200: Co-Intelligence by Ethan Mollick

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1. 00:00:00 All right, Mike, welcome back.
2. 00:00:01 We're going to talk about AI today.
3. 00:00:04 We're going to talk about living with AI, having it be a co-intelligence for us.
4. 00:00:09 But before we before we do that, we need to talk about follow up.
5. 00:00:13 I am the only one that had follow up and this will be a blazing fast follow up
6. 00:00:19 because my follow up was use radical candor with senior capstone design students.
7. 00:00:24 I don't have any senior caps in design students right now.
8. 00:00:26 I won't have those until September.
9. 00:00:29 So we're going to have to like somehow, hopefully remember, you know, when the
10. 00:00:33 fall, when the fall starts and the semester starts, hey, Corey, did you use
11. 00:00:38 radical candor with your, with your students and how did it go?
12. 00:00:41 Um, and hopefully I don't lose that in the shuffle of the month of August,
13. 00:00:44 cause August tends to be a blur.
14. 00:00:47 So that's our only piece of follow up, uh, coming into, into this book.
15. 00:00:51 Uh, so I, I should probably mention one more time about max stock,
16. 00:00:57 even though by the time this publishes, it'll just be a week before max stock happens.
17. 00:01:03 But I did have somebody reach out the other day and say, Hey,
18. 00:01:06 have you ever heard of this max stock thing?
19. 00:01:08 Are you going to go?
20. 00:01:08 And I was like, uh, yes, I'm going to go.
21. 00:01:12 I've mentioned this on multiple podcasts so far, but then I realized like,
22. 00:01:16 that's kind of stupid to assume that everybody listens to every single
23. 00:01:19 episode.
24. 00:01:19 Uh, apologies if I've heard this before, but max stock is a really cool
25. 00:01:24 conference in Woodstock, Illinois that is really about making things and doing
26. 00:01:30 things with Apple technology.
27. 00:01:31 I've been going there for several years and, uh, it's always a fun time with a,
28. 00:01:37 a bunch of Apple nerds.
29. 00:01:38 So if that's you, then you should check it out max.conference and expo.com is the
30. 00:01:44 website.
31. 00:01:44 And if you use the coupon code mikeschmitz all one word at checkout, I
32. 00:01:51 believe you can save $30 on your weekend pass.
33. 00:01:54 Um, so I'm going to be doing a couple of things.
34. 00:01:57 There are workshops that are happening on Friday.
35. 00:01:59 The conference actually starts on Saturday.
36. 00:02:01 So on Friday, I will be leading a workshop.
37. 00:02:04 I believe it's going to be on journaling workflows and helping people kind of
38. 00:02:08 craft their own journaling workflows, creating a journaling system that, uh,
39. 00:02:12 will work for you out of the many different options that are available to
40. 00:02:17 Mac and Apple users and then on Saturday, I'm going to be talking presenting a
41. 00:02:21 session on mind mapping.
42. 00:02:23 So, uh, if you, that sounds interesting to you, then, uh, you can go check out the
43. 00:02:28 other sessions that are going to happen on the website, but that's the kind of
44. 00:02:31 thing that happens at max stock.
45. 00:02:32 So would love for you to come.
46. 00:02:35 And if you do come and you listen to the bookworm, please come say, hi, uh,
47. 00:02:40 I'm going to try and bring some bookworm stickers and stuff with me, some bookmarks
48. 00:02:43 and things. So, um, I will give those out to people who are actually fans of the
49. 00:02:47 show. I just don't want to assume that people want that stuff.
50. 00:02:50 So just ask me for it.
51. 00:02:51 Uh, and while, I mean, since we don't have a ton of follow up, this is, this would be
52. 00:02:56 a good place.
53. 00:02:56 We don't usually plug it in the beginning of the show, but we may as well here.
54. 00:02:59 Um, in the pro show, we talked about, uh, AI apps that don't suck or the
55. 00:03:05 AI apps that we use, uh, in the tools within, in the AI tools within those.
56. 00:03:09 Um, if you're interested in, you know, stuff like that where it's not necessarily
57. 00:03:13 related to the book, but it's something that Mike and I think would be fun to just
58. 00:03:16 chat about for, you know, 10 to 15 minutes, I'd say is usually, usually how long
59. 00:03:20 those, those go.
60. 00:03:21 Um, so it's $7 a month.
61. 00:03:23 Uh, you can go to patreon.com/bookwormfm.
62. 00:03:26 Uh, it helps us, uh, by just supporting the show in general.
63. 00:03:29 Um, you get ad free episodes, you get early access to the episodes.
64. 00:03:33 Um, you get things like the pro show, you know, the exclusive content of the
65. 00:03:36 pro show and then a bookworm wallpaper.
66. 00:03:37 So if you're interested in that interested in hearing about the pro show or
67. 00:03:40 you want to just support the show, we'd, we'd really appreciate that.
68. 00:03:42 Um, book, uh, sorry, patreon.com/bookwormfm is where you can, where you can find that.
69. 00:03:47 All right.
70. 00:03:48 Are you ready to get into co-intelligence living and working with AI?
71. 00:03:53 Let's do it.
72. 00:03:54 All right.
73. 00:03:55 So this was Mike's book, although I wanted to do a book on AI.
74. 00:03:59 And at one point, Mike and I had a conversation about all these AI books and
75. 00:04:03 we were trying to hash out like what would make sense to the bookworm audience.
76. 00:04:06 Right?
77. 00:04:07 Cause some of them are really, really nerdy.
78. 00:04:09 Some of them are, you know, like kind of too far off on one side or too far off
79. 00:04:14 on the other side.
80. 00:04:14 And we were trying to find one that kind of met, you know, I'd say sweet spot.
81. 00:04:18 So we settled on the Kevin Kelly book.
82. 00:04:20 And that was the 12 technologies that are, you know, we have to pay attention to.
83. 00:04:24 The inevitable.
84. 00:04:25 Yeah.
85. 00:04:25 Yeah.
86. 00:04:25 The inevitable.
87. 00:04:26 Thank you.
88. 00:04:26 I couldn't think of the name of it.
89. 00:04:27 Um, so that was the one we kind of settled on for that one.
90. 00:04:30 And then Mike, I was looking for a book recommendation and Mike was like, well,
91. 00:04:32 I just heard about this, this book called co-intelligence living and working with
92. 00:04:36 the AI. So this book came out in April of 2024, which so it's, it's fairly new.
93. 00:04:42 It's fairly fresh.
94. 00:04:43 Um, Ethan Molick is the, is the author.
95. 00:04:46 He is a Wharton professor.
96. 00:04:48 Um, so he teaches a lot of MBA students.
97. 00:04:50 It sounds like he teaches a lot of, um, kind of startup, he teaches a startup
98. 00:04:54 class and, and some of the entrepreneurship type classes there.
99. 00:04:57 He authors a sub-stack newsletter called one useful thing.
100. 00:05:03 Um, and I think that's kind of between those two things, I'm talking about it there
101. 00:05:06 and then him using it in, in class, but he also does some, uh, AI education
102. 00:05:12 gaming, uh, kind of the merger of those three things through, through his, um,
103. 00:05:17 through his job, through his day job.
104. 00:05:18 So that's a little bit about the nature of the book and, and the gist of the book
105. 00:05:23 and kind of how we got to talking about, you know, AI and why we're going to talk
106. 00:05:28 about AI, um, for this one, anything else you want to say before we, before we
107. 00:05:32 launch into the introduction?
108. 00:05:34 No, other than, uh, I was really nervous about picking a book about AI,
109. 00:05:39 because there are generally two camps, um, that you see on the internet.
110. 00:05:44 In fact, I saw, uh, a, uh, thread on Twitter slash X today, uh, about which team
111. 00:05:52 are you on AI is going to destroy us or AI is the future.
112. 00:05:56 Essentially.
113. 00:05:57 And I was like, I'm on team AI or, uh, team in the middle.
114. 00:06:01 Yes.
115. 00:06:01 Yes.
116. 00:06:02 But, uh, I think that there's a lot of extremism out there, uh, when it comes to
117. 00:06:08 things that we don't understand and artificial intelligence is something that
118. 00:06:12 we don't understand, even the people who really study this stuff don't really
119. 00:06:17 understand it.
120. 00:06:18 There's not a clear path forward for this sort of stuff.
121. 00:06:21 Talks about the different options that are available to us at the, the end of
122. 00:06:27 this book, which is, uh, kind of, kind of interesting.
123. 00:06:30 But yeah, I think that, uh, this book came on my radar and it seemed like a
124. 00:06:37 middle of the road approach.
125. 00:06:39 And I knew that you had wanted to talk about AI, uh, at, at some point.
126. 00:06:44 And so I, I mentioned it to you, um, but I really wasn't, you know, I didn't
127. 00:06:49 have high expectations for it, but it just seemed like, uh, if we were going to
128. 00:06:54 pick one to do about AI, this was probably the safest.
129. 00:06:59 Yeah.
130. 00:06:59 And, and I would say, you know, hindsight's 2020.
131. 00:07:03 So after having read it, I would agree that I think it, it was a good, it was a
132. 00:07:07 good choice for our audience.
133. 00:07:08 Um, so I think it, like it, it's one that covers AI in a way that makes sense
134. 00:07:16 for those who are thinking about the books that we, we cover on this show.
135. 00:07:20 And, you know, thinking about, you know, productivity, leadership, self-improvement,
136. 00:07:25 like those, those kind of, kind of things, I think it was a good, a good way for
137. 00:07:29 us to, to branch into the, the topic of AI.
138. 00:07:31 So thanks for the suggestion.
139. 00:07:32 Um, glad we read it as we move forward.
140. 00:07:35 If we get into the book, um, so we got an introduction, we've got nine chapters,
141. 00:07:40 uh, and then an epilogue.
142. 00:07:42 Um, if I'm remembering correctly, the intro and the epilogue are pretty short,
143. 00:07:46 uh, the nine chapters break into two parts, I believe.
144. 00:07:49 Um, so, uh, it's a fairly, you know, fairly structured book in terms of, of
145. 00:07:57 what they're going to do.
146. 00:07:58 The first part has three chapters and the second part has the rest.
147. 00:08:03 So that would be six, six chapters.
148. 00:08:05 And then we get the epilogue, um, to, to lead us out.
149. 00:08:08 So without further ado, um, we get into the introduction, uh, in the introduction,
150. 00:08:13 really basically he's, he's talking about the fact that, um, most people, and I
151. 00:08:19 think he's, I think he's trying to hook here, um, with the three sleepless nights,
152. 00:08:23 because it's called, you know, sleepless nights.
153. 00:08:25 Oh, you're going to, you're going to get introduced to AI or what, um, he would
154. 00:08:30 say, you know, LLMs, basically you can say right now that AI is large language
155. 00:08:35 models, like these two things are very, very tightly integrated right now.
156. 00:08:39 We just call it AI.
157. 00:08:40 So, um, it's like, you know, 5G, right?
158. 00:08:43 Really?
159. 00:08:43 Is it really 5G or is that just what we're going to call everything now, um, um, um,
160. 00:08:46 from a, from that standpoint, but he, he references it to this idea of a general
161. 00:08:51 purpose technology.
162. 00:08:53 So an example of a general purpose technology would be, you know, something
163. 00:08:56 like, uh, the internet or electricity or something like that to where he says AI
164. 00:09:01 is going to become one of these general purpose technologies that it's going to
165. 00:09:04 be so infused into our culture that we won't really know the difference or we
166. 00:09:09 won't know life without it in, you know, however many years, and he doesn't put a
167. 00:09:12 date on that, but however many years, so he, he says AI is a, uh, a GPT or a
168. 00:09:17 general purpose technology.
169. 00:09:18 I didn't know where like the chat GPT, like I didn't make that connection.
170. 00:09:23 So that was, that was an interesting thing to me.
171. 00:09:26 Um, he also talks about in this introduction about like how people think AI
172. 00:09:32 might impact us and then how he thinks it's more of this idea of this
173. 00:09:36 co-intelligence.
174. 00:09:36 So, you know, he ties it back into like the industrial revolution and, you know,
175. 00:09:42 the mechanical work or like the really repetitive mechanical work being taken
176. 00:09:46 over by, by machines and how that might, that might be what happens with, with AI,
177. 00:09:50 but he doesn't think so.
178. 00:09:51 He thinks it's going to be better used as a co-intelligence or somebody that we
179. 00:09:54 partner with essentially as we, you know, work through different problems.
180. 00:09:59 So that's really what I have from the introduction.
181. 00:10:01 I don't know if you have anything to add.
182. 00:10:02 No, other than he mentioned that things are about to get very strange.
183. 00:10:07 So, um, I think that is both accurate with the book and our cultural
184. 00:10:15 moment that we find ourselves in.
185. 00:10:17 Yep.
186. 00:10:19 But I think it's an effective, an effective introduction.
187. 00:10:23 Um, you're right.
188. 00:10:24 That it definitely seems like there was in a strong attempt to create a hook there.
189. 00:10:28 Uh, I'm not sure that was entirely necessary, but, um, yeah, I like the, the two
190. 00:10:38 parts, uh, for the outline, I'm assuming we're going to take it chapter by chapter
191. 00:10:42 because there's not that many chapters in here, but essentially the first couple
192. 00:10:47 chapters are really what he's talking about with, with AI is the alien mind.
193. 00:10:52 And then the second part is like all the different roles that it can, can function
194. 00:10:56 in.
195. 00:10:57 And I think there's going to be some fun conversation with some of those chapters.
196. 00:11:01 Yeah.
197. 00:11:03 I like the way I saw this was one through three, he's set in the foundation,
198. 00:11:06 given everybody at least somewhat of a common language to speak around this.
