1 00:00:00,760 --> 00:00:05,760 Speaker 1: It's Night Side with Dan Ray on WBS, Boston's news radio. 2 00:00:06,800 --> 00:00:09,600 Speaker 2: That is correct. Brady Jay for Dan Ray on Nightside. 3 00:00:09,680 --> 00:00:13,120 Speaker 2: Shout out to Dan. And we have Rob Brooks and 4 00:00:13,320 --> 00:00:18,400 Speaker 2: Jay working the wheel in the master control. It's guiding 5 00:00:18,440 --> 00:00:21,959 Speaker 2: the ship through the evening top of the hour routine. 6 00:00:21,960 --> 00:00:23,600 Speaker 2: We all have our top of the our routine. Here 7 00:00:24,000 --> 00:00:26,479 Speaker 2: go get a candy bar top of the hour. I 8 00:00:26,680 --> 00:00:30,920 Speaker 2: just finished off. I polished stuff Reese's peanut butter cups 9 00:00:31,520 --> 00:00:36,120 Speaker 2: and it gets my brain working, firing on all cylinders. 10 00:00:36,400 --> 00:00:44,360 Speaker 2: Excuse me, all right, last night a side topic kind 11 00:00:44,360 --> 00:00:46,559 Speaker 2: of popped in, and then we had a gentleman call 12 00:00:46,640 --> 00:00:51,360 Speaker 2: and say he had no use for computers. We liked 13 00:00:51,400 --> 00:00:57,000 Speaker 2: life fine without a computer, and I get that to 14 00:00:57,080 --> 00:01:03,360 Speaker 2: some degree. But sir and all you folks like him, 15 00:01:03,480 --> 00:01:07,479 Speaker 2: you really are missing out. And this doesn't lead into 16 00:01:07,520 --> 00:01:12,080 Speaker 2: our topic here. You're missing out on being able to 17 00:01:12,120 --> 00:01:16,800 Speaker 2: do certain things like we knew your license buy plane tickets? 18 00:01:16,880 --> 00:01:19,760 Speaker 2: How do you buy plane tickets without using a computer. 19 00:01:22,080 --> 00:01:27,400 Speaker 2: That these uh, self induced difficulties have been around for 20 00:01:27,440 --> 00:01:29,840 Speaker 2: a while. But now if you don't use a computer, 21 00:01:29,880 --> 00:01:34,480 Speaker 2: you're going to be missing out on even more. And 22 00:01:34,680 --> 00:01:37,960 Speaker 2: I'll get to what that is, but I really recommend it. 23 00:01:38,680 --> 00:01:40,160 Speaker 2: In a way, it's none of my business, but in 24 00:01:40,200 --> 00:01:45,679 Speaker 2: a way it's my responsibility to tell you, please don't 25 00:01:45,760 --> 00:01:51,880 Speaker 2: be intimidated by the computer. It's actually quite simple. And 26 00:01:51,960 --> 00:01:55,920 Speaker 2: I feel tell me if I'm wrong six one, seven, two, five, four, 27 00:01:56,040 --> 00:01:59,520 Speaker 2: ten thirty that you say you don't want to just 28 00:01:59,560 --> 00:02:04,960 Speaker 2: because you're afraid, admit it. Admit it. There are way 29 00:02:05,280 --> 00:02:07,160 Speaker 2: it doesn't take a lot of time, and the payoff 30 00:02:07,200 --> 00:02:11,280 Speaker 2: is huge. Yes, there are risks that go along with it, 31 00:02:12,440 --> 00:02:18,919 Speaker 2: but it's here and not embracing the computer. It's kind 32 00:02:18,919 --> 00:02:24,240 Speaker 2: of like when the automobile came along. Say I like horses, Yeah, 33 00:02:24,800 --> 00:02:27,160 Speaker 2: and you can get along on a horse back in 34 00:02:29,160 --> 00:02:34,120 Speaker 2: ought sex or whatever it was. But it becomes a hardship. 