WEBVTT NOTE This file was generated by Descript 00:00:00.161 --> 00:00:04.091 all right, well, what is interesting to you in AI these days? 00:00:05.231 --> 00:00:05.921 Oh, man. 00:00:06.011 --> 00:00:06.191 Um. 00:00:07.466 --> 00:00:11.186 Well, my AI governance book is gonna be out in about three weeks. 00:00:11.336 --> 00:00:11.996 Oh, it is. 00:00:12.086 --> 00:00:12.326 Excellent. 00:00:12.326 --> 00:00:14.006 Uh, so I'm really excited about that. 00:00:14.216 --> 00:00:21.926 Um, yeah, I've, uh, just finished, uh, the proofing a couple of weeks ago, and then the publisher says, congrats. 00:00:21.926 --> 00:00:23.216 It's now at the printer. 00:00:23.681 --> 00:00:33.701 And, uh, and, and then he confirmed that the, the availability date showing on Amazon, you know, where it's available for pre-order is, is pretty close. 00:00:33.761 --> 00:00:37.421 Uh, I'll have my own personal copies maybe two weeks ahead of that. 00:00:37.931 --> 00:00:42.581 So maybe first or second week of January I'll have them in my hand. 00:00:42.821 --> 00:00:44.231 Man, that's pretty exciting. 00:00:44.231 --> 00:00:47.201 So tell me about your, about your book. 00:00:47.201 --> 00:00:48.551 What do you, what is the. 00:00:49.001 --> 00:00:50.441 The premise of it. 00:00:50.531 --> 00:00:51.191 Yeah. 00:00:51.221 --> 00:00:51.671 Let's see. 00:00:51.671 --> 00:00:55.361 I gotta, I gotta move you over to the right window here, so I'm, yeah, all good. 00:00:55.361 --> 00:00:55.991 Looking at you. 00:00:55.991 --> 00:00:58.151 And not off in the corner of the room somewhere. 00:00:58.601 --> 00:00:59.381 Alright, there we are. 00:00:59.771 --> 00:01:02.446 Uh, so this is a book about, uh. 00:01:03.176 --> 00:01:16.196 Um, it, it's, it's for professionals and, and companies who are serious about governance and want to bring AI into, into their governance, um, program. 00:01:16.436 --> 00:01:25.976 Or if they don't have governance and they don't know how, then, you know, my book also helps, helps them, you know, build, build government governance from scratch. 00:01:26.021 --> 00:01:28.541 Great if, if they need to. 00:01:28.631 --> 00:01:44.681 Uh, and this is a study guide for a certification called A IGP, um, AI Governance Professional, which is, uh, which was done a couple of years ago by the International Association of Privacy Professionals. 00:01:45.746 --> 00:02:03.116 Which is highly, highly respected in the areas of privacy and AI and privacy are kind of go hand in hand here, um, because many AI systems are trained with personal information about us. 00:02:03.116 --> 00:02:03.126 Mm-hmm. 00:02:03.746 --> 00:02:09.776 And so, you know, existing and emerging privacy laws, um, there it is. 00:02:09.776 --> 00:02:10.136 Yeah. 00:02:10.976 --> 00:02:12.341 Privacy laws are, um. 00:02:13.796 --> 00:02:20.996 Um, run kind of at odds with the way that AI systems work and are trained. 00:02:20.996 --> 00:02:29.876 And so there are some, some kind of, some caution areas in, uh, in AI governance, uh mm-hmm. 00:02:30.116 --> 00:02:34.107 You know, training, training and AI system with PII. 00:02:34.316 --> 00:02:35.141 And then the way that. 00:02:36.026 --> 00:02:39.686 Society and applicable laws expect companies to treat. 00:02:39.686 --> 00:02:44.216 PII are are directly at odds with one another. 00:02:44.876 --> 00:02:45.386 So, 00:02:46.526 --> 00:02:47.066 uh, yeah. 00:02:47.516 --> 00:02:51.126 So how did you go about writing this book? 00:02:52.376 --> 00:02:58.496 Like, did you take the, the test and look at it and say, what do I need people to understand or? 00:02:58.706 --> 00:03:00.326 Yeah, good question. 00:03:00.326 --> 00:03:11.756 So in, in my last position when I was the senior director of, uh, cyber risk and compliance at, uh, this Alaska based telecom where I worked for. 00:03:12.446 --> 00:03:14.096 Four plus years. 00:03:14.456 --> 00:03:22.046 Um, I was put, one of the last big things I did was, uh, I was asked to put together an AI governance program. 00:03:22.406 --> 00:03:22.466 Hmm. 00:03:22.496 --> 00:03:25.651 So, I, I found that IAPP. 00:03:26.741 --> 00:03:31.451 Had a really great training course on AI governance. 00:03:31.511 --> 00:03:34.841 And so I took the course and I ran through the course three times. 00:03:34.841 --> 00:03:36.551 It was a web-based on-demand course. 00:03:36.556 --> 00:03:36.706 Mm-hmm. 00:03:36.786 --> 00:03:38.501 So I could watch modules over and over. 00:03:39.281 --> 00:03:47.591 Um, and after I went all through that, that really helped me figure out, okay, now I, now I know what I have to do here in this company. 00:03:47.621 --> 00:03:55.091 And, and I built governance structures for identity and access management and for risk management and, and. 00:03:55.526 --> 00:03:56.306 For privacy. 00:03:56.306 --> 00:04:09.536 So, so it, it wasn't entirely new to me other than just some of the new things that AI introduces that, that other technologies don't. 00:04:10.196 --> 00:04:17.726 Um, and so after going through that training and I thought, gosh, you know, I've written like, you know, 40 certification exam guides. 00:04:17.726 --> 00:04:19.736 Maybe I should write one for this one. 00:04:19.736 --> 00:04:24.596 And so I shot a proposal off to the publisher and they said, yeah, do it. 00:04:25.211 --> 00:04:25.841 Let's do it. 00:04:25.901 --> 00:04:27.281 So it's like, alright. 00:04:28.061 --> 00:04:28.421 Yeah. 00:04:28.751 --> 00:04:31.661 And you've written like a hundred books now, right? 00:04:32.321 --> 00:04:33.911 Well, I dunno, A fee seems like a hundred. 00:04:33.911 --> 00:04:41.651 It's, um, it, depending on how you count, I'm looking at the list now, it's like in the sixties. 00:04:42.281 --> 00:04:44.321 Um, I've. 00:04:45.701 --> 00:04:49.841 Written 43 full length manuscripts from scratch. 00:04:49.871 --> 00:04:54.161 So the difference there is that some books have gone into repeat editions. 00:04:54.191 --> 00:04:54.281 Mm-hmm. 00:04:54.491 --> 00:05:00.851 Which doesn't mean I have to write a full length manuscript from scratch, I only have to revise what's already there. 00:05:00.851 --> 00:05:00.911 Yeah. 00:05:01.901 --> 00:05:03.431 And that's over 25 years. 00:05:03.851 --> 00:05:04.121 Yeah. 00:05:04.121 --> 00:05:07.001 And you've got 42 titles on Amazon. 00:05:07.031 --> 00:05:12.746 So there are, there are some that are not on Amazon that, um, you can't even. 00:05:13.646 --> 00:05:16.496 Get, uh, that aren't printed anymore, right? 