1 00:00:00,240 --> 00:00:04,600 Speaker 1: Now here's a highlight from Coast to Coast AM on iHeartRadio. 2 00:00:04,920 --> 00:00:07,960 Speaker 2: All right, So justin Harrison, the CEO, and so I 3 00:00:08,080 --> 00:00:10,240 Speaker 2: look at this and I said, it's one of those 4 00:00:11,160 --> 00:00:14,920 Speaker 2: it's like one of those acronyms. When I look at it, 5 00:00:15,040 --> 00:00:17,680 Speaker 2: I want to say you because I see the V 6 00:00:17,840 --> 00:00:21,560 Speaker 2: and it looks like the Latin you, right, and so 7 00:00:21,920 --> 00:00:25,920 Speaker 2: I go, like, you've, it looks like but it's it's you've. 8 00:00:26,760 --> 00:00:29,760 Speaker 3: Is that our you've? You call it you've? Okay, all right, 9 00:00:29,840 --> 00:00:31,240 Speaker 3: so you only virtual? 10 00:00:32,080 --> 00:00:33,480 Speaker 1: You got it? Okay? Good. 11 00:00:33,840 --> 00:00:36,840 Speaker 2: So, so you were just saying though that here you were, 12 00:00:36,880 --> 00:00:40,720 Speaker 2: you were coming out of surgery after having you know, 13 00:00:40,920 --> 00:00:43,839 Speaker 2: been in this wreck. Your father comes down, mister engineer, 14 00:00:43,920 --> 00:00:47,839 Speaker 2: mister linear thinker, and he is talking to you, but 15 00:00:47,960 --> 00:00:50,559 Speaker 2: you're not making any sense because you're still you know, 16 00:00:50,640 --> 00:00:52,879 Speaker 2: coming down off the painkillers and whatever. 17 00:00:53,280 --> 00:00:57,040 Speaker 3: So you've you felt like you were speaking gibberish. So 18 00:00:57,080 --> 00:00:57,640 Speaker 3: what happened? 19 00:00:59,600 --> 00:01:02,680 Speaker 4: Well, you know, so he's going back and forth with it, 20 00:01:02,760 --> 00:01:06,640 Speaker 4: and he's probably me story obviously, you know, and retroactively, 21 00:01:06,720 --> 00:01:12,600 Speaker 4: and and you know, he says to me, I had 22 00:01:12,600 --> 00:01:15,399 Speaker 4: this huge sense of relief because you were getting so 23 00:01:15,480 --> 00:01:18,320 Speaker 4: annoyed with me, and I go, you know, and I'm like, 24 00:01:18,360 --> 00:01:21,039 Speaker 4: why would you believe that I'm getting annoyed? And he goes, yeah, 25 00:01:21,080 --> 00:01:24,600 Speaker 4: you know, you're you're exhaling hard, you're rolling your eyes, 26 00:01:24,680 --> 00:01:26,920 Speaker 4: and you were still there. 27 00:01:27,560 --> 00:01:28,120 Speaker 3: Yeah. 28 00:01:28,200 --> 00:01:31,480 Speaker 4: I knew that your head was okay. I knew that 29 00:01:31,520 --> 00:01:33,800 Speaker 4: my kid was still there. I knew you were still you. 30 00:01:34,440 --> 00:01:36,000 Speaker 4: And that for me, when he said that to me, 31 00:01:36,120 --> 00:01:38,600 Speaker 4: was a light bulb to me, which was I'm not 32 00:01:38,800 --> 00:01:42,319 Speaker 4: trying to save the information about my mom. I'm trying 33 00:01:42,319 --> 00:01:45,520 Speaker 4: to save what I know is her, what I know 34 00:01:45,680 --> 00:01:49,160 Speaker 4: her to be. And it opened up this whole thought 35 00:01:49,200 --> 00:01:53,600 Speaker 4: process about the fact that you know, my mom, as 36 00:01:53,640 --> 00:01:57,280 Speaker 4: we all are, was many different people to many different people. 