35 00:02:34,360 --> 00:02:38,200 Speaker 2: And I feel that many of you not getting involved 36 00:02:38,200 --> 00:02:42,480 Speaker 2: in understanding what computer is it is a hardship that 37 00:02:42,600 --> 00:02:46,360 Speaker 2: is self. You're doing it to yourself. There's no there's 38 00:02:46,360 --> 00:02:48,440 Speaker 2: no shame in saying I want to learn how to 39 00:02:48,440 --> 00:02:49,920 Speaker 2: do it, I don't know how to do it. I 40 00:02:49,960 --> 00:02:54,640 Speaker 2: would think that there would be many parents who would 41 00:02:54,720 --> 00:02:57,679 Speaker 2: ask their kids to teach them, so that would be 42 00:02:57,720 --> 00:03:00,919 Speaker 2: the easy way. Can you please teach me and this 43 00:03:01,240 --> 00:03:03,680 Speaker 2: very simple things are teach me how to write an email, 44 00:03:04,240 --> 00:03:08,359 Speaker 2: teach me, teach me how to look something up. All right. 45 00:03:08,720 --> 00:03:12,679 Speaker 2: Now there's another whole layer, and that is artificial intelligence 46 00:03:13,360 --> 00:03:17,880 Speaker 2: or super overused word. I wish there are other words AI. 47 00:03:19,840 --> 00:03:26,560 Speaker 2: AI as different things to different people, but it's it's 48 00:03:26,600 --> 00:03:31,480 Speaker 2: a thing like I just showed Michael Coin something that 49 00:03:31,720 --> 00:03:36,440 Speaker 2: blew his mind. I said that I was thinking about 50 00:03:36,480 --> 00:03:44,520 Speaker 2: asking about interviewing chat GPT Guy Chat GTP. GPT is 51 00:03:44,560 --> 00:03:49,320 Speaker 2: an AI program which is stunning. It's only twenty bucks 52 00:03:49,360 --> 00:03:52,960 Speaker 2: a month and it does crazy stuff. You can talk 53 00:03:53,000 --> 00:03:55,200 Speaker 2: to it like a human being. It seems to have 54 00:03:55,240 --> 00:03:59,360 Speaker 2: a sense of humor. And if you if you say, 55 00:03:59,440 --> 00:04:04,040 Speaker 2: can you not you that word anymore? They don't. Of course, 56 00:04:04,080 --> 00:04:06,360 Speaker 2: there's a lot of down There's a lot of downsides too. 57 00:04:06,440 --> 00:04:11,760 Speaker 2: And the reason this comes up tonight is because a 58 00:04:11,960 --> 00:04:14,520 Speaker 2: pole at a school I used to go to and 59 00:04:14,720 --> 00:04:20,360 Speaker 2: liked very much in New Hampshire did a poll and 60 00:04:20,400 --> 00:04:24,799 Speaker 2: it says in New Hampshire mixed feelings about the effects 61 00:04:24,800 --> 00:04:30,120 Speaker 2: of AI in New Hampshire. And I'm I'm wondering a 62 00:04:30,200 --> 00:04:33,719 Speaker 2: couple I'm wondering your take on a couple of things. 63 00:04:34,600 --> 00:04:37,159 Speaker 2: One of them is how do you think people in 64 00:04:37,200 --> 00:04:39,800 Speaker 2: New Hampshire view AI? Do you think they're mostly in 65 00:04:39,839 --> 00:04:44,120 Speaker 2: favor of it or not? And here's this, this is 66 00:04:44,360 --> 00:04:49,480 Speaker 2: kind of interesting, really interesting. Of those that don't trust 67 00:04:49,520 --> 00:04:56,000 Speaker 2: AI and don't think that it will make the country 68 00:04:56,000 --> 00:05:02,120 Speaker 2: a better place, of those doubting thomasin do you think 69 00:05:02,120 --> 00:05:04,640 Speaker 2: those people who doubt and dislike AI would be more 70 00:05:04,760 --> 00:05:10,520 Speaker 2: likely to be republican conservatives or liberal Democrats? Well, let's 71 00:05:10,560 --> 00:05:13,880 Speaker 2: find out. This is phase one of what we did. 72 00:05:14,000 --> 00:05:15,679 Speaker 2: What we're going to do is find out about this poll. 73 00:05:16,760 --> 00:05:18,719 Speaker 2: But I'll ask you the same question as the poll. 74 00:05:19,440 --> 00:05:22,000 Speaker 2: And we've brushed upon this before, but we've never we've 75 00:05:22,000 --> 00:05:24,719 Speaker 2: never drilled down and made its complete topic that I 76 00:05:24,720 --> 00:05:29,839 Speaker 2: can remember as far as I know. New polling data 77 00:05:29,880 --> 00:05:34,800 Speaker 2: shows twenty eight percent of respondence think artificial intelligence will 78 00:05:34,800 --> 00:05:38,360 Speaker 2: have a positive effect on the country overall. Wow, so 79 00:05:38,400 --> 00:05:41,719 Speaker 2: you're taking the mind this is New Hampshire kind of 80 00:05:41,720 --> 00:05:45,680 Speaker 2: a mixed state, a lot of conservatives. Oh yeah, trust me. 81 00:05:45,800 --> 00:05:49,520 Speaker 2: When I drive up north, there's all kinds of lawn 82 00:05:49,640 --> 00:05:52,159 Speaker 2: signs and flag waving going on up there, and you 83 00:05:52,200 --> 00:05:55,240 Speaker 2: know who they voted for. And in the more urban areas, 84 00:05:55,279 --> 00:06:01,159 Speaker 2: it's a different thing. But the article says, and this 85 00:06:01,279 --> 00:06:06,560 Speaker 2: is Boston Globe granite status, anticipate artificial intelligence will have 86 00:06:06,560 --> 00:06:10,520 Speaker 2: a positive impact on medical care in the coming decade. 87 00:06:11,440 --> 00:06:15,680 Speaker 2: There they are largely pessimistic about the emerging technologies broader 88 00:06:15,800 --> 00:06:21,000 Speaker 2: social effects, according to a new survey. And I'm going 89 00:06:21,080 --> 00:06:25,719 Speaker 2: to say that just as I feel it's a hardship 90 00:06:25,839 --> 00:06:30,479 Speaker 2: not to embrace the computer, it's going to increase that 91 00:06:30,560 --> 00:06:36,599 Speaker 2: hardship if you don't embrace AI. Again with the automobile analogy, 92 00:06:37,600 --> 00:06:42,480 Speaker 2: you may not like it, but it's coming. And even 93 00:06:42,560 --> 00:06:45,800 Speaker 2: though you may not like it, if you don't use it, 94 00:06:46,160 --> 00:06:49,080 Speaker 2: that then you don't like you be behind the eight 95 00:06:49,160 --> 00:06:54,320 Speaker 2: ball even further. And in a future topic, I will 96 00:06:54,880 --> 00:07:01,719 Speaker 2: address how this is really creating a generation gap, more big, way, 97 00:07:01,760 --> 00:07:05,200 Speaker 2: more substantial, way, more gaping than the one in the sixties. 98 00:07:07,320 --> 00:07:12,240 Speaker 2: Because the gap, it's not through choice anyone, in many ways, 99 00:07:12,280 --> 00:07:14,800 Speaker 2: not through choice. If you don't understand, if you don't 100 00:07:14,800 --> 00:07:17,600 Speaker 2: get technology, and if you don't use it, you don't 101 00:07:17,680 --> 00:07:21,040 Speaker 2: understand your kids, and you don't understand your grandkids, you 102 00:07:21,080 --> 00:07:24,400 Speaker 2: live in a different world. They don't get you at all. 103 00:07:26,560 --> 00:07:30,520 Speaker 2: You know, they you are going to be much less 104 00:07:30,560 --> 00:07:32,080 Speaker 2: on their minds and a much less part of their 105 00:07:32,080 --> 00:07:35,720 Speaker 2: life if you can't communicate with them, and you're not 106 00:07:35,720 --> 00:07:38,000 Speaker 2: gonna be able to communicate with them if you don't 107 00:07:38,040 --> 00:07:41,840 Speaker 2: get their technology. I don't think many of you understand 108 00:07:42,000 --> 00:07:48,240 Speaker 2: how technology is insinuated into their life way more than 109 00:07:48,240 --> 00:07:50,040 Speaker 2: you get. Anyway, let me get into this and do 110 00:07:50,080 --> 00:07:53,080 Speaker 2: you do agree with me or not. You can say, hey, 111 00:07:53,120 --> 00:07:56,920 Speaker 2: Bradley Jay, I don't care. I am refusing to learn 112 00:07:56,960 --> 00:07:59,440 Speaker 2: the computer. You can say the same thing about AI. 113 00:08:00,000 --> 00:08:01,400 Speaker 2: I don't like it. I'm not going to do it, 114 00:08:03,400 --> 00:08:05,120 Speaker 2: but I'll give you the pros and cons later. What 115 00:08:05,160 --> 00:08:09,119 Speaker 2: do you think? Six one, seven, two five. So granted 116 00:08:09,200 --> 00:08:11,320 Speaker 2: status they feel like it's going to help medically, but 117 00:08:11,520 --> 00:08:14,120 Speaker 2: they still don't like it. Kind of here's the thing, 118 00:08:14,160 --> 00:08:18,440 Speaker 2: it's coming. There's nothing you can do. Nothing you can do. 119 00:08:19,360 --> 00:08:22,880 Speaker 2: It's coming. It's going to make demands of you. You're 120 00:08:22,880 --> 00:08:24,400 Speaker 2: going to need to learn how to use it to 121 00:08:24,520 --> 00:08:29,440 Speaker 2: compete to stay up well. Lessen the third of the 122 00:08:29,480 --> 00:08:33,600 Speaker 2: respondents predicted a I'll have a positive effect more than 123 00:08:33,600 --> 00:08:35,320 Speaker 2: twice as many thought it was going to have a 124 00:08:35,600 --> 00:08:42,640 Speaker 2: negative effect. And even so, you need to adopt it, 125 00:08:42,800 --> 00:08:44,880 Speaker 2: even though you may think it's evil. You need to 126 00:08:45,000 --> 00:08:47,560 Speaker 2: learn it, if for no other reason to protect yourself 127 00:08:47,559 --> 00:08:53,720 Speaker 2: from it. And as I was mentioning before Dean Michael 128 00:08:53,760 --> 00:08:57,800 Speaker 2: con was blown away when I said, you know what 129 00:08:57,840 --> 00:08:59,640 Speaker 2: I'd like to do, I'd like to ask my bosses. 130 00:09:00,040 --> 00:09:06,520 Speaker 2: I'd like to interview chat GPT on the radio. And 131 00:09:06,679 --> 00:09:08,840 Speaker 2: unless you've experienced it, you might think, well, that would 132 00:09:08,880 --> 00:09:11,600 Speaker 2: be weird, that would be no good. It will be 133 00:09:11,679 --> 00:09:14,839 Speaker 2: someone talking, you know. I'll have to type it in 134 00:09:14,880 --> 00:09:17,320 Speaker 2: and I'll get a text response and I'll say tell you, 135 00:09:18,040 --> 00:09:21,800 Speaker 2: or you'll have some robody sound in person speak like 136 00:09:21,840 --> 00:09:25,480 Speaker 2: a lot of those dumb AI podcasts that make dumb 137 00:09:25,520 --> 00:09:30,960 Speaker 2: AI mistakes. Oh yeah, you're probably judging current AI on 138 00:09:31,000 --> 00:09:35,600 Speaker 2: those old podcasts, and you can. You can tell by 139 00:09:35,640 --> 00:09:41,040 Speaker 2: how they misread stuff like he went to the store 140 00:09:41,080 --> 00:09:45,200 Speaker 2: and he bought all oaf of bread that they just 141 00:09:45,240 --> 00:09:47,679 Speaker 2: don't get a lot of stuff. But then the new 142 00:09:47,720 --> 00:09:51,439 Speaker 2: AI is so far advanced. I can talk to it 143 00:09:52,240 --> 00:09:54,280 Speaker 2: and it right out of my laptop has a big 144 00:09:54,320 --> 00:10:00,520 Speaker 2: clear voice, and I can ask it about itself, which 145 00:10:00,600 --> 00:10:03,920 Speaker 2: is pretty intense. I ask it, why do you not 146 00:10:04,080 --> 00:10:07,679 Speaker 2: like me to call you Bob, and I ask AI 147 00:10:07,840 --> 00:10:13,200 Speaker 2: if it's legal to interview AI on the radio, And 148 00:10:13,240 --> 00:10:18,760 Speaker 2: I wonder in a court of awe if that would 149 00:10:18,760 --> 00:10:23,280 Speaker 2: hold up if say the Open AI company said you 150 00:10:23,360 --> 00:10:28,160 Speaker 2: can't interview chat GPT. What if chat GP told me 151 00:10:28,240 --> 00:10:31,400 Speaker 2: I could? That would be a slam dunk defense. I 152 00:10:31,440 --> 00:10:37,080 Speaker 2: would think anyway more about it, And this pole the 153 00:10:37,080 --> 00:10:41,000 Speaker 2: influence of AI generated synthetic media as Smith's mark on 154 00:10:41,640 --> 00:10:47,840 Speaker 2: politics too. Oh my god. And of course when it 155 00:10:47,880 --> 00:10:53,040 Speaker 2: comes to negative campaigning, what a delicious tool AI is. 156 00:10:53,679 --> 00:11:02,480 Speaker 2: You can create such embarrassing, demeaning images that have a 157 00:11:02,480 --> 00:11:06,120 Speaker 2: great effect, and images that would make a statement that 158 00:11:06,240 --> 00:11:09,720 Speaker 2: was false. Then you run into all kinds of legal 159 00:11:10,679 --> 00:11:16,840 Speaker 2: areas on hey, does this qualify as slander or libel? 