1 00:00:00,080 --> 00:00:04,720 Speaker 1: Tell you what does stop, unfortunately is Tech Tuesday with 2 00:00:04,840 --> 00:00:06,720 Speaker 1: rich Dmurrow. Wish we could do that forever because I 3 00:00:06,760 --> 00:00:07,520 Speaker 1: have so many questions. 4 00:00:07,600 --> 00:00:10,680 Speaker 2: Good morning, rich Oh thanks Bill. I thought you said 5 00:00:10,680 --> 00:00:13,120 Speaker 2: it's going to stop. I was like, oh, wow, no, no. 6 00:00:13,360 --> 00:00:17,000 Speaker 1: Yeah, Well, unfortunately it's going to stop because we are limited. 7 00:00:17,040 --> 00:00:20,480 Speaker 1: When we talk about these lawsuits for crazy those are 8 00:00:20,480 --> 00:00:24,160 Speaker 1: not limited. The amount of time we have together is limited. 9 00:00:24,440 --> 00:00:27,880 Speaker 1: That's where I was going. You know that anyone can 10 00:00:27,960 --> 00:00:28,640 Speaker 1: sue for anything. 11 00:00:28,680 --> 00:00:31,560 Speaker 2: I mean, when it's that kind of high level thing, 12 00:00:31,640 --> 00:00:33,000 Speaker 2: that's a whole other world. 13 00:00:33,720 --> 00:00:37,159 Speaker 1: Yeah, this is the Texas Attorney General doing it. It's 14 00:00:37,159 --> 00:00:40,440 Speaker 1: not just some crazy ass guy out there. But anyway, 15 00:00:41,080 --> 00:00:45,120 Speaker 1: let's get into our stuff because I think this actually 16 00:00:45,320 --> 00:00:52,680 Speaker 1: is more fun. And so let's talk about fake AI receipts. Now, 17 00:00:52,800 --> 00:00:54,120 Speaker 1: what is that about? 18 00:00:55,040 --> 00:00:57,120 Speaker 2: Yeah, this is and this is something that's been kind 19 00:00:57,120 --> 00:00:59,800 Speaker 2: of going on for a bit. And now there's a 20 00:01:00,120 --> 00:01:03,720 Speaker 2: port from the Financial Times that businesses are getting tricked 21 00:01:03,760 --> 00:01:07,319 Speaker 2: by these AI generator receipts. And we know that if 22 00:01:07,319 --> 00:01:10,360 Speaker 2: you do expense reports. Number one, they're annoying. Number two, 23 00:01:10,560 --> 00:01:12,480 Speaker 2: they require a lot number three, A lot of it's 24 00:01:12,520 --> 00:01:16,520 Speaker 2: automated because these companies have seated control to these giant 25 00:01:17,280 --> 00:01:20,360 Speaker 2: concurs of the world and things like that, and employees 26 00:01:20,400 --> 00:01:26,440 Speaker 2: are now apparently submitting ultra realistic fake expense receipts thanks 27 00:01:26,480 --> 00:01:26,919 Speaker 2: to AI. 28 00:01:27,640 --> 00:01:30,080 Speaker 3: And so one of these software companies ramps As. 29 00:01:30,000 --> 00:01:32,880 Speaker 2: They flagged over a million dollars in fake receipts in 30 00:01:33,040 --> 00:01:38,000 Speaker 2: just ninety days. And they look really real, So the 31 00:01:38,040 --> 00:01:41,120 Speaker 2: AI can even make them look like they're wrinkled paper. 32 00:01:41,680 --> 00:01:44,959 Speaker 2: They can put signatures on them, and people, you know 33 00:01:45,040 --> 00:01:47,840 Speaker 2: these AI companies, they put like a watermark in them 34 00:01:48,120 --> 00:01:50,800 Speaker 2: so that you can't really submit them because the software 35 00:01:50,840 --> 00:01:51,680 Speaker 2: will figure that out. 36 00:01:51,920 --> 00:01:53,040 Speaker 3: So what do the employees do. 37 00:01:53,080 --> 00:01:54,960 Speaker 2: They just screenshot it or take a picture of it 38 00:01:55,000 --> 00:01:58,520 Speaker 2: with their phone and they get