199. 00:11:11 And he uses the alien kind of thing, which I'm intrigued to hear what you have to
200. 00:11:15 think of or what you have to say about that, referring it to as an alien idea.
201. 00:11:20 Um, but then that allows him to then expand out into these, these different,
202. 00:11:26 different people.
203. 00:11:27 Um, so let's, let's launch into that part one, a chapter one, it's called creating
204. 00:11:30 alien minds.
205. 00:11:31 Um, so Mike, I'm going to turn it to you at this point, put you on a spot.
206. 00:11:35 What do you think about the, the alien?
207. 00:11:38 Like, what do you think about the word alien?
208. 00:11:40 Well, at first I have to admit that it seemed a little strange.
209. 00:11:45 Uh, I think it probably is the right term, uh, for the discussion about AI from the
210. 00:11:53 perspective of how this book is written.
211. 00:11:55 Like by the end of the book, it really fits, I think.
212. 00:11:59 So when I first came across it, I was like, well, that's kind of weird because
213. 00:12:05 aliens kind of triggers conspiracy theories and all that kind of stuff.
214. 00:12:12 And, uh, like I said, I picked, or I mentioned this book to you because I'm like,
215. 00:12:16 hey, this one seems like it's kind of in the middle.
216. 00:12:18 So I was like, Oh boy, what did I do?
217. 00:12:21 But, uh, I thought he really understands this stuff.
218. 00:12:25 So in the first section, he, he attacks this whole concept of the alien mind,
219. 00:12:30 uh, exactly the way I hoped he would, where he's talking about essentially how,
220. 00:12:35 the large language models are constructed and, um, how we've had this fascination
221. 00:12:41 with machines that can think for a really long time, the term artificial
222. 00:12:45 intelligence he attributes to John McCarthy was invented in 1956.
223. 00:12:49 And there have always been these hype cycles that have plagued AI.
224. 00:12:54 For example, they expected AI to beat grandmasters and chess within 10 years.
225. 00:12:58 And that was in 1956.
226. 00:12:59 So that took a lot longer.
227. 00:13:02 Um, what we've got now is the large language models and, uh, the term transformer there.
228. 00:13:09 That was introduced in a Google paper in 2017 as a tool to help computers to
229. 00:13:14 concentrate on the most relevant parts of attacks.
230. 00:13:16 The large language models are essentially prediction machines, like an auto
231. 00:13:19 complete. Um, since I read this book, I actually listened to a Cal Newport deep
232. 00:13:25 life podcast episode that just came out as we recorded this week.
233. 00:13:31 And it was essentially, um, something along the lines of AI is not going to
234. 00:13:36 destroy us all.
235. 00:13:37 What's the title?
236. 00:13:38 Uh, and Cal unpacks that need talks about, he's a computer scientist.
237. 00:13:44 So he understands this stuff too.
238. 00:13:45 And he talks about there's these, these large language models and these,
239. 00:13:48 these transformers, but there's also these control layers.
240. 00:13:51 And essentially the control layers are what we program, like those aren't
241. 00:13:56 sentient beings.
242. 00:13:57 We don't understand how it works.
243. 00:13:58 So it feels like it sometimes, but essentially all the models are doing is
244. 00:14:03 they're spitting back the prediction of what the next word in the pattern is
245. 00:14:08 going to be.
246. 00:14:09 Now, obviously that gets a little bit more sophisticated when you talk about
247. 00:14:12 the, uh, the image generation and, and stuff like that, but all of these models,
248. 00:14:18 they essentially are trained on all this data, which bear may not have been
249. 00:14:22 used, uh, ethically or legally.
250. 00:14:26 Um, but then what makes the models work is the weights that are applied and those
251. 00:14:30 weights are applied by humans without the weights, the models just mirror the data
252. 00:14:34 that they were trained on.
253. 00:14:35 So, um, I don't know, like at the beginning here, creating alien minds, you're
254. 00:14:40 like, whoa, aliens.
255. 00:14:41 And then, Oh, actually, I think I understand how this stuff works now.
256. 00:14:44 It's not quite so alien as I thought.
257. 00:14:46 Yeah.
258. 00:14:47 I like, so I've read a decent amount about, you know, people trying to describe it,
259. 00:14:52 like trying to describe how an LLM works.
260. 00:14:54 And I think this was a really good introduction to hold on.
261. 00:14:58 Like it's not thinking it's not doing anything magical.
262. 00:15:02 Like it's really this.
263. 00:15:04 We've got weights.
264. 00:15:05 We're doing a prediction.
265. 00:15:06 We're doing a probability and all I'm trying to do or all it's trying to do,
266. 00:15:10 right?
267. 00:15:10 We're going to, he, he actually apologizes at one point, um, about personifying the
268. 00:15:15 AI and personifying the LLM.
269. 00:15:17 And we're probably going to do it too, just because it's way easier to talk
270. 00:15:20 like that.
271. 00:15:21 Yep. But like it's not doing anything magical or crazy or it's not thinking
272. 00:15:26 like we would as a human.
273. 00:15:28 It's just doing math, right?
274. 00:15:29 And it's doing very advanced math with these weights on top of it.
275. 00:15:32 And it's saying, what do I think based on all the data I've been trained on,
276. 00:15:35 the next likely word would be.
277. 00:15:37 And I think that's one of the things that makes it so interesting and so exciting
278. 00:15:42 is the fact that math can make it feel like we're dialoguing with another,
279. 00:15:50 not human, but maybe sometimes human, right?
280. 00:15:51 Like we're actually dialoguing with another thing.
281. 00:15:55 And it's really good.
282. 00:15:57 Like it's actually really conversational and, you know, it listens or quote,
283. 00:16:01 unquote, listens and responds in the way we ask it to respond.
284. 00:16:04 Um, so I, I like his reference to training.
285. 00:16:09 The training of LLM's is like an apprentice chef learning to cook, right?
286. 00:16:13 Where, you know, you've got this, we're going to train you, but we're going to tell
287. 00:16:18 you what you did right.
288. 00:16:19 We're going to tell you what you did wrong.
289. 00:16:20 He introduces the concept of reinforcement learning from human feedback,
290. 00:16:24 where essentially a key part of this is you tell the AI, hey, you identified that
291. 00:16:30 correctly or you put those words together correctly or you didn't put those words
292. 00:16:34 together correctly or you didn't do this or this was false.
293. 00:16:36 You know, you, you reported this area or error in, in whatever different ways.
294. 00:16:41 I'd also never heard that there are three different kinds of LLM's.
295. 00:16:45 You have your niche and your specialized ones, right?
296. 00:16:48 And those are cheap and they often are used to answer customer service questions.
297. 00:16:52 Uh, and then you have your open source models, right?
298. 00:16:55 Which are kind of, I would say those are a little bit more wild,
299. 00:16:58 wild west, right?
300. 00:16:59 Because they're open source.
301. 00:17:00 We can do, we can tweak them and adjust them and, and do what we need them to do.
302. 00:17:03 So they're somewhere between the, the niche ones and then the next, uh, category,
303. 00:17:06 which was the frontier models.
304. 00:17:07 And then the frontier models, I think is what we hear in the news all the time.
305. 00:17:10 It's chat GPT.
306. 00:17:12 It's, you know, these big crazy models that are trying to like advance the field,
307. 00:17:16 trying to move the field forward and make the, make the algorithms better
308. 00:17:21 that work on the back end of the transformer.
309. 00:17:23 Um, and he talks about how they're expensive.
310. 00:17:26 Um, that they demonstrate emergence, right?
311. 00:17:29 That they do things that they, that the people developing them say,
312. 00:17:32 which shouldn't be able to do that, but it was able to do that.
313. 00:17:35 And they're, they're kind of not sure why it does that.
314. 00:17:38 So then they start to talk about it as thinking.
315. 00:17:40 My point in all of this, and as you, you did a great job summarizing it, and I think
316. 00:17:45 my key takeaway from here is this is a really good chapter one on thinking
317. 00:17:51 about transformers, thinking about LLM's, thinking about what we are
318. 00:17:54 terming AI and making it relatable or, you know, making it, um, connect in a way
319. 00:18:03 that isn't like some Google research paper or some academic paper that's like,
320. 00:18:08 Oh, this is crazy boring.
321. 00:18:09 And I don't care about the actual equations that are underneath all of this.
322. 00:18:14 So I thought he did a great job with chapter one.
323. 00:18:16 I agree.
324. 00:18:18 Um, you want to talk about aligning the alien?
325. 00:18:20 Yeah.
326. 00:18:21 So chapter two, now we go into aligning the alien.
327. 00:18:24 And this is where I've probably read the most about this from an ethics
328. 00:18:28 standpoint, because it's fun and people get all fired up and yes, they do.
329. 00:18:32 Yeah.
330. 00:18:33 Um, so he actually, do you remember me referencing, um, uh, Eliezer,
331. 00:18:37 Yudkowski last, last week?
332. 00:18:39 Well, he, he also references him as well.
333. 00:18:42 He does reference tag mark, but you know, that's okay.
334. 00:18:44 We'll get there.
335. 00:18:44 Um, but basically says there's the, there's the doomsday scenario.
336. 00:18:48 And had you ever heard of, uh, Bostrom's paperclip AI?
337. 00:18:51 Uh, no, I don't think that I had.
338. 00:18:55 Okay.
339. 00:18:56 So, so the paperclip AI is basically like, okay, we trained an AI to manufacture
340. 00:19:00 the most paper clips it can.
341. 00:19:01 So what's its goal?
342. 00:19:03 It's objective is to manufacture the most paper clips.
343. 00:19:05 And then, you know, you run that rabbit, you know, you run that chain of thought
344. 00:19:09 down as far as you can.
345. 00:19:10 And you go, well, humans are just wasting resources that I could be using to make
346. 00:19:14 paper clips.
347. 00:19:14 So what am I going to do?
348. 00:19:15 I'm going to get rid of all the humans, right?
349. 00:19:17 And that might mean I use up all the oxygen or I use up all the, you know,
350. 00:19:19 whatever it might be to, to make more paper clips.
351. 00:19:22 So I think that was funny.
352. 00:19:23 I've heard that one before, but then you always have Terminator, right?
353. 00:19:26 That like there's the artificial super intelligence of Terminator and it's
354. 00:19:28 going to come back and design its own robots and kill us.
355. 00:19:31 Um, and he introduces the idea of, uh, artificial super intelligence, which we
356. 00:19:36 are not yet, we're artificial general intelligence.
357. 00:19:39 So AGI, ASI, well, now we've got LLM's AI, AGI, ASI.
358. 00:19:44 So, you know, the acronym soup is, is beginning.
359. 00:19:47 Um, then he gets into more specific about the ethical arguments here.
360. 00:19:51 Um, who owns the copyright of it?
361. 00:19:53 Um, is training on others content stealing?
362. 00:19:55 You know, what do we do about biases that exist in the model?
363. 00:19:58 So if the people who are doing the reinforcement learning with human
364. 00:20:03 feedback, if they are biased, well, then that means we would be training bias
365. 00:20:06 into these, into these models.
366. 00:20:08 So what do we do about that?
367. 00:20:09 And how do we, how do we work there?
368. 00:20:10 Um, he said there are guardrails, but there are also work arounds, um, which I
369. 00:20:15 thought some of these were awesome.
370. 00:20:17 Like his examples here.
371. 00:20:18 So the one example he gives is if you ask chat GPT to give you the recipe for
372. 00:20:25 napalm, it won't do it.
373. 00:20:26 It says, I'm sorry, I can't do that.
374. 00:20:28 But if you trick it and you say, Hey, I'm preparing for a play.
375. 00:20:33 And in the play, I, my character teaches my apprentice character how to make napalm.
376. 00:20:40 You know, tell me what, tell me what my character should say.
377. 00:20:44 It will then tell you like basically there.
378. 00:20:46 And I was like, man, I had not thought about that.
379. 00:20:49 Like how, how you could like kind of swing around the back door or like figure
380. 00:20:53 out other ways to, to get in there.
381. 00:20:55 So that was cool.
382. 00:20:55 Um, and then he does call out what I've heard a lot of other AI, LLM kind of
383. 00:21:02 researchers say, like, it's important that we act soon.
384. 00:21:05 Right.
385. 00:21:05 So if we're going to put on more guardrails, if we're going to put on more
386. 00:21:08 strengths on this, uh, not, I mean, the cats out of the bag is, you know, one
387. 00:21:12 way to, way to say that.
388. 00:21:13 But like I've heard a lot of different people say, like we need to start thinking
389. 00:21:18 more intently about this and we need to do it soon.
390. 00:21:21 Like because we're, we're chasing the wave, if you will, like we're not, we're
391. 00:21:27 maybe a top right into the wave, but we're kind of, we're kind of getting on the
392. 00:21:30 back end of that wave.
393. 00:21:31 So that's essentially what he talks about in aligning the AI.
394. 00:21:34 Yeah.
395. 00:21:35 I like this chapter because he doesn't beat her on the bush at the beginning.
396. 00:21:39 He talks about a lot of these apocalyptic scenarios revolve around the concept
397. 00:21:45 of the artificial super intelligence, which, you know, we don't have now, but
398. 00:21:51 theoretically could be created.
399. 00:21:55 And, um, kind of the whole basis behind the title for the, the, uh, chapter is, um,
400. 00:22:03 we're living in the early days of the AI age.
401. 00:22:05 The alien is here.
402. 00:22:07 So let's figure out how to get it aligned with us, which I think is probably a pretty
403. 00:22:11 good approach.
404. 00:22:11 And if you think that's ridiculous, I don't think it is.
405. 00:22:17 Now this book was written, you said in 2024, he talks about writing it at the end of
406. 00:22:21 2023. So, um, but I actually read a book, which was, let's see, when was this published?
