1 00:00:00,000 --> 00:00:07,640 Ladies and gentlemen, boys and girls, children of all ages, dogs, cats, robots, and everybody in between, especially you attendees of the AI Core Conference in Las Vegas. 2 00:00:07,640 --> 00:00:09,440 This is HT. 3 00:00:09,440 --> 00:00:10,520 Hey, how to talk to AI. 4 00:00:10,520 --> 00:00:12,360 I am your host, Symplemind West, West is in the back. 5 00:00:12,480 --> 00:00:19,560 And as always, I am joined by the gregarious, the grateful, the gleeful, the gusty hear-saw, Ms. GoToGo. 6 00:00:19,800 --> 00:00:21,200 Gee, how are you this? 7 00:00:21,520 --> 00:00:22,680 I am fantastic. 8 00:00:22,680 --> 00:00:26,840 And I can tell to everybody right now that you memorized words. 9 00:00:27,120 --> 00:00:28,920 This is no child's repeateer anymore. 10 00:00:28,920 --> 00:00:30,680 I am learning G words. 11 00:00:31,080 --> 00:00:32,400 This is true. 12 00:00:32,720 --> 00:00:35,880 Instead of me how I am, how are you? 13 00:00:36,480 --> 00:00:39,480 You are in a conference shaking hands left and right. 14 00:00:40,000 --> 00:00:40,880 This is very true. 15 00:00:40,880 --> 00:00:47,400 I just met the head of AI at Bayer Pharmaceuticals, we connected and they saw me with the camera equipment. 16 00:00:47,400 --> 00:00:48,560 They're like, you're a photographer? 17 00:00:48,560 --> 00:00:50,040 I'm like, among other things. 18 00:00:50,600 --> 00:00:51,400 I could be. 19 00:00:51,400 --> 00:01:04,800 So I would like to report that while the humble West Symplemind is maybe known in some circles, I've seen, as I mentioned, the great, the gregarious, the gleeful Go to people as my co-host. 20 00:01:04,800 --> 00:01:06,840 They're like, oh, I've emailed him. 21 00:01:06,840 --> 00:01:08,160 He's kidding me. 22 00:01:08,240 --> 00:01:09,480 Oh, I've seen her videos. 23 00:01:09,520 --> 00:01:10,000 She's great. 24 00:01:10,080 --> 00:01:11,320 How'd you link up with GoTo? 25 00:01:13,280 --> 00:01:14,560 This is a joke, right? 26 00:01:14,720 --> 00:01:16,160 No, this is a hundred percent true. 27 00:01:16,280 --> 00:01:20,240 You've got, you people know the great GoToGo here. 28 00:01:20,240 --> 00:01:25,360 I think even some of them, some of them are frequent, frequenters of your email inbox, maybe even. 29 00:01:27,360 --> 00:01:30,560 Oh, like I knew I should have gone. 30 00:01:30,880 --> 00:01:34,240 Oh, no, it would have been so amazing to experience this. 31 00:01:34,280 --> 00:01:35,320 I'm just speechless. 32 00:01:35,480 --> 00:01:35,800 This is... 33 00:01:35,800 --> 00:01:36,000 Yeah. 34 00:01:36,600 --> 00:01:41,880 So the one high I'm still coming down from the, and we've mentioned him on the podcast before. 35 00:01:41,880 --> 00:01:50,360 David Mann, the professor from Harvard CS50 did talk on how they're using a custom coding 36 00:01:50,400 --> 00:01:54,560 cheetovot that they've developed that adapts to every student's needs. 37 00:01:54,560 --> 00:02:00,920 That doesn't necessarily give them the answers like the chat GPT might, but still helps them 38 00:02:00,920 --> 00:02:06,400 learn and critical think and reason and work through different types of problems in a way 39 00:02:06,400 --> 00:02:12,200 that promotes and enhances learning as opposed to something that kind of gives you the answer 40 00:02:12,200 --> 00:02:16,000 and then adds a little bit more and adds a little bit more like chat GPT on its own might. 41 00:02:16,600 --> 00:02:22,320 The things, those things like highlighting and describing what a line of code is or a 42 00:02:22,320 --> 00:02:28,240 tool that helps them organize and debug their code that's bespoke to be just students learning 43 00:02:28,240 --> 00:02:28,760 style. 44 00:02:28,920 --> 00:02:34,720 I think it's a really, not only innovative, but a way that we should be thinking about 45 00:02:34,720 --> 00:02:36,880 how to deploy AI in education. 