around that. So this 39 00:01:58,600 --> 00:02:02,440 Speaker 2: is a huge issue, I think for companies, and it's 40 00:02:02,440 --> 00:02:04,680 Speaker 2: something that I if you're an employee and you're doing this, 41 00:02:04,840 --> 00:02:06,680 Speaker 2: I don't know why you would do this because you 42 00:02:06,680 --> 00:02:08,320 Speaker 2: don't want to get caught doing this. 43 00:02:08,840 --> 00:02:12,480 Speaker 1: Yeah, but I have a question, why can't anybody use 44 00:02:12,840 --> 00:02:17,960 Speaker 1: existing technology and just put in a fake expense report? 45 00:02:18,120 --> 00:02:21,480 Speaker 1: Or are you talking about providers that putting that in 46 00:02:21,520 --> 00:02:24,440 Speaker 1: there and getting money sent to them for products they 47 00:02:24,480 --> 00:02:26,800 Speaker 1: didn't send into the company, didn't sell the company. 48 00:02:26,800 --> 00:02:28,600 Speaker 3: I don't know who is doing this. 49 00:02:29,760 --> 00:02:31,560 Speaker 2: Well, I think it's the employees that are going on 50 00:02:32,080 --> 00:02:36,720 Speaker 2: company travel and so they are they're padding. It's it's 51 00:02:36,760 --> 00:02:39,320 Speaker 2: kind of a new way of what's been going on forever, 52 00:02:39,360 --> 00:02:42,160 Speaker 2: which is padding the expense report, right, except this time 53 00:02:42,440 --> 00:02:45,799 Speaker 2: it's ultra realistic. And because I think the the idea 54 00:02:45,840 --> 00:02:49,080 Speaker 2: here is because AI is fact checking the expense reports 55 00:02:49,120 --> 00:02:53,440 Speaker 2: before they go for payment. Majority of the time, you know, 56 00:02:53,480 --> 00:02:55,920 Speaker 2: I think that AI just looks at the receipt says, oh, 57 00:02:55,960 --> 00:02:59,160 Speaker 2: that looks good, and it passes it through. So but 58 00:02:59,320 --> 00:03:03,000 Speaker 2: even say concur which you know, they scan a lot 59 00:03:03,000 --> 00:03:05,839 Speaker 2: of these receipts, one of their executives said, you can't 60 00:03:05,840 --> 00:03:09,680 Speaker 2: even trust your eyes anymore because these receipts look so good. 61 00:03:10,880 --> 00:03:13,880 Speaker 2: And so my question is, if you're an employee doing this, 62 00:03:13,960 --> 00:03:14,919 Speaker 2: you're gonna get fired. 63 00:03:15,360 --> 00:03:17,760 Speaker 3: So probably not a good idea. 64 00:03:18,840 --> 00:03:21,200 Speaker 1: Well, I mean, it's just another way of stealing money. 65 00:03:21,600 --> 00:03:24,440 Speaker 1: I'm assuming right, you pitch in a fake report, which 66 00:03:24,480 --> 00:03:27,960 Speaker 1: people do all day long and until they don't. I 67 00:03:28,160 --> 00:03:30,839 Speaker 1: what you're saying is AI just makes it easier for 68 00:03:30,880 --> 00:03:34,000 Speaker 1: an employee to rip off the employer bottom line. 69 00:03:34,080 --> 00:03:36,240 Speaker 3: It makes this It makes it very easy. 70 00:03:36,240 --> 00:03:38,800 Speaker 2: And I remember seeing a story about this a couple 71 00:03:38,840 --> 00:03:41,080 Speaker 2: of months ago where this was just starting to happen, 72 00:03:41,400 --> 00:03:44,640 Speaker 2: and this was before the AI image generators have gotten 73 00:03:44,680 --> 00:03:48,440 Speaker 2: so good now, and I remember trying to generate one 74 00:03:48,480 --> 00:03:50,560 Speaker 2: of these receipts and I could not believe my eyes. 75 00:03:50,640 --> 00:03:52,880 Speaker 2: I mean, it was so good that it's like, oh 