00:05:16.766 --> 00:05:22.226 Well, it's, that's a part of the story of why there aren't as many. 00:05:22.616 --> 00:05:29.846 The other reason is now if you stop right there, you see that, that one that's called getting, oh, wait, no, that's not it. 00:05:29.936 --> 00:05:35.916 Um, there I've written several, what are called custom publications for mm-hmm. 00:05:36.326 --> 00:05:43.736 Wiley Publishing, and those are books commissioned by vendors that are not sold on Amazon, but one of them did appear here. 00:05:43.736 --> 00:05:53.186 So if you scrolled slowly, I'll, I'll show you where one of those books is and you probably want to go the other way. 00:05:53.276 --> 00:05:53.816 Other way. 00:05:54.026 --> 00:05:54.236 Okay. 00:05:54.236 --> 00:05:55.856 Um, all right. 00:05:55.856 --> 00:05:56.636 Keep going. 00:05:56.636 --> 00:05:57.986 Keep going. 00:05:59.126 --> 00:06:01.646 I think there's one of those in there. 00:06:02.036 --> 00:06:04.016 One of those custom pubs. 00:06:05.246 --> 00:06:07.406 Um, it's a Dummies. 00:06:07.466 --> 00:06:08.426 It's a dummies book. 00:06:08.426 --> 00:06:09.086 It's, uh, yeah, so 00:06:09.086 --> 00:06:14.036 there's this dummies right here where it's just somebody took a picture of it, like they're reselling it right 00:06:14.336 --> 00:06:14.576 now. 00:06:14.576 --> 00:06:15.836 That was a real book. 00:06:15.836 --> 00:06:17.336 I mean, that was like 23 years ago. 00:06:17.336 --> 00:06:18.266 Larry and I wrote that. 00:06:18.806 --> 00:06:18.926 Yeah. 00:06:19.226 --> 00:06:23.936 And it, it didn't celebrate, so it, it didn't go into repeat editions. 00:06:24.266 --> 00:06:24.746 Oh, okay. 00:06:24.746 --> 00:06:26.816 So none of the custom pubs are in here. 00:06:26.816 --> 00:06:28.496 Maybe one of them used to be. 00:06:29.156 --> 00:06:33.116 So examples of these, um, just. 00:06:33.626 --> 00:06:46.706 Going up my list here, like, um, advanced Physical Access Control for Dummies, which was commissioned by HID Global, which is like the, the security entrance card vendor. 00:06:47.246 --> 00:06:55.256 Um, another one Stopping Zero Date Exploits for Dummies, which was written by an Israeli security company called Trust Deer. 00:06:55.586 --> 00:06:55.826 Hmm. 00:06:56.036 --> 00:06:58.436 Um, and several others. 00:06:58.526 --> 00:06:58.976 Um. 00:06:59.606 --> 00:07:07.946 I was, for about 5, 6, 7 years, I wrote, I don't know, maybe 15 of these custom pubs. 00:07:08.846 --> 00:07:12.956 Uh, and they're short books anywhere from like 25 to 60 pages. 00:07:12.956 --> 00:07:14.456 Some of them I can write in a weekend. 00:07:14.876 --> 00:07:15.296 Um. 00:07:16.211 --> 00:07:17.681 So, yeah, those were fun. 00:07:17.681 --> 00:07:19.241 I don't have time to do those right now. 00:07:19.571 --> 00:07:20.021 Yeah. 00:07:20.021 --> 00:07:28.331 Um, but, um, that, that is mostly the difference between what I claim and what Amazon lists. 00:07:28.336 --> 00:07:28.606 Yeah. 00:07:28.901 --> 00:07:29.111 Yeah. 00:07:29.111 --> 00:07:29.921 That's pretty good. 00:07:29.981 --> 00:07:30.641 Very cool. 00:07:31.121 --> 00:07:40.571 Um, so, so you got the AI governance and so for a, a school focused audience, which is, is who's usually listening to this, um. 00:07:41.636 --> 00:07:51.926 The way I'm interpreting that is that, uh, governance means things like school boards and people overseeing the operations of the school, things like that. 00:07:52.196 --> 00:07:54.446 Is that the same thing that you're talking about? 00:07:54.806 --> 00:07:54.896 Yes. 00:07:55.046 --> 00:07:56.426 Or are you talking about something different? 00:07:56.636 --> 00:08:08.366 No, this governance in, in a kind of a generic term, represents formal structures established so that executive management. 00:08:08.921 --> 00:08:29.501 Or board of directors can exert over or can exert control over a part of the business and then receive information back in the form of re uh, metrics and KPIs and readouts and briefings to give them feedback on how it's going. 00:08:30.221 --> 00:08:36.821 Uh, so it's, it's generally in, in more formal companies, governance is something that, that boards do. 00:08:37.211 --> 00:08:37.391 Mm-hmm. 00:08:37.721 --> 00:08:44.261 Uh, but, and then in many, many larger companies have what you would call an IT steering committee. 00:08:44.261 --> 00:08:50.681 And this goes back like to the sixties where stakeholders from across a business will. 00:08:51.071 --> 00:08:56.711 Meet from time to time, maybe monthly or quarterly, to deliberate what's happening in it? 00:08:57.311 --> 00:09:05.831 What does the business need in terms of, you know, new capabilities, updates, and so forth for, you know, any of many reasons. 00:09:06.161 --> 00:09:11.291 Uh, and then how can that all be kind of prioritized to best meet the business' needs? 00:09:11.981 --> 00:09:12.041 Yeah. 00:09:12.041 --> 00:09:15.221 And so that would be an example of governance structure. 00:09:16.856 --> 00:09:22.556 WW without having mentioned, you know, some of the minutia, like, you know, the metrics and, and things like that. 00:09:23.006 --> 00:09:23.426 Yeah. 00:09:23.786 --> 00:09:42.981 Well, and this is a really fascinating topic because like you mentioned before, the, the way that they go about collecting the information is, is definitely at odds with how we are supposed to, uh, manage and take care of, um. 00:09:43.841 --> 00:09:48.311 The information of the people that we are serving and so, right. 00:09:48.311 --> 00:10:00.431 That, that's a, that's a big issue right there, that it's, it'd be cool to use AI to help with that governance and, and pay attention to things, but you run into issues. 00:10:00.491 --> 00:10:09.731 If the AI was trained in an inappropriate way, like how do you, how do you reconcile that because you, you really don't have control over how the AI was trained. 00:10:10.151 --> 00:10:10.811 And 00:10:11.231 --> 00:10:12.191 Well, it depends. 00:10:12.191 --> 00:10:13.841 I mean, you should have control over it. 00:10:13.841 --> 00:10:27.551 I mean, if, if you're a company, let's say you're, uh, you know, let's say you're, you're a university or you're just, you know, like a K 12 school system and you want to better understand. 00:10:28.826 --> 00:10:39.236 Courses and grades and test results, let's say, and be able to do some prediction of that by bringing data in. 00:10:39.236 --> 00:10:40.646 I'm just making this up on the fly. 00:10:40.646 --> 00:10:40.946 Right. 00:10:40.946 --> 00:10:43.676 Well, let me, let me give you a good example on that. 00:10:43.796 --> 00:10:53.096 So we, we know that if kids are not reading by grade three and we have tests to determine whether or not they're grade, they're reading. 00:10:53.876 --> 00:10:58.976 We know that if they're not reading by grade three, then things are going to be more difficult for them. 00:10:59.426 --> 00:11:03.926 We also are required to keep data about students for years. 00:11:03.956 --> 00:11:09.956 Some, some pieces of data for a hundred years after the student graduates from the high school. 