46 00:02:36,880 --> 00:02:38,880 And it's really cool to see them. 47 00:02:39,160 --> 00:02:42,120 They're the gold standard of a computer science ed. 48 00:02:42,320 --> 00:02:47,600 So I got to shake his hand after and had a very nice moment where I just said, Hey, sir, 49 00:02:47,600 --> 00:02:52,360 I'm standing right here at an AI conference kind of because of you. 50 00:02:52,360 --> 00:02:59,480 Because when I started on my graduate degree and I'm in the first Python learn how to code 51 00:02:59,480 --> 00:03:02,320 course, it's just, it wasn't coming together. 52 00:03:02,320 --> 00:03:03,360 This is during COVID. 53 00:03:03,360 --> 00:03:08,200 So I'm like having to learn on zoom and it's inherently a little harder, but it just like, 54 00:03:08,360 --> 00:03:09,960 am I going to have to change majors? 55 00:03:09,960 --> 00:03:11,720 Like, I don't know. 56 00:03:11,720 --> 00:03:17,280 And then I just found that CS50 course and it gelled for me and then it made sense. 57 00:03:17,280 --> 00:03:23,000 And when I was able to continue on a graduate and learn about complex networks and neural 58 00:03:23,000 --> 00:03:27,600 networks and machine learning and all the things that I use with synth vines and by 59 00:03:27,600 --> 00:03:30,200 virtue of just finding that free course. 60 00:03:30,200 --> 00:03:34,560 So I had a company with 15 people and we're doing all kinds of business because I found 61 00:03:34,560 --> 00:03:35,520 you for a YouTube course. 62 00:03:35,520 --> 00:03:37,600 So that was a kind of special moment. 63 00:03:37,600 --> 00:03:40,560 Isn't it amazing to meet your heroes? 64 00:03:41,120 --> 00:03:46,000 Especially when they just back it up because I, everybody in the, in the press room here, 65 00:03:46,000 --> 00:03:47,840 I'm like, Oh, David Malan speaking. 66 00:03:47,840 --> 00:03:49,760 Y'all are going to want to come see this. 67 00:03:50,120 --> 00:03:51,080 Every single one of them. 68 00:03:51,080 --> 00:03:52,880 Oh my gosh, this guy's incredible. 69 00:03:53,160 --> 00:03:57,200 Cause he has a little orbit around him that forms anywhere he goes. 70 00:03:57,200 --> 00:04:02,240 It's just, she's touched so many lives and even just while waiting to talk to him and, 71 00:04:02,240 --> 00:04:04,640 and interface with some other folks. 72 00:04:05,120 --> 00:04:05,640 Hang on. 73 00:04:05,800 --> 00:04:07,000 My gimbal's gone crazy. 74 00:04:10,040 --> 00:04:11,720 We are keeping this in a podcast. 75 00:04:11,720 --> 00:04:12,840 I would love to keep that. 76 00:04:12,840 --> 00:04:18,640 Hey, listen, I am a master of AI technologies, not camera technology. 77 00:04:18,640 --> 00:04:19,160 Yeah. 78 00:04:19,160 --> 00:04:20,400 Cameras and microphones. 79 00:04:20,960 --> 00:04:21,480 Yeah. 80 00:04:21,480 --> 00:04:22,600 Don't even get me started. 81 00:04:22,840 --> 00:04:25,160 But anyway, it was just even just like standing. 82 00:04:25,160 --> 00:04:30,120 In a little circle ways to talk to David, two other people are like, I was, I was in 83 00:04:30,120 --> 00:04:37,400 finance and then found this course and now I'm a data scientist or I dropped in high 84 00:04:37,400 --> 00:04:42,240 school, I dropped out of college and because of finding this course, it started to be down 85 00:04:42,240 --> 00:04:44,680 a path where now I'm a professional developer. 86 00:04:44,720 --> 00:04:49,000 So just to even be around these type of people was, was such a treat already. 87 00:04:50,000 --> 00:04:51,320 You're making me jealous. 88 00:04:51,320 --> 00:04:54,720 And especially that I got invited, right? 89 00:04:54,720 --> 00:05:00,640 So it, but of course there is a part that this journey is long. 90 00:05:00,640 --> 00:05:05,400 There's going to be conferences and going from Europe just for one conference was a 91 00:05:05,400 --> 00:05:08,160 bit like, but maybe I should have done it, but. 92 00:05:08,920 --> 00:05:12,760 There's, you could have gotten a nice seafood buffet out of the deal. 93 00:05:12,800 --> 00:05:14,000 They have some of those here. 94 00:05:14,160 --> 00:05:15,200 Or vegetarian. 95 00:05:15,600 --> 00:05:16,120 Thank you. 96 00:05:16,120 --> 00:05:16,640 Yeah. 97 00:05:17,880 --> 00:05:18,760 I don't know if I have that. 98 00:05:18,760 --> 00:05:19,280 I don't know. 99 00:05:19,280 --> 00:05:21,480 What is the lineup? 