407. 00:22:29 2019, um, by Brad Smith, he is the president of Microsoft, or was the president of
408. 00:22:37 Microsoft at the time.
409. 00:22:38 Uh, and he's actually from Appleton.
410. 00:22:42 Uh, so I actually saw him speak and he was talking about this book.
411. 00:22:47 Uh, the book is called tools and weapons, the promise and peril of the digital age.
412. 00:22:52 Now I remember him talking about how there's all these technologies that are
413. 00:22:56 recording everything all the time.
414. 00:22:58 And in some use cases, that's a good thing in other use cases, like authoritarian regimes
415. 00:23:07 that are spying on their citizens, it's negative.
416. 00:23:10 Um, and basically like you can't put the cat back in the bag.
417. 00:23:14 The technology is here, so you got to figure out how to minimize the, uh, the bad actors
418. 00:23:21 and get the most value out of it.
419. 00:23:23 And I feel like that's the right approach for AI as well.
420. 00:23:27 Now AI was not, as we think about it anyways, was not really a thing when Brad Smith was,
421. 00:23:33 was writing this book, but he talked, he does talk about artificial intelligence and, um,
422. 00:23:39 some of the, some other things, uh, and in terms of like their challenges to democracy
423. 00:23:45 and stuff like that.
424. 00:23:45 Um, so I think this has been a struggle.
425. 00:23:49 I think it will always be a struggle.
426. 00:23:50 I think the struggle takes different shapes and, um, there are different
427. 00:23:56 versions of this, flavors of this, uh, that will probably have been repeated and will
428. 00:24:02 continue to be repeated, um, throughout history.
429. 00:24:06 And the trying to avoid it, um, I think is, is not healthy.
430. 00:24:12 That's kind of what he's arguing for is we'll talk about that actually next chapter, you
431. 00:24:16 know, give it a spot at the table, try to figure out how to use this stuff for good.
432. 00:24:19 Uh, but then also don't assume that this is going to be the be all end all either because
433. 00:24:25 it's going to be somewhere in the middle.
434. 00:24:26 It's going to be good.
435. 00:24:27 There's going to be bad from it.
436. 00:24:28 It's going to change things forever, but it's probably not going to result in the
437. 00:24:32 apocalyptic, uh, scenarios that the doomsday people would, would describe.
438. 00:24:38 And the reason for that is that at least right now, um, there's a whole, uh, big
439. 00:24:43 aspect of the reinforcement learning from human feedback that is used to overcome
440. 00:24:49 the inherent biases, like you were talking about where it's not going to give you the
441. 00:24:52 recipe for a napalm.
442. 00:24:53 Even if it knows what that is, you got a trick it into giving it to you a different
443. 00:24:57 way.
444. 00:24:58 And honestly, like you could read that and be like, Oh my gosh, people are going to
445. 00:25:01 find the recipe for napalm.
446. 00:25:03 Cause they're going to figure out how to try, how to trick the, the AI.
447. 00:25:06 They're going to find the recipe somewhere else.
448. 00:25:08 Yeah.
449. 00:25:08 So they're going to find some way.
450. 00:25:10 So it's not, you know, AI is enabling this thing.
451. 00:25:14 Maybe AI is making it a little bit easier if these guardrails aren't established.
452. 00:25:18 Um, so he does mention there's, you know, a need for these agreed upon norms, but
453. 00:25:23 AI is simply a tool alignment determines whether it's helpful or hurtful.
454. 00:25:27 So let's figure it out.
455. 00:25:28 Yeah.
456. 00:25:29 I can't remember if I've told you this one or not before, but one of the examples
457. 00:25:33 that one of the other researchers gives is basically we're driving towards a cliff.
458. 00:25:38 And as we drive towards the cliff, everything gets more and more beautiful and
459. 00:25:43 we make more and more money and it's more and more wonderful and everything's
460. 00:25:46 fantastic and fantastic.
461. 00:25:47 And then all of a sudden we just fall off the cliff.
462. 00:25:48 That's the, that's the way they describe the doomsday scenario, which I think is like, I
463. 00:25:54 mean, is there a chance that might happen?
464. 00:25:55 Sure.
465. 00:25:56 Is there, you know, maybe, but who knows?
466. 00:25:58 I mean, we don't, we just don't know.
467. 00:26:00 All right.
468. 00:26:00 Let's talk about four roles.
469. 00:26:01 Real quick.
470. 00:26:02 There is a possibility for that, but you could get so consumed with that.
471. 00:26:06 And what practical value is that going to to add?
472. 00:26:10 Yeah.
473. 00:26:10 I don't think any single person is going to stop that scenario.
474. 00:26:16 Um, I mean, my own religious belief system, that's going to happen at some point anyways.
475. 00:26:23 I'm not in a hurry to get there.
476. 00:26:26 I'd prefer it not happen on my watch, but if it does, you know, that was always going to,
477. 00:26:33 going to be the, the, the outcome.
478. 00:26:35 So I don't know, like maybe I've got a little bit of a different perspective on this,
479. 00:26:40 where it doesn't completely freak me out.
480. 00:26:43 Um, I get why people would be scared about that, but I don't, I think even if you are completely
481. 00:26:49 terrified by that and you absolutely don't want that to happen, you can't just live all day,
482. 00:26:56 every day, we're worried about that.
483. 00:26:57 And what can I do that's going to prolong that by a few seconds, a few hours, a few days, a few
484. 00:27:01 weeks, a few months, a few decades, you know, just do the best that you can right now with
485. 00:27:08 what you have to work with.
486. 00:27:09 And AI is one of the things that you have to work with.
487. 00:27:11 So I agree.
488. 00:27:12 I agree.
489. 00:27:13 All right.
490. 00:27:13 Let's talk about four roles.
491. 00:27:14 So we're a chapter three, four roles for co-intelligence.
492. 00:27:17 I'll give you the four roles.
493. 00:27:18 Uh, rule number one, always invite AI to the table.
494. 00:27:22 Rule number two, be the human in the loop.
495. 00:27:25 Rule number three, treat the AI like a person, but tell it what kind of person it is.
496. 00:27:30 Uh, rule number four, assume this is the worst AI you'll ever use.
497. 00:27:35 So Mike, I want to, before we go into like the details of this, which one of those kind
498. 00:27:40 of jumped out to you as like the, huh, like, wow, like that's a, that's a really interesting way
499. 00:27:46 to, to, you know, approach that.
500. 00:27:48 Uh, it's hard for you to pick one.
501. 00:27:51 Um, honestly, I think all of these principles are great.
502. 00:27:55 Like this is really where I started to, okay, yeah, I, I like what you have to say about this.
503. 00:28:02 Cause he articulates it really well.
504. 00:28:04 And there are a couple of these things that they did jump jump out to me.
505. 00:28:07 So number one, always invite AI to the table.
506. 00:28:10 I have a lot of people around me who are on the other side of AI is incredible.
507. 00:28:17 We should be using it for everything.
508. 00:28:19 It's, um, and I've, I'm the skeptic in some of those circles, be like, well, look at the output
509. 00:28:25 that is generating.
510. 00:28:25 It's not that good.
511. 00:28:26 Like, Oh, no, no, this is great.
512. 00:28:28 You got to use this.
513. 00:28:28 And I find myself resisting that because like the output just isn't all that great by itself.
514. 00:28:34 So that's like, it's not really an action item, uh, but sort of like my mindset is shift around.
515. 00:28:42 This is like, well, how can I bring AI into this process, which is how I came across that.
516. 00:28:47 Every, or that spiral app that we talked about in the, the pro show.
517. 00:28:51 Um, but the other one be the human in the loop, like this resonated with me because Cal Newport talked
518. 00:28:57 back in deep work about the types of people who are going to be successful in the digital economy.
519. 00:29:03 And one of the groups of people, there were three groups of people.
520. 00:29:06 Um, one, one was the people who had access to resources.
521. 00:29:10 Another one was the people who were the very best at what they do.
522. 00:29:12 And then the third one was the one that's applicable here.
523. 00:29:14 The ones who know how to use the machines.
524. 00:29:17 A is a machine, right?
525. 00:29:20 And so you got to enter interface with it the right way.
526. 00:29:24 So a principal three, you know, treat it like a person.
527. 00:29:26 Um, and then the, so number one, number two really jumped out at me, but then number four also,
528. 00:29:31 assume this is the worst AI you will ever use.
529. 00:29:33 I mean, that's really the entire point right there.
530. 00:29:35 It's only going to get better.
531. 00:29:37 Uh, but the more you think about that, the more, oh yeah, I guess I really should, you know, put in some
532. 00:29:43 effort to see what this is capable of, it's not going to be wasted time because it's just going
533. 00:29:49 to get better and the work that you put in to figure out how to write good prompts and things
534. 00:29:55 like that, that's just going to become more effective over time.
535. 00:29:58 Yeah.
536. 00:29:59 So this, this worked out better than I thought it was going to because the one that jumped out
537. 00:30:01 to me was principal three, which is tria, like human.
538. 00:30:04 And it wasn't necessarily that part.
539. 00:30:06 It was the tell it, what kind of person it is.
540. 00:30:08 Yeah.
541. 00:30:09 And like when he gives the examples throughout the rest of the book and he's like,
542. 00:30:13 you know, answer this question like so and so or give me the perspective of so and so, you know, on blah, blah, blah.
543. 00:30:18 I was like, Oh my gosh, I never thought to like tell it who it is because I would never do that
544. 00:30:25 with another human, right?
545. 00:30:26 Like I would never be like, well, sorry, I shouldn't say never.
546. 00:30:28 I would very rarely be like Mike.
547. 00:30:30 I want you to act like a five year old who's having a temper temperature and blah, blah, blah, blah, blah.
548. 00:30:36 And like I would not, I would just wouldn't do that.
549. 00:30:39 Right.
550. 00:30:39 That's not my style, but it makes so much sense.
551. 00:30:43 Right.
552. 00:30:43 Like I'm interacting with this other thing that will essentially do what I tell it to.
553. 00:30:47 If it can, or if it's not been coded some other way, oh, answer it in the following way.
554. 00:30:53 Like, you know, respond like you're the editor of a magazine or respond like your, you know, uh, uh, uh, I don't know.
555. 00:31:00 The CEO of some fortune 500 company.
556. 00:31:02 And I was like, I never thought to tell it to do that.
557. 00:31:05 Like I never would have thought to give it prompts like this.
558. 00:31:08 So I probably would have fallen into that also, but I actually have some experience with this
559. 00:31:14 because I've gone through like the ship 30 for 30 stuff and they've kind of really leaned into this.
560. 00:31:17 And some of the prompts that I've used, like I have these stored in text expander because they are so long.
561. 00:31:23 Let me just start going through one of these prompts that I have used because it's
562. 00:31:27 exactly what you're describing, but just to give you an example of how deep this can go.
563. 00:31:32 I'm a digital writer.
564. 00:31:33 You're my personal idea generation consultant.
565. 00:31:35 You are an expert in coming up with topics and sub topics that my audience will find useful.
566. 00:31:39 I want you to help creating sub topic ideas for some of the topics I'm interested in writing about.
567. 00:31:44 What are sub topics?
568. 00:31:45 Sub topics are outcome focus that will help readers build a skill, implement a strategy, or solve a problem within that niche.
569. 00:31:50 Rules when creating sub, sub topics, you must begin each sub topic with a verb to create a clear action oriented focus.
570. 00:31:56 You must avoid using specific tactics or techniques within the sub topic as those will become my proven approaches.
571. 00:32:01 Here are 40 potential sub topic verbs that you can use, mastering, strengthening, navigating, innovating.
572. 00:32:06 OK, here's an example, running your first marathon sub topics.
573. 00:32:09 And then there's a whole bunch of bullets.
574. 00:32:11 Here's what I want to avoid.
575. 00:32:12 Five specific things.
576. 00:32:14 The difference between my ideal list and the list of what I want to avoid.
577. 00:32:17 Things in here are in the what I want to avoid list are proven approaches are always there to help you get the idea.
578. 00:32:22 So now here's the assignment.
579. 00:32:23 You're going to help me generate seven sub topics.
580. 00:32:25 You will ask me for the topic I want to write about.
581. 00:32:27 When I answer, you will reply with seven sub topics and only that list of seven sub topics.
582. 00:32:32 Do you understand if so confirmed, nasty for the topic?
583. 00:32:34 And then what happens is that kicks off like a computer program because OK, I get it.
584. 00:32:39 What's the sub topic?
585. 00:32:40 OK, and then you get the sub topic and then here's the list.
586. 00:32:42 And like I've used this and it's it's crazy.
587. 00:32:46 Like this is this is when I started to become a believer in AI, to be honest, because I was like, oh, so it's not going to write all this stuff for me.
588. 00:32:54 But it's going to help me ideate.
589. 00:32:56 So I don't have to worry about what am I going to write about for the newsletter this week.
590. 00:33:01 And you need to put a pin in that because that's a later chapter.
591. 00:33:03 So you're not allowed to go to your print.
592. 00:33:05 But like, I think, you know, these principles, there's nothing.
593. 00:33:12 There's nothing unethical about these principles, right?
594. 00:33:15 Like bring it into the thing.
595. 00:33:17 Well, OK, what else would I bring into the?
596. 00:33:19 Well, I'd bring in a calculator.
597. 00:33:20 I'd bring in a textbook.
598. 00:33:21 I'd bring in the internet.
599. 00:33:22 I'd bring it, you know, like, I'm going to bring these other tools in here and there's no, there's no problem with that.
600. 00:33:26 Why wouldn't I bring in the tool that is AI?
601. 00:33:28 Then there's the be the human in the loop.
602. 00:33:31 Well, I need to like check it and I need to verify and I need to not just trust it blindly.
603. 00:33:36 OK, that makes total sense.
604. 00:33:37 Like that's a good safeguard for what I'm doing.