00:11:10.016 --> 00:11:12.241 That's, that's what some data retention policies are like 00:11:12.386 --> 00:11:12.626 by 00:11:12.626 --> 00:11:14.396 name with grades and things like that. 00:11:14.666 --> 00:11:14.996 Yes. 00:11:15.116 --> 00:11:16.856 Depending on the, on the thing. 00:11:16.856 --> 00:11:21.326 So it's not, it's when it's a hundred years, it's not that detailed like. 00:11:22.061 --> 00:11:33.161 So every year the district has to archive and save the the grades or the assignments and the scores on the assignments that made up the final grade for that student. 00:11:33.521 --> 00:11:43.331 So school districts particularly have all of this stuff there and they have access to it, some of which is digitized, some of which is hard Copy. 00:11:43.886 --> 00:12:02.306 But the idea is you could essentially go back for the last 50 years of your school district and you could say, let's look at everybody who was at this level in third grade, and let's track how they graduated and what their rank was, for example, in the rest in their graduating class. 00:12:02.546 --> 00:12:06.146 And the data exists to be able to do that. 00:12:06.986 --> 00:12:10.856 Now, with it being the district data, it seems like. 00:12:11.861 --> 00:12:20.111 They could use that and perhaps anonymize it and, and do something to make it so that they're not revealing personal information. 00:12:20.711 --> 00:12:27.551 But then they could say, when a kid finishes this test in third grade, here's the trajectory we map for them. 00:12:27.851 --> 00:12:29.111 If nothing changes. 00:12:29.921 --> 00:12:35.801 That idea is like the holy grail for education that will know where someone ends up. 00:12:36.221 --> 00:12:46.301 Well, and AI could be a great tool to analyze a very broad set of data points per student as well as if you could follow those students. 00:12:46.706 --> 00:12:48.386 Post education. 00:12:48.806 --> 00:12:49.046 Yep. 00:12:49.076 --> 00:12:56.006 Then a, a well-trained AI system could, could show you what the success factors are. 00:12:56.516 --> 00:12:56.726 Yeah. 00:12:56.726 --> 00:12:57.086 Right. 00:12:57.086 --> 00:13:00.506 And that would be something that educators would love to know. 00:13:00.506 --> 00:13:00.926 Right. 00:13:01.106 --> 00:13:07.826 Now, you, you touched on a, on a point about anonymizing the data, and certainly you'd want do that however. 00:13:08.921 --> 00:13:23.411 That's harder than it seems on the surface, because if you don't anonymize it, well then data records are subject to being able to be re-identified. 00:13:23.861 --> 00:13:24.221 Mm-hmm. 00:13:24.551 --> 00:13:30.346 You know, for instance, in, in a different example, I use, um, let's say, you know, you've got, uh. 00:13:31.751 --> 00:13:42.791 Give me all the, the vice presidents in the software business living in this zip code who make more than $200,000 a year and are married. 00:13:43.241 --> 00:13:43.331 Mm-hmm. 00:13:43.571 --> 00:13:46.781 Well, you can anonymize that, but there might only be one person. 00:13:47.111 --> 00:13:47.441 Right. 00:13:47.771 --> 00:13:48.161 Right. 00:13:48.161 --> 00:13:49.991 And it's like, well, we know who that is. 00:13:50.531 --> 00:13:50.771 Yeah. 00:13:50.951 --> 00:13:53.891 It's not hard to, uh, you know, put, put that back together. 00:13:54.041 --> 00:13:59.771 So one of the ways that this, that, that's a very real example in the education world is that we, um. 00:14:00.206 --> 00:14:04.976 We, uh, aggregate and disaggregate data based on tests. 00:14:05.186 --> 00:14:14.726 That's based on, that's test scores based on, uh, different, um, uh, characteristics like race and gender and things like that. 00:14:14.786 --> 00:14:15.026 Right. 00:14:15.026 --> 00:14:15.716 Demographics. 00:14:15.716 --> 00:14:15.926 Yeah. 00:14:16.046 --> 00:14:16.406 Yeah. 00:14:16.466 --> 00:14:18.536 And so if there are, uh. 00:14:18.956 --> 00:14:26.936 If there are less than 10, for example, then it won't show on certain reports because it says it. 00:14:27.326 --> 00:14:34.076 If we do this, if we show this, then you'll know exactly who it is, and that defeats the purpose of this being anonymous data. 00:14:34.076 --> 00:14:38.906 So it just says less than 10, and so there's no, there's essentially no data. 00:14:39.416 --> 00:14:39.626 Right. 00:14:39.691 --> 00:14:44.366 But going back to this idea of being able to predict in the future and being able to. 00:14:45.641 --> 00:14:52.301 Use this historical data to train a model on what we can do going forward. 00:14:52.871 --> 00:14:58.721 There's, there's a lot of value to that and things that are very worthwhile for that. 00:14:58.991 --> 00:15:00.041 Mm, tremendous 00:15:00.041 --> 00:15:00.581 value. 00:15:00.586 --> 00:15:00.656 Yeah. 00:15:00.661 --> 00:15:01.001 Yeah. 00:15:01.001 --> 00:15:04.091 So, so how would you go about anonymizing that data? 00:15:04.121 --> 00:15:13.331 What things would you suggest people put in place so that they do train the data in a, in a way that respects personally identifiable information? 00:15:14.861 --> 00:15:15.401 Well, 00:15:17.471 --> 00:15:22.511 you know, you just sort of gotta sit down and look at what the data set is, how are you gonna use it? 00:15:22.541 --> 00:15:26.321 I mean, you know, that's kind of a, you know, that gets pretty detailed. 00:15:26.321 --> 00:15:30.431 Uh, you know, kind of like, well, how would we rebuild this engine anyway? 00:15:30.431 --> 00:15:32.231 It's like, well, I don't know. 00:15:32.231 --> 00:15:34.181 It's kind of hard to walk through it, but it's easier. 00:15:34.181 --> 00:15:37.121 Like, let's just find an example and do it. 00:15:37.481 --> 00:15:40.301 Um, yeah, it's, it's kind of tricky. 00:15:40.301 --> 00:15:41.201 It takes a good. 00:15:42.041 --> 00:15:48.671 Data scientist who understands data and how it can be used together with a privacy professional. 00:15:48.671 --> 00:15:51.131 And sometimes that's, you know, those are both in one person. 00:15:51.131 --> 00:15:51.221 Mm-hmm. 00:15:51.461 --> 00:15:52.121 Sometimes not. 00:15:52.751 --> 00:15:55.211 Um, it depends. 00:15:55.271 --> 00:15:56.861 There's, there's so many things. 00:15:56.861 --> 00:15:58.871 Uh, the context is also important. 00:15:58.871 --> 00:16:02.261 You know, are these customers, are they employees, are they students? 00:16:02.291 --> 00:16:03.731 Are they just members of the public? 