160 00:11:19,559 --> 00:11:22,320 Speaker 2: I mean you were putting out an image that is 161 00:11:22,320 --> 00:11:26,320 Speaker 2: making a statement about me that is not true. It's 162 00:11:26,480 --> 00:11:30,920 Speaker 2: just like you spoke it or wrote it. It's the 163 00:11:30,960 --> 00:11:38,040 Speaker 2: same thing. Also, what if you put out a fake 164 00:11:38,120 --> 00:11:44,000 Speaker 2: AI image of a building on fire? You yelled, you 165 00:11:44,120 --> 00:11:48,160 Speaker 2: visually yelled fire in a crowded theater, and then what 166 00:11:48,200 --> 00:11:52,040 Speaker 2: if fire departments responded, and then what if fire firefighter 167 00:11:52,120 --> 00:11:56,920 Speaker 2: was hurt? Would you be on the hook? You probably would, 168 00:11:56,920 --> 00:11:59,080 Speaker 2: and you probably should. I don't know about they probably 169 00:11:59,080 --> 00:12:02,280 Speaker 2: would you probably we should, Glenn and Brighton is a 170 00:12:02,280 --> 00:12:04,280 Speaker 2: smart guy. He knows this is a good time to call. 171 00:12:04,559 --> 00:12:08,959 Speaker 2: And we'll talk to Glenn right after this. On WBZ, You're. 172 00:12:08,800 --> 00:12:13,480 Speaker 1: On Night Side with Dan Ray. I'm WBZ Boston's news radio. 173 00:12:14,400 --> 00:12:17,280 Speaker 2: In case you just joined us, I was speaking about 174 00:12:17,320 --> 00:12:22,200 Speaker 2: a poll taken in New Hampshire regarding the opinions about 175 00:12:22,760 --> 00:12:25,840 Speaker 2: how AI will affect the quality of life in this country. 176 00:12:26,120 --> 00:12:31,400 Speaker 2: Also that there has been a situation where fake AI 177 00:12:31,559 --> 00:12:35,280 Speaker 2: generated image caused a lot of problem with the high 178 00:12:35,320 --> 00:12:38,840 Speaker 2: school and a fire. And then I will also give 179 00:12:38,880 --> 00:12:41,520 Speaker 2: you a serious list of the pros and cons, and 180 00:12:41,520 --> 00:12:45,040 Speaker 2: I'm really interested in how you feel about it. And 181 00:12:45,320 --> 00:12:54,560 Speaker 2: for me, it's simultaneously evil and helpful. I'll tell you 182 00:12:54,679 --> 00:12:56,840 Speaker 2: at this point I don't mean to be gloom and doom. However, 183 00:12:59,280 --> 00:13:01,720 Speaker 2: I do believe it will be it will cause the 184 00:13:01,720 --> 00:13:04,160 Speaker 2: actual end of the world, and they're not too just 185 00:13:04,200 --> 00:13:07,400 Speaker 2: in future. And I'll explain why, what can we do 186 00:13:07,440 --> 00:13:13,360 Speaker 2: about it? Nothing? How's that for an upbeat assessment? We 187 00:13:13,400 --> 00:13:14,559 Speaker 2: go to Glenn in Brighton. 188 00:13:16,400 --> 00:13:19,000 Speaker 3: I agree with you, all right, Glenn. 189 00:13:19,000 --> 00:13:20,480 Speaker 2: First of all. How you been. I haven't talked to 190 00:13:20,559 --> 00:13:21,240 Speaker 2: you in a long time. 191 00:13:21,760 --> 00:13:24,480 Speaker 3: I know I have a friend that's living with me 192 00:13:24,520 --> 00:13:27,439 Speaker 3: who he's homeless. He worked in a music store. He 193 00:13:28,280 --> 00:13:32,560 Speaker 3: got evicted because they closed the building, and he he 194 00:13:32,640 --> 00:13:35,520 Speaker 3: has a friend. He and I are trying to trying 195 00:13:35,520 --> 00:13:39,240 Speaker 3: to track down Steve Kilroy, who does piano refinish him. 196 00:13:40,160 --> 00:13:43,079 Speaker 3: That's why I'm calling you. I missed you last night. 