.756 --> 00:12:40.996 Or you can like cut bait and invest in people and make things better today with what you got. 00:12:41.296 --> 00:12:41.476 Right? 00:12:41.806 --> 00:12:47.806 And so like I'm really like, you know, I don't work for open ai, I don't work for Microsoft, I don't work for Amazon. 00:12:48.206 --> 00:12:48.716 I don't. 00:12:49.301 --> 00:12:56.711 Have skin in the game, you know, I just want people to like have good processes and be aware of the decisions they're making and be able to measure the impact, you know. 00:12:57.491 --> 00:12:57.731 Second. 00:12:59.186 --> 00:12:59.726 Eli Davis: Yeah. 00:13:00.536 --> 00:13:00.896 Yep. 00:13:02.186 --> 00:13:02.486 All right. 00:13:02.486 --> 00:13:03.116 Here we go. 00:13:03.366 --> 00:13:03.786 All right. 00:13:03.786 --> 00:13:10.396 So, the future of human AI collaboration, how do you envision the future of AI collaboration? 00:13:10.816 --> 00:13:14.736 if you could possibly break it down to. 00:13:15.076 --> 00:13:23.639 the people who like, like my fraternity brother, he had never even opened up the app section on his iPhone. 00:13:24.110 --> 00:13:30.290 Peter Swimm: I guess the good news for people like that is the technology's coming for you even if you aren't like an early adopter. 00:13:30.350 --> 00:13:36.380 Like they're putting it an Apple message and they're putting it everywhere, and there's gonna be a taste of it at some point everywhere. 00:13:36.920 --> 00:13:52.800 The problem is like if you're not used to how it operates you may put reliance on something that is like, may not be like a hundred percent, you know, or it may be something like, for example, um, I mentioned my I'm very bad at text messaging. 00:13:53.310 --> 00:13:59.610 And the text messaging on my iPhone used to be pretty good at where I would just type and it would just like, correct. 00:13:59.880 --> 00:14:00.180 Right? 00:14:00.440 --> 00:14:10.140 And it can't do it at all anymore for me, like I have to like very carefully type again because it's using AI to auto correct my words. 00:14:10.380 --> 00:14:19.080 Incorrectly all the time because, you know, as opposed to like doing like whatever cheap version of autocorrect was 10 years ago, that worked for me. 00:14:19.410 --> 00:14:23.910 It's using some sort of thing based on how people text, which is not how I text. 00:14:24.210 --> 00:14:28.700 And so, it's there's a writer, Cory Doctor calls this in acidification. 00:14:28.800 --> 00:14:32.670 So the product has been in, you know, Google's been inify all that's been in, 00:14:33.130 --> 00:14:33.700 So. 00:14:34.225 --> 00:14:40.015 So a lot of people will first encounter these technologies when they notice that something they relied on is much worse now. 00:14:40.495 --> 00:14:40.765 Right? 00:14:41.245 --> 00:14:43.965 And so those people kind have a problematic view. 00:14:43.995 --> 00:14:48.195 'cause they won't understand why they think, oh no, am I getting old? 00:14:48.195 --> 00:14:49.395 Can I not type anymore? 00:14:49.485 --> 00:14:50.505 You know, that's my first thing. 00:14:50.505 --> 00:14:53.085 And like, oh, I just can't type anymore. 00:14:53.085 --> 00:14:57.675 But, and then I realized, oh yeah, it's, the autocorrect is much different for me. 00:14:58.125 --> 00:15:06.855 And I think that's kind of like an important thing where, if AI is used correctly, you don't notice it, and it's just like better. 00:15:07.245 --> 00:15:10.905 But if it's used incorrectly, you do notice it and it's frustrating. 00:15:11.145 --> 00:15:14.655 And then there's other people who don't notice it and it's wrong. 00:15:14.775 --> 00:15:15.825 And now you look stupid. 00:15:17.085 --> 00:15:25.345 And so like you're turning a paper that doesn't make any sense to your teacher, and you can't explain why you wrote that because it's, you plagiarized it from a prompt. 00:15:25.630 --> 00:15:25.930 Eli Davis: Yeah. 00:15:25.930 --> 00:15:27.105 And it's and it's super vague. 00:15:27.655 --> 00:15:33.495 Peter Swimm: Like I use I don't use AI tools for writing, but I do use AI tools for my outlines. 00:15:34.305 --> 00:15:42.025 'cause I feel like that is helpful for me as a neurodiverse person to like just kind of talk through problems and make a bold list of what I want to talk about. 00:15:42.145 --> 00:15:42.835 And then I write. 