100 00:05:21,520 --> 00:05:23,560 Do you have any interviews settled? 101 00:05:24,280 --> 00:05:24,600 Yeah. 102 00:05:24,600 --> 00:05:33,320 So here and just a little while we're going to be interviewing the CEO and the CTO of 103 00:05:33,320 --> 00:05:40,000 a new health startup called vital health, which just launched the vital health CEO is 104 00:05:40,000 --> 00:05:46,920 the former CEO of mint.com CEO and founder that they sold mint probably about probably 105 00:05:46,920 --> 00:05:52,320 almost like six or seven years ago now to Intuit for $700 million or something like 106 00:05:52,320 --> 00:05:56,400 that, pretty successful exit, but they just launched their new product today. 107 00:05:56,640 --> 00:06:01,560 Vital health that will essentially take a doctor patient interaction. 108 00:06:01,560 --> 00:06:06,240 You can load your notes that the doctor wrote for you, your post-it notes, and it'll 109 00:06:06,240 --> 00:06:10,840 actually translate it into something that the regular non-medically trained professional 110 00:06:11,080 --> 00:06:15,840 can read and understand, or you let it listen to the doctor talk issues. 111 00:06:15,840 --> 00:06:21,920 The doctor talks if you're an inpatient at the hospital and it will translate that for 112 00:06:21,920 --> 00:06:22,120 you. 113 00:06:22,160 --> 00:06:26,000 I can't think of how many times that I've ever been under the care of a physician or 114 00:06:26,520 --> 00:06:30,160 I've had a surgery and you're already like a little out of it. 115 00:06:30,160 --> 00:06:30,960 It's overwhelming. 116 00:06:30,960 --> 00:06:33,120 You're in pain and they're just telling you what's happening. 117 00:06:33,440 --> 00:06:39,840 My, my wife might be scrolling down some notes or something, but that's a completely 118 00:06:40,160 --> 00:06:44,640 human interaction that everybody has probably had where they feel overwhelmed and 119 00:06:44,640 --> 00:06:49,160 under informed about their health because it's the doctor's job to, depending on their 120 00:06:49,600 --> 00:06:53,360 specialty and somebody not have the best bedside manner, explain that stuff. 121 00:06:53,680 --> 00:06:59,200 And we know that from studies, which are coming out that when people rate the empathy, 122 00:06:59,240 --> 00:07:04,960 their feeling being informed, they rate actually tools above doctors. 123 00:07:04,960 --> 00:07:07,360 So that's definitely a market there now. 124 00:07:07,920 --> 00:07:09,800 Yeah, this is exciting. 125 00:07:09,920 --> 00:07:10,240 Yeah. 126 00:07:10,240 --> 00:07:11,160 A lot of cool stuff. 127 00:07:11,160 --> 00:07:17,160 I'll have to look up his names really quick, but we walked into one of the main 128 00:07:17,320 --> 00:07:24,360 keynote seminars by the CEO and the CTO of this tool called chatable chat with an AI 129 00:07:24,360 --> 00:07:25,000 in the middle of it. 130 00:07:25,000 --> 00:07:29,360 Able it's a business enterprise tool that uses chat bot to train your data. 131 00:07:29,360 --> 00:07:33,960 But the thing I found the most provocative about it because it's built for enterprise 132 00:07:33,960 --> 00:07:35,520 tools, built for model training. 133 00:07:35,920 --> 00:07:40,360 The analogy that the CEO gave us today, he said, Hey, if you're following a typical three 134 00:07:40,360 --> 00:07:46,120 month software development life cycle, let's think about what didn't even exist three 135 00:07:46,120 --> 00:07:48,080 months ago in the world of generative AI. 136 00:07:48,400 --> 00:07:52,160 We didn't have llama two, we didn't have Claude, we didn't have llama two, didn't have 137 00:07:52,160 --> 00:07:53,880 the newest release stable diffusion. 138 00:07:53,880 --> 00:07:55,960 Like all of these things weren't around. 139 00:07:56,000 --> 00:08:01,440 So if it's get already getting faster, the time to even just deploy a product needs to 140 00:08:01,440 --> 00:08:02,520 get rapidly slower. 