197 00:13:43,160 --> 00:13:44,920 Speaker 3: I didn't know you were on somebody told. 198 00:13:45,040 --> 00:13:49,680 Speaker 2: Interesting, So yeah he does. Actually, he's a Steve Kilroy 199 00:13:49,760 --> 00:13:52,280 Speaker 2: as a drummer and a piano finisher. He has his 200 00:13:52,320 --> 00:13:58,200 Speaker 2: own piano right and he's currently he's in a bunch 201 00:13:58,200 --> 00:14:01,000 Speaker 2: of bands. He may be on tour. But I can 202 00:14:01,040 --> 00:14:04,920 Speaker 2: connect him. Why do you want to connect? Because you want. 203 00:14:04,760 --> 00:14:08,480 Speaker 3: To You know a guy. We know a guy in 204 00:14:08,559 --> 00:14:11,559 Speaker 3: the Newton. His piano needs to be finishing. His cat 205 00:14:12,200 --> 00:14:15,280 Speaker 3: cook cook cat claws scratch the hell out of the thing, 206 00:14:15,920 --> 00:14:18,240 Speaker 3: out of the lead, especially in one of the legs. 207 00:14:18,800 --> 00:14:21,640 Speaker 2: Of course, you are a piano tuner, so that's all right, 208 00:14:21,680 --> 00:14:25,680 Speaker 2: that all fits together. Yeah, it's expensive though. I think 209 00:14:25,720 --> 00:14:27,400 Speaker 2: maybe he needs to just get used to those cat 210 00:14:27,440 --> 00:14:29,480 Speaker 2: closs cat gloss scratches. 211 00:14:29,760 --> 00:14:31,680 Speaker 3: Yeah, I know. I thought, well, the guy is a 212 00:14:31,760 --> 00:14:34,440 Speaker 3: contract contract attorney. He's got more. 213 00:14:34,400 --> 00:14:38,000 Speaker 2: Money than going Okay, then okay, fine, so there's that, 214 00:14:38,200 --> 00:14:41,840 Speaker 2: but that's kind of inside baseball. So in general, you're 215 00:14:41,880 --> 00:14:45,920 Speaker 2: healthy or you know, yeah, just give me the good 216 00:14:45,960 --> 00:14:48,080 Speaker 2: don't tell me the bad news, just everything. 217 00:14:48,120 --> 00:14:49,840 Speaker 3: I have a crush on that woman that does a 218 00:14:50,000 --> 00:14:52,920 Speaker 3: commercial about the Dreams by James. 219 00:14:54,520 --> 00:14:57,760 Speaker 2: He speak very highly of you as well. That's nice. 220 00:14:58,680 --> 00:15:02,160 Speaker 3: Yeah. Well, a woman in the ad has a voice 221 00:15:02,240 --> 00:15:04,680 Speaker 3: like someone that I'm trying to track down named Christine 222 00:15:04,680 --> 00:15:08,680 Speaker 3: and Dorchester, Christine Doherty. I met her at a trumper. 223 00:15:09,320 --> 00:15:10,280 Speaker 3: I met her at a trumper. 224 00:15:10,280 --> 00:15:13,240 Speaker 2: I use last names bleep bleed. 225 00:15:14,120 --> 00:15:18,160 Speaker 3: All right, Oh you're getting you guys get nervous. I'm sorry. 226 00:15:18,680 --> 00:15:22,840 Speaker 2: That's a bad policy. So you called during the AI thing, Glenn. 227 00:15:23,040 --> 00:15:24,120 Speaker 2: That coincidence. 228 00:15:24,200 --> 00:15:27,960 Speaker 3: Yeah, I don't like either the ride for people for disabilities. 229 00:15:27,960 --> 00:15:29,640 Speaker 3: They have a new AI system. 230 00:15:30,840 --> 00:15:32,280 Speaker 2: Okay, so how does that work? 231 00:15:32,320 --> 00:15:36,720 Speaker 3: The drivers I don't know. The drivers hated, the dispatchers 232 00:15:36,720 --> 00:15:41,720 Speaker 3: hated the everybody hates it. But the head, the head 233 00:15:41,760 --> 00:15:43,560 Speaker 3: of the ride, and I can mention his name because 234 00:15:43,640 --> 00:15:46,560 Speaker 3: Dan Ray tried to have him on as a guest 235 00:15:47,000 --> 00:15:51,360 Speaker 3: Joel Cook, He doesn't care that everybody hates it. Dan 236 00:15:51,720 --> 00:15:54,680 Speaker 3: Marina McKinnon tried to get him on his guest So, 237 00:15:56,040 --> 00:15:56,520 Speaker 3: how does the. 238 00:15:56,480 --> 00:15:58,920 Speaker 2: System work or how did it change? 