00:16:03.731 --> 00:16:04.181 Are they. 00:16:04.571 --> 00:16:09.071 Something else, you know, like, you know, former inmates of our jail or mm-hmm. 00:16:09.311 --> 00:16:11.861 You know, what class do, do they belong to? 00:16:12.281 --> 00:16:18.821 Um, and you know, then you kind of over, you gotta overlay privacy laws to see, okay, what are we required to do? 00:16:19.301 --> 00:16:27.641 And then in the context of the industry, there are, you know, many industries that, that prescribe minimums and maximums for. 00:16:28.046 --> 00:16:44.066 The retention of business records and often you cannot, I, you know, anonymize those, you know, for instance, employers are required to keep HR data for all employees for, what is it, you know, termination plus seven years or plus 10 or something like that. 00:16:44.066 --> 00:16:44.156 Mm-hmm. 00:16:44.846 --> 00:16:49.646 And you can't anonymize that in terms of the business records you're required to keep. 00:16:49.676 --> 00:16:50.736 Although if you wanted to. 00:16:52.091 --> 00:16:59.441 Play around with AI in that, you know, you could anonymize it and throw it into your AI to get kind of some generic workforce data. 00:16:59.441 --> 00:17:07.901 You know, like if you wanted to better understand the characteristics of the more successful employees in your company, you know, that might help you to find what those factors are. 00:17:07.901 --> 00:17:11.526 And then that would help you, you know, hire better in the future or, you know, mm-hmm. 00:17:11.606 --> 00:17:14.171 Take care of your current workforce better. 00:17:14.621 --> 00:17:19.511 Um, there's a, a tremendous amount of detail that that. 00:17:20.726 --> 00:17:28.616 Potentially, you know, has to go into, you know, studying, okay, how do we, how do we go about this for this thing? 00:17:29.126 --> 00:17:29.396 Mm-hmm. 00:17:30.241 --> 00:17:39.026 The, the other big thing about AI that I write about in the book, um, so back to privacy. 00:17:39.986 --> 00:17:46.586 One of the things that's codified into GDPR, I don't remember if it's in California's privacy laws or not. 00:17:46.586 --> 00:17:48.057 CPRA and CCPA. 00:17:48.581 --> 00:17:55.391 Um, but I have a feeling that it's kind of coming anyway because people are just so twisted up about mm-hmm. 00:17:55.631 --> 00:17:58.541 You know, marketing information that is just nonstop. 00:17:58.931 --> 00:18:02.471 And that is that, that concept about the right to be forgotten. 00:18:03.011 --> 00:18:03.281 Mm-hmm. 00:18:03.611 --> 00:18:07.991 Which organizations are obligated to. 00:18:08.546 --> 00:18:17.786 Do just that on request, as long as it doesn't conflict with a different law that says you must keep your business records for a minimum of this long. 00:18:18.716 --> 00:18:18.776 Yeah. 00:18:18.806 --> 00:18:31.796 So as long as you don't run into other statutory re uh, requirements on required retention, then organizations do need to go ahead and, and remove that data. 00:18:31.796 --> 00:18:34.106 Although anonymizing, it is allowed. 00:18:34.591 --> 00:18:39.031 Provided they do it well enough that it can't be re-identified. 00:18:39.241 --> 00:18:39.451 Mm-hmm. 00:18:40.051 --> 00:18:56.671 But then when you're talking about an AI system, you know, so let's say, you know that such a organization, whatever business it's in, had this data about customers or employees or whoever they are, and they trained their AI with it to make their business better in some way. 00:18:56.941 --> 00:19:00.841 And then one, then one person comes along and says, okay, I want you to forget about me. 00:19:01.436 --> 00:19:10.826 Well, in ai, there's no such thing as removing one record from a training set where the system's already been trained. 00:19:10.886 --> 00:19:10.946 Yeah. 00:19:10.976 --> 00:19:12.206 There's just no one undo. 00:19:12.926 --> 00:19:14.606 So what do you do about that? 00:19:14.786 --> 00:19:21.956 I mean, that is, that is the big, that's the big head-on collision with AI and privacy. 00:19:22.016 --> 00:19:25.226 Uh, and, and copyright. 00:19:25.226 --> 00:19:26.486 What I recommend is. 00:19:27.461 --> 00:19:44.381 You gotta either pseudonymized or anonymize that data unless the thing you want to do with the AI requires, you still have to know precisely who is the person and then you know what you might end up having to do. 00:19:45.791 --> 00:20:04.511 Is like if you've gotta have, you know, real PII in there and you gotta put it in your AI because of whatever you decide you're gonna do as a business, then you might have to do something like, you know, monthly or quarterly retraining where you remove the records in the next retraining cycle and maybe that's how you do it. 00:20:04.511 --> 00:20:05.171 Mm-hmm. 00:20:05.186 --> 00:20:05.406 Um. 00:20:06.796 --> 00:20:07.086 Yeah. 00:20:07.086 --> 00:20:07.096 But 00:20:07.751 --> 00:20:09.431 yeah, the devil's in the details here. 00:20:09.671 --> 00:20:25.301 Um, I mean, AI is capable of doing great things, but there are a few new gotchas here that are unique to AI because before ai, I mean, you know, removing a data record for privacy purposes, no big deal. 00:20:26.051 --> 00:20:26.471 Um. 00:20:28.766 --> 00:20:29.156 Yeah. 00:20:29.156 --> 00:20:36.356 And, and it's, it's so interesting because it, some of those things you wouldn't even think about. 00:20:36.596 --> 00:20:45.476 Um, and if, uh, I mean a lot of things related to privacy, uh, people just don't think about period, right? 00:20:45.806 --> 00:20:45.836 Yeah. 00:20:45.836 --> 00:20:49.196 I mean that's, that's one of the real challenges that we, that we currently face. 00:20:49.736 --> 00:20:56.606 Um, but then when you think about retraining the data and taking out the people who have asked to be forgotten. 00:20:57.041 --> 00:21:00.581 That, that seems more complicated and 00:21:01.091 --> 00:21:01.331 Yeah. 00:21:01.901 --> 00:21:12.671 Um, and there would be situations where it would be very valuable to have, uh, PII, so for example, there's a, there's an AI tool called Libra, L-E-B-R-A. 00:21:13.151 --> 00:21:17.921 And what they do is their goal is to improve culture in a school. 00:21:18.431 --> 00:21:22.511 And so, um, so what it does is it, uh. 00:21:23.291 --> 00:21:30.821 It, it brings in your information from your Office 365 or whatever, so that it knows who everybody is, what their relationship is. 00:21:30.821 --> 00:21:44.051 And then, um, it, it allows you to use AI to write notes and short and like do recognitions of things that may have taken you too long to do, to do otherwise. 00:21:44.051 --> 00:21:47.081 That, uh, just makes that part of it seamless. 00:21:47.261 --> 00:21:50.471 So having the PII is essential because you need to know. 00:21:51.296 --> 00:22:06.266 That you know, so and so a third grade teacher at this school is the person and that they work at that school, and you're not really training the AI on that person per se, but they are included in the data so that when. 00:22:06.986 --> 00:22:15.746 That third grade teacher at that school does something, and it is noted in the data, it will surface to the supervisors or the people that are connected to her. 00:22:16.076 --> 00:22:20.246 So those things, those connections and relationships do exist. 