37 00:01:57,800 --> 00:02:01,080 Speaker 4: You know, you, you're a personality, he isn't this sort 38 00:02:01,120 --> 00:02:05,680 Speaker 4: of universal truth right here were thousands of personalities, really, 39 00:02:06,120 --> 00:02:09,360 Speaker 4: and in that personality that is he in or is 40 00:02:09,560 --> 00:02:13,480 Speaker 4: justin is rightly just an aggregation of tons of smaller 41 00:02:13,520 --> 00:02:18,760 Speaker 4: personalities and really truly made me realize the biggest tragedy 42 00:02:18,800 --> 00:02:22,920 Speaker 4: of death is that for the survivors, this personality of 43 00:02:22,960 --> 00:02:25,680 Speaker 4: mine that came out from my mom for forty years, 44 00:02:25,760 --> 00:02:29,239 Speaker 4: by the time she died, it was just going to 45 00:02:29,360 --> 00:02:31,680 Speaker 4: go away. It was just going to be stuck there. 46 00:02:31,720 --> 00:02:33,840 Speaker 4: I mean, it doesn't go away. I'm alive. I still 47 00:02:33,919 --> 00:02:35,639 Speaker 4: that part of me is still there that wants to 48 00:02:36,000 --> 00:02:38,639 Speaker 4: connect with her, but there's nobody for me to pull 49 00:02:38,639 --> 00:02:42,040 Speaker 4: it out for because I only had one mom and 50 00:02:42,120 --> 00:02:44,200 Speaker 4: that's when we really it clicked for us. So we 51 00:02:44,240 --> 00:02:48,359 Speaker 4: don't actually build a virtual personality. We call them villas. 52 00:02:49,080 --> 00:02:53,560 Speaker 4: We build dozens depending on who wants to remember the 53 00:02:53,600 --> 00:02:56,079 Speaker 4: person who wants to connect with that person, because it's 54 00:02:56,120 --> 00:02:58,720 Speaker 4: going to be a different relationship. And I think you 55 00:02:58,760 --> 00:03:01,800 Speaker 4: had started this question asking how we do it, and 56 00:03:01,840 --> 00:03:04,520 Speaker 4: it's really looking when you think about everything that a 57 00:03:04,560 --> 00:03:07,600 Speaker 4: relationship is based on its communication, Right, how do you 58 00:03:07,639 --> 00:03:10,360 Speaker 4: communicate with your friends, How do you communicate with your partner? 59 00:03:10,360 --> 00:03:12,560 Speaker 4: How do you communicate with your parents, your children, et cetera, 60 00:03:12,600 --> 00:03:15,520 Speaker 4: et cetera, and those that's where they see and experience 61 00:03:15,560 --> 00:03:18,120 Speaker 4: the difference is how you communicate with them. And so 62 00:03:18,200 --> 00:03:22,400 Speaker 4: we analyze the communication. And that's evergreen. Right, as long 63 00:03:22,440 --> 00:03:25,880 Speaker 4: as you have the communications, whether it's email or text 64 00:03:25,960 --> 00:03:29,520 Speaker 4: or phone calls that you recorded or videos, that's all 65 00:03:29,560 --> 00:03:33,320 Speaker 4: we need. So the person whose past never had to 66 00:03:33,360 --> 00:03:36,760 Speaker 4: have recorded anything as long as we have some samples 67 00:03:36,760 --> 00:03:40,080 Speaker 4: of their communication, we can analyze what the patterns were 68 00:03:40,120 --> 00:03:43,600 Speaker 4: that were unique to your two's relationship dynamics. 69 00:03:43,920 --> 00:03:46,480 Speaker 3: You know, there's a there's this survey. 70 00:03:46,520 --> 00:03:48,040 Speaker 2: I've seen it done a bunch of times, and the 71 00:03:48,480 --> 00:03:55,040 Speaker 2: percentage may vary, but that if somebody got us forty 72 00:03:55,160 --> 00:04:00,000 Speaker 2: percent forty percent of who we knew we are and 73 00:04:00,000 --> 00:04:02,960 Speaker 2: forty percent of how we think. If they got us 74 00:04:03,040 --> 00:04:07,800 Speaker 2: forty percent, then they really did know us. But that 75 00:04:07,920 --> 00:04:10,960 Speaker 2: still leaves this huge part of us that is not 76 00:04:11,160 --> 00:04:14,000 Speaker 2: known to that person. And we might feel a kind 77 00:04:14,000 --> 00:04:17,839 Speaker 2: of kinship, almost a kind of twinship, maybe even with 78 00:04:18,000 --> 00:04:21,480 Speaker 2: somebody else, but they don't really get us. They get 79 00:04:21,520 --> 00:04:23,840 Speaker 2: a part of us and they and there's all sorts 80 00:04:23,839 --> 00:04:27,800 Speaker 2: of psychological surveys about relationships and communication that would back 81 00:04:27,839 --> 00:04:28,240 Speaker 2: that up. 82 00:04:29,480 --> 00:04:31,960 Speaker 4: Well, you know, I think that's interesting. One of my 83 00:04:32,000 --> 00:04:36,400 Speaker 4: philosophies internal is we have to think small to think they. 84 00:04:36,240 --> 00:04:36,880 Speaker 1: I would. 85 00:04:38,160 --> 00:04:41,320 Speaker 4: I would say that no one really knows anybody in 86 00:04:41,320 --> 00:04:44,000 Speaker 4: that thing. Most people don't know themselves. I don't know. 87 00:04:44,480 --> 00:04:49,320 Speaker 4: People are taking mental note of the fact that you 88 00:04:49,560 --> 00:04:53,360 Speaker 4: change dramatically throughout the entire day depending on who you're 89 00:04:53,360 --> 00:04:53,880 Speaker 4: interacting with. 90 00:04:54,320 --> 00:04:55,000 Speaker 3: That's really true. 91 00:04:55,600 --> 00:04:57,400 Speaker 4: There's just no way you go home to your wife 92 00:04:58,000 --> 00:05:00,600 Speaker 4: and you speak to her the same way you spoke 93 00:05:00,680 --> 00:05:04,320 Speaker 4: to your drinking buddies or your or your colleague or 94 00:05:04,320 --> 00:05:06,559 Speaker 4: your boss. I mean, they're just very different people. 95 00:05:07,160 --> 00:05:10,880 Speaker 3: Or you do that at your peril, right right. 96 00:05:12,000 --> 00:05:13,680 Speaker 4: If you get home and start talking to your wife 97 00:05:13,720 --> 00:05:15,240 Speaker 4: the same way you talk to the guys down at 98 00:05:15,279 --> 00:05:17,360 Speaker 4: the bar, you may not have a white longer. 99 00:05:17,920 --> 00:05:19,839 Speaker 2: And we actually have a rule in our house too 100 00:05:19,920 --> 00:05:23,560 Speaker 2: that you have to bring the best to the nest. 101 00:05:24,000 --> 00:05:24,680 Speaker 3: That's our rule. 102 00:05:25,080 --> 00:05:28,640 Speaker 2: And so you can't just give the world your best 103 00:05:28,680 --> 00:05:33,560 Speaker 2: and then come home and be all grumbly and unhappy. 104 00:05:32,800 --> 00:05:35,880 Speaker 2: The point of every day is that you come home 105 00:05:35,920 --> 00:05:39,320 Speaker 2: and you recharge your batteries at home, but you still 106 00:05:39,360 --> 00:05:41,880 Speaker 2: have to retain that best part of you. You can't 107 00:05:41,920 --> 00:05:44,040 Speaker 2: take it out on the people that you love the most. 108 00:05:45,160 --> 00:05:47,520 Speaker 4: I mean, I think that's absolutely beautiful to start, and 109 00:05:48,480 --> 00:05:50,440 Speaker 4: the world will probably be a better place if everybody 110 00:05:51,000 --> 00:05:54,760 Speaker 4: you know, adopted that. But I think that really, you know, 111 00:05:54,839 --> 00:05:57,080 Speaker 4: I started noticing it for myself when I started analyzing 112 00:05:57,080 --> 00:06:00,320 Speaker 4: my own communications. You know, there's all kinds of varying answers. 113 00:06:00,480 --> 00:06:03,120 Speaker 4: I mean, really from the tone of my voice, from 114 00:06:03,120 --> 00:06:07,000 Speaker 4: the tempo, the speed in which I respond, the language 115 00:06:07,040 --> 00:06:12,080 Speaker 4: I use. You know, you know, everything changes. I mean, 116 00:06:12,120 --> 00:06:14,880 Speaker 4: it's just so drastically different that you know, you could 117 00:06:14,880 --> 00:06:17,440 Speaker 4: put how I'm hanging out with my buddies when We're 118 00:06:17,480 --> 00:06:22,159 Speaker 4: riding motorcycles and going to bars and you know, doing 119 00:06:22,160 --> 00:06:25,440 Speaker 4: whatever we're doing to how I, you know, would be 120 00:06:25,640 --> 00:06:26,600 Speaker 4: speaking to my mother. 