00:15:43.375 --> 00:15:44.995 And I think that's super helpful for me. 00:15:44.995 --> 00:15:53.635 Like, but I constantly have to tell the model to like, quote me verbatim, don't rewrite what I say because it will like write it like less spicy. 00:15:54.145 --> 00:16:03.325 And like as a writer, you're trying to be, you're trying to be fun and spicy and interesting and, everything in the AI just kind of pushes stuff to the boring middle, right? 00:16:03.605 --> 00:16:06.665 And so like, you don't get any of interesting insights and stuff. 00:16:06.665 --> 00:16:08.605 You do get from things. 00:16:08.605 --> 00:16:18.085 So, you know, it's really good for writing a business email, you know, like an email that has like a. Or anything that is just like very rote, like you need to email your landlord or whatever. 00:16:18.085 --> 00:16:18.325 Yeah. 00:16:18.415 --> 00:16:19.735 Like AI can write it for you. 00:16:19.735 --> 00:16:21.260 That's beneath you. 00:16:21.745 --> 00:16:26.245 You know, that's like not a good use of your time to like struggle with writing a proper email. 00:16:26.585 --> 00:16:29.255 But if you're writing an email to your grandma, you know, you should write that. 00:16:29.315 --> 00:16:29.585 Right. 00:16:29.705 --> 00:16:30.035 You know? 00:16:30.500 --> 00:16:30.680 Eli Davis: Right. 00:16:31.730 --> 00:16:32.060 All right. 00:16:32.180 --> 00:16:34.745 So, I'm a musician and I'm a writer too. 00:16:36.170 --> 00:16:56.255 I also used artificial intelligence to, to see how I can I. Play it like I play my guitar, you know, you know, I, I know also that there's gonna be multiple iterations of these large language models and how you could write with them and all. 00:16:56.765 --> 00:17:06.265 But I also know I want, you know, I started playing the guitar when I was 27 years old, and now, you know, I've been playing I'm 51 now, so I've been playing for a very long time. 00:17:07.225 --> 00:17:11.125 But I also know where I started. 00:17:11.125 --> 00:17:14.035 I. You know, and how I started with practice. 00:17:14.245 --> 00:17:23.205 So, so what I'm trying to do is there's going to be some emergence that happens with artificial intelligence. 00:17:23.655 --> 00:17:30.055 You know, what is going to emerge from this technology, especially when it comes to creativity, right? 00:17:30.395 --> 00:17:32.105 I love I'm not an artist. 00:17:32.105 --> 00:17:44.945 I can't draw, but I love to blow into artificial intelligence, you know, particularly chat GBT and see what I can create from my imagination now using this new tool, you know? 00:17:45.065 --> 00:17:49.955 So, that just goes into the segment of now what kind of weird music do you make? 00:17:50.955 --> 00:17:53.655 Peter Swimm: So yeah, I, so I like. 00:17:54.240 --> 00:17:57.150 There's three kinds of music I like and I try to put it in the same music. 00:17:57.150 --> 00:18:01.620 I like old video game music, so like old Nintendo music and stuff like that. 00:18:01.770 --> 00:18:08.390 I like eighties new wave and electronic music and like industrial, I like heavy metal and I like surf. 00:18:09.530 --> 00:18:17.280 So I try to like put them together in ways that don't make sense and yeah, I think like you, like, I play music because I like it. 00:18:17.760 --> 00:18:21.210 Like all the stuff that AI is offered to do for me is like the parts I like. 00:18:21.240 --> 00:18:22.530 So why would I want to do that? 00:18:22.830 --> 00:18:23.340 You know what I mean? 00:18:23.580 --> 00:18:31.790 Like, it's kinda like, if my hobby was portrait painting and then I just tell AI to make it like, 'cause it, the idea is not the idea, right? 00:18:31.790 --> 00:18:32.990 It's the process of 00:18:33.005 --> 00:18:33.125 Eli Davis: I. 00:18:33.260 --> 00:18:33.620 Peter Swimm: the thing. 00:18:34.040 --> 00:18:38.300 And like the process of like taking an hour of your day and like just learning how a song works. 00:18:38.300 --> 00:18:40.160 And like, to me that's like. 00:18:40.745 --> 00:18:45.155 I'm not trying to release a Top 40 song, you know, I'm not trying to like, make a product. 00:18:45.635 --> 00:18:54.305 And I think the, that, that question of like process versus product is kind of like at the heart of using these things for creative endeavors like music. 