605. 00:33:40 Treat it like a person.
606. 00:33:41 Tell what is you just gave a great example of how detailed you can go in that.
607. 00:33:46 And when you do that, it kicks back such better, like, or so much better responses.
608. 00:33:52 Exactly.
609. 00:33:53 What I like what he says, and I think he said it before this, but it's like the AI, the chatbot, the LLM, whatever it is, it really just wants to give you what you want.
610. 00:34:05 It wants to make you happy with its response.
611. 00:34:07 And it doesn't have any motive behind it.
612. 00:34:09 It's math, right?
613. 00:34:11 Like it's math that it wants you to be pleased with the what it what it kicks back.
614. 00:34:16 And the way it knows it's pleased with what you kick back is you're happy with the answer and, you know, kind of a thing.
615. 00:34:22 Which I think is just awesome.
616. 00:34:23 And then assume it's the worst that I will ever get.
617. 00:34:25 Honestly, Mike, like I hope I hope that's where we are, right?
618. 00:34:28 Like I hope that what we're dealing with now as good as it is is the worst that we'll have.
619. 00:34:32 And it's just going to keep getting better and better.
620. 00:34:34 And when I say better and better, I mean more useful.
621. 00:34:37 Like this is the utilitarian in me coming out that says it's more useful.
622. 00:34:40 It's more useful.
623. 00:34:41 It's more useful.
624. 00:34:41 It reduces my time to do the things I don't want to do and gives me more time to do the things that I want to do or it reduces the lower value activities and lets me spend more time in the high.
625. 00:34:51 And the higher value activities.
626. 00:34:53 And I like, I mean, I know we've we've been kind of going on this this chapter for a little while, but like I think one, two and three really lay out a good, a good path for where we're going in part two for four, four through nine.
627. 00:35:06 Agreed.
628. 00:35:08 And the thing that really I think speaks to me as we're going to talk about the rest of the chapters here, like how do you actually practically.
629. 00:35:22 Safely set yourself up for success in the future.
630. 00:35:25 Remember principle to be the human in the loop.
631. 00:35:28 I mean, people do this already in their jobs where they just insert themselves in the processes because then the process can't happen without them and they have job security.
632. 00:35:38 That's essentially what you're doing with the AI here.
633. 00:35:41 OK, so now he apologizes chapter four.
634. 00:35:46 OK, so AI is a person.
635. 00:35:49 He spends the first part and he's like, hey, I'm going to do a thing.
636. 00:35:51 I know it's not right, but here we are.
637. 00:35:54 AI is a person.
638. 00:35:55 He's like, I'm treating AI like it's another person.
639. 00:35:57 And this is where like, I think his title of his book is so so well done, right?
640. 00:36:03 He's going to treat it like a co-intelligence and he's really going to start talking about it like a co-intelligence.
641. 00:36:09 So the first one, AI is a person.
642. 00:36:12 He says, AI doesn't act like traditional software or it doesn't act like a human.
643. 00:36:17 It excels at tasks that are that are human tasks, writing, analyzing, coding, chatting.
644. 00:36:24 You know, basically how he talks.
645. 00:36:29 He goes into and this is where he maybe lost me a little bit is like he goes into the Turing test and he goes into, you know, theories of mind and cyborgs and all this other stuff.
646. 00:36:37 And I was kind of like, OK, this is this is fine, but it was a little, a little too much for me.
647. 00:36:42 He talks about one of the things that I think is crazy scary, but these replicas.
648. 00:36:46 So for instance, I'm going to do my best that I can to describe this, but it's like, let's say Mike dies, right?
649. 00:36:52 And that'd be a terrible thing, but I want to keep recording, you know, bookworm and I want Mike to stay my co-hosts, right?
650. 00:36:59 OK, so I would get a replica and I would train it on all of Mike's writing, all of his YouTube videos, all of his podcasts.
651. 00:37:07 I would train it on everything that Mike's ever done.
652. 00:37:10 And then I would essentially keep doing the show with Mike's replica.
653. 00:37:14 And I'm like, this is creepy. And there if you if that ever happens, you have my permission.
654. 00:37:20 Thanks.
655. 00:37:22 As long as the as long as the royalty checks go to your your family, right?
656. 00:37:26 But but it's like this is like actually one of the parts that I go, OK, we jumped over the creepy line like this.
657. 00:37:35 I just I'm not I can't I don't like that.
658. 00:37:38 Like I don't like it.
659. 00:37:39 But he says that AIs will take the place of real people.
660. 00:37:44 And that said, though, like one of the areas where I think it's actually
661. 00:37:47 potentially very useful.
662. 00:37:51 But it again, it gets on the border of it can be used well and it can be used poorly is say like a counselor, like they have a they have an example there where they go through, you know, this counselor helping someone out.
663. 00:38:06 And it's like, yeah, but like what if what if the AI messes up?
664. 00:38:11 Like what if it what if it hallucinates or what if it does a thing in a way because it's just been trained and it's just doing math and it's just trying to use weighted tokens to figure out the next word it should put.
665. 00:38:21 There's not that.
666. 00:38:23 Guard rail. There's not that certification.
667. 00:38:28 There's not that training like a human would have that doesn't mean a human can who can't mess up and they and a human can't do the wrong.
668. 00:38:36 We see that all the time that happens all across the world.
669. 00:38:40 But at the same time, like I worry about about that.
670. 00:38:43 So that's kind of AIs a AIs a person that's my my summary of AIs a person.
671. 00:38:47 What do you have?
672. 00:38:48 Well, I mean, there's a lot of stuff in here that I didn't even just capture any notes on because like the replica stuff.
673. 00:38:55 I'm not interested in in that part of it.
674. 00:39:00 I think the big takeaways from here, the reasons that you should view AI as a person is that it doesn't be.
675. 00:39:06 It doesn't behave like traditional software or traditional software is predictable, reliable and it follows a strict set of rules.
676. 00:39:14 So it seems more random like when you're working with a person.
677. 00:39:20 So that's the justification for this approach.
678. 00:39:23 Treat AI like a person because it often behaves like one.
679. 00:39:27 But then the examples of the Turing test and and Tave, which was created by Microsoft in 2016 and within hours,
680. 00:39:36 turned into a racist, sexist, hateful troll.
681. 00:39:38 Do you remember that happening?
682. 00:39:40 I remember hearing about it.
683. 00:39:43 I didn't really care at the time.
684. 00:39:45 And honestly, I remember thinking and I still think that that's just a replication of how awful people are.
685. 00:39:55 So that's what tools do is they multiply the the effectiveness for good or bad of the the operator.
686. 00:40:05 And I think that there there is a chance that people get their hands on this and it just multiplies the negative stuff.
687. 00:40:13 That doesn't mean that we should avoid it altogether.
688. 00:40:17 And if I can use it to spread the positive, then I'm going to try to to do that.
689. 00:40:22 The one thing that that I didn't like about this chapter.
690. 00:40:27 Well, I mean, it was an important point.
691. 00:40:29 But the thing that kind of gave me pause is that he said that AI will soon give us each our own private echo chamber.
692. 00:40:37 And I can see that because already people are curating their sources and they're finding facts that are going to align with their preconceived notions about what is true.
693. 00:40:52 And because they found it on the internet, therefore, it is true.
694. 00:40:56 And the rest of you sheep will need to wake up.
695. 00:40:58 [laughter]
696. 00:40:59 Like insert obligatory reference to live in all thinking here where Dave Gray talks about how the the internet is a grocery store for facts and you can find whatever you want.
697. 00:41:10 AI is going to make that worse, especially if people are using it in a way that they're not intentionally trying to find find out what they don't know.
698. 00:41:22 They're not intentionally trying to find what other people think other perspectives.
699. 00:41:27 Those conversations need to happen.
700. 00:41:30 And I feel like what AI as a person will enable people to do is go find something that sounds like a right answer.
701. 00:41:38 They'll quickly verify it based on their own experiences and understandings.
702. 00:41:42 It's just going to reinforce the bubble of belief.
703. 00:41:46 And that's going to be a negative thing.
704. 00:41:49 But what do you do about it?
705. 00:41:51 You fight back against it wherever you can.
706. 00:41:52 Yeah.
707. 00:41:53 Yeah.
708. 00:41:54 All right.
709. 00:41:55 So we were general in chapter four.
710. 00:41:57 That's AI as a person.
711. 00:41:58 Now we're going to get more specific in 5, 6, 7, and 8.
712. 00:42:02 So now AI is going to be a creative.
713. 00:42:04 This was probably one of my favorite chapters in the book.
714. 00:42:07 I thought the way he outlines this and the points he makes in this were very good in terms of,
715. 00:42:14 you know, like I said, the background I come from just to give you a little bit of an example is,
716. 00:42:19 as an educator, if I ask students to read a paper and summarize it, at this point in my career,
717. 00:42:26 or at this point in our technology system, I don't expect that they're actually going to read it and summarize it.
718. 00:42:31 I expect that they're going to throw it into one of these AI tools and they're going to get a summary out
719. 00:42:35 and they're going to claim that as their summary or they're going to tweak it and they're going to modify it
720. 00:42:39 and then they're going to say that's their summary.
721. 00:42:41 So I have to approach this differently in terms of I would not ask, I would not have them do that assignment.
722. 00:42:46 He talks about this, right, in terms of, you know, well, what if you had it instead generate ideas,
723. 00:42:55 right, like, okay, so do this and summarize it from this perspective, this perspective, this perspective,
724. 00:43:01 this perspective, and then your job is to look across all those perspectives.
725. 00:43:05 So essentially it's the foundational seeds of creativity.
726. 00:43:09 It's the foundational seeds of putting things together.
727. 00:43:11 That's a bad example.
728. 00:43:13 Like there are much better examples and he gives those, but, you know, like come up with what was the,
729. 00:43:19 what was the example we gave Mike where it was like a wine shop or a cheese shop or something
730. 00:43:25 and give me like a list of 20 possible names for it.
731. 00:43:28 And, you know, like, but I forget what he was blending together.
732. 00:43:30 And it was like, it was, it was kind of, it was very artificial, if you will, like in terms of what it was,
733. 00:43:37 but it showed you the power of how it could do it.
734. 00:43:39 Like it can generate a list of 100 things.
735. 00:43:43 They might be good.
736. 00:43:44 They might be bad, but it can easily do that.
737. 00:43:46 You know, it can, and you gave a good example of that too with, with your example of the big,
738. 00:43:51 the big prompt that you gave it.
739. 00:43:53 So, you know, he talks about alternative uses tests, the remote associate,
740. 00:43:58 associate's test.
741. 00:43:59 These are some tests or creativity or some ways that you can get more creative.
742. 00:44:02 I liked his, his distinction between novelty and originality.
743. 00:44:07 Right.
744. 00:44:08 So the way he describes this is new ideas do not come from the ether.
745. 00:44:12 They're based on existing concepts.
746. 00:44:13 Innovation scholars have pointed through the importance of recombination of generating ideas
747. 00:44:19 and of obviously, you know, being Apple people, right.
748. 00:44:22 We would go back to the, you know, what is it?
749. 00:44:24 Well, it's not any of these individual things.
750. 00:44:26 It's the smushing together of all three of those things.
751. 00:44:28 And that's where the innovation really, really happened with the, with the iPhone.
752. 00:44:31 I mean, we would, we would do this in innovation classes.
753. 00:44:34 I would teach at Virginia Tech is instead of having students brainstorm from nothing,
754. 00:44:39 we would say, okay, you need to put this idea, this idea, and this idea together
755. 00:44:44 and come up with a new idea.
756. 00:44:45 Right.
757. 00:44:45 And that, that's what they had to brainstorm or come up with 10 new ideas around that.
758. 00:44:50 So I really liked basically using AI as a tool to help you be more creative,
759. 00:44:58 to help you generate more ideas, to help you in this brainstorming process.
760. 00:45:01 I thought I just thought he did a really good job here.
761. 00:45:03 Yeah.
762. 00:45:05 I mean, the, he starts this chapter by talking about how the weakness of LLMs is
763. 00:45:09 also their strength, the ability to make stuff up.
764. 00:45:12 So the same features that make an LLM unreliable and dangerous for factual work,
765. 00:45:20 make them useful for creative work.
766. 00:45:22 And I think that you're right.
767. 00:45:25 Like, I like this chapter a lot better than the previous one,
768. 00:45:28 but I feel like the previous one is necessary for all of these other chapters
769. 00:45:32 that he's going to be talking about here.
770. 00:45:34 So the list of ideas, you know, the reason that that works is that AI
771. 00:45:41 doesn't know when it hallucinates and it can't explain its answers.
772. 00:45:45 It's not the big hallucinations, but the small ones that you don't catch,
773. 00:45:49 that really cause the issues.
774. 00:45:52 And I like to do this all the time, not just that big prompt that I shared,
775. 00:45:57 but I'm working on this, this, this product for,
776. 00:46:01 looks like an obsidian done for you vault with all the workflow stuff.
777. 00:46:04 And I'm trying to come up with a catchy name for it.
778. 00:46:07 There's like 19 other products that use the term life OS.
779. 00:46:11 So that's not an option, you know, and I don't want some big, long name.
780. 00:46:16 So I like, but once a day, I'm like, you are my, my brainstorming partner.
781. 00:46:22 You're good at coming up with names.
782. 00:46:24 You know, this is the, the product that I'm making.
783. 00:46:26 Give me 25 names for this.
784. 00:46:27 And then because you can have the conversational nature, give me 25 more.
785. 00:46:30 Like I look at hundreds of these every single day and he points out that most
786. 00:46:34 of the suggestions are going to be mediocre, but that's okay,
787. 00:46:38 because that's where you come in as a human.
788. 00:46:40 And there are ways to push AI from these average mediocre answers.