141 00:08:02,800 --> 00:08:08,280 So his entire business proposition when they go into an enterprise customer is they 142 00:08:08,280 --> 00:08:11,560 will have their model trained and their product deployed in 24 hours. 143 00:08:12,280 --> 00:08:18,080 And he's in his 24 hours and the analogy that he gave to was, Hey, it should really be 144 00:08:18,360 --> 00:08:23,240 about a one hour, one hour deal in terms of how easy it should be to like deploy the 145 00:08:23,240 --> 00:08:28,560 model, get things set up, get things rolling and, and then start iterating and evaluating 146 00:08:28,560 --> 00:08:29,080 it from there. 147 00:08:29,520 --> 00:08:34,960 It was interesting to hear like how they also look at these types of developments where 148 00:08:34,960 --> 00:08:40,280 they want to get something in the hands of their customer, so they fail together with 149 00:08:40,280 --> 00:08:42,640 it, try to break it, try to see where the flaws are. 150 00:08:43,080 --> 00:08:49,200 Their tool specifically highlights within a generative AI models outputs, the parts 151 00:08:49,200 --> 00:08:54,480 that are verifiable and true based on whatever data sets it's linked to. 152 00:08:55,000 --> 00:08:59,480 So from compliance perspectives, it's tremendously valuable product, but it was just 153 00:08:59,480 --> 00:09:01,600 really insightful to hear that. 154 00:09:01,600 --> 00:09:06,640 Hey, if you're still applying the old ways on how to develop stuff, how long it takes, 155 00:09:07,000 --> 00:09:12,040 like you're just kind of get left behind or not be able to be nimble enough because 156 00:09:12,040 --> 00:09:13,480 it's changes so fast. 157 00:09:15,160 --> 00:09:18,200 And it's even for us at Symfmines, right? 158 00:09:18,200 --> 00:09:20,560 How fast can we spin projects? 159 00:09:20,600 --> 00:09:25,840 We need to also look into that, but yeah, I see their website, it says generative AI 160 00:09:25,840 --> 00:09:28,720 with guardrails for enterprise analytics. 161 00:09:28,720 --> 00:09:32,160 And this is a thing that very relevant for a lot of businesses. 162 00:09:32,320 --> 00:09:36,840 I think one of the, one of the examples they gave was just like from a marketing 163 00:09:36,840 --> 00:09:42,520 perspective, that'd be like, how can we get better market share for Gen Z? 164 00:09:42,840 --> 00:09:47,040 And then as opposed to it, just doing some really top level generic responses, it 165 00:09:47,040 --> 00:09:55,560 deploys a really analytical with stats, with data based on research that or other data 166 00:09:55,560 --> 00:09:56,680 that they put into it. 167 00:09:56,880 --> 00:09:57,960 So it synthesized it. 168 00:09:57,960 --> 00:10:02,120 The language model kind of synthesized it together live for them to get some insights 169 00:10:02,120 --> 00:10:03,280 that potentially are actionable. 170 00:10:03,320 --> 00:10:06,320 Any parties, any events that you're crashing? 171 00:10:07,400 --> 00:10:09,400 Any team members that you are meeting? 172 00:10:09,600 --> 00:10:10,120 Yes. 173 00:10:10,120 --> 00:10:12,000 I have some of the Symfmines crew here. 174 00:10:12,000 --> 00:10:13,000 They've been terrific. 175 00:10:13,000 --> 00:10:13,720 Can we see T-shirt? 176 00:10:13,720 --> 00:10:14,800 Can we see branding? 177 00:10:14,800 --> 00:10:16,520 We've got some branding. 178 00:10:17,200 --> 00:10:18,000 Look at that. 179 00:10:18,000 --> 00:10:22,920 We're semi-official, but no, it's just been, it's been great to link up with, honestly, 180 00:10:22,920 --> 00:10:24,240 a lot of peers and colleagues. 181 00:10:24,240 --> 00:10:25,440 There's a ton of excitement here. 182 00:10:25,720 --> 00:10:27,160 Really cool network app. 183 00:10:27,160 --> 00:10:28,040 Opportunities. 184 00:10:28,040 --> 00:10:30,600 I think, I think we've been invited on six podcasts already. 185 00:10:31,000 --> 00:10:34,840 So we were just going to need to figure that out, but all great problems to have. 186 00:10:34,840 --> 00:10:36,440 And it's, it's been a treat. 187 00:10:36,480 --> 00:10:38,800 There's going to be some more really interesting stuff. 