121 00:06:26,680 --> 00:06:29,719 Speaker 2: I mean those are night and day people, right, or 122 00:06:29,800 --> 00:06:33,560 Speaker 2: business meeting or you know, going to the dentist. 123 00:06:33,680 --> 00:06:36,599 Speaker 4: Doesn't really my or when the sec is listening to 124 00:06:36,640 --> 00:06:38,800 Speaker 4: the radio. You know, I'm seking totally different than I 125 00:06:38,800 --> 00:06:41,640 Speaker 4: would be, you know, so it's different, it's a different person. 126 00:06:41,800 --> 00:06:44,240 Speaker 4: Does that make it any less mean, not at all, 127 00:06:44,640 --> 00:06:46,880 Speaker 4: you know, it's it's just a different mean, it's a 128 00:06:46,880 --> 00:06:48,000 Speaker 4: different version of myself. 129 00:06:48,520 --> 00:06:53,600 Speaker 2: So how then, what is the process then for capturing 130 00:06:53,680 --> 00:06:59,039 Speaker 2: those data points of your personality that could create a versona, 131 00:06:59,640 --> 00:07:04,240 Speaker 2: a liable versona as you call them, that would help 132 00:07:04,360 --> 00:07:06,200 Speaker 2: somebody in a grief process. 133 00:07:08,480 --> 00:07:12,920 Speaker 4: So you know, what we do is we analyze data. 134 00:07:13,360 --> 00:07:17,040 Speaker 4: I mean it's looking at data points. So the proprietary 135 00:07:17,160 --> 00:07:20,200 Speaker 4: nature of what we do is we have thousands of 136 00:07:20,280 --> 00:07:22,840 Speaker 4: data points and I think a lot of people get 137 00:07:22,840 --> 00:07:26,360 Speaker 4: freaked out by the idea of AI and then machine 138 00:07:26,440 --> 00:07:29,680 Speaker 4: learning underneath that, and you know, really all that is 139 00:07:29,680 --> 00:07:33,320 Speaker 4: is we're just telling when we go into code a 140 00:07:33,360 --> 00:07:35,960 Speaker 4: program to work, all we're doing is saying, hey, and 141 00:07:36,040 --> 00:07:38,440 Speaker 4: if you find something else, add it to the code base. 142 00:07:39,200 --> 00:07:43,000 Speaker 4: Not nothing crazier than that. So it's basically what we 143 00:07:43,040 --> 00:07:46,960 Speaker 4: say is, here's a hundred data points to start that 144 00:07:47,000 --> 00:07:50,040 Speaker 4: are unique to these communications, that vary from sort of 145 00:07:50,160 --> 00:07:55,400 Speaker 4: universal natural language models. Right, find something different about these 146 00:07:55,400 --> 00:07:58,240 Speaker 4: what is unique within these two communications, and as you 147 00:07:58,280 --> 00:08:03,320 Speaker 4: find other patterns that don't exist in other places, add them, right, 148 00:08:03,840 --> 00:08:05,520 Speaker 4: And so that what happens is then when you when 149 00:08:05,560 --> 00:08:09,560 Speaker 4: you when you apply a VERSONA to a natural language model, 150 00:08:09,600 --> 00:08:12,560 Speaker 4: and you could use any of them, it's drawing from 151 00:08:12,560 --> 00:08:15,960 Speaker 4: a set of data that is completely unique between these 152 00:08:15,960 --> 00:08:20,720 Speaker 4: two people. And so the answers and the responses and 153 00:08:20,840 --> 00:08:26,120 Speaker 4: the questions and the the prompts come out very uniquely 154 00:08:26,840 --> 00:08:32,439 Speaker 4: to what the surviving person is expecting to hear. And 155 00:08:32,440 --> 00:08:34,480 Speaker 4: and that's I think what makes it so special is 156 00:08:34,520 --> 00:08:39,400 Speaker 4: that it's it's literally thousands upon thousands of thousands of 157 00:08:39,480 --> 00:08:44,400 Speaker 4: data points you know, learned by our by our technology 158 00:08:44,520 --> 00:08:47,840 Speaker 4: in a matter of minutes, and then when you go 159 00:08:47,960 --> 00:08:51,120 Speaker 4: to speak with it, it behaves in the way that 160 00:08:51,160 --> 00:08:52,200 Speaker 4: you expect it to behave. 