00:22:20.666 --> 00:22:26.066 And that is, um, those are just things that we haven't never really had to think about before. 00:22:26.546 --> 00:22:33.176 Um, especially with the black box nature of AI as a whole, because we don't know what's actually going on in there. 00:22:33.446 --> 00:22:39.236 Whereas with the computer program, we just delete that record and then poof, they're, they're gone. 00:22:39.296 --> 00:22:40.646 But that's not how it is with ai. 00:22:40.916 --> 00:22:41.786 Right, right. 00:22:41.786 --> 00:22:44.576 I mean, these, these are, these are some of the challenges. 00:22:44.606 --> 00:22:45.386 I mean, they're. 00:22:46.661 --> 00:23:02.801 They're solvable and one of the areas where I think we may end up seeing some innovation is, uh, one of the AI vendors might figure out a way to remove individual records from training data. 00:23:03.161 --> 00:23:03.251 Mm-hmm. 00:23:03.491 --> 00:23:03.821 Now, 00:23:04.631 --> 00:23:06.546 I don't understand enough about how. 00:23:07.346 --> 00:23:09.386 AI training works. 00:23:09.596 --> 00:23:11.666 I just have never gone that deep. 00:23:12.296 --> 00:23:18.026 Um, I've heard discussions about it and it sounds like I'd have to like go back to school for 80 hours. 00:23:18.476 --> 00:23:18.866 Yeah. 00:23:19.016 --> 00:23:26.936 My head down and just be deeply immersed in this to have any kind of a clue as to what this all means. 00:23:27.326 --> 00:23:28.151 Um, but. 00:23:29.261 --> 00:23:35.771 Eventually somebody may figure out a way of having an AI system unlearn a single record. 00:23:35.861 --> 00:23:37.511 But I don't know. 00:23:37.511 --> 00:23:39.761 I'm not optimistic based on things I've read. 00:23:40.301 --> 00:23:48.371 Um, but you know, in terms of the age of ai, you know, it's still, well, it's in, in some forms. 00:23:48.371 --> 00:23:54.821 It's been around for 50 years, but it's been used in volume for, what, five years? 00:23:55.601 --> 00:23:55.991 So. 00:23:56.396 --> 00:23:56.816 Yeah. 00:23:57.566 --> 00:23:59.486 Ish, five-ish years. 00:24:00.956 --> 00:24:01.256 Hmm. 00:24:01.286 --> 00:24:04.076 So there's, there's different ways of doing it. 00:24:04.136 --> 00:24:22.046 Uh, one, one that comes to mind is that you could have an index of students and their names and maybe their student ID, and then some other kind of an identifying number. 00:24:22.046 --> 00:24:25.201 And then when you import your training data. 00:24:26.066 --> 00:24:32.066 You don't import the student ID or the student's name, but this other index value that points back to it. 00:24:32.846 --> 00:24:42.026 And if a student wants to be forgotten, I know it's a bad business case, but if, if a student wants to be forgotten, you can keep the roster. 00:24:42.296 --> 00:24:46.796 But all you do is in this intermediate index file, you just delete the record. 00:24:46.826 --> 00:24:51.116 So now what happens is in your AI system, you've got a pointer. 00:24:51.506 --> 00:24:54.356 That that won't resolve and point you back to a student. 00:24:54.356 --> 00:24:56.816 It points back to, well, this was deleted. 00:24:57.206 --> 00:24:57.326 Yeah. 00:24:57.331 --> 00:24:59.366 And so you won't know who it was. 00:24:59.846 --> 00:25:08.666 But depending on what the other data points are that were imported for that record, you know, you still got have that re-identification risk. 00:25:09.536 --> 00:25:09.986 Yeah. 00:25:10.076 --> 00:25:19.196 You know, that's something that I personally have done where we've had this district student id, we've had the student name, and then we've had the. 00:25:20.111 --> 00:25:34.691 Jethro's school ID and I import just that Jethro School id, which is a number that I just, I just took all the kids in in, in Excel, and I just did number one for the first kid and then down to 560 for that. 00:25:34.691 --> 00:25:35.651 You need your own index. 00:25:35.651 --> 00:25:36.611 Yeah, yeah, exactly. 00:25:36.971 --> 00:25:39.786 And so that became the unique identifier for me. 00:25:40.471 --> 00:25:45.811 And I could always go back and find out who that kid was when I needed to find out who they were. 00:25:46.141 --> 00:25:53.131 But the system didn't understand who they were, anybody else looking, couldn't understand who they were and couldn't see who they were. 00:25:53.131 --> 00:25:56.341 And you had to have the, the key to make. 00:25:57.071 --> 00:25:59.951 To unlock the data that that made sense for them. 00:26:00.371 --> 00:26:00.911 Right. 00:26:01.391 --> 00:26:10.331 But the problem here is that a lot of companies are, are diving into the deep end of the AI pool right away without knowing these little things. 00:26:10.631 --> 00:26:10.991 Yeah. 00:26:11.111 --> 00:26:17.081 Only, and, and some of 'em are gonna get into trouble and some of 'em are gonna have to go, oh, well that didn't work. 00:26:17.081 --> 00:26:18.461 We're gonna have to start over. 00:26:18.491 --> 00:26:18.941 Okay. 00:26:18.971 --> 00:26:26.231 AI Project 2.0, let's try it again With lessons learned and um. 00:26:27.131 --> 00:26:29.591 Well, and, and I think that's gonna happen a lot. 00:26:29.741 --> 00:26:33.911 Um, it to all kinds of different tools and, and what people are using. 00:26:34.421 --> 00:26:38.981 And eventually I believe we're gonna find a way to manage that. 00:26:39.281 --> 00:26:46.601 But I don't think that anybody is, uh, anybody has the need to do that just yet. 00:26:47.081 --> 00:26:50.651 And that is a, an area that is concerning because. 00:26:51.101 --> 00:26:55.031 Uh, the damage is, we're not going to be able to go back. 00:26:55.031 --> 00:27:08.471 And when we look at, for example, the, um, the, the current lawsuit of copyright owners against OpenAI and Anthropic, um, I think Anthropic just paid like a billion dollars or something. 00:27:08.621 --> 00:27:13.061 I'm, I am in that, I am a member of that class. 00:27:13.211 --> 00:27:19.781 I was gonna say, I have had to file a lot of paperwork because many of my books are. 00:27:20.591 --> 00:27:22.091 A part of that class. 00:27:22.091 --> 00:27:42.131 So yeah, I, someone tipped it off to me, although I was gonna find out anyway, but someone, someone tipped me off early to it maybe four months ago, and so I got on board and yeah, and started doing my homework and I'm in the class and they'll be paying that out next year. 00:27:43.466 --> 00:27:48.656 Um, and there are several other lawsuits still pending and probably some have settled now. 00:27:48.686 --> 00:27:52.436 Like, um, Getty Images is suing open ai. 00:27:52.556 --> 00:27:52.946 Yeah. 00:27:53.156 --> 00:27:55.886 I mean, and I don't have to tell you why, right? 