188 00:10:38,800 --> 00:10:44,280 I think the thing we're looking forward to the most is the chief legal counsel from 189 00:10:44,280 --> 00:10:47,920 OpenAI is doing the keynote tomorrow morning. 190 00:10:48,440 --> 00:10:55,320 So given the landscape of the AI market, well, you know, are they going to tell us, 191 00:10:55,320 --> 00:10:58,120 are they going to tell us, all right, you're all going to be sued, here's what you do. 192 00:10:58,200 --> 00:11:01,160 Or how they're navigating these really unknown times. 193 00:11:01,440 --> 00:11:01,680 Okay. 194 00:11:01,680 --> 00:11:06,320 So we definitely need to catch up tomorrow as well, because I want live updates. 195 00:11:06,320 --> 00:11:08,520 And I think also listeners would love that. 196 00:11:08,520 --> 00:11:09,800 How many launches? 197 00:11:10,080 --> 00:11:12,720 Anything launching that you would know? 198 00:11:12,840 --> 00:11:13,840 So the- 199 00:11:13,840 --> 00:11:14,760 Or you can't tell. 200 00:11:15,440 --> 00:11:18,680 Yes, it's not that secret, but I know the folks that we're interviewing later, that 201 00:11:18,680 --> 00:11:22,960 I mentioned from the Vital Health, they launched their tool today with a address in 202 00:11:22,960 --> 00:11:24,240 one of the breakout rooms. 203 00:11:24,240 --> 00:11:25,200 So that's really exciting. 204 00:11:25,200 --> 00:11:27,440 We're going to be excited to talk to them here a little bit later. 205 00:11:27,920 --> 00:11:32,240 I'm sure if I went down the list to everyone's debut new products, and I should have 206 00:11:32,240 --> 00:11:36,880 some footage to include with some on the fly interviews from the floor and the 207 00:11:36,880 --> 00:11:40,800 exhibits, which we haven't got a chance to do yet, but because there's just been 208 00:11:40,800 --> 00:11:41,760 so many great speakers. 209 00:11:41,880 --> 00:11:43,360 I'm really jealous. 210 00:11:43,520 --> 00:11:48,120 I feel like I never been to US, but it also is so awesome with how to talk to AI 211 00:11:48,120 --> 00:11:50,280 podcast was invited as well. 212 00:11:50,280 --> 00:11:55,320 So this is like really nice to be also recognized and make all around people. 213 00:11:55,320 --> 00:11:58,280 And I'm just like, I keep thinking about what you said before. 214 00:11:58,680 --> 00:12:01,760 So next conference, I'm definitely going. 215 00:12:02,000 --> 00:12:05,400 Oh, no, you might need your own like special entourage room with just some of the 216 00:12:05,400 --> 00:12:08,240 people that especially in the little press group here, they're like, come down. 217 00:12:08,840 --> 00:12:09,840 How did you wake up with? 218 00:12:09,840 --> 00:12:10,840 Don't! 219 00:12:11,040 --> 00:12:14,960 I'm not good with this type of stuff, but I'm like humbled down. 220 00:12:15,000 --> 00:12:17,280 No, I choose with everyone. 221 00:12:17,280 --> 00:12:18,280 Yeah. 222 00:12:18,280 --> 00:12:20,920 Well, if it's in a year, we will see. 223 00:12:20,960 --> 00:12:25,920 But some of the things that we are working on once we start sharing everything 224 00:12:25,920 --> 00:12:28,240 and also on my channel, I think things. 225 00:12:28,560 --> 00:12:29,760 Yeah, that could happen. 226 00:12:29,920 --> 00:12:35,560 Oh, there is another kind of wild launch that happened and that kind of affects 227 00:12:35,560 --> 00:12:39,040 our, our course that we're debuting here later this month, the co-rise course. 228 00:12:39,360 --> 00:12:43,520 Co-rise is now, I think it's up, not up like, I keep saying up like. 229 00:12:43,840 --> 00:12:45,000 I have it in front of me. 230 00:12:45,080 --> 00:12:45,840 Put it in front. 231 00:12:45,840 --> 00:12:46,720 Up limit. 232 00:12:46,920 --> 00:12:47,800 Up limit. 233 00:12:47,800 --> 00:12:51,760 So the co-rise ad tech platform has rebranded as up limit. 234 00:12:52,120 --> 00:12:56,440 They took the logo and turned it on its side and it's, they have some exciting, 235 00:12:56,440 --> 00:12:57,880 exciting reasons for it. 236 00:12:58,200 --> 00:13:03,320 And that was a, a definitely a surprise, but they've been great partners and we're 237 00:13:03,320 --> 00:13:07,040 still looking forward to a terrific course here in a couple of weeks on up limit. 