161 00:08:53,240 --> 00:08:56,439 Speaker 3: So I'm not freaked out by AI, and I'm not. 162 00:08:59,400 --> 00:09:02,760 Speaker 2: I I respect a lot about what you're doing and 163 00:09:02,800 --> 00:09:09,240 Speaker 2: what this application can mean. I don't think, however, that 164 00:09:09,320 --> 00:09:13,520 Speaker 2: I have seen evidence yet in general, not this application, 165 00:09:13,600 --> 00:09:18,600 Speaker 2: but in general on the value of AI and machine learning, 166 00:09:19,559 --> 00:09:24,679 Speaker 2: which is resulting in students not even writing a paper 167 00:09:25,360 --> 00:09:28,120 Speaker 2: where they just as you you know, as we say, 168 00:09:28,280 --> 00:09:32,360 Speaker 2: type in some data points, hit a button, write a paper, 169 00:09:32,640 --> 00:09:36,800 Speaker 2: go back to the bar. That's not an education, and 170 00:09:38,080 --> 00:09:41,560 Speaker 2: it's fairly transparent. I don't think they see it that way, 171 00:09:42,640 --> 00:09:44,760 Speaker 2: but you know, and we can make all the comments 172 00:09:44,760 --> 00:09:47,560 Speaker 2: we want about you know, they're only treating themselves and whatever, 173 00:09:47,600 --> 00:09:51,760 Speaker 2: but are you really getting an education by doing that? 174 00:09:52,200 --> 00:09:55,120 Speaker 3: And at some point that might just show up, believe 175 00:09:55,120 --> 00:09:57,920 Speaker 3: it or not. And I'm I'm. 176 00:09:57,760 --> 00:10:00,679 Speaker 2: Less excited about that aspect of A than I am 177 00:10:00,720 --> 00:10:03,800 Speaker 2: about this. What I like about this is it actually 178 00:10:04,000 --> 00:10:13,920 Speaker 2: replicates using technology a fairly traditional way of understanding place 179 00:10:14,800 --> 00:10:15,480 Speaker 2: and family. 180 00:10:18,120 --> 00:10:22,440 Speaker 4: Well, yeah, absolutely, and then that's you know, we call 181 00:10:22,520 --> 00:10:26,960 Speaker 4: our users community members. I mean, there's almost everybody in 182 00:10:27,000 --> 00:10:29,920 Speaker 4: the company is related or has personal relationships, so we're 183 00:10:30,040 --> 00:10:34,680 Speaker 4: very about familial bonds and replicating that with technology. But 184 00:10:34,720 --> 00:10:39,640 Speaker 4: one thing I would say about AI is it ultimately 185 00:10:39,720 --> 00:10:43,800 Speaker 4: it's artificial intelligence as an umbrella, is just allowing for 186 00:10:43,920 --> 00:10:51,960 Speaker 4: computer software to integrate new information into its code so 187 00:10:52,000 --> 00:10:54,120 Speaker 4: that they can do new and better things. So when 188 00:10:54,160 --> 00:10:56,680 Speaker 4: we think about the value of AI, I mean, you know, 189 00:10:56,720 --> 00:10:59,360 Speaker 4: I think about cancer research, I think about what we 190 00:10:59,400 --> 00:11:02,400 Speaker 4: are doing, you know. I mean you can have something 191 00:11:02,440 --> 00:11:05,160 Speaker 4: that's continuously learning and finding better ways to do it. 192 00:11:05,200 --> 00:11:07,240 Speaker 4: I mean, that's the real value when it comes to 193 00:11:07,840 --> 00:11:10,360 Speaker 4: certain you know, I think really more