00:18:54.305 --> 00:18:58.685 Like I've used all the music generating tools and I've not been impressed, right? 00:18:58.685 --> 00:19:04.175 Because this like, it's like, oh, I wanna hear a ska version of Marvin Gaye, you know, or whatever. 00:19:04.175 --> 00:19:05.705 Like, interesting. 00:19:05.705 --> 00:19:06.905 But it's like also like. 00:19:07.610 --> 00:19:08.570 One, it's stolen. 00:19:08.750 --> 00:19:14.660 You know, like I know that as artists, I know what it takes to make music and it, you know, it's difficult, but it's not impossible. 00:19:14.660 --> 00:19:20.600 And like you can go get a guitar and you can put in your 10,000 hours and learn how thing works. 00:19:20.600 --> 00:19:23.810 And I think that's a worthwhile human investment. 00:19:23.810 --> 00:19:25.700 And I don't think AI takes that away. 00:19:26.150 --> 00:19:27.410 What is cool applicants? 00:19:27.410 --> 00:19:33.210 The ai, like I use AI to rip stems from records and I sample them. 00:19:34.290 --> 00:19:45.510 So, you know, like I could take a baseline off a record and like automatically take out the drums and the vocals and I can isolate a bass note that I like the sound of and use that to make something new, right. 00:19:45.840 --> 00:20:00.660 To me, I think that's like transformative and it's not derivative in the way that like, you know, putting Rihanna acapella over some be their computer made is, you know, and and I think like a lot of creative endeavors are like that. 00:20:00.660 --> 00:20:05.660 Like, I. If I told the AI to make what is a guitar that's three 30 feet long? 00:20:05.660 --> 00:20:08.480 Sound like, you know, like, that's interesting, right? 00:20:08.480 --> 00:20:20.820 Because you can't physically do it and you're gonna have a computer like, just imagine what that is and make that happen versus like, you know, do a yacht rock version of you know, yacht rock, version of a journey song or something like that. 00:20:21.120 --> 00:20:27.870 And it's just like, and I think there's a mistake that ideas are important as execution. 00:20:28.530 --> 00:20:37.650 With these things like like people all the time, like you, you have a friend who always like, claims they invented something because they thought of an idea for something, but they didn't build it. 00:20:37.650 --> 00:20:39.960 You know, they didn't make it so they don't get to be the inventor. 00:20:40.050 --> 00:20:49.880 You know, like, and I think like there's something be said about like, take an idea to the end, then nurturing and all that is still kind of like an important human process. 00:20:50.330 --> 00:20:51.650 And that's kind of like how I, you know. 00:20:52.805 --> 00:21:01.685 I do my music because I like doing music and I use AI to write my business email because I hate writing business emails and it's good enough, right? 00:21:02.045 --> 00:21:10.955 It's like not like, I don't think like business email format is like the number one most plagiar realizable thing in the world because it doesn't matter. 00:21:11.195 --> 00:21:11.435 You know? 00:21:11.435 --> 00:21:18.615 It's just like, it's just like a way to get a job done and and that's kind of like my holistic view of like. 00:21:19.710 --> 00:21:30.480 And I've had a lot of friends who won't work with me because I have the word ai, my thing, and I, because I think there's a lot of people in the AI industry who has no respect for knowledge or IP or anything like that. 00:21:30.900 --> 00:21:31.770 And I'm not one of them. 00:21:31.950 --> 00:21:40.670 You know, like I, you know, I, all the logos and design my website, I pay the artists to write, you know, I don't play with, I don't play with you know, making studio. 00:21:41.670 --> 00:21:45.660 MEMS and stuff like that because it's just like, it's boring, you know? 00:21:45.710 --> 00:21:53.130 I like doing art and making a computer do art for me is not fun other than like, you know, a 10 minute thing where you play like, 00:21:53.580 --> 00:21:56.520 You know, making a computer art, make a art sound or something. 00:21:56.520 --> 00:22:00.240 It's funny, but it's not like, it's not like the process that I like to do, 00:22:00.290 --> 00:22:00.530 Eli Davis: yeah. 00:22:00.740 --> 00:22:05.590 I think I I feel you on that, and that's very interesting because. 