00:27:55.916 --> 00:27:56.096 Yeah. 00:27:56.186 --> 00:28:09.776 And then the music industry has been suing somebody, um, one of the, one of the AI companies, uh, because, you know, this is another, you know, collision area here Ha. 00:28:10.016 --> 00:28:11.126 Having to do with. 00:28:11.981 --> 00:28:15.011 You know what constitutes fair use of copyright content. 00:28:15.281 --> 00:28:15.551 Yeah. 00:28:15.551 --> 00:28:22.421 In any form, you know, whether it's a book or performance art or something else. 00:28:22.511 --> 00:28:30.206 Uh, and so, you know, copyright law is, needs to address this and it doesn't. 00:28:30.971 --> 00:28:31.331 Yeah. 00:28:31.391 --> 00:28:31.721 Yeah. 00:28:32.051 --> 00:28:32.411 Yep. 00:28:32.891 --> 00:28:34.661 And, and that's gonna take time too. 00:28:34.661 --> 00:28:49.721 But the other, the other part of that is that content creators like Disney can partner with open AI and in this, on the same day, send a cease and desist letter to, um, to Google. 00:28:50.081 --> 00:28:59.531 But then the question becomes, going back to what we were talking about before, how does Google take out any Disney related references from. 00:29:00.221 --> 00:29:10.151 They're from their training data and I, I can't imagine that they even understand how to get rid of all that stuff that's already in there. 00:29:10.361 --> 00:29:13.061 Yeah, I mean that's, that's, that's the problem. 00:29:13.061 --> 00:29:18.371 And, and that was the risk they took when they decided to train their systems with copyright data. 00:29:18.611 --> 00:29:23.231 I am just so sure that they talked about it. 00:29:23.636 --> 00:29:29.396 In the planning stages, like, okay, we wanna build this giant AI system, what are we gonna train it on? 00:29:29.546 --> 00:29:30.386 Everything. 00:29:30.386 --> 00:29:30.446 Yeah. 00:29:30.746 --> 00:29:32.006 What about copyrights? 00:29:32.216 --> 00:29:33.836 Oh, we'll worry about that later. 00:29:34.106 --> 00:29:34.466 Yep. 00:29:35.276 --> 00:29:35.636 For sure. 00:29:36.176 --> 00:29:40.616 That just absolutely seems to be what, what path they 00:29:40.616 --> 00:29:40.976 took 00:29:41.036 --> 00:29:41.366 and, 00:29:41.366 --> 00:29:50.036 yeah, and, and to, to the point exactly that we as educators, when we adopt this stuff, we have to. 00:29:50.501 --> 00:30:00.461 Think about those things right now, because if we do something to damage a kid, for example, um, we're, we're gonna be held responsible for that. 00:30:00.461 --> 00:30:09.071 And, and we, we have to be prepared to, to manage that and deal with it in a way that we don't even understand how it could be possible. 00:30:09.491 --> 00:30:10.781 Right, right. 00:30:10.781 --> 00:30:19.121 Well, and that, what that comes down to, and, and privacy laws have been testing this for, oh, I don't know, 15 years. 00:30:19.721 --> 00:30:26.381 Or more, oh, actually, probably 20 years is what constitutes harm. 00:30:26.891 --> 00:30:26.981 Mm-hmm. 00:30:27.221 --> 00:30:29.261 What does it mean when harm occurs? 00:30:29.531 --> 00:30:37.691 Um, there have been a number of big security breaches that have occurred where massive numbers of. 00:30:39.056 --> 00:30:43.346 PII have been breached because some company wasn't paying attention. 00:30:43.346 --> 00:30:43.676 Right. 00:30:43.676 --> 00:30:50.246 And some, you know, cyber criminals came in and stole all this data and either used it or published it or sold it. 00:30:51.266 --> 00:30:56.126 And there have been class action lawsuits or, and non-class action lawsuits. 00:30:56.516 --> 00:31:00.746 And the judge says, where's the harm? 00:31:00.956 --> 00:31:04.826 How, you know, to the plaintiffs whose PII has been stolen? 00:31:04.946 --> 00:31:06.206 He says, show me. 00:31:06.296 --> 00:31:08.366 How have you been harmed by this? 00:31:08.786 --> 00:31:09.236 Yeah, 00:31:09.506 --> 00:31:21.206 well, you know, it's an invasion of your privacy, but where's the harm and lawsuits have been dismissed because just because someone steals your identity doesn't mean you've been harmed. 00:31:21.206 --> 00:31:23.606 You've only been harmed when they do something with it. 00:31:23.606 --> 00:31:27.146 Like open credit cards in your name and run 'em up and then not pay 'em. 00:31:27.746 --> 00:31:27.926 Yeah, 00:31:27.926 --> 00:31:33.356 then the harm comes, but then, you know, associating that with that breach over there. 00:31:33.566 --> 00:31:33.776 Yeah. 00:31:34.016 --> 00:31:34.436 Good luck. 00:31:34.766 --> 00:31:35.726 Yeah, no kidding. 00:31:35.726 --> 00:31:35.906 Good luck. 00:31:35.996 --> 00:31:41.336 And not to mention when you put everything out publicly on social media anyway, you know, oh yeah. 00:31:41.336 --> 00:31:50.816 And then that makes it even more difficult to say, you know, you, yes, maybe the data was, was breached and, and they got this information. 00:31:51.506 --> 00:31:57.266 But I could also spend 10 minutes Googling you and get all this information as well. 00:31:57.476 --> 00:31:57.866 Oh yeah. 00:31:57.956 --> 00:32:11.816 So it's, it's not like that is, you know, and, and that's a, a sickening feeling, I imagine, to, to think that you have really been harmed and then have a judge say, sorry, there's no evidence of harm here. 00:32:11.816 --> 00:32:13.796 That's, that's gotta really stink. 00:32:13.916 --> 00:32:17.641 But at the same time, you know, there's, there's some reality to that also. 00:32:18.716 --> 00:32:23.336 Yeah, I mean, you know, feeling uncomfortable does not mean you're a victim of a crime. 00:32:23.336 --> 00:32:23.396 Yeah. 00:32:23.606 --> 00:32:27.596 It might mean that you may become a victim of a crime, but Yeah. 00:32:27.926 --> 00:32:29.216 It doesn't mean you will. 00:32:29.786 --> 00:32:36.806 And until you do, I mean, you know, un until we have a department of, of pre-crime, um, know the reference. 00:32:36.806 --> 00:32:36.896 Right? 00:32:36.986 --> 00:32:37.256 Yeah. 00:32:37.526 --> 00:32:37.766 Exactly. 00:32:37.766 --> 00:32:42.776 Never hit then, you know, and, and I'm not sure I want that. 00:32:43.256 --> 00:32:44.306 No, definitely not. 00:32:45.296 --> 00:32:45.536 Yeah. 00:32:45.536 --> 00:32:51.986 Uh, 'cause, you know, people can, can commit crimes in their, in their heart and their mind and, and never carry them out, you know, and 00:32:52.046 --> 00:32:52.466 Yeah. 00:32:52.676 --> 00:32:53.366 Are they guilty? 00:32:54.026 --> 00:32:54.596 I know, I know. 00:32:55.616 --> 00:32:56.006 Yeah. 00:32:56.186 --> 00:33:10.166 Um, I, my, my thought on that briefly is if they can commit the crime in their head and heart, then they can also, uh, uh, seek forgiveness of that crime in their head and heart as well. 00:33:10.166 --> 00:33:10.646 So. 00:33:10.856 --> 00:33:14.036 If, if it works that way, then it's gotta work the other way too. 00:33:14.066 --> 00:33:17.126 So, uh, we do have a question from Kenny. 