what you're you're 194 00:11:10,400 --> 00:11:13,160 Speaker 4: thinking about is you know, natural language processing and things 195 00:11:13,160 --> 00:11:16,680 Speaker 4: of that nature. Yeah, there's some questions around it. I think, 196 00:11:17,400 --> 00:11:20,320 Speaker 4: you know, my attorney friends aren't super enthusd by this. 197 00:11:20,440 --> 00:11:22,439 Speaker 4: But when you think about a lot of the things 198 00:11:22,440 --> 00:11:26,080 Speaker 4: that a program like artificial you know, like a chat GPT, 199 00:11:26,280 --> 00:11:28,520 Speaker 4: let's say, which I think I love what open AI 200 00:11:28,679 --> 00:11:33,160 Speaker 4: is doing, and but if you think about the access 201 00:11:33,240 --> 00:11:37,439 Speaker 4: that creates, you know, not everybody can afford four or 202 00:11:37,480 --> 00:11:39,640 Speaker 4: five hundred dollars an hour, whatever the going rate for 203 00:11:39,679 --> 00:11:42,600 Speaker 4: an attorney is to get some advice before they have 204 00:11:42,679 --> 00:11:45,880 Speaker 4: to go represent themselves in a small claims court or 205 00:11:46,200 --> 00:11:49,520 Speaker 4: before they sign a contract. And so I think there's 206 00:11:49,760 --> 00:11:53,040 Speaker 4: there's a ton of really valuable applications for that kind 207 00:11:53,040 --> 00:11:56,680 Speaker 4: of technology that goes far beyond the sort of the 208 00:11:56,800 --> 00:11:59,000 Speaker 4: media frenzy about students are cheating. 209 00:11:59,000 --> 00:12:03,240 Speaker 2: Now, it's not a frenzy man, it's not a frenzy man. 210 00:12:03,600 --> 00:12:05,480 Speaker 2: Do you just gotta you gotta trust me on this 211 00:12:05,559 --> 00:12:08,960 Speaker 2: as a professor, you just gotta trust me. It's not 212 00:12:09,200 --> 00:12:13,280 Speaker 2: like media friendly, like we all lost our perspective. I 213 00:12:13,360 --> 00:12:18,200 Speaker 2: teach media and this is a problem and it's going 214 00:12:18,280 --> 00:12:23,240 Speaker 2: to get worse. And when we even with the idea 215 00:12:23,480 --> 00:12:28,199 Speaker 2: of these open sources for say, for example, it's one 216 00:12:28,200 --> 00:12:31,040 Speaker 2: thing to have like a zoom where there's a format 217 00:12:31,120 --> 00:12:36,800 Speaker 2: and there's a but the amount of the kiting of 218 00:12:36,920 --> 00:12:40,839 Speaker 2: information and the way things are going to be pilfered 219 00:12:40,920 --> 00:12:46,880 Speaker 2: here and there, I'm I would say your company and 220 00:12:46,960 --> 00:12:49,880 Speaker 2: your idea is a great one. I'm not sure you 221 00:12:49,920 --> 00:12:53,079 Speaker 2: would enjoy it so much if somebody were coming along 222 00:12:53,120 --> 00:12:55,280 Speaker 2: and they were taking out little bits of it in 223 00:12:55,320 --> 00:12:59,480 Speaker 2: an untraceable way and then using it to create their 224 00:12:59,559 --> 00:13:03,360 Speaker 2: own work. When you did the all of the original 225 00:13:03,400 --> 00:13:04,080 Speaker 2: heavy lifting. 226 00:13:06,200 --> 00:13:08,640 Speaker 4: No, for sure, not. I mean, but I also think that, 227 00:13:10,120 --> 00:13:14,560 Speaker 4: you know, one that's already happening. I mean, let's be real, 228 00:13:14,679 --> 00:13:18,880 Speaker 4: but happening. It's already happening us. You know, we've already 229 00:13:18,920 --> 00:13:22,240 Speaker 4: seen people popping up and using some of the same strategies. 