00:22:06.590 --> 00:22:10.010 I wouldn't want artificial intelligence to play the guitar for me. 00:22:10.190 --> 00:22:11.150 Not at all. 00:22:11.810 --> 00:22:12.440 You know? 00:22:12.510 --> 00:22:16.400 Because I love the way the f the strings finger, the feel underneath my fingers. 00:22:16.400 --> 00:22:17.420 I love the bounce. 00:22:17.420 --> 00:22:18.650 I love the rhythm. 00:22:18.650 --> 00:22:21.680 I love so much of the physical aspects of it. 00:22:23.000 --> 00:22:29.550 For me, I think it's very interesting to see what this new tech technology can do. 00:22:29.610 --> 00:22:44.980 And I like to mess around with it because, you know, as a, I'm a, I'm an educator and I I work in mostly marginalized situations and what I what I see is the potential to to be able to, to. 00:22:45.980 --> 00:22:50.930 Get people to think about things in a new way with using artificial intelligence. 00:22:51.950 --> 00:23:07.590 You know, especially for people who have, may not been able to have the kind of, I don't know your education, but you seem smart to me, you know, you know, so, but working in the inner city of Birmingham, Alabama. 00:23:08.160 --> 00:23:11.070 And they haven't had access to that, that, to that. 00:23:11.070 --> 00:23:18.920 You know, I think that using artificial intelligence as a tool to teach and also a tool to create to birth new ideas. 00:23:19.470 --> 00:23:26.040 I thought it was real cool that Bill Gates, I was watching the video and Bill Gates was like, okay, this is intelligence. 00:23:26.070 --> 00:23:28.320 Like, like this is a form of intelligence. 00:23:28.710 --> 00:23:41.170 And now we are gonna be able to offer this form of intelligence to to me how I look at it to this community who have been just left behind, you know? 00:23:41.240 --> 00:23:42.665 And if Go ahead. 00:23:43.680 --> 00:23:44.160 Peter Swimm: No, go ahead. 00:23:44.430 --> 00:23:48.440 Well, I was just gonna say that, you know, I think access to technology is like the. 00:23:49.360 --> 00:23:51.460 The pro predominant problem of our age. 00:23:51.460 --> 00:23:51.790 Right? 00:23:52.180 --> 00:24:03.160 And if having access to gen AI levels, the playing field for people in like the market of the economy and stuff like that, that all means it should happen, right? 00:24:03.160 --> 00:24:25.330 Like, I think you have to be very careful the execution of like, you know, I grew up, I'm, I grew up in the north side of Chicago and, you know, I was, I always tell people that I'm the last analog kid because like, I'm an elder millennial and like every, you know, I went to audio school for music and they taught me on tape machines, and the year after I graduated, they got. 00:24:25.930 --> 00:24:27.550 Pro tools and computers and stuff. 00:24:27.850 --> 00:24:44.440 So like, and like all the, you know, all the guys I came up working with are like the old guys who like in the tech industry's guys who worked on, you know, big magnetic tapes and, you know, programming and fortran and, you know, I think there's like access to things that just happens to people. 00:24:44.740 --> 00:24:52.510 And you know, I grew up a poor white person, but I even still had the privilege of like, you know, I can clean up and put on glasses and I talk smart. 00:24:52.750 --> 00:25:01.945 I didn't go to college, you know, I didn't, I. I, I too neuro spicy for college and I worked my way up, but like I wouldn't be able to pull that off if I didn't already look like a tech person 00:25:02.050 --> 00:25:04.360 Eli Davis: I love that neural spicy. 00:25:04.690 --> 00:25:05.200 Oh my goodness. 00:25:05.530 --> 00:25:06.520 That is amazing. 00:25:06.820 --> 00:25:06.880 I 00:25:06.925 --> 00:25:06.926 Peter Swimm: Yeah. 00:25:07.720 --> 00:25:16.795 but, and I think like technology is a way for people to like interface in the world that meets them more individualistically. 00:25:16.825 --> 00:25:21.955 Like I could set up my computer the way I like it and help me do things and all that, and that's a super powerful. 00:25:22.480 --> 00:25:25.450 Force multiplier for what I like and what I'm good at. 00:25:25.920 --> 00:25:27.930 And I think everyone should have the opportunity to do that. 00:25:28.270 --> 00:25:39.940 I think the difficult thing is like these open AI of the world and all that, they've taken, you know, a free and open internet and they pay all day, right? 