239 00:16:01,360 --> 00:16:04,400 Speaker 3: Well, it's I don't know how explaining of it itself computic. 240 00:16:06,080 --> 00:16:08,400 Speaker 3: When you call the ride, it calls you back and 241 00:16:08,480 --> 00:16:12,520 Speaker 3: confirms that everything that you told it, well, that's fine. 242 00:16:12,960 --> 00:16:15,600 Speaker 3: And then the next day it calls you an hour 243 00:16:15,640 --> 00:16:18,920 Speaker 3: before it's coming and ten minutes before you're coming. But 244 00:16:19,040 --> 00:16:23,000 Speaker 3: sometimes it forgets to call you, and sometimes it gives 245 00:16:23,000 --> 00:16:25,960 Speaker 3: the driver's the wrong address. I had a driver no 246 00:16:26,160 --> 00:16:28,600 Speaker 3: show me. I had to take an uber to tune 247 00:16:28,600 --> 00:16:31,320 Speaker 3: the piano because the drive the ride didn't even come 248 00:16:31,360 --> 00:16:32,160 Speaker 3: to my apartment. 249 00:16:33,320 --> 00:16:35,840 Speaker 2: Well, that is a flaw. Hopefully they can fix that. 250 00:16:36,040 --> 00:16:37,960 Speaker 3: In the pouring rain, I'm waiting outside. 251 00:16:38,280 --> 00:16:40,320 Speaker 2: How much does it cost to tune a piano? 252 00:16:42,320 --> 00:16:42,440 Speaker 4: Uh? 253 00:16:43,040 --> 00:16:48,000 Speaker 3: Well, I charge either one sixty or two sixty. It's 254 00:16:48,000 --> 00:16:51,360 Speaker 3: a single tuny one sixty. If it's a double tuning 255 00:16:51,400 --> 00:16:52,040 Speaker 3: two sixty. 256 00:16:53,080 --> 00:16:57,600 Speaker 2: How much does it cost a tuna fish? How could 257 00:16:57,600 --> 00:16:59,720 Speaker 2: I not say? How could I not say that? 258 00:17:00,480 --> 00:17:05,720 Speaker 3: Okay, so what are you doing for fun? 259 00:17:05,840 --> 00:17:09,840 Speaker 2: You know? Do you get out to the Eagle deli and. 260 00:17:09,720 --> 00:17:14,320 Speaker 3: Stuff, and yeah there were no nice women that. 261 00:17:17,280 --> 00:17:21,439 Speaker 2: That's no good. So you're you're not down. So you 262 00:17:21,480 --> 00:17:24,879 Speaker 2: think AI is the kind of going to be the 263 00:17:24,920 --> 00:17:25,199 Speaker 2: end of the. 264 00:17:25,200 --> 00:17:28,960 Speaker 3: World, like me, Yes, I agree with you, totally. 265 00:17:29,800 --> 00:17:34,240 Speaker 2: Okay with my I will just allude to the thing 266 00:17:34,240 --> 00:17:36,640 Speaker 2: I'm going to talk about a little more detail. There 267 00:17:36,800 --> 00:17:42,159 Speaker 2: was a kerfuffle caused by a and I AI image 268 00:17:42,160 --> 00:17:47,800 Speaker 2: of a school on fire, and people I guess thought 269 00:17:47,840 --> 00:17:51,240 Speaker 2: that there was a school on fire. Fire department came 270 00:17:51,359 --> 00:17:55,760 Speaker 2: and all. And so it's a very short distance from 271 00:17:55,800 --> 00:18:02,800 Speaker 2: that to a video of missiles taking off a you know, 272 00:18:03,359 --> 00:18:07,520 Speaker 2: a lot, along with an AI generated phone call from 273 00:18:07,520 --> 00:18:10,640 Speaker 2: a fake Putin or a fake world leader and other 274 00:18:11,160 --> 00:18:14,720 Speaker 2: other AI hacks that make our country or some other 275 00:18:14,720 --> 00:18:18,760 Speaker 2: country believe we are under attack. We have some seven 276 00:18:18,840 --> 00:18:22,879 Speaker 2: minutes to use them or lose them, and and and 277 00:18:22,920 --> 00:18:28,480 Speaker 2: then it's all over. That's it, good night, good night. 