230 00:13:22,320 --> 00:13:25,240 Speaker 4: And we could run around, and you know, we we 231 00:13:25,320 --> 00:13:27,400 Speaker 4: have our technology pack, and we could run around and 232 00:13:27,400 --> 00:13:29,959 Speaker 4: start filing lawsuits and whatever. But you know, there's two 233 00:13:30,000 --> 00:13:34,800 Speaker 4: things for me that really matter. The first is if 234 00:13:35,000 --> 00:13:37,240 Speaker 4: people are applying this and doing something good with it, 235 00:13:37,240 --> 00:13:40,320 Speaker 4: if it's if it's alleviating loneliness, if it's alleviating grief, 236 00:13:40,720 --> 00:13:44,120 Speaker 4: and we're not getting a cut of that financially, it's fine. 237 00:13:44,200 --> 00:13:45,520 Speaker 4: I can live with that. 238 00:13:45,559 --> 00:13:47,040 Speaker 1: I think. 239 00:13:47,040 --> 00:13:50,520 Speaker 4: The other thing is that, you know, we've put in 240 00:13:50,920 --> 00:13:55,360 Speaker 4: an extraordinary amount of work in building a trust with 241 00:13:55,520 --> 00:13:58,360 Speaker 4: our community members and with users and being out in 242 00:13:58,360 --> 00:14:01,040 Speaker 4: the world and explo mean why we've done this. You know, 243 00:14:01,520 --> 00:14:04,440 Speaker 4: it's not easy if I'm being honesty and to tell 244 00:14:04,440 --> 00:14:06,360 Speaker 4: the story of my mother dying, to tell the story 245 00:14:06,360 --> 00:14:09,800 Speaker 4: of myself almost dying over and over and over again. 246 00:14:09,840 --> 00:14:13,520 Speaker 4: But I feel a responsibility to be vulnerable and open 247 00:14:13,559 --> 00:14:16,199 Speaker 4: like that, so that people feel safe using our technology 248 00:14:16,240 --> 00:14:20,240 Speaker 4: and understand our motivation. And so, you know, I think 249 00:14:20,360 --> 00:14:25,440 Speaker 4: ultimately people will use our product, and people will feel 250 00:14:25,440 --> 00:14:27,480 Speaker 4: comfortable with the product because they trust us as a 251 00:14:27,520 --> 00:14:31,120 Speaker 4: company and they know why we're doing it. And and 252 00:14:31,160 --> 00:14:33,240 Speaker 4: to your point, you know, I was a high school teacher, 253 00:14:33,280 --> 00:14:36,560 Speaker 4: and I was I was long done teaching before you know, 254 00:14:36,640 --> 00:14:40,240 Speaker 4: the Chatchy pet revolution. But certainly I've seen many of 255 00:14:40,560 --> 00:14:42,680 Speaker 4: many of the techniques of cheating, as we all do 256 00:14:42,800 --> 00:14:48,440 Speaker 4: as educators. And I hear you, and I'm very firmly 257 00:14:48,480 --> 00:14:51,240 Speaker 4: in the camp of you only cheat yourself when you cheat. 258 00:14:52,400 --> 00:15:00,000 Speaker 4: And ultimately, you know, I think that especially with something 259 00:15:00,160 --> 00:15:05,040 Speaker 4: like media, you know, nobody is paying too much attention 260 00:15:05,120 --> 00:15:07,800 Speaker 4: to what grade you got in school. So if you 261 00:15:07,880 --> 00:15:10,400 Speaker 4: cheated your way through it, and you can't, you can't 262 00:15:10,400 --> 00:15:13,320 Speaker 4: put together real or you can't put together a piece 263 00:15:13,360 --> 00:15:17,040 Speaker 4: of journalism that blows an editor in mind, it doesn't 264 00:15:17,080 --> 00:15:19,120 Speaker 4: matter anyway, you know. And so I always sort of 265 00:15:19,120 --> 00:15:24,240 Speaker 4: feel like, you know, charmerically and universally, and you know, 266 00:15:24,280 --> 00:15:26,560 Speaker 4: when you do things with good intentions or bad intentions, 267 00:15:26,880 --> 00:15:29,840 Speaker 4: the results are going to reflect that. And I don't 268 00:15:29,880 --> 00:15:32,880 Speaker 4: overly worry about that sort of thing, and I understand 269 00:15:33,080 --> 00:15:34,800 Speaker 4: the good far outweighs the bad. 270 00:15:35,240 --> 00:15:38,320 Speaker 2: And I just I want to hear you feel that 271 00:15:38,520 --> 00:15:42,360 Speaker 2: confident about a team of surgeons who fake their way 272 00:15:42,360 --> 00:15:45,880 Speaker 2: through med school, who are now looking at your broken body. 273 00:15:46,520 --> 00:15:50,080 Speaker 2: And I'm not trying to exaggerate. And this is not hysterical. 