76 00:03:52,920 --> 00:03:55,640 Speaker 2: my gosh. So I think for maybe here's what I 77 00:03:55,640 --> 00:04:00,800 Speaker 2: think happens. Maybe employees tiptoe into this by saying, oh shoot, 78 00:04:00,840 --> 00:04:03,920 Speaker 2: I forgot to get that receipt or I need that receipt, 79 00:04:04,160 --> 00:04:06,880 Speaker 2: and so maybe they recreate it, and then all of 80 00:04:06,920 --> 00:04:08,960 Speaker 2: a sudden, you know, some other people just realize that 81 00:04:09,000 --> 00:04:10,320 Speaker 2: you can just make this very simple. 82 00:04:11,240 --> 00:04:16,840 Speaker 1: Okay, Rich anti recognition glasses. I don't even know what 83 00:04:16,880 --> 00:04:19,640 Speaker 1: that means other than I don't recognize you. 84 00:04:21,120 --> 00:04:21,440 Speaker 3: Yeah. 85 00:04:21,480 --> 00:04:25,240 Speaker 2: So imagine you put these glasses on and you try 86 00:04:25,240 --> 00:04:27,680 Speaker 2: to use face ID on your phone and it does 87 00:04:27,720 --> 00:04:31,400 Speaker 2: not work. That's what we're talking about here. So this 88 00:04:31,440 --> 00:04:34,680 Speaker 2: is a company called Zeny Optical. They've come out with 89 00:04:34,839 --> 00:04:37,600 Speaker 2: over the years a bunch of glasses for like blocking 90 00:04:37,920 --> 00:04:40,480 Speaker 2: blue light and things like that. But now they've got 91 00:04:40,480 --> 00:04:44,800 Speaker 2: this new technology called zenny id Guard, and it blocks 92 00:04:44,880 --> 00:04:50,279 Speaker 2: infrared facial recognition systems from tracking you. So I thought 93 00:04:50,320 --> 00:04:52,280 Speaker 2: this was kind of cool, kind of interesting, kind of 94 00:04:52,320 --> 00:04:55,400 Speaker 2: a sign of our times. So the coding on these glasses, 95 00:04:55,440 --> 00:04:58,200 Speaker 2: which is a subtle pink so that you know, you 96 00:04:58,320 --> 00:05:00,799 Speaker 2: kind of signal to other people that hey, I'm blocking 97 00:05:00,839 --> 00:05:04,760 Speaker 2: this stuff, reflects up to eighty percent of near infrared light, 98 00:05:04,839 --> 00:05:07,320 Speaker 2: which is what a lot of cameras and biometric systems 99 00:05:07,440 --> 00:05:10,400 Speaker 2: use to identify faces. And you can test this out 100 00:05:10,480 --> 00:05:13,400 Speaker 2: very simply by trying face idea on your phone or 101 00:05:13,440 --> 00:05:16,800 Speaker 2: something like Windows Hello on a Windows PC, and. 102 00:05:16,720 --> 00:05:17,560 Speaker 3: They will not work. 103 00:05:18,080 --> 00:05:20,480 Speaker 2: So the idea here is that you wear these glasses, 104 00:05:20,520 --> 00:05:24,159 Speaker 2: you can walk around town and facial recognition cameras that 105 00:05:24,160 --> 00:05:26,600 Speaker 2: are trying to capture your biometrics are just not going 106 00:05:26,680 --> 00:05:27,000 Speaker 2: to work. 107 00:05:27,440 --> 00:05:28,200 Speaker 3: So you can. 108 00:05:28,480 --> 00:05:31,760 Speaker 2: I don't know if you're completely anonymous when you wear these, 109 00:05:31,800 --> 00:05:35,000 Speaker 2: but it's just one step in kind of protecting your identity. 