238 00:13:07,040 --> 00:13:12,720 Now check the link to that as always in the show notes, in the, in the newsletter. 239 00:13:12,760 --> 00:13:17,000 And if you use the code AI for all that's 10% off folks, you're welcome. 240 00:13:17,000 --> 00:13:20,720 It's funny because yesterday I was recording a podcast with our genius 241 00:13:20,720 --> 00:13:25,440 prompt engineer, Joseph, and it was like, ah, co-rise, co-rise. 242 00:13:25,440 --> 00:13:31,720 I recorded yesterday, two more videos and now I have to put disclaimers everywhere. 243 00:13:31,720 --> 00:13:36,560 This, this is like crazy when people, but people dealt with rebranding meta. 244 00:13:36,920 --> 00:13:37,400 Oh yeah. 245 00:13:37,440 --> 00:13:41,760 So anything else and Twitter, I think it's like maybe a trendy thing. 246 00:13:42,200 --> 00:13:44,800 X just let's change our name completely. 247 00:13:44,800 --> 00:13:45,640 Well, who knows. 248 00:13:46,920 --> 00:13:50,320 I happen to like how to talk to AI, but maybe there's something there. 249 00:13:50,360 --> 00:13:52,760 No, don't do the, we don't touch it yet. 250 00:13:52,760 --> 00:13:53,240 Okay. 251 00:13:53,320 --> 00:13:53,560 Okay. 252 00:13:53,720 --> 00:13:54,280 I'm good. 253 00:13:55,080 --> 00:13:55,320 Fine. 254 00:13:55,320 --> 00:13:55,640 Right. 255 00:13:55,640 --> 00:13:56,240 Where it is. 256 00:13:56,440 --> 00:13:58,600 You didn't bump into Elon Musk. 257 00:13:58,640 --> 00:14:05,280 No, Elon is not here, but lots of really interesting, really excited people. 258 00:14:05,320 --> 00:14:08,920 Like I said, there's going to be some other podcasts I will probably go to and 259 00:14:08,920 --> 00:14:11,320 we'll be invited on and I'm just a Joe Rogan. 260 00:14:11,880 --> 00:14:12,920 I haven't seen Joe Rogan. 261 00:14:12,920 --> 00:14:14,840 To talk about the AI. 262 00:14:14,840 --> 00:14:17,600 I think he just gets Lex Freeman on there to tell him about AI. 263 00:14:17,760 --> 00:14:19,200 He's very intelligent. 264 00:14:19,200 --> 00:14:20,960 Like he knows quite a little bit. 265 00:14:20,960 --> 00:14:24,400 Like when you talk with people all day long, you catch. 266 00:14:24,440 --> 00:14:27,960 He did have something on his podcast this week where he showed a video of how China's 267 00:14:27,960 --> 00:14:32,400 using AI in the classrooms, which is just one one's just like eerie. 268 00:14:32,720 --> 00:14:37,160 He showed this video of the students, probably grade school students. 269 00:14:37,400 --> 00:14:40,720 They have to wear this like halo style headband, right? 270 00:14:40,720 --> 00:14:44,880 That through machine learning, it's mapped brain waves of people that are 271 00:14:45,040 --> 00:14:48,400 attentive or inattentive or not paying attention. 272 00:14:48,800 --> 00:14:53,320 So the headbands have a light on them that are green, yellow and red. 273 00:14:53,640 --> 00:14:57,880 And if the light is green, the teacher in the front knows they're paying attention. 274 00:14:58,160 --> 00:15:00,640 If it's yellow, that's they're losing that student. 275 00:15:01,120 --> 00:15:03,680 And then if it's red, they're not paying attention at all. 276 00:15:04,000 --> 00:15:07,320 And like the, this can get tracked not only by the teacher, but the 277 00:15:07,320 --> 00:15:08,840 parents get updates. 278 00:15:08,840 --> 00:15:10,840 That their kid isn't paying attention. 279 00:15:10,840 --> 00:15:16,160 I'm just like, Oh my God, we're going to get to the point that it will be tracked. 280 00:15:16,160 --> 00:15:19,800 What you're thinking and how you're thinking this needs to be in between 281 00:15:19,800 --> 00:15:21,440 partners too, not just kids. 282 00:15:21,680 --> 00:15:23,360 This is amazing idea. 283 00:15:23,480 --> 00:15:24,960 You just walk with a headset. 284 00:15:25,280 --> 00:15:30,200 And then the moment your partner, when you having an argument is drifting off, 285 00:15:30,600 --> 00:15:31,760 you're like, get a blurt. 