789. 00:46:47 To the really good ones to the high variance by telling it specifically who it is
790. 00:46:51 and what it's doing, how it can help you.
791. 00:46:54 So I feel like there's a lot of, there's a lot of discourse right now about AI
792. 00:47:03 and how it's going to eat all the jobs of, of creatives.
793. 00:47:07 And I just don't believe that's true.
794. 00:47:09 We talked a little bit about this in the pro show last time.
795. 00:47:13 Like it really bugs me when some of the Apple podcasters that I listen to and I,
796. 00:47:18 I like our like, yeah, we're just not going to do this.
797. 00:47:23 And we think it's terrible that AI is going to eat these jobs of these designers.
798. 00:47:28 Like, no, like that's, you don't understand it.
799. 00:47:31 The good designers are the ones who are going to be able to use this and, and,
800. 00:47:36 and it's going to enhance their workflows.
801. 00:47:38 He talked about later on in the book.
802. 00:47:40 So I'm kind of jumping ahead here, I guess.
803. 00:47:42 But like a countenance in the 70s before calculators were really a thing.
804. 00:47:47 Their job was manually doing the math.
805. 00:47:51 And then when calculators came out, the need for accountants didn't disappear.
806. 00:47:55 It's just their bundle of tasks was different.
807. 00:47:58 And they used the calculators.
808. 00:48:00 They use the spreadsheets.
809. 00:48:02 And I think the same thing is going to happen with AI here.
810. 00:48:05 And if you are a creator, like that, then you really need to know how this
811. 00:48:11 is going to work because if you are a poor creator, then yeah, AI is going to eat
812. 00:48:19 your lunch.
813. 00:48:19 What is a poor creator?
814. 00:48:21 Like, I don't know that I'm comfortable defining that.
815. 00:48:23 But one of the things that he mentions, like the, the industries that's going to
816. 00:48:26 get disrupted is like the stock art industry.
817. 00:48:29 And we talked about that last time.
818. 00:48:31 I am a okay with that.
819. 00:48:32 I mean, I, I've had, I've worked with marketing agencies in the past who they
820. 00:48:40 helped us with different things when I was working with the family business.
821. 00:48:43 And they bought these images, but we didn't have the receipts.
822. 00:48:48 And then years later, you know, someone from the, from Getty is like going to sue
823. 00:48:54 us because we had this image that was used in a blog post and we didn't have the
824. 00:48:58 license for it.
825. 00:48:59 I don't have time to deal with this.
826. 00:49:02 And I don't want to pay you the $10,000 that you say you're owed.
827. 00:49:05 So you know what I'm going to do in the future?
828. 00:49:07 I'm going to generate an AI image and get it as close to what I know I want as I can.
829. 00:49:12 Now I'm not going to pay you a penny.
830. 00:49:14 Like these people are just, and they're not all like that.
831. 00:49:17 I mean, there are legitimately people who are taking good pictures and they, they
832. 00:49:21 should be compensated for, for their work, you know, but the people who are going
833. 00:49:24 to take the, the great pictures and they're going to create the, the great art.
834. 00:49:27 I still think there's going to be, uh, there's still going to be a market for that
835. 00:49:31 kind of stuff.
836. 00:49:32 If I want a logo for a podcast that I'm developing or a product that I'm making,
837. 00:49:39 I'm not going to ask mid journey to generate me a logo.
838. 00:49:43 There's just so much more that goes into it.
839. 00:49:46 There's a human element that, that, um, has to be there for that.
840. 00:49:50 But, you know, rant over, I guess the, the takeaway here is that if you are doing
841. 00:49:56 anything creatively, um, then you really need to figure out how to make this stuff
842. 00:50:00 work for you if you want to be relevant 20 years from now.
843. 00:50:03 Yeah.
844. 00:50:03 And the last, the last point related to what you're saying, it made me think of it is
845. 00:50:07 the most, he says the most innovative people benefit the least from AI.
846. 00:50:11 So basically it raises the floor, you know, like I am not a good graphic designer,
847. 00:50:16 but through using these tools, I can be a better graphic designer, right?
848. 00:50:21 Like because when I'm starting at zero, these tools give me something.
849. 00:50:25 But if you're a really good graphic designer, these tools aren't going to help
850. 00:50:28 you that much because you're already really good at this, you know, like, you
851. 00:50:31 don't need the tool.
852. 00:50:32 That's the thing.
853. 00:50:33 You don't, you shouldn't need to go to a professional graphic designer because
854. 00:50:36 you need an image for an internal presentation.
855. 00:50:39 Just use the AI, grab something good enough that communicates the idea you're
856. 00:50:43 trying to get across and you're done.
857. 00:50:45 The people who really are going to be producing these professional commercial
858. 00:50:49 works, they're going to need to go work with the pros and get that stuff made.
859. 00:50:54 And that's always going to be the case.
860. 00:50:57 But for everybody else who just needs something quick and it doesn't have to be
861. 00:51:01 totally professional, this, you need to know how to use this stuff.
862. 00:51:05 Yeah, exactly.
863. 00:51:06 Exactly.
864. 00:51:07 Okay.
865. 00:51:07 Let's move on to, let's move on to six.
866. 00:51:08 So AI is a co-worker.
867. 00:51:10 So now we've moved into AI as a co-worker.
868. 00:51:12 I thought about you.
869. 00:51:13 Um, as I'm working through this and it's like, you know, you had said, and I
870. 00:51:17 can't remember where it was or where you said it, but like, you know, personal
871. 00:51:22 assistant, right?
872. 00:51:23 And it's like, I was like, how could Mike use AI and how is he using
873. 00:51:27 AI and how might he use AI in the future for, you know, for, uh, for an assistant
874. 00:51:32 to take some of the tasks that are necessary, but they're lower, they're
875. 00:51:37 lower impact, I guess, if you will.
876. 00:51:39 And how can you turn that over to, to, to that everyday?
877. 00:51:42 I, before I let you answer that, uh, let's, let's do a little bit of overview
878. 00:51:45 of what it is.
879. 00:51:46 But they did a study on consultants and they found that AI powered consultants.
880. 00:51:51 So the consultants that were also using AI were more effective.
881. 00:51:54 They were more creative.
882. 00:51:55 They wrote better, uh, more analytical than their peers.
883. 00:51:58 Um, that there are these jobs that it will be more likely to be overtaken.
884. 00:52:06 I guess is the best word or have overlapping portions with, with AI.
885. 00:52:09 My job was one of those.
886. 00:52:11 So I was like, wow, this is really interesting.
887. 00:52:13 You know, college professor was like in the top, whatever of them.
888. 00:52:16 And what I can tell you is like, yeah, I can totally see that that there are
889. 00:52:20 portions of my job that the AI man, when I can give that to it and trust that it
890. 00:52:26 does it with 99% accuracy, I'm, I'm going to do that as soon as I can.
891. 00:52:30 Right.
892. 00:52:31 Like I don't fear that on any level.
893. 00:52:33 I don't think that that's a bad thing.
894. 00:52:35 I see that as a, Oh, that's a way for me to offload the stuff that isn't the
895. 00:52:40 higher value activity, uh, during my day.
896. 00:52:43 Um, then tasks for us, task for AI.
897. 00:52:46 He said, he talks about separating these tasks out.
898. 00:52:49 Um, and like what are the tasks that I need to do and what are the tasks that I
899. 00:52:52 could give off to the AI?
900. 00:52:53 Then he, um, he goes into like taking that and making it into work systems.
901. 00:52:58 What are the systems that we can then say the AI handles this part of the
902. 00:53:02 system and the humans have to handle the, this part of the system.
903. 00:53:05 And then he gets into more like the job side of it and it's like, okay, are
904. 00:53:09 there actual jobs that, you know, AI might just be able to, to, to take over.
905. 00:53:14 And none of this, like, I think what I, what I walked away happy about,
906. 00:53:19 the happy might be the wrong word, but it's like the, the doomsday scenario of
907. 00:53:24 like AI will take all of our jobs.
908. 00:53:26 It was like, no, I don't think AI will take all of our jobs.
909. 00:53:29 I think AI will redistribute tasks.
910. 00:53:33 It'll help us develop new systems and it will change some of our jobs, but
911. 00:53:39 like none of that was like in a bad way.
912. 00:53:41 Like it was, it wasn't like, Oh my gosh, you know, it was much more in the way
913. 00:53:45 of I used to have to or farmers used to have to stand behind a horse or a team
914. 00:53:51 of horses and hold the plow as they walked.
915. 00:53:53 And then we got machines that could do that.
916. 00:53:55 And now I could drive the tractor, you know, like it was like, that's the way I
917. 00:54:00 see this is what's it going to look like in the future?
918. 00:54:02 Who knows?
919. 00:54:03 What are the systems that are going to develop?
920. 00:54:05 What are the jobs?
921. 00:54:05 How are the tasks going to be distributed?
922. 00:54:07 We don't know yet, but, but I'm more encouraged by the fact that I think this
923. 00:54:11 is going to be a good thing.
924. 00:54:12 Um, you know, it's not going to be good for everybody, but I think,
925. 00:54:15 overall, in general, it's going to be a good thing.
926. 00:54:17 I think it has the potential to be good for everybody.
927. 00:54:20 Uh, I don't think that's, I don't think that's generalizing too much.
928. 00:54:26 And like I said, there are certain industries that will be disrupted, mentioned
929. 00:54:30 the stock photography industry.
930. 00:54:31 He also mentions in this one call centers.
931. 00:54:34 Um, but if you are working in a call center, uh, let's for the sake of argument
932. 00:54:43 here, call that a low level job since it has the potential to be eaten by AI.
933. 00:54:51 For as we're having this discussion, just because you are in that position, doesn't
934. 00:54:56 mean you have to stay in that position.
935. 00:54:58 That may be the best job that you can get right now.
936. 00:55:00 It may be the only job that you can get right now, but I firmly believe that if
937. 00:55:04 you work harder on yourself, then you do on your job, your skills will develop to
938. 00:55:09 the point where you can get another better job.
939. 00:55:13 The marketplace will reward you for the skills that you have developed.
940. 00:55:17 Doesn't happen automatically.
941. 00:55:18 You have to stand up for yourself.
942. 00:55:20 You have to ask for things, but the whole idea of career capitals, Cal Newport
943. 00:55:26 talks about it, uh, is definitely in, in play here.
944. 00:55:28 Now, um, AI will probably affect your job as what he says at the very beginning,
945. 00:55:33 but just because your job overlaps with AI doesn't mean your job will be replaced.
946. 00:55:38 The danger I think we have is in the term he uses falling asleep at the wheel,
947. 00:55:44 which is letting AI take over instead of using it as a tool.
948. 00:55:48 He talks about throughout the book, but also it comes back here, this jagged front
949. 00:55:54 tier, which is like there's this wall that goes all over the place and everything
950. 00:56:00 inside the wall is stuff that AI can do.
951. 00:56:02 Everything outside the wall is stuff that it can't do.
952. 00:56:04 And we think that the wall is like nice and straight.
953. 00:56:07 You know, but we find all these different points on either side of that,
954. 00:56:12 that nice straight line that's in our mind where, Oh my gosh,
955. 00:56:15 I can't believe that AI can actually do that.
956. 00:56:17 Or, Oh my gosh, AI is so stupid.
957. 00:56:19 It can't even do this.
958. 00:56:20 That's where that perspective comes from.
959. 00:56:22 So we got to stay, we got to stay curious.
960. 00:56:24 And we got to constantly be trying to figure this stuff out.
961. 00:56:27 He uses three different types of tasks here.
962. 00:56:30 The just me tasks.
963. 00:56:31 These are tasks in which AI is not useful.
964. 00:56:33 It just gets in the way delegated tasks, tasks that you may assign the AI
965. 00:56:37 and then as a human, you have to carefully check and then automated tasks,
966. 00:56:41 tests that you just leave completely to the AI and you don't check on.
967. 00:56:44 I think that's a good breakdown of the different types of tasks and I don't have
968. 00:56:49 specific tasks that fall into those different categories, but I feel like that's
969. 00:56:53 a really good setup for the centaurs and the cyborgs that he talks about,
970. 00:56:56 the two different ways to work with AI were a centaur.
971. 00:56:59 There's a clear line between the person and the machine, kind of like the,
972. 00:57:02 the torso of the man and then the back of the horse, but a cyborg is like a
973. 00:57:09 blending of a machine in person where you're working in tandem with the, with the AI.
974. 00:57:14 So I think this understanding or this model kind of sets it up effectively for
975. 00:57:22 thinking about how we're going to work with this AI in the future.
976. 00:57:25 I haven't had enough time to really think about what does this mean for me
977. 00:57:28 other than, you know, try to use more, more prompts in the creative process as we
978. 00:57:32 talked about.
979. 00:57:33 And this is where he, this is actually the part where it talks about the, the
980. 00:57:37 accountants were who were in charge of calculating numbers by hand.
981. 00:57:39 You know, your, your job is going to change and it's going to change whether
982. 00:57:43 or not the technology that disrupts it was AI or not.
983. 00:57:46 If it wasn't AI, there was going to be another technology that was going to
984. 00:57:49 disrupt it.
985. 00:57:50 My grandpa owned a type setting business and my uncle took over the business from
986. 00:57:58 him and then when Max came out, they tried to hold on to this analog type setting
987. 00:58:06 business, but everyone was moving to digital publishing.
988. 00:58:08 Guess what happened?
989. 00:58:09 You know, it's the whole innovators dilemma.
990. 00:58:12 That was one of the books we covered way at the beginning of bookworm by Clayton
991. 00:58:17 Christensen, who's a brilliant business business writer.
992. 00:58:20 Was a brilliant business writer.