00:33:17.126 --> 00:33:22.256 Oh, he asks, uh, I think we've already an answered the first question. 00:33:22.256 --> 00:33:23.876 Do the AI companies give a crap? 00:33:24.236 --> 00:33:26.936 And I think we'd say probably not about most of this. 00:33:27.506 --> 00:33:29.036 Uh, he's got, 00:33:30.326 --> 00:33:36.206 what do you think they, it depends on their exit strategy, but certainly for any of these companies, I mean, if they wanna. 00:33:36.911 --> 00:33:42.791 If they want to get big and the founders wanna get rich, then that means they gotta stay in business for a little while longer. 00:33:42.971 --> 00:33:43.451 Yes. 00:33:43.511 --> 00:33:43.601 Mm-hmm. 00:33:43.841 --> 00:33:47.951 Um, but you know how companies are, uh, some of them. 00:33:50.681 --> 00:33:53.141 Are are, are not altruistic. 00:33:53.141 --> 00:34:04.151 They, they care about themselves and they care about, you know, and, and to them, the exit strategy is that the founders are gonna make a billion dollars a piece and, and go, you know, yeah. 00:34:04.151 --> 00:34:07.661 Live in, on the Riviera, uh, or, you know, wherever. 00:34:08.141 --> 00:34:10.391 Um, and they, and they don't care what happens. 00:34:10.571 --> 00:34:10.941 Um, mm-hmm. 00:34:11.816 --> 00:34:15.806 But you know, their companies and, and their founders are all over the place. 00:34:15.836 --> 00:34:19.376 Um, I mean, they will need to care because. 00:34:20.321 --> 00:34:26.561 You know, there are some laws where directors, as in board directors are personally accountable when things go wrong. 00:34:26.891 --> 00:34:27.251 Mm-hmm. 00:34:27.761 --> 00:34:28.991 Yeah, exactly. 00:34:29.501 --> 00:34:36.611 Um, so I, I would say for the most part, they, they care so long as they can continue doing what they're doing. 00:34:36.936 --> 00:34:42.606 And if it becomes a thing where they can't continue what doing what they're doing, then they will care. 00:34:42.666 --> 00:34:45.816 And yeah, I mean that's just the, the sad reality. 00:34:46.266 --> 00:34:52.866 So Kenny also asks, how does it impact what schools should be doing this AI as a whole? 00:34:53.226 --> 00:34:57.936 How like, should we even be teaching reading and basic, basic skills? 00:34:57.996 --> 00:35:01.206 Uh, do you think brick and mortar schools are gonna go away? 00:35:01.566 --> 00:35:02.941 What are your thoughts on that kind of stuff? 00:35:06.596 --> 00:35:13.586 I've never been a professional in education, you know, either, you know, K 12, you know, primary or, or mm-hmm. 00:35:15.026 --> 00:35:16.316 Uh, beyond. 00:35:16.886 --> 00:35:33.416 Um, I, I don't have expertise there, but I, I guess I, I could only say that that AI is proving already to be really impactful. 00:35:34.901 --> 00:35:37.091 Um, and that's both, you know, good and bad. 00:35:37.121 --> 00:35:43.631 It, you know, organizations and individuals can use it to do great things, but also terrible things if they're not careful. 00:35:44.351 --> 00:35:44.741 Yeah. 00:35:44.951 --> 00:35:47.171 Uh, or if they don't have a good moral compass. 00:35:47.171 --> 00:35:47.501 Right. 00:35:47.891 --> 00:35:54.941 Um, one thing though that I, that I will say, because I use AI sometimes in my work 00:35:55.241 --> 00:35:55.421 mm-hmm. 00:35:56.081 --> 00:36:00.731 And a couple of thoughts about it, um, I consider AI. 00:36:01.151 --> 00:36:04.931 As if it were a fresh out of college. 00:36:05.861 --> 00:36:09.671 Um, really eager but not so bright research assistant. 00:36:09.971 --> 00:36:10.301 Mm-hmm. 00:36:10.541 --> 00:36:21.131 Who doesn't always get things right but is, is good at, you know, fairly good at grammar and, and so forth, can, you know, can actually, you know, write some decent content even if they're kind of locked into a style. 00:36:21.521 --> 00:36:21.881 Yeah. 00:36:21.941 --> 00:36:26.561 So, so that's, that's one thought I have, but the other is that. 00:36:27.236 --> 00:36:33.656 So for me, I use AI to supplement why my work with subject matter. 00:36:33.656 --> 00:36:35.216 I'm already an expert in 00:36:35.696 --> 00:36:35.966 Yes. 00:36:35.966 --> 00:36:38.786 So when AI says something good, I recognize it. 00:36:38.906 --> 00:36:38.966 Yeah. 00:36:38.966 --> 00:36:42.086 When it's, when it gets something wrong, I recognize it. 00:36:42.506 --> 00:36:42.596 Mm-hmm. 00:36:42.836 --> 00:36:52.136 But the caution, but there's a caution here is if you start using AI to help you in an area where you lack expertise, then it might be hard to know. 00:36:52.901 --> 00:37:02.711 Unless you start doing a lot of fact checking it, it, it will often be hard to know whether whatever AI told you is correct or not. 00:37:03.131 --> 00:37:03.431 Mm-hmm. 00:37:03.851 --> 00:37:04.091 Okay. 00:37:04.091 --> 00:37:04.811 That's one thing. 00:37:05.051 --> 00:37:09.581 Now in, in the learning part, um. 00:37:10.706 --> 00:37:13.616 I think professional educators know what the words are for this. 00:37:13.616 --> 00:37:15.866 I don't, I only know what it feels like. 00:37:15.986 --> 00:37:16.406 Sure. 00:37:16.496 --> 00:37:27.566 And in my career, um, I've been a computer programmer, a software engineer, network engineer, systems engineer, security engineer, DBA IT manager. 00:37:28.401 --> 00:37:41.931 Um, security manager, security director, and with, with many, many different things I've been responsible for over the years, and I've, and my, my career has been one of continuous learning. 00:37:42.111 --> 00:37:53.661 I've, in almost every year of my career, I dove into something deep and learn something entirely new, like a new programming language or a new, uh, you know, just some new skill. 00:37:54.291 --> 00:37:54.711 Um. 00:37:55.931 --> 00:38:01.061 What I found is learning is hard and learning requires struggle. 00:38:01.451 --> 00:38:01.991 Yes. 00:38:02.051 --> 00:38:02.141 Yeah. 00:38:02.141 --> 00:38:02.201 You 00:38:02.201 --> 00:38:03.641 learn in the struggle. 00:38:04.121 --> 00:38:17.831 Now, if I could rewind my career back to the beginning, and if I had had chat GPT by my side since 1979, when I started full-time professional work, what if I had AI do those things for me? 00:38:18.911 --> 00:38:22.361 What kind of person would I have been as a result? 00:38:23.966 --> 00:38:30.986 AI would've taken the struggle off my shoulders, but then I wouldn't have personally done the learning. 00:38:31.856 --> 00:38:32.696 Exactly right. 00:38:32.696 --> 00:38:32.876 Yep. 00:38:32.876 --> 00:38:32.966 And 00:38:33.356 --> 00:38:47.246 I would have not really been an expert, but more like a messenger transferring data from an AI system over to a book page or a presentation or what, something. 