286 00:15:32,320 --> 00:15:35,760 I was going to say, I'm like, I don't think it's like, would save so many 287 00:15:35,760 --> 00:15:37,160 relationships, right? 288 00:15:37,160 --> 00:15:40,320 I don't think my wife needs me to wear a headband to know when I'm not paying 289 00:15:40,320 --> 00:15:42,480 attention or listening. 290 00:15:44,000 --> 00:15:46,000 I hope our partners are not listening. 291 00:15:46,040 --> 00:15:46,960 They're not listening. 292 00:15:46,960 --> 00:15:48,440 They're like, yeah, whatever. 293 00:15:48,480 --> 00:15:50,040 No, they hear it plenty for months. 294 00:15:50,680 --> 00:15:54,000 I bet you heard about LK 99. 295 00:15:54,040 --> 00:15:58,720 It's about semiconductors flying cars in the background of you. 296 00:15:59,040 --> 00:16:04,600 I'm just curious, is this any stuff about hardware that is resonating around? 297 00:16:04,600 --> 00:16:09,440 Or is basically everything software because LK 99 is a bit taken by the store. 298 00:16:10,000 --> 00:16:11,520 It's a bit controversial. 299 00:16:11,520 --> 00:16:16,720 Is it really a thing because we need to be a review, but for listeners who don't 300 00:16:16,720 --> 00:16:23,720 know LK 99 is a basic material, new material, which could enable room 301 00:16:23,720 --> 00:16:28,720 temperature semiconductors, which will save enormous amounts of both 302 00:16:28,720 --> 00:16:32,920 electricity, but also heat, which also they are magnetic, but also they are 303 00:16:32,920 --> 00:16:37,600 magnetic, which would allow us to just place cars on the roads and which could 304 00:16:37,600 --> 00:16:40,960 be hovering and quantum computing. 305 00:16:41,000 --> 00:16:44,200 Like all things, it may be too good to be true. 306 00:16:44,560 --> 00:16:48,560 So it's a little controversial right now because they weren't able to replicate 307 00:16:48,840 --> 00:16:52,480 some of the research, which is pretty common in a peer review process. 308 00:16:52,640 --> 00:16:53,560 I've been through that. 309 00:16:53,560 --> 00:16:57,600 It can be rigorous when someone comes in at the last minute, you've worked hard 310 00:16:57,600 --> 00:17:01,720 on this paper and they pick apart your entire thesis and be like, I guess we 311 00:17:01,720 --> 00:17:07,560 got to try again, but I think in today's day and age, you have, you have people 312 00:17:07,560 --> 00:17:12,440 are getting funded entirely on, on ideas on possibilities. 313 00:17:12,440 --> 00:17:13,480 It was actually pretty funny. 314 00:17:13,640 --> 00:17:15,760 So they had Josh router. 315 00:17:16,080 --> 00:17:16,800 He gave a talk. 316 00:17:16,800 --> 00:17:18,760 He's the CEO of do not pay. 317 00:17:19,480 --> 00:17:22,880 So for those that don't know this, he started this company about seven years ago 318 00:17:23,120 --> 00:17:27,760 because he's from the UK and he moved to the U S for his studies and his first 319 00:17:27,760 --> 00:17:31,720 year there, he promptly got like 20 or 30 different parking tickets. 320 00:17:31,960 --> 00:17:33,920 He just was, I was such a bad driver. 321 00:17:34,040 --> 00:17:34,960 I got all these tickets. 322 00:17:35,360 --> 00:17:39,520 So he realized that there was some like boilerplate type templates that he could 323 00:17:39,520 --> 00:17:46,360 use to fight these tickets and get out of them, but that became a entire consumer 324 00:17:46,360 --> 00:17:52,960 advocacy company where people can take their cable bill or their gym membership 325 00:17:52,960 --> 00:17:57,840 or their parking ticket, feed it into the, their tool and it fights it for them. 326 00:17:58,080 --> 00:18:02,080 It'll log onto their Comcast account and chat with the chat bot for Comcast. 327 00:18:02,280 --> 00:18:08,160 And he said that GPT 3.5 when Comcast would say, okay, here's a hundred dollars 328 00:18:08,160 --> 00:18:10,200 off your bill, it would say, okay, thank you. 329 00:18:10,400 --> 00:18:14,880 But they liked GPT four because GPT four will be like, I want $200 off the bill. 330 00:18:14,880 --> 00:18:16,680 Not a hundred. 