110 00:05:35,839 --> 00:05:36,039 Speaker 3: Yeah. 111 00:05:36,120 --> 00:05:42,080 Speaker 1: I know, there's entire industries that are anti where technology 112 00:05:42,120 --> 00:05:46,320 Speaker 1: comes up with something and then instantly there are now 113 00:05:46,560 --> 00:05:50,640 Speaker 1: is a group, a small group or a company to say, 114 00:05:50,640 --> 00:05:53,560 Speaker 1: oh no, no, we're going to fight that and it seems 115 00:05:53,600 --> 00:05:56,960 Speaker 1: like it's one industry following another industry. So if you 116 00:05:56,960 --> 00:05:59,480 Speaker 1: don't want your face right, if you don't want facial 117 00:05:59,520 --> 00:06:01,880 Speaker 1: recognition you walk around all day with, if you don't 118 00:06:01,880 --> 00:06:06,000 Speaker 1: need glasses with these playing lenses with no prescription, and 119 00:06:06,040 --> 00:06:08,120 Speaker 1: then you're theoretically. 120 00:06:07,560 --> 00:06:11,479 Speaker 3: Fine, Do I have that right? Yes? Huh? 121 00:06:12,000 --> 00:06:15,000 Speaker 1: Is it easier just to wear like ninja ninja masks 122 00:06:15,680 --> 00:06:18,120 Speaker 1: and you know those cost six those cost six bucks. 123 00:06:19,279 --> 00:06:21,719 Speaker 2: It's simpler than the ninja mask because this still lets 124 00:06:21,800 --> 00:06:23,880 Speaker 2: people see your eyes. But it's just you know, look, 125 00:06:23,960 --> 00:06:25,719 Speaker 2: I think when it comes to what you said to 126 00:06:25,760 --> 00:06:29,039 Speaker 2: your point of any industry that pops up, there's always 127 00:06:29,040 --> 00:06:31,719 Speaker 2: going to be a reactionary industry. 128 00:06:31,920 --> 00:06:33,720 Speaker 3: And I think that you know, is. 129 00:06:33,680 --> 00:06:36,000 Speaker 2: This a bit of a ploy to get people to 130 00:06:36,000 --> 00:06:38,200 Speaker 2: spend money on these glasses they may or may not need. 131 00:06:38,320 --> 00:06:41,960 Speaker 2: Is this really protecting your you know, your facial identification? 132 00:06:42,240 --> 00:06:42,919 Speaker 3: Like who knows? 133 00:06:43,400 --> 00:06:46,000 Speaker 2: But the reality is that we see this stuff happen 134 00:06:46,040 --> 00:06:49,279 Speaker 2: all the time, and you know, it's one way that 135 00:06:49,320 --> 00:06:52,960 Speaker 2: people can feel more secure if they feel like they're 136 00:06:52,960 --> 00:06:54,800 Speaker 2: getting id'd in too many places. 137 00:06:55,400 --> 00:06:56,520 Speaker 3: And I think it is interesting. 138 00:06:56,560 --> 00:06:58,719 Speaker 2: We've heard over and over bill that they use this 139 00:06:58,760 --> 00:07:01,880 Speaker 2: facial recognition in places like concerts. If you look and 140 00:07:02,040 --> 00:07:05,279 Speaker 2: this is you know, because I love technology, so anywhere 141 00:07:05,279 --> 00:07:07,920 Speaker 2: I go, if you look at like small print on 142 00:07:08,120 --> 00:07:11,600 Speaker 2: signs at places you go, a majority of them are 143 00:07:11,640 --> 00:07:14,560 Speaker 2: now mentioning that they are using some sort of automated 144 00:07:15,240 --> 00:07:19,560 Speaker 2: facial recognition or facial tracking at grocery stores and different 145 00:07:19,600 --> 00:07:22,440 Speaker 2: retailers that you go to. So when you walk in there, yeah, 146 00:07:22,440 --> 00:07:25,000 Speaker 2: they're they're figuring out like, oh, okay, this person comes in. 147 00:07:25,040 --> 00:07:26,840 Speaker 2: They may not have a name to you just yet, 148 00:07:27,160 --> 00:07:29,200 Speaker 2: but they know who you are based on the fact 149 00:07:29,200 --> 00:07:30,960 Speaker 2: that you come in there three times a week. And 150 00:07:31,040 --> 00:07:33,640 Speaker 2: eventually they could layer that with some sort of third 151 00:07:33,680 --> 00:07:36,800 Speaker 2: party database and figure out, hey, this is Rich he's 152 00:07:36,800 --> 00:07:37,760 Speaker 2: in here every Wednesday. 