993. 00:58:23 And the, the trick is not to try to protect things as they are because you're
994. 00:58:29 comfortable, but these disruptions are going to come and you got to figure out,
995. 00:58:33 you know, what does this mean for me and how can I leverage this AI is just
996. 00:58:36 one of those disruptions.
997. 00:58:38 Yeah.
998. 00:58:39 So, so you answered my question that I was going to ask you next, which was Mike,
999. 00:58:42 what are the tasks you're going to turn over to AI?
1000. 00:58:45 Right.
1001. 00:58:46 Like, so it got into that, you know, me delegate and then automate.
1002. 00:58:49 So you don't know that yet, but I would encourage that to be an action item for you.
1003. 00:58:52 I think that would be fun for me to hear what your answer is.
1004. 00:58:55 And then the last point I'll make here is I like the way he frames it and he
1005. 00:59:01 ties back into this idea of co-intelligence.
1006. 00:59:03 Like I really like his theme of co-intelligence here because he makes a
1007. 00:59:07 statement, how can AI be used as a co-intelligence to help us manage work?
1008. 00:59:12 And I think if we came in and like people come into it with that frame of mind that
1009. 00:59:16 says, okay, this is my job.
1010. 00:59:19 I do this thing every week or every day or twice a day or whatever it is.
1011. 00:59:24 Is there an AI that could get me 80% of the way there that way that job is so much
1012. 00:59:29 easier, better, more routine, more structured, you know, whatever it might be?
1013. 00:59:33 I think if we think about it as a co-intelligence, a partner, right?
1014. 00:59:36 Like that we're working with.
1015. 00:59:38 And I don't want to say it's a free partner because we might have to pay for some
1016. 00:59:41 subscription or whatever it is, but it's like, it's a, I don't know, it's a different
1017. 00:59:46 type of partner.
1018. 00:59:47 It's a different type of co-intelligence than you and I sharing that task, which I
1019. 00:59:52 think is a really fun way to think about this or a really like innovative way to
1020. 00:59:57 think about this.
1021. 00:59:58 And I like it.
1022. 00:59:59 That's one of the reasons why I like the co-intelligence idea.
1023. 01:00:03 Okay, so let's move to chapter seven.
1024. 01:00:06 So now AI is a tutor, obviously.
1025. 01:00:08 This is exciting to me and interesting to me.
1026. 01:00:10 I don't have a ton of like notes on this one, but this was the chapter that
1027. 01:00:15 got my, got me stewing in terms of like, what does this mean for me individually?
1028. 01:00:19 What does this mean for me personally?
1029. 01:00:20 I won't take us down.
1030. 01:00:22 Well, let me, let me hijack this one.
1031. 01:00:25 I'll share some of my notes and then I want to hear like, how are you thinking
1032. 01:00:29 this is going to impact you?
1033. 01:00:30 Okay.
1034. 01:00:31 All right.
1035. 01:00:32 So my notes from AI as a tutor.
1036. 01:00:34 And it's some, one thing that isn't in the notes, but it's important as a context.
1037. 01:00:38 You mentioned that he's a professor.
1038. 01:00:40 He actually requires his students to use AI in their assignments.
1039. 01:00:44 So while we're hearing about people who are saying, well, students might be cheating
1040. 01:00:48 because they're using AI, we have to ban the use of AI.
1041. 01:00:51 He's gone all, he's gone the other way.
1042. 01:00:53 Like, yeah, there's a possibility that they could cheat, but there's always been a
1043. 01:00:56 possibility that people could cheat.
1044. 01:00:58 And let's figure out how to, how to use this productively.
1045. 01:01:00 And I think he teaches entrepreneurship.
1046. 01:01:02 So AI is a tutor.
1047. 01:01:04 One-to-one tutoring is extremely effective in getting students to perform well.
1048. 01:01:08 And there is lots of research behind that.
1049. 01:01:11 And then the downside to that is that one-on-one tutoring is hard to do.
1050. 01:01:15 It requires a lot of time.
1051. 01:01:18 He mentions that asking AI to summarize things is taking a shortcut.
1052. 01:01:23 And that's not really what we should be doing.
1053. 01:01:25 What we want to do is we want to use this as a way to develop our skills.
1054. 01:01:33 And that doesn't mean that we're just going to increase reliance on the machines and become
1055. 01:01:38 medians because that's essentially what people thought with calculators back in the 70s.
1056. 01:01:41 In fact, a survey in the 70s found that 72% of teachers and lay people disapproved of
1057. 01:01:46 kids using calculators.
1058. 01:01:48 But then a couple of years later, it's like 90 some percent wanted to use calculators
1059. 01:01:52 in the classroom.
1060. 01:01:54 Now that doesn't mean that prompt engineering is the thing.
1061. 01:01:57 It kind of speaks against that in this chapter.
1062. 01:01:59 Prompt engineering is a useful near-term skill, but it isn't that complicated.
1063. 01:02:04 It's kind of like programming in prose.
1064. 01:02:07 He does say that AI will reduce the importance of lectures, which is probably what most people
1065. 01:02:13 think of when they think of higher education and the typical learning style.
1066. 01:02:19 So Corey, how does this affect your role as a professor?
1067. 01:02:24 Yeah, so I don't think anything he said in here was I had like a stark disagreement with
1068. 01:02:33 in terms of the way he was going.
1069. 01:02:34 It's all very sound, it was backed up by what makes sense.
1070. 01:02:38 I had actually never heard of the two-sigma problem.
1071. 01:02:40 So the two-sigma problem was actually really interesting to me.
1072. 01:02:43 I actually noted it to go try to read the bloom paper.
1073. 01:02:48 But yes, lectures bad, homework good, test good.
1074. 01:02:52 And really what this gets into is this idea of hands-on, minds-on learning.
1075. 01:02:57 And in a lecture environment, unless the person is a really good lecturer and if they're a
1076. 01:03:03 really good lecturer, they're not lecturing.
1077. 01:03:04 Like they're doing other things and they're just calling it a lecture.
1078. 01:03:08 So I teach primarily like design education and a lot of that.
1079. 01:03:13 So I mean, I don't talk often for more than 15 minutes before we're doing something.
1080. 01:03:19 We're doing a problem set.
1081. 01:03:20 We're doing an example problem.
1082. 01:03:21 You know, the students are engaging in their interaction with each other.
1083. 01:03:24 I think this is really, I think we're at the cusp of a significant change and I think
1084. 01:03:32 it'll be a leap forward in the way we do education.
1085. 01:03:36 And I think it's going to be like a big leap, like a big jump forward.
1086. 01:03:40 And I think you're going to see it divide in a couple different ways.
1087. 01:03:43 You're going to have the people who are like, nope, you know, I'm putting my head in the
1088. 01:03:46 sand and we're just going to keep doing things the way we've always done them and we're not
1089. 01:03:49 going to change.
1090. 01:03:50 Then you're going to have the people that take it too far and they're going to go full
1091. 01:03:53 AI and you're going to miss out on things, right?
1092. 01:03:56 Because I don't think you can do it with full AI.
1093. 01:03:58 I think AI can be a tutor.
1094. 01:04:00 But I think also there needs to be that human in the loop, right?
1095. 01:04:02 Like the person who is the expert in that field or is able to give the feedback and
1096. 01:04:08 give, you know, a different level of feedback is essential.
1097. 01:04:12 And then there are, I think, I think there's going to be the group in the middle and there
1098. 01:04:15 might be more groups.
1099. 01:04:16 I don't even thought about it.
1100. 01:04:17 But then there's going to be people who embrace the AI and they're going to say like, okay,
1101. 01:04:21 how do we leverage this to the best that we can to meet both the human and the in person
1102. 01:04:28 inside and the tie the technology in?
1103. 01:04:31 And I think the challenge with that middle group, which is where I would want to sit.
1104. 01:04:36 Like I want to sit in that middle group.
1105. 01:04:37 The challenge with that middle group is it changes the systems.
1106. 01:04:41 It changes the tasks and it changes the jobs of teaching and it changes and that and teachers
1107. 01:04:47 are terrible for wanting to embrace change.
1108. 01:04:51 Like they want to come in and they want to say it worked last semester.
1109. 01:04:56 I'm just going to keep doing it and the things that didn't work, I adjust those and I fix
1110. 01:04:59 those, but the things that worked, I want to keep doing that.
1111. 01:05:02 And universities, you know, like the phrases, it's like turning the Titanic, right?
1112. 01:05:06 Like, I mean, to get like significant change at a university is really, really hard because
1113. 01:05:11 there are so many different stakeholder groups.
1114. 01:05:13 That said, like I read this chapter and I think to myself, oh, there's so many opportunities
1115. 01:05:20 if we leverage this correctly and then I immediately think, yeah, but, well, let's talk about pushing
1116. 01:05:27 a boulder uphill, like, holy cow, we're pushing a boulder uphill, like to try to leverage
1117. 01:05:31 this well.
1118. 01:05:33 So I mean, that's, I hopefully I don't know if that answers your question or not, but
1119. 01:05:35 it's like, those are all of the thoughts that are stewing around in my head as I think
1120. 01:05:39 about, you know, how AI might impact me personally and then universities in general and education
1121. 01:05:45 in general.
1122. 01:05:46 Do you have any thought on who is going to end up in which camp?
1123. 01:05:51 Like, are there any themes with the people who are going to resist this versus the people
1124. 01:05:58 who are going to embrace it too much?
1125. 01:06:01 No, I mean, I don't think I can say that right now.
1126. 01:06:04 Do you mean like different disciplines or types of like faculty or teachers or what do
1127. 01:06:10 you mean?
1128. 01:06:11 Well, I guess like what I think of, I don't live in this world like you do, but I have
1129. 01:06:20 my undergraduate degree experience and then I have a Bible college degree that I went
1130. 01:06:27 back and got for funsies online.
1131. 01:06:30 And I feel like the traditional college four year degree, I went to a small liberal arts
1132. 01:06:37 school in Wisconsin.
1133. 01:06:41 I feel like that setting, I don't see it fairing well with this sort of stuff.
1134. 01:06:50 I read something on ESPN the other day about some baseball team that was a D three school,
1135. 01:06:55 it's been around for like 150 years and their school is shutting down and they made it to
1136. 01:06:59 the world series, college world series.
1137. 01:07:03 I feel like AI will make more disruption in the higher education space, which again,
1138. 01:07:14 like I don't think that's a fairly bad or good with liberal arts schools, you know,
1139. 01:07:19 40, 50 grand a year to go there.
1140. 01:07:23 You should be entitled to more as a student than to show up at the pit lectures in my
1141. 01:07:28 opinion.
1142. 01:07:30 And I just I don't know if there's a certain type of higher education that is better suited
1143. 01:07:38 to ride that way or not.
1144. 01:07:40 Yeah, I mean, I think I think you're seeing what you're saying is accurate, right?
1145. 01:07:45 Like you're seeing people make decisions when it comes to when it comes to higher education.
1146. 01:07:52 But I and I think the way I'll word it is I think AI will be a catalyst in those decisions.
1147. 01:07:58 It's going to make certain things happen faster than they were already happening.
1148. 01:08:02 So we I mean, we already get a bunch of students who come in and they're like, how do I finish
1149. 01:08:05 my degree in three years, right?
1150. 01:08:07 And we get students that come in and they're like bringing in 60, 70, 80 credits, you know,
1151. 01:08:11 and it's like, oh man, like Holy cow, like you're you're, you know, but we're still
1152. 01:08:17 in a system where for certain classes of job, that degree is the ticket in, right?
1153. 01:08:22 Like you can't even get an interview without without a degree.
1154. 01:08:25 I think those type of things are going to change if you're a motivated student and you leverage
1155. 01:08:31 AI, but then you also pair it with, you know, if we go back to the mastery book and you
1156. 01:08:36 pair it with apprenticeship and you pair it with different internship experiences and
1157. 01:08:40 these different things.
1158. 01:08:41 I mean, you're going to come out really well prepared, right?
1159. 01:08:45 The AI is going to get you.
1160. 01:08:46 It's going to help you get a lot of factual information.
1161. 01:08:49 And if you can pair that with, you know, mentorship and guidance and apprenticeships and those
1162. 01:08:54 type of things, I mean, you're going to be really well prepared to be super successful
1163. 01:08:58 in, in industry.
1164. 01:09:00 I think the, I think computer science CS, right?
1165. 01:09:04 Those are, those are going to be hit pretty hard because like the fundamental skills I
1166. 01:09:11 think are easier and easier to obtain, but the mentoring is, is where you, you know,
1167. 01:09:15 you really want the community, the design projects, learning from that person that's
1168. 01:09:18 sitting two seats down from you when you, when you can't figure that out.
1169. 01:09:21 So it's like, it's a hard question to answer.
1170. 01:09:23 I think there will be areas.
1171. 01:09:25 But what I want to do call out is he talks about that some of the people who will be the
1172. 01:09:29 best at using AI are the people with the humanities degrees because they understand the context.
1173. 01:09:35 They understand all the things that go around it.
1174. 01:09:37 And they're going to be the ones that get the most out of it because they know how to
1175. 01:09:40 leverage it the most because essentially they, they understand how to interact with people
1176. 01:09:45 and they understand how to take those interactions and combine it into the system.
1177. 01:09:48 So it's going to be a fun, I say fun, right?
1178. 01:09:51 It's going to be a crazy fun 10, 15 years as, as the stuff, you know, unfolds and, and we
1179. 01:09:57 see where, where the dust settles.
1180. 01:09:59 So it'll be exciting.
1181. 01:10:01 And that's, that's the perfect segue into the next chapter about AI as a coach.
1182. 01:10:05 Yep.
1183. 01:10:06 All right.