00:25:40.120 --> 00:25:47.920 So like open, if open AI is like all Reddit and Wikipedia and none of that money goes back to making Reddit, Wikipedia possible. 00:25:48.595 --> 00:25:53.635 Those people are gonna start, like, stop making that content and they're gonna start giving away for free, and then no one's gonna have it. 00:25:53.635 --> 00:25:53.875 Right. 00:25:54.295 --> 00:26:04.495 And I think like, you know, you're probably around my age too, like where you remember before the internet and after the internet and before cell phones and after cell phones. 00:26:04.985 --> 00:26:10.295 And there's a whole, you know, more than half of the Americans right now can't. 00:26:10.895 --> 00:26:12.575 Imagine a world before the internet. 00:26:13.175 --> 00:26:22.545 Like I, I had a friend, I when I used to live in New York City, I had a friend who doesn't use a cell phone, still doesn't, and like we were talking to a millennial and they couldn't get it. 00:26:23.115 --> 00:26:25.635 They didn't understand like, well, how do you meet up with people? 00:26:25.635 --> 00:26:29.265 It's like, well, I email them and we make plans and I meet them at that time. 00:26:29.745 --> 00:26:36.345 You know, like, so there's all kinds of like dependence and technology that like people don't get, and. 00:26:36.835 --> 00:26:51.275 I think there's a real risk of being overdependent on these AI technologies where the benefit of like having that force multiplier is unevenly applied and, you know, people become dependent on it in ways that other people with privilege aren't. 00:26:51.665 --> 00:26:59.605 You know, like, like the whole agentic thing is being posited by tech CEOs who have access to the 1000 smartest human beings on the planet. 00:27:00.100 --> 00:27:00.340 Right. 00:27:00.820 --> 00:27:07.280 And so they think agents are, is that, you know, like everyone's gonna have access to 1000 smartest people on the planet. 00:27:07.310 --> 00:27:10.910 It's like, no, you were the only person who gets to talk to the expensive people. 00:27:10.910 --> 00:27:12.200 Everyone else gets the agent, 00:27:12.665 --> 00:27:12.955 Eli Davis: Yeah. 00:27:14.000 --> 00:27:17.450 Peter Swimm: you know, everyone else talks to the abstraction of expertise. 00:27:17.930 --> 00:27:26.640 And that, I think that's where the big risk comes in there, you know, where it's just like, okay I get the Temo expertise and you get the Amazon Prime expertise, you know? 00:27:27.045 --> 00:27:35.105 Eli Davis: Yeah, I I wrote an article I write articles and I publish on Medium, and I wrote an article about this idea of name brand, LLMs. 00:27:35.945 --> 00:27:36.935 You know what I mean? 00:27:36.965 --> 00:27:40.055 It's that it is just gonna be like the genes and I am around your age. 00:27:40.595 --> 00:27:48.450 And when I went to school, well, you know, I don't know for sure, but from the content that you're saying I, I understand. 00:27:48.960 --> 00:27:50.800 And and I'm from Milwaukee, Wisconsin. 00:27:50.800 --> 00:27:51.385 What's upsta? 00:27:52.270 --> 00:27:52.510 Peter Swimm: Yeah. 00:27:52.510 --> 00:27:52.870 Yeah. 00:27:53.870 --> 00:27:55.900 Eli Davis: My my sister, she she lives in Chicago. 00:27:56.390 --> 00:28:02.400 So, well, I think that there is a, there's this moment I study black history, right? 00:28:02.490 --> 00:28:12.140 As an educator, but particularly I'm interested in you know, the impact, the historical impact of enslavement and how that like bam and what that done and all this stuff. 00:28:12.660 --> 00:28:14.100 Through that historical impact. 00:28:14.100 --> 00:28:23.390 I think that this is really one of the first times that African American people or people who have been historically marginalized will be able to have. 00:28:24.005 --> 00:28:30.605 Access to this fundamental and very new technology like never before. 00:28:31.065 --> 00:28:40.835 I think because of cell phones and which gives you internet access and and you know, and of course, you know, people have computers and stuff. 00:28:41.045 --> 00:28:44.425 I think that this is one of the first times that we have had. 00:28:45.425 --> 00:28:53.680 This level of access to something that it will be as foundational or as revolutionary as the artificial intelligence. 