153 00:07:38,120 --> 00:07:40,960 Speaker 1: Hey, are we reaching the point where you know, For example, 154 00:07:41,080 --> 00:07:47,760 Speaker 1: China uses face recognition basically for every single citizen. They 155 00:07:47,800 --> 00:07:50,400 Speaker 1: know what every single one of their people looks like 156 00:07:51,440 --> 00:07:53,800 Speaker 1: with technology, which I don't understand because they look all 157 00:07:53,800 --> 00:07:57,640 Speaker 1: the same to me. But you've got an entire country 158 00:07:57,680 --> 00:07:59,600 Speaker 1: doing this. Are we heading in that direction? You think 159 00:07:59,640 --> 00:08:02,240 Speaker 1: where we're all going to be somehow surveilled. 160 00:08:04,560 --> 00:08:07,720 Speaker 2: I think it's already happening. I think that it's already happening. 161 00:08:07,960 --> 00:08:12,200 Speaker 2: And I mean, look, when I travel, I go from 162 00:08:12,440 --> 00:08:17,360 Speaker 2: my car to the airport, to the security to the 163 00:08:17,360 --> 00:08:22,120 Speaker 2: next country without speaking to a human being, or in 164 00:08:22,160 --> 00:08:25,040 Speaker 2: most cases, without even showing any ID. I mean, when 165 00:08:25,040 --> 00:08:27,920 Speaker 2: you go to LAX and you park on the parking 166 00:08:28,480 --> 00:08:30,920 Speaker 2: structures there, it reads your license plate. 167 00:08:31,040 --> 00:08:33,360 Speaker 3: If you've already prepaid, it opens the gate. 168 00:08:33,720 --> 00:08:37,960 Speaker 2: You go into the LAX, you've got facial recognition, You 169 00:08:38,040 --> 00:08:40,320 Speaker 2: walk up to a camera, it snaps a picture of you. 170 00:08:40,480 --> 00:08:43,360 Speaker 2: It says, okay, you're rich Demiro. The gate opens up, 171 00:08:43,400 --> 00:08:46,400 Speaker 2: you go in. When you enter the other country, you 172 00:08:46,440 --> 00:08:49,920 Speaker 2: scan your passport or your face and it just says, hey, okay, 173 00:08:49,960 --> 00:08:53,079 Speaker 2: come on in. So we're already living in a society 174 00:08:53,120 --> 00:08:57,040 Speaker 2: where the government already knows who you are. They've got 175 00:08:57,040 --> 00:09:00,560 Speaker 2: your biometrics, they've got your face, and you know, we've 176 00:09:00,559 --> 00:09:02,920 Speaker 2: all sort of agreed to this. And it's I think 177 00:09:02,920 --> 00:09:06,360 Speaker 2: the private companies that are catching up to this, they 178 00:09:06,440 --> 00:09:09,200 Speaker 2: probably have more information than the government because we've handed 179 00:09:09,240 --> 00:09:12,320 Speaker 2: it to them. So I think, you know, we're going 180 00:09:12,400 --> 00:09:14,120 Speaker 2: to see a renaissance of people saying I want to 181 00:09:14,160 --> 00:09:16,160 Speaker 2: reclaim some of my privacy. 182 00:09:16,640 --> 00:09:18,439 Speaker 3: I'm not sure, that's going to be possible in the future. 