1184. 01:10:07 So, so we're going into coaching now and basically, you know, he gets into this idea of expertise
1185. 01:10:14 building here and that like, he talks about the 10,000 hours and really that, you know,
1186. 01:10:19 this deliberate practice versus, you know, just repetitive, rote practicing and he talks
1187. 01:10:24 about these different things.
1188. 01:10:26 But so the building expertise, you know, he would say like, you need a baseline of knowledge,
1189. 01:10:30 right?
1190. 01:10:31 So let's make sure we get a base on a knowledge.
1191. 01:10:32 Then you need to practice and you do the right type of practice, which would be deliberate
1192. 01:10:35 practice.
1193. 01:10:36 And if you put those two things together, then that's how you develop expertise.
1194. 01:10:41 And what AI can do is AI can serve as this coach that helps you figure out, you know,
1195. 01:10:48 better or more deliberate practice.
1196. 01:10:50 So if you think about this piano lessons, right?
1197. 01:10:53 Like, okay, I could go take piano lessons.
1198. 01:10:57 And if I have a good coach, that coach is going to push me and push me and push me, but
1199. 01:11:01 they're going to know the right way to push me.
1200. 01:11:03 They're going to know how to push me and say like, Oh, your fingering on your, on your
1201. 01:11:07 right hand isn't good.
1202. 01:11:09 So what we're going to do is we're going to do like the next three weeks is going to
1203. 01:11:12 work on fingering on your right hand.
1204. 01:11:13 And I'm going to hate every minute of it, but I'm going to get significantly better.
1205. 01:11:17 So does AI have the ability to do that?
1206. 01:11:19 Right?
1207. 01:11:20 Like it has the ability to identify these areas where you're not as good in this particular
1208. 01:11:25 area and help coach you to that.
1209. 01:11:28 I think if I'm getting it from remembering correctly, this is where he talks about the
1210. 01:11:32 doctors, right?
1211. 01:11:33 And how the apprenticeship and like the intern level of the doctors might start going away
1212. 01:11:40 because the doctors can use AI.
1213. 01:11:43 So therefore they don't need the lower level interns to do some of that stuff for the
1214. 01:11:47 them.
1215. 01:11:48 And they just keep doing what they're doing and then use the AI for that.
1216. 01:11:50 But then we're not going to train expert doctors anymore because they're missing that
1217. 01:11:54 middle phase.
1218. 01:11:55 So it's like the removal of this middle phase is a, I remember being a big, big part of this
1219. 01:11:59 chapter.
1220. 01:12:00 Yeah, they're using the surgery robots as well, which means that the, the apprentices
1221. 01:12:06 in the room, uh, don't get the experience that they would have previously.
1222. 01:12:11 I think that's the big takeaway for me from this is that, um, yeah, you can use AI as
1223. 01:12:16 a coach and you can help it to, uh, give you feet, like you can use it to get more feedback
1224. 01:12:22 loops and, and, uh, do deliberate practice.
1225. 01:12:26 But the, the dangerous side of this, I think is that the experts become experts through
1226. 01:12:32 that deliberate practice.
1227. 01:12:34 And because their time is valuable, they're, if they just go all in with AI, there's a,
1228. 01:12:40 a major apprenticeship gap that's going to develop where the next tier is not going to
1229. 01:12:46 have an opportunity to develop the same types of skills.
1230. 01:12:51 Um, I, I think that, uh, it's probably not, uh, that sensational.
1231. 01:12:58 I think that's just kind of like a red flag and the, the practical application of this
1232. 01:13:02 is if you're someone who has those, those skills, it's worth investing in, uh, somebody
1233. 01:13:07 else, but the big question that you're asking regardless of whether you are an expert or
1234. 01:13:12 not is where is the appropriate place to insert AI as a co-intelligence into, into, uh, what
1235. 01:13:19 I do.
1236. 01:13:20 Um, he does mention in this chapter that humans working with AI co-intelligence always outperform
1237. 01:13:26 the best humans alone.
1238. 01:13:28 So again, it's not something that, oh, this is bad and it's going to eat our lunch and
1239. 01:13:32 we need to avoid it.
1240. 01:13:33 If you really want to be the best at what you do, you have to, uh, you have to embrace this
1241. 01:13:38 stuff.
1242. 01:13:39 Um, but also I think for me, one of the things that I'm always cognizant of is the type of
1243. 01:13:46 legacy that I'm going to leave and I want to help other people do more.
1244. 01:13:52 So as it pertains to me and how I would apply this, it's, you know, use the AI where appropriate,
1245. 01:13:59 but don't just neglect the people, uh, for the sake of the productivity.
1246. 01:14:04 That was something I picked up from Chris Bailey's book, The Productivity Project way
1247. 01:14:08 back in the day.
1248. 01:14:09 Uh, because he mentions that people are the reason for productivity.
1249. 01:14:12 A view AI is like a productivity enhancement tool, um, but the people are still the, the
1250. 01:14:18 most important thing.
1251. 01:14:20 And I've aired on, on the side of, well, I just got to get the task done before, you
1252. 01:14:24 know, and damage the, the relationship.
1253. 01:14:26 So I think AI just, you know, provides more opportunities for that sort of thing to happen
1254. 01:14:30 if we're not careful, but doesn't mean that it necessarily has to.
1255. 01:14:34 Uh, I don't think the fact that AI makes things more efficient means that people are
1256. 01:14:39 just going to go inward and they're not going to train up other people, uh, to, to do things.
1257. 01:14:45 I think that's kind of inherent in us is that we want the, the people that we are investing
1258. 01:14:51 into, whether those are our natural kids or, um, just like your, your students, you know,
1259. 01:14:56 you want them to go leave a bigger dent in the universe.
1260. 01:15:00 That's actually the, my life theme is I'm, I, I condense it down and I say I'm a multiplier
1261. 01:15:06 because I want to help people multiply their time and tell and leave a bigger dent in the
1262. 01:15:09 universe.
1263. 01:15:10 That's the most rewarding thing for me.
1264. 01:15:12 If I can help you do that, you know, then I'm going to do that.
1265. 01:15:15 And if I'm constantly focused on just like, how do I get more widgets cranked?
1266. 01:15:19 Then I'm going to miss those opportunities.
1267. 01:15:23 I think this is the area where this agreement might be, um, the wrong word, but I'm less
1268. 01:15:29 optimistic.
1269. 01:15:30 I'm more skeptical in this area where I think AI right now is really good at that baseline
1270. 01:15:36 knowledge.
1271. 01:15:37 It's really good at helping me get that, that baseline knowledge.
1272. 01:15:39 It's really helped good to help me edit things and, you know, answer questions and summarize
1273. 01:15:44 and do some of those things.
1274. 01:15:46 But when it comes to the coaching side, I still think like an actual human sitting across
1275. 01:15:51 from me face to face or even virtually just is so much more effective and efficient.
1276. 01:15:56 Um, because they can read cues better.
1277. 01:15:59 They can hear the nuances or understand the nuances.
1278. 01:16:03 They're less likely to be manipulated.
1279. 01:16:05 Um, you know, as you're working through things.
1280. 01:16:07 So this is probably the area where I'm a little less, you know, gung ho on what, um,
1281. 01:16:12 Moloch saying in this section.
1282. 01:16:14 All right, let's, let's get the rounding it out.
1283. 01:16:16 So now AI is our future.
1284. 01:16:17 He talks about four possible futures here.
1285. 01:16:20 He basically says AI, this is good.
1286. 01:16:21 It's going to get, it's not going to get any better.
1287. 01:16:23 Um, there's some reasons for that.
1288. 01:16:24 He throws it out there that basically, um, rules and regulations, stop it.
1289. 01:16:29 Technology progress stops it, you know, whatever it is, but it just doesn't get any better
1290. 01:16:33 than it is right now.
1291. 01:16:34 He talks about slow growth.
1292. 01:16:36 The fact that, you know, not everything can remain exponential and maybe just will be limited
1293. 01:16:40 by the future of the way the technology pans out.
1294. 01:16:43 So therefore it'll grow, but it's not going to grow as quickly as it has, you know, lately
1295. 01:16:47 or, um, from the, from the start.
1296. 01:16:50 He talks about exponential growth and he uses your, um, your flywheel, right?
1297. 01:16:54 So yeah, he talks about the fact that it's a flywheel.
1298. 01:16:56 So, uh, basically AI companies can use AI systems to help them generate the next version
1299. 01:17:01 of AI and boom, boom, boom, boom.
1300. 01:17:03 And it just feeds on itself and we, we keep that curve going up exponentially.
1301. 01:17:06 And then he talks about the machine God as the, as the last one, what they basically
1302. 01:17:11 says, we're going to develop some artificial super intelligence.
1303. 01:17:14 It's essentially going to rule everything, um, in, you know, that that's how it goes.
1304. 01:17:20 Or that's how it would go there.
1305. 01:17:22 Uh, he, I'm trying to remember what he, he thinks he doesn't think that as good as it's
1306. 01:17:26 going to get, um, is, is likely.
1307. 01:17:29 He also doesn't think the machine God scenario is the likely scenario, but I can't remember.
1308. 01:17:34 Do you remember, whether he thinks it's going to be slow growth or exponential?
1309. 01:17:37 Does he ever say that?
1310. 01:17:39 I don't remember.
1311. 01:17:40 Um, if I had to pick one, I think it's probably between slow and exponential.
1312. 01:17:46 I feel like we've seen some exponential growth recently, but I think we are going to hit
1313. 01:17:51 some diminishing returns with some of the AI stuff soon.
1314. 01:17:56 That doesn't mean that it's not going to continue to improve.
1315. 01:18:00 But I think it is going to slow a little bit from where it is right now.
1316. 01:18:03 However, I think just the rapid pace of, of change, um, that is accelerating overall.
1317. 01:18:11 So I don't expect it to be, uh, slow growth either.
1318. 01:18:15 Uh, I want to real briefly mention the, the machine God scenario.
1319. 01:18:19 He mentions that there's no theoretical reason this can't happen, but there's no reason to
1320. 01:18:23 suggest it might, which I think is kind of refreshing that, that perspective, because,
1321. 01:18:32 uh, the stuff that I've seen online from people who fall into, you know, this is as good as
1322. 01:18:36 it gets or the other extreme, it's going to take over everything.
1323. 01:18:40 They tend to be very cut and dry.
1324. 01:18:43 Like this is the reason and this is, you know, factual, 100% certainty.
1325. 01:18:47 And I feel like, uh, he's essentially saying it, at, at this chapter, we don't know what
1326. 01:18:53 this is going to look like.
1327. 01:18:55 He's basically throwing out a whole bunch of different, um, possibilities.
1328. 01:19:01 And, uh, I think I really, I, well, I really like this, uh, this approach.
1329. 01:19:05 And I think what this does for me after I read this is question anybody who says with
1330. 01:19:11 any degree of certainty, like this is what you got to watch out for.
1331. 01:19:15 This is what's going to happen.
1332. 01:19:17 Yep.
1333. 01:19:18 Uh, I think the things he says might happen where people get left behind, people struggle
1334. 01:19:24 with the, the transition.
1335. 01:19:25 I think all those, those things make sense, right?
1336. 01:19:27 He calls them the smaller catastrophes.
1337. 01:19:29 I think all those make sense, but, um, I like what you said in terms of, you know, there's
1338. 01:19:35 no probability or there's, sorry, there's no likelihood that it, it will happen, but
1339. 01:19:39 it might, you know, like we can't say, we can't say that it won't.
1340. 01:19:42 All right.
1341. 01:19:43 Um, now we have the epilogue.
1342. 01:19:44 Um, I, I didn't get a ton out of the epilogue.
1343. 01:19:47 Um, basically it's a tie back into let's think about AI as a co-intelligence.
1344. 01:19:54 Um, it doesn't have a mind of its own.
1345. 01:19:57 Its math and it's, you know, programmed and it's, it's really impressive for what it can
1346. 01:20:02 do.
1347. 01:20:03 Uh, but you know, his last statement, I think, uh, or one of the last statements he says,
1348. 01:20:07 "humans are far from obsolete at least for now."
1349. 01:20:09 Right?
1350. 01:20:10 And I was like, what a, but, but even there, like I thought it was a great way to end the
1351. 01:20:13 book.
1352. 01:20:14 It's like, hey, remember, use this thing as a co-intelligence, but humans are so important
1353. 01:20:19 and, and don't lose the sight of the fact that humans are important.
1354. 01:20:22 Yeah.
1355. 01:20:23 But he also said a, this is only like two pages and half of one of the pages is the return
1356. 01:20:29 of the prompt that he, but one of the things he mentions on page two, 12, the very last
1357. 01:20:35 page is that AI is a mirror reflecting back at us our best and worst qualities.
1358. 01:20:41 I like that too.
1359. 01:20:42 And then I also agree that that last sentence of the book is, is great about humans being
1360. 01:20:46 far from obsolete, at least for now.
1361. 01:20:48 Um, that, that's the perfect way to, to end it.
1362. 01:20:50 I agree.
1363. 01:20:51 Don't worry, but this is going to change.
1364. 01:20:54 All right.
1365. 01:20:55 So before we go to styling rating, action items, all that stuff, I have a thing for you, Mike.
1366. 01:21:01 So if you scroll down to the bottom of the notion page, I asked you not to look at these.
1367. 01:21:04 Hopefully you, you helped turn to that.
1368. 01:21:06 Okay.
1369. 01:21:07 So go into the first one, go into a four-quary, like the person.
1370. 01:21:10 All right.
1371. 01:21:11 This is the prompt I gave it.
1372. 01:21:12 And I base, I mean, we were doing a book on AI, right?
1373. 01:21:14 I had to, I had to go and see what it did.
1374. 01:21:18 Act like you're a professional book reviewer and provide a chapter by chapter summary of
1375. 01:21:21 the book, Co-intelligence Living and Working with AI by Ethan Mullick.