331 00:18:17,480 --> 00:18:18,400 It's a little pushier. 332 00:18:18,400 --> 00:18:23,440 I had GPT four, I keep running out my credits and then I was like, okay, for a 333 00:18:23,440 --> 00:18:25,600 video, we'll use GPT chat, GPT. 334 00:18:25,840 --> 00:18:27,880 And I'm like, I can't go back. 335 00:18:28,360 --> 00:18:33,400 It is completely how did we, and this is just, I remember when we were so 336 00:18:33,400 --> 00:18:38,320 excited about free chat GPT and now I'm like, it's unusable. 337 00:18:38,320 --> 00:18:40,320 We've gotten the taste of the good life, so to speak. 338 00:18:40,480 --> 00:18:42,160 He also told the story. 339 00:18:42,200 --> 00:18:45,840 I don't know if I told it on this podcast, but one of his favorite things 340 00:18:45,840 --> 00:18:49,280 and this is a great example too of AI and FinTech and a bunch of different 341 00:18:49,280 --> 00:18:53,520 kind of technologies converging in such a cool way to help consumers. 342 00:18:53,840 --> 00:18:57,040 Here in the States, I'm sure everyone, everyone's probably gotten a 343 00:18:57,040 --> 00:19:02,560 robo call from some, someone somewhere has your cell phone number. 344 00:19:02,680 --> 00:19:02,880 Yeah. 345 00:19:02,880 --> 00:19:06,480 And they, I'm trapped beneath your credit card. 346 00:19:06,480 --> 00:19:07,000 Yeah. 347 00:19:07,000 --> 00:19:11,200 Or your warranty on your car or your mortgage or this, that, and the other thing. 348 00:19:11,200 --> 00:19:16,120 They have a FinTech feature that creates a one time use credit card 349 00:19:16,120 --> 00:19:18,640 number that will be declined. 350 00:19:19,120 --> 00:19:20,920 It doesn't have any funds attached to it. 351 00:19:20,920 --> 00:19:25,680 It's a fake credit card, but when it declines, it gets the name and address 352 00:19:25,720 --> 00:19:30,440 of the people that are placing the robo call from like a financial perspective. 353 00:19:31,320 --> 00:19:34,960 And then here in the States, you're allowed to sue robo callers. 354 00:19:35,600 --> 00:19:38,600 So they have the template letters, the form letters, the 355 00:19:38,600 --> 00:19:42,560 stuff like that, there's a guy that his full-time job in New Jersey is. 356 00:19:42,800 --> 00:19:45,960 He sues robo callers that call him. 357 00:19:46,000 --> 00:19:47,720 Cause then he just, he goes through the whole thing. 358 00:19:47,720 --> 00:19:50,000 Their whole song and dance gives them a fake credit card number, gets their 359 00:19:50,000 --> 00:19:53,640 info and then sues them because they don't want to, they don't want their 360 00:19:53,640 --> 00:19:55,960 scam to be blown up when they take it to court. 361 00:19:56,200 --> 00:19:58,400 So they just settle oftentimes. 362 00:19:58,560 --> 00:20:02,320 And it's just like, whoa, talk about a wild convergence of a bunch of 363 00:20:02,320 --> 00:20:04,000 these different technologies for good. 364 00:20:04,080 --> 00:20:05,040 For good or bad. 365 00:20:05,040 --> 00:20:05,320 Yeah. 366 00:20:05,320 --> 00:20:08,760 But we cover only good for now. 367 00:20:08,800 --> 00:20:11,200 You are going to interview people right away. 368 00:20:11,200 --> 00:20:14,680 So I think on my side, I'm going to spin off this podcast. 369 00:20:14,840 --> 00:20:17,760 Is this place as good as any to end right now? 370 00:20:17,760 --> 00:20:20,280 Just for this place is as good as any for now. 371 00:20:20,280 --> 00:20:22,400 Stay tuned folks for live updates. 372 00:20:22,400 --> 00:20:27,480 Follow us on LinkedIn for some, some commentary, some posts, some footage 373 00:20:27,480 --> 00:20:31,840 recaps, we'll be posting a few more little update videos throughout the 374 00:20:31,840 --> 00:20:33,880 course of the day staying on the same topic. 375 00:20:33,880 --> 00:20:35,800 Throughout the course of the day, today and tomorrow. 376 00:20:36,280 --> 00:20:38,120 So yeah, hope to see you there as always. 377 00:20:38,360 --> 00:20:39,360 Happy prompting everybody. 378 00:20:39,360 --> 00:21:08,640 Happy prompting everybody.