274 00:15:50,760 --> 00:15:56,080 Speaker 2: This is an ancient problem. This is an ongoing problem 275 00:15:56,120 --> 00:15:59,160 Speaker 2: with people who go to and they can they can 276 00:15:59,200 --> 00:16:02,080 Speaker 2: come up with the same credentials as the next person, 277 00:16:02,880 --> 00:16:05,040 Speaker 2: and then the only way they're going to as you 278 00:16:05,080 --> 00:16:08,560 Speaker 2: point out, you know, it's one thing if you're just saying, well, 279 00:16:08,600 --> 00:16:12,120 Speaker 2: you know, media is a talent driven business. So if 280 00:16:12,160 --> 00:16:15,960 Speaker 2: you've got the talent, you'll rise. If you don't, you know, 281 00:16:16,560 --> 00:16:19,120 Speaker 2: then it will reveal itself over time. 282 00:16:19,840 --> 00:16:22,200 Speaker 3: Well do you want to be that ninth patient? 283 00:16:23,360 --> 00:16:26,680 Speaker 2: I don't think so, and I and I think there's 284 00:16:26,720 --> 00:16:30,400 Speaker 2: a there there are enough problems right now with it 285 00:16:31,080 --> 00:16:35,040 Speaker 2: that I just think we're way in front of any 286 00:16:35,160 --> 00:16:37,600 Speaker 2: kind of backstop to it. 287 00:16:38,400 --> 00:16:40,160 Speaker 3: And that's what concerns me. 288 00:16:40,760 --> 00:16:45,400 Speaker 2: And so it's not that I think the whole idea 289 00:16:45,520 --> 00:16:48,480 Speaker 2: is terrible and I'm not being some old guy with 290 00:16:48,560 --> 00:16:51,840 Speaker 2: a hose saying get off my lawn. What I'm saying is, 291 00:16:51,920 --> 00:16:57,080 Speaker 2: like anything, we need to always be looking twenty blocks 292 00:16:57,160 --> 00:17:01,560 Speaker 2: down the road, not just three blocks behind us. And 293 00:17:02,080 --> 00:17:04,520 Speaker 2: that's the part I'm worried about, not this though, because 294 00:17:04,520 --> 00:17:09,160 Speaker 2: I really like what this is a tool for helping 295 00:17:09,320 --> 00:17:13,240 Speaker 2: individuals manage grief. And like I said, it comes from 296 00:17:13,240 --> 00:17:18,199 Speaker 2: a long tradition right of people, whether it's even if 297 00:17:18,240 --> 00:17:21,520 Speaker 2: we just looked at it photographs and that was one 298 00:17:21,640 --> 00:17:24,119 Speaker 2: that was an innovation in and of itself, was to 299 00:17:24,160 --> 00:17:27,280 Speaker 2: take a photograph of somebody who had died so that 300 00:17:27,359 --> 00:17:29,240 Speaker 2: you would have that to look at them, so you 301 00:17:29,280 --> 00:17:29,720 Speaker 2: didn't have. 302 00:17:29,720 --> 00:17:34,840 Speaker 3: To rely only on your memory. It's really that kind 303 00:17:34,880 --> 00:17:36,040 Speaker 3: of a grief. 304 00:17:37,720 --> 00:17:40,159 Speaker 2: It's sort of a grief manager. It's sort of a 305 00:17:40,200 --> 00:17:43,760 Speaker 2: tool for grief managing that has a that goes back, 306 00:17:44,040 --> 00:17:45,240 Speaker 2: you know, hundreds of years. 307 00:17:45,720 --> 00:17:49,000 Speaker 1: Listen to more Coast to Coast AM every weeknight at 308 00:17:49,000 --> 00:17:52,280 Speaker 1: one am Eastern and go to Coast to coastam dot 309 00:17:52,280 --> 00:17:53,080 Speaker 1: com for more