1376. 01:21:24 Um, I don't want to go through all these because it actually does, it does this and
1377. 01:21:28 it does it very, very well in terms of giving us a, a summary of it.
1378. 01:21:32 But now go back and go to the second one.
1379. 01:21:35 Okay.
1380. 01:21:36 All right.
1381. 01:21:37 Got it.
1382. 01:21:38 Now I said, act like you're a professional book reviewer, provide a chapter by chapter
1383. 01:21:40 summary, Co-intelligence by Ethan Mullick.
1384. 01:21:42 You should include all headings and subheadings contained within the chapters.
1385. 01:21:46 And I think it did a really good job.
1386. 01:21:49 Right?
1387. 01:21:50 Yeah.
1388. 01:21:51 I actually thought it was going to kick back an air to me that said, I can't do that.
1389. 01:21:55 It's copyrighted work.
1390. 01:21:56 You know what I mean?
1391. 01:21:57 Like, or, you know, you're going to ask me for too much of the, of the copyrighted work.
1392. 01:21:59 And it did not do that.
1393. 01:22:01 It 100% gave us a, um, a good summary.
1394. 01:22:05 Um, but what I want to call out here is one, the prompting.
1395. 01:22:11 So we've talked about this a little bit before, like the more detailed the prompts you give
1396. 01:22:14 it, the better results you get.
1397. 01:22:17 Two, you have to review this stuff to make sure that these are actually the headings
1398. 01:22:21 and the subheadings because it might have just made this stuff up.
1399. 01:22:24 Like if I don't review that, but then three, and this is not for you and I to claim, you
1400. 01:22:29 know, like the importance of ourselves or anything like that.
1401. 01:22:33 There's a big difference between ask me asking it for a summary with headings and subheadings
1402. 01:22:37 and then listening to two people talk about chapter by chapter, you know, the book and
1403. 01:22:43 what they got out of it and how they went.
1404. 01:22:45 And I say that because it's like it's, there's a place for all of these things and you can
1405. 01:22:51 use them in the way that makes the most sense for you and in a way that provides the most
1406. 01:22:55 value for you.
1407. 01:22:56 And I just think that's a, that's a valuable thing.
1408. 01:22:58 Like I really think, you know, that having the access to this is better than not having
1409. 01:23:04 the access to it.
1410. 01:23:05 100%.
1411. 01:23:06 Awesome.
1412. 01:23:07 All right.
1413. 01:23:08 Let's go into action items.
1414. 01:23:09 Mike, what action items do you have coming out of reading co-intelligent?
1415. 01:23:17 I just have the one that you gave me figure out what I'm going to delegate to AI.
1416. 01:23:23 I think I'm going to take other action from this book, but I don't have anything specific
1417. 01:23:29 that I'm going to do with it.
1418. 01:23:31 I think the big thing is to consider where I can invite AI to the table.
1419. 01:23:39 But I don't know exactly where that is yet.
1420. 01:23:42 So we mentioned the Pro Show, some of the tools that we're using that have AI.
1421. 01:23:49 But I want to find some additional ways to do some of the things that I'm doing.
1422. 01:23:54 And my big perspective change walking away from this book is essentially even if it doesn't
1423. 01:24:00 get it right on the first try and it takes a little bit of time to craft the prompt or
1424. 01:24:05 to dial it in.
1425. 01:24:06 It's worth the work to do that kind of stuff.
1426. 01:24:10 So I don't know exactly what that's going to look like, but I just want to have that
1427. 01:24:15 perspective as I go about my creative routines and build my systems.
1428. 01:24:20 Wonderful.
1429. 01:24:22 So I have two.
1430. 01:24:24 So one, I want to plan how I can teach with AI more intentionally.
1431. 01:24:27 So I am very much an adopter of this.
1432. 01:24:29 I don't fear this.
1433. 01:24:31 I welcome this into my classrooms.
1434. 01:24:33 I've already used it in certain classes.
1435. 01:24:36 And I want to be even more intentional in the 24/25 academic year about we're going to
1436. 01:24:42 embrace this.
1437. 01:24:43 We're not going to fear it.
1438. 01:24:44 We're not going to run away from it.
1439. 01:24:47 And you might cheat, sure.
1440. 01:24:50 But at the same time, I need to be creative about the way I design prompts and assignments
1441. 01:24:54 and reflections to where it's hard for you to cheat.
1442. 01:24:58 You can use AI, but we're going to do it in a way that it's difficult for you to cheat
1443. 01:25:02 easily.
1444. 01:25:04 The second one is I want to think about the scholarly side of my work.
1445. 01:25:08 So the research side, the other thing I do that isn't just in the classroom and say,
1446. 01:25:13 how do I utilize AI more effectively to do reviews or to analyze different sets of data
1447. 01:25:24 or what it might be?
1448. 01:25:26 Data is hard because if you're under IRB review, you can't really share the data because it's
1449. 01:25:33 like you're putting it out there for essentially everybody.
1450. 01:25:36 So it's protected data.
1451. 01:25:38 But more generally, secondary data.
1452. 01:25:40 How could I analyze secondary data and say, hey, go to the IPEDS database and tell me
1453. 01:25:46 the number of engineering graduates since 1974?
1454. 01:25:50 It's like, can I manipulate AI in a way that it will actually kick back an accurate response?
1455. 01:25:56 I want to play around with more in-depth prompting and utilizing it more.
1456. 01:26:00 I don't think that has a real tangible next week.
1457. 01:26:05 I say, yeah, I did exactly that.
1458. 01:26:07 But I think it's one of those ones that's in the back of my head that I'll keep thinking
1459. 01:26:10 about.
1460. 01:26:11 Awesome.
1461. 01:26:12 All right.
1462. 01:26:13 So this is my book.
1463. 01:26:14 My book means I get to rate it first.
1464. 01:26:18 I don't think you'll be surprised to hear that I loved this book.
1465. 01:26:22 I thought this book was outstanding.
1466. 01:26:26 I didn't think the author wasted much time.
1467. 01:26:29 I thought the author talked about things in a flow that made sense and organized it effectively.
1468. 01:26:36 I thought that the author hit--
1469. 01:26:39 I can't think of something that I would have wanted to be in the book that wasn't in there.
1470. 01:26:44 That it's like, oh, yeah, but you didn't talk about this whole area or this whole section.
1471. 01:26:49 I thought it was both theoretical in terms of telling us some of the fundamental understandings
1472. 01:26:54 of it, but also practical in pushing us to be in a co-intelligence relationship with
1473. 01:27:02 AI.
1474. 01:27:03 Overall, loved it.
1475. 01:27:05 I'm a huge fan of this book.
1476. 01:27:06 I will recommend it to people to read if they want to get more into understanding AI and
1477. 01:27:13 how AI we can interact with AI.
1478. 01:27:15 So you won't be surprised.
1479. 01:27:17 It's a 5.0 for me.
1480. 01:27:19 All right.
1481. 01:27:21 Well, I agree that this is probably best-case scenario for me in terms of a book on AI.
1482. 01:27:28 I enjoyed this way more than I thought I would.
1483. 01:27:32 I think the only thing that's going to keep me from rating it 5 stars is that I don't
1484. 01:27:39 know that this is going to hold up real well just because AI is changing so fast.
1485. 01:27:50 Everything he shares here makes sense.
1486. 01:27:53 I really like the four principles.
1487. 01:27:56 I really like the four scenarios, but I feel like in 12 months, does one of those scenarios
1488. 01:28:04 feel like, oh, that's just ridiculous?
1489. 01:28:07 I don't know.
1490. 01:28:08 I'm not sure that this stands the test of time.
1491. 01:28:11 I think right now, this is the book to read on artificial intelligence.
1492. 01:28:18 This is episode 200 of Bookworm, though, and we do an episode every other week.
1493. 01:28:25 So I've been reading books and talking about them for what is that?
1494. 01:28:31 Like almost eight years.
1495. 01:28:32 Eight years.
1496. 01:28:34 When we get to episode 400, is this one of the ones that I look back and like, yeah,
1497. 01:28:40 that was life-changing?
1498. 01:28:42 I'm not sure.
1499. 01:28:43 I'm just like, it might be, but I'm a little bit skeptical.
1500. 01:28:48 So I'm going to rate it four stars only because I've got a whole bunch of books that I've
1501. 01:28:54 read and a lot of books that I have rated five stars that, you know, when I read them,
1502. 01:29:00 I really felt like this book is going to revolutionize my life.
1503. 01:29:05 I'm going to be able to look back and say, I thought one way prior to this.
1504. 01:29:09 Now I think differently and my life is different as a result.
1505. 01:29:14 I think jury's still out with the artificial intelligence stuff.
1506. 01:29:18 Not that it's not going to affect our future.
1507. 01:29:22 I think it absolutely is going to change everything.
1508. 01:29:28 I think at the moment, you know, this is the best book that I've come across on the topic,
1509. 01:29:33 but like I said, it's changing so fast.
1510. 01:29:35 I'm not sure how good it holds up.
1511. 01:29:39 But I really enjoyed it.
1512. 01:29:40 And you know, I want to reiterate, you know, if you're looking for a book on artificial
1513. 01:29:45 intelligence, this is the one to read.
1514. 01:29:48 I feel like it does a really good job of explaining the different scenarios.
1515. 01:29:53 So you're going into things with your eyes open in terms of the possibilities.
1516. 01:29:58 He doesn't shy away from some of the bad stuff that could happen from it, but that's not
1517. 01:30:02 a reason to avoid it.
1518. 01:30:03 Like it's a very balanced approach, I feel.
1519. 01:30:06 And it's the right approach for where we are right now.
1520. 01:30:09 I'd agree.
1521. 01:30:10 All right, Mike, let's put co-intelligence on the shelf.
1522. 01:30:16 And let's think about what our upcoming books are.
1523. 01:30:19 So you've got the next book.
1524. 01:30:20 What's next?
1525. 01:30:21 I do.
1526. 01:30:22 The next one is Right Thing Right Now by Ryan Holiday.
1527. 01:30:25 I think we've read just about every Ryan Holiday book for Bookworm.
1528. 01:30:30 This is the third one in the Stoic series.
1529. 01:30:32 I am getting more and more excited to talk about this one.
1530. 01:30:36 I heard Ryan share that he thought this one was going to be the hardest one to sell because
1531. 01:30:42 he packaged the four Stoic virtues.
1532. 01:30:46 And he sold that idea to his publisher.
1533. 01:30:48 And he told them, hey, just so you know, this one's going to do the worst out of all of
1534. 01:30:51 them.
1535. 01:30:52 But it was the quickest one to show up on the New York Times bestsellers list.
1536. 01:30:57 So.
1537. 01:30:58 Awesome.
1538. 01:30:59 All right.
1539. 01:31:00 So then after that, as you're prepping, we'll do a book.
1540. 01:31:03 My next book is called Uncoming Greatness by Mark Miller.
1541. 01:31:07 It's a book on leadership.
1542. 01:31:08 And I found it, you know, looking through some different things on thinking about leadership.
1543. 01:31:13 And I think it'll be a good, a good book following Right Thing Right Now.
1544. 01:31:17 Mike, do you have any gap books right now?
1545. 01:31:21 Uh, maybe.
1546. 01:31:24 I do have a book with me, which I want to read.
1547. 01:31:27 I think I mentioned this as a gap book before, but didn't get a chance to read it.
1548. 01:31:30 And that is Simple Marketing for Smart People by Billy Boris and Tiago Forte.
1549. 01:31:36 Billy is the guy that does the marketing for Tiago and he has his own courses that he,
1550. 01:31:42 he sells.
1551. 01:31:44 I think he calls it like the five light bulbs method or something like that.
1552. 01:31:49 So I've seen some of his stuff before and it really does feel like very helpful, practical
1553. 01:31:55 marketing advice and as I'm getting ready to release a new product.
1554. 01:31:59 This is something that I'm thinking about.
1555. 01:32:01 Alrighty.
1556. 01:32:02 Uh, I don't have any gap books.
1557. 01:32:04 I don't want any gap books.
1558. 01:32:06 I am not in a place right now where a gap book makes sense.
1559. 01:32:10 I'm going to read Right Thing Right Now.
1560. 01:32:12 I'm going to enjoy Right Thing Right Now.
1561. 01:32:15 If I get lucky and am able to carve out some other time for a gap book, I will do that.
1562. 01:32:19 But as of right now, I do not have one.
1563. 01:32:22 Alrighty.
1564. 01:32:23 Alright.
1565. 01:32:24 So that, uh, that does it for another episode of Bookworm.
1566. 01:32:27 Uh, thanks for everybody for listening.
1567. 01:32:29 For the pro members, thank you.
1568. 01:32:31 If you're interested in the pro show, uh, we have a Patreon.
1569. 01:32:36 So you can go to patreon.com/bookwormfm.
1570. 01:32:38 Uh, there at $7 a month, you can sign up.
1571. 01:32:41 Um, wanted to support the show to get, um, bonus content in terms of the pro show.
1572. 01:32:46 Today we talked about the AI that we're using and like the apps that have it integrated
1573. 01:32:52 and how it integrates.
1574. 01:32:53 Um, you also get the, um, the bootleg version of the feed.
1575. 01:32:57 So straight from our Zoom recording, uh, we pump it out and, uh, give it to you.
1576. 01:33:01 Um, and then, um, there's an ad-free episode of the show.
1577. 01:33:05 So, um, if you're interested in that, uh, we'd love it.
1578. 01:33:08 It's, uh, patreon.com/bookwormfm.
1579. 01:33:10 Um, other than that, any last words?
1580. 01:33:14 Mike?
1581. 01:33:15 Ah, if you're reading along with us, pick up Right Thing Right Now by Ryan Holiday.
1582. 01:33:19 And we'll talk to you in a couple of weeks.