183 00:09:18,480 --> 00:09:20,679 Speaker 1: Now, I mean, how do you do that without legislation, 184 00:09:21,160 --> 00:09:24,560 Speaker 1: without laws that tell these companies and these organizations you 185 00:09:24,640 --> 00:09:27,920 Speaker 1: cannot do that anymore short of that. 186 00:09:28,160 --> 00:09:29,480 Speaker 3: And you're right. It just occurred to me. 187 00:09:29,520 --> 00:09:31,920 Speaker 1: I just came back from Europe a few weeks ago, 188 00:09:32,480 --> 00:09:36,640 Speaker 1: and as I was getting off the airplane and there's 189 00:09:36,679 --> 00:09:40,400 Speaker 1: a line for American citizens and people who have foreign passports, 190 00:09:40,440 --> 00:09:43,160 Speaker 1: and literally I have my passport in my pocket. And 191 00:09:43,200 --> 00:09:48,920 Speaker 1: as I'm taking my passport out, the TSA person or 192 00:09:49,080 --> 00:09:53,240 Speaker 1: in this case, the customs and the customs people say no, no, 193 00:09:53,480 --> 00:09:56,320 Speaker 1: just walk through here, just look at the camera. And 194 00:09:56,360 --> 00:10:00,520 Speaker 1: I was kind of floored. And there's my picture. I 195 00:10:00,520 --> 00:10:02,600 Speaker 1: mean there's is there such thing as a good looking 196 00:10:02,640 --> 00:10:06,079 Speaker 1: picture on driver's licenses or any of this? Uh, any 197 00:10:06,120 --> 00:10:06,920 Speaker 1: of these programs. 198 00:10:08,280 --> 00:10:10,520 Speaker 2: I mean, I don't mind mine, but they don't let 199 00:10:10,600 --> 00:10:11,760 Speaker 2: you take another one. 200 00:10:11,840 --> 00:10:14,160 Speaker 3: I mean, I you can't drive you. 201 00:10:14,240 --> 00:10:16,760 Speaker 1: In yours, because yeah you can for your driver's license. 202 00:10:17,280 --> 00:10:19,240 Speaker 1: You can say, well, you take another one. Let you 203 00:10:19,280 --> 00:10:21,880 Speaker 1: look at it, and Kate will because. 204 00:10:21,640 --> 00:10:23,439 Speaker 3: They wouldn't let me look. They just said that's okay, 205 00:10:23,720 --> 00:10:24,160 Speaker 3: you're good. 206 00:10:24,240 --> 00:10:27,959 Speaker 1: No, no, you ask Hey, can I take a look. 207 00:10:28,000 --> 00:10:29,319 Speaker 1: Do you mind if I take a look, Because it's 208 00:10:29,320 --> 00:10:31,320 Speaker 1: a digital take somebody, you're. 209 00:10:31,160 --> 00:10:33,520 Speaker 3: Gonna you're gonna gum up the system. They got to 210 00:10:33,520 --> 00:10:34,720 Speaker 3: get people there. 211 00:10:34,880 --> 00:10:36,520 Speaker 1: Yeah, I know. But then they have to they take 212 00:10:36,559 --> 00:10:38,720 Speaker 1: the picture because what if someone blinks, what if someone 213 00:10:38,800 --> 00:10:39,959 Speaker 1: has their eyes closed? 214 00:10:40,080 --> 00:10:42,520 Speaker 2: They look for that, they they all they're looking for 215 00:10:42,640 --> 00:10:45,120 Speaker 2: is just to make sure that the measurements for biometrics 216 00:10:45,120 --> 00:10:45,480 Speaker 2: are fine. 217 00:10:45,480 --> 00:10:45,760 Speaker 3: They're not. 218 00:10:45,800 --> 00:10:47,920 Speaker 2: They don't care if you have you know, if you 219 00:10:48,000 --> 00:10:48,360 Speaker 2: look good. 220 00:10:48,400 --> 00:10:50,120 Speaker 1: No, I'm talking about No, No, I'm talking about I'm 221 00:10:50,120 --> 00:10:52,199 Speaker 1: talking about that. Yeah, I think we're on different pages here. 222 00:10:52,240 --> 00:10:55,800 Speaker 1: I'm talking about the driver's licenses photos. That's where I 223 00:10:55,880 --> 00:10:59,200 Speaker 1: was going. Okay, okay, I'm glad we missed each other 224 00:10:59,280 --> 00:11:02,280 Speaker 1: and two ships passing in the night. Rich, we'll catch 225 00:11:02,280 --> 00:11:04,400 Speaker 1: you at this Saturday, eleven am to two pm right 226 00:11:04,440 --> 00:11:05,040 Speaker 1: here on KFI. 227 00:11:05,160 --> 00:11:08,439 Speaker 3: You have a good win. Thank you, you got it.