00:28:54.130 --> 00:28:56.740 There has been no other time in history in which we have had it. 00:28:57.190 --> 00:28:59.510 So this is why I'm all in. 00:29:00.020 --> 00:29:00.860 Let's see what you could do. 00:29:01.250 --> 00:29:02.000 You know what I mean? 00:29:02.225 --> 00:29:02.405 Peter Swimm: Yeah. 00:29:02.405 --> 00:29:02.406 Yeah. 00:29:02.470 --> 00:29:02.760 Yeah. 00:29:02.810 --> 00:29:08.790 Eli Davis: I 100% understand where you coming from and making sure that human is in the loop. 00:29:09.540 --> 00:29:10.260 You know what I mean? 00:29:10.310 --> 00:29:11.380 That is so very important. 00:29:11.500 --> 00:29:17.050 So, just to honor your time, Peter I just wanna say thank you for coming on amazing con conversation. 00:29:17.050 --> 00:29:18.940 And we should probably do it again if you don't mind. 00:29:19.020 --> 00:29:19.470 Peter Swimm: Oh yeah 00:29:19.520 --> 00:29:25.095 Eli Davis: so, go ahead and just give the people where they can find you, where they can get you and what they can get when they got you. 00:29:25.950 --> 00:29:26.340 Peter Swimm: sure. 00:29:26.640 --> 00:29:28.320 Well, you can reach me on my website. 00:29:28.440 --> 00:29:30.060 It's toy.com. 00:29:30.060 --> 00:29:32.250 That's ITS toil, TIL. 00:29:33.570 --> 00:29:35.190 BILL e.com. 00:29:35.490 --> 00:29:39.090 And the one thing I wanna pitch is I do an office hours. 00:29:39.360 --> 00:29:49.750 So every Wednesday morning on Twitch and LinkedIn and YouTube I just have like office hours, like a professor would where you can come and ask whatever questions you want and we just talk about the issues of the week. 00:29:50.050 --> 00:29:58.710 So for people who are figuring out where they fit in this world, or wanna ask questions or maybe want to play with the stuff at work or at school, feel free to come on by and we could just talk about stuff. 00:29:59.760 --> 00:30:00.510 Eli Davis: Sounds good. 00:30:00.810 --> 00:30:02.220 All right, well, thank you very much. 00:30:02.220 --> 00:30:11.230 And also thank you for sharing your creative expression and you know, one of my favorite cities in the world is Chicago, so, you know what I mean? 00:30:11.230 --> 00:30:12.580 I spent a lot of time in Chicago. 00:30:12.580 --> 00:30:17.910 So you just exemplify and exude that favorite city energy. 00:30:19.350 --> 00:30:31.790 Peter Swimm: Well, I gotta say, like, I, I know we're at time, but Chicago I really credit for my approach to things, you know, where I grew up on North Side, Chicago and Devon Avenue, where every block is a different country of the world. 00:30:32.690 --> 00:30:44.820 You know, like, you know, Indians and Koreans and Syrians and, diversity kind of prepared me like I don't know nothing and and that all kinds of people know something and it takes all kinds of people to do things. 00:30:44.820 --> 00:30:52.380 So I think Chicago is really that kind of like, it's of all the major American cities, I think it, it has that kind of like variety of diversity. 00:30:52.380 --> 00:30:59.550 I think that is really important for as we go into this kind of like blended society where everyone's in the same social networks and 00:30:59.620 --> 00:31:01.215 Eli Davis: Yeah that's really interesting. 00:31:01.265 --> 00:31:11.645 My father who have now passed away, God rest his soul, you know, but he's, he stayed around Divine Avenue on the north side of Chicago. 00:31:11.645 --> 00:31:16.325 My sister stays on the west side, you know, so, so I'm familiar with that area. 00:31:16.325 --> 00:31:19.055 And they have a amazing food over there. 00:31:19.170 --> 00:31:19.685 Peter Swimm: Oh, yeah. 00:31:19.805 --> 00:31:19.985 Yeah. 00:31:20.075 --> 00:31:20.585 I grew up right. 00:31:20.585 --> 00:31:24.555 I lived my whole childhood right by that little league that runs stadium. 00:31:24.990 --> 00:31:27.570 A little miniature Wrigley field that they have there. 00:31:27.865 --> 00:31:29.095 Eli Davis: I'm not, I don't know it like that. 00:31:29.470 --> 00:31:29.560 Peter Swimm: Okay. 00:31:30.250 --> 00:31:33.490 But so someone listening to your podcast because they'd be like, fill. 00:31:34.490 --> 00:31:36.470 Alright, well, thank you for having me. 00:31:36.905 --> 00:31:37.595 Eli Davis: Oh, man. 00:31:37.625 --> 00:31:39.165 Peter thank you very much. 00:31:39.220 --> 00:31:40.331 Everybody Peter Swin.