00:38:49.256 --> 00:38:52.106 Well, that, that is exactly my thought. 00:38:52.106 --> 00:38:55.076 We do still need to teach all these skills. 00:38:55.166 --> 00:39:02.396 Uh, brick and mortar schools might go away, but I don't think that's because of AI specifically. 00:39:02.396 --> 00:39:07.736 I think it's because of the other things that we're doing in schools that are not serving kids and families well. 00:39:08.156 --> 00:39:13.946 And, and so that I see as, uh, definitely an area to, to be concerned. 00:39:14.546 --> 00:39:19.826 But it really comes down to the necessity for what we in education call productive struggle. 00:39:19.886 --> 00:39:26.846 Where it is struggle that is worthwhile, that teaches you something, and, and that is nece a necessity for learning. 00:39:26.846 --> 00:39:31.286 You have to go through that to be able to, to make those neural pathways 00:39:31.346 --> 00:39:32.096 stick 00:39:32.186 --> 00:39:34.436 and remember the things that you're, that you're learning. 00:39:34.646 --> 00:39:35.726 It's reinforcement. 00:39:35.756 --> 00:39:36.116 Yeah. 00:39:36.146 --> 00:39:36.356 Yeah. 00:39:36.356 --> 00:39:37.796 The learning and retention. 00:39:38.201 --> 00:39:39.911 Comes through reinforcement. 00:39:40.121 --> 00:39:40.181 Yeah. 00:39:40.301 --> 00:39:50.561 One, one little vignette of an example I can think of here is, is in the books I write, sometimes I'll have a write ai, write a passage or a short section, say a couple hundred words. 00:39:50.831 --> 00:39:51.131 Yeah. 00:39:51.491 --> 00:39:56.981 And then I'll, I'll throw the words into my document, and then I'll kind of tweak it so that it, it sounds right. 00:39:56.981 --> 00:40:05.621 What I find is that I will not have as good a recollection of having written that content as it. 00:40:06.251 --> 00:40:09.281 In the same way as if I had written it from scratch. 00:40:09.461 --> 00:40:09.761 Yep. 00:40:09.821 --> 00:40:18.731 It was easier, but there was less of me in it and, and less retention for what I had written. 00:40:19.871 --> 00:40:20.351 Yeah. 00:40:20.411 --> 00:40:32.201 And, and there's a really powerful thing that happens when you do that, that when you write your own words and or when you use Chacha PT to do it, the feel is very different. 00:40:32.231 --> 00:40:33.311 And so you want. 00:40:34.211 --> 00:40:53.111 We don't, I don't believe we know how to fully articulate that yet, but that is a key area where, you know, I can, I can have AI do something for me and it is not nearly as valuable to me or as to somebody else as when I do it myself. 00:40:53.201 --> 00:40:58.661 And I think about it like this when it comes to things like empathy and character. 00:40:59.186 --> 00:40:59.996 Things like that. 00:40:59.996 --> 00:41:08.786 What makes empathy from another human valuable is that they, they are limited in the amount of empathy they can give someone. 00:41:09.386 --> 00:41:22.436 And what makes empathy in an AI completely ridiculous and just seems Han, is that the AI never runs out of time or energy to have empathy. 00:41:22.796 --> 00:41:30.806 And, and empathy is really valuable, but what makes it valuable is the, the limit on how much of it we can give. 00:41:30.986 --> 00:41:31.676 And so 00:41:31.886 --> 00:41:33.146 Well, yeah. 00:41:33.146 --> 00:41:34.916 Also the source of it. 00:41:35.306 --> 00:41:35.546 Yeah. 00:41:36.056 --> 00:41:37.646 AI empathy is counterfeit. 00:41:38.096 --> 00:41:39.086 Yes, totally. 00:41:39.091 --> 00:41:41.846 It's, it's like, uh, you know, processed sugar. 00:41:41.846 --> 00:41:41.906 Yeah. 00:41:43.556 --> 00:41:43.856 Yep. 00:41:44.231 --> 00:41:46.676 It's, and, and that is really the issue. 00:41:46.676 --> 00:41:49.406 That's, that's to me, what it really comes down to that. 00:41:50.066 --> 00:42:00.296 AI can only mimic what is real and only we as humans can still create things that actually are real. 00:42:00.596 --> 00:42:03.326 And even like artwork, for example, or music. 00:42:03.836 --> 00:42:06.381 AI can do a much better job creating that than I can. 00:42:07.691 --> 00:42:09.941 That was never one of my skill sets. 00:42:09.941 --> 00:42:12.311 That was never one of my gifts in this life. 00:42:12.341 --> 00:42:15.281 And I've never been good at artistic things. 00:42:15.851 --> 00:42:30.011 But now I can use AI to help me be more artistic with some things, but it still lacks the, uh, the feeling and emotion of me putting myself into it. 00:42:30.041 --> 00:42:31.061 And it may be. 00:42:31.436 --> 00:42:42.056 Better produced, it may look better, but a handwritten note that I write to my wife means a lot more than a, a, an AI typed note to her. 00:42:42.446 --> 00:42:42.896 Period. 00:42:42.896 --> 00:42:43.406 End the story. 00:42:43.406 --> 00:42:43.496 Right? 00:42:43.501 --> 00:42:52.346 Or, or, or what if you hired an AI agent to watch your texts and auto reply, and what if you wife is going, oh, I'm feeling really down today. 00:42:52.346 --> 00:42:55.376 And then your AI agent goes, oh, I'm sorry to hear that. 00:42:55.376 --> 00:42:56.726 Blah, blah, blah, blah, blah, blah, blah. 00:42:56.756 --> 00:42:57.086 You know? 00:42:57.086 --> 00:42:59.156 And then she might think, oh, he really cares. 00:42:59.156 --> 00:43:00.506 And, and Jethro's going. 00:43:01.931 --> 00:43:02.831 Did I say that? 00:43:03.131 --> 00:43:06.401 Yeah, I was, I was out chopping wood when I supposedly said that. 00:43:06.401 --> 00:43:06.761 Yeah. 00:43:07.196 --> 00:43:07.486 Yeah. 00:43:07.491 --> 00:43:08.231 It's counterfeit. 00:43:08.561 --> 00:43:08.891 Yeah. 00:43:08.891 --> 00:43:10.451 I didn't know you were struggling today. 00:43:11.021 --> 00:43:11.861 That would be something. 00:43:12.401 --> 00:43:15.101 Alright, well, uh, Peter, this has been awesome chatting with you. 00:43:15.101 --> 00:43:16.391 Thank you so much for your time. 00:43:16.391 --> 00:43:16.481 Sure. 00:43:16.571 --> 00:43:17.531 And for sharing. 00:43:17.591 --> 00:43:18.011 Um. 00:43:18.896 --> 00:43:27.116 Once again, the book that he was talking about before was the A IGP, artificial Intelligence Governance Professional Study Guide. 00:43:27.656 --> 00:43:30.356 Um, and so, uh, go check that out. 00:43:30.416 --> 00:43:32.336 And Peter, thanks so much for being here today. 00:43:32.336 --> 00:43:32.966 I appreciate it. 00:43:33.266 --> 00:43:33.446 Yeah. 00:43:33.446 --> 00:43:34.046 Hey, thanks. 00:43:34.046 --> 00:43:35.606 Uh, thanks for putting me on Jethro. 00:43:35.606 --> 00:43:36.266 Really appreciate it. 00:43:36.266 --> 00:43:37.226 Yeah, my pleasure. 00:43:37.436 --> 00:43:37.646 Enjoy. 00:43:37.651 --> 00:43:38.096 Have a good one. 00:43:38.726 --> 00:43:38.936 Yep, 00:43:38.996 --> 00:43:39.176 you too. 00:43:39.176 --> 00:43:39.461 We'll see 00:43:39.461 --> 00:43:39.541 ya.