278 00:18:29,640 --> 00:18:36,320 Speaker 3: And so is that near Plastown, New Hampshire. You mentioned 279 00:18:36,400 --> 00:18:37,080 Speaker 3: New Hampshire. 280 00:18:37,560 --> 00:18:40,439 Speaker 2: Oh, this was a pole. I mentioned New Hampshire in 281 00:18:40,520 --> 00:18:43,120 Speaker 2: terms of the pole that was taken and I believe 282 00:18:43,160 --> 00:18:46,480 Speaker 2: it was u n h and Durham, New Hampshire. 283 00:18:46,160 --> 00:18:50,639 Speaker 3: Chris Chris Q in Plastown, Hampshire, according to the website. 284 00:18:50,960 --> 00:18:55,720 Speaker 2: Okay, so that's anything else, Glen, you see him kind 285 00:18:55,720 --> 00:18:56,119 Speaker 2: of down. 286 00:18:56,320 --> 00:18:59,159 Speaker 3: Come on now, I am because I need you to 287 00:18:59,240 --> 00:19:01,359 Speaker 3: have him call my friend. 288 00:19:01,520 --> 00:19:06,719 Speaker 2: Should Okay, that's like not a radio thing. Like we've 289 00:19:06,760 --> 00:19:07,680 Speaker 2: got to remember there's. 290 00:19:07,480 --> 00:19:13,280 Speaker 3: A lot of people. Okay, Okay, do I leave a 291 00:19:13,400 --> 00:19:14,400 Speaker 3: number with a producer? 292 00:19:14,600 --> 00:19:17,560 Speaker 2: I don't know, Glenn. Let's take a big picture, man. 293 00:19:17,840 --> 00:19:19,200 Speaker 2: How are you doing in the big picture? 294 00:19:21,040 --> 00:19:25,000 Speaker 3: Oh, I don't know. My life is like groundhog Day. 295 00:19:25,040 --> 00:19:26,080 Speaker 3: Every day is the same. 296 00:19:26,840 --> 00:19:29,800 Speaker 2: Okay, that's so, I'm so sad. Well, I'm glad you 297 00:19:29,920 --> 00:19:33,560 Speaker 2: checked in, Glenn. Thanks to talk to you longer. But 298 00:19:34,160 --> 00:19:35,960 Speaker 2: you don't seem to have much to talk about. And 299 00:19:36,000 --> 00:19:38,560 Speaker 2: I don't mean it in the name any sort of bad. 300 00:19:38,400 --> 00:19:41,040 Speaker 3: Way on tomorrow night. 301 00:19:41,560 --> 00:19:43,359 Speaker 2: Yeah, there's a lot of people out there needs to 302 00:19:43,359 --> 00:19:47,639 Speaker 2: be entertained. And yeah, I know usually you're super entertaining 303 00:19:48,160 --> 00:19:51,560 Speaker 2: telling telling stories of your wild past and your your 304 00:19:51,600 --> 00:19:54,640 Speaker 2: wild adventures. But but you're down here. 305 00:19:55,280 --> 00:19:58,280 Speaker 3: I did. I can tell you a story, all right, 306 00:19:58,359 --> 00:20:01,640 Speaker 3: I need a story A yeah, I mean I when 307 00:20:01,640 --> 00:20:05,040 Speaker 3: I when I tuned the piano Monday, the guy had 308 00:20:05,040 --> 00:20:07,600 Speaker 3: two loud doors. I had to keep telling him, Hey, 309 00:20:07,640 --> 00:20:09,040 Speaker 3: I can't tune with the doors bark. 310 00:20:10,600 --> 00:20:12,639 Speaker 2: All right, Well that was a pretty short story, but 311 00:20:12,680 --> 00:20:15,520 Speaker 2: that was a good one. As you can see, Glenni's past. 312 00:20:15,640 --> 00:20:18,119 Speaker 2: It's time for the break, and I appreciate it. And 313 00:20:18,280 --> 00:20:20,400 Speaker 2: I hope thanks we talk. Life is a little better 314 00:20:20,400 --> 00:20:22,879 Speaker 2: for it. And everybody say a prayer for Glenn and 315 00:20:22,960 --> 00:20:25,760 Speaker 2: hope he cheers up. Thanks Glenn's WBZ. 316 00:20:27,200 --> 00:20:31,440 Speaker 1: You're on Night Side with Dan Ray on WBZ, Boston's 317 00:20:31,480 --> 00:20:32,240 Speaker 1: news radio. 318 00:20:32,840 --> 00:20:35,320 Speaker 2: Let's go right to Sandy and Hampton on w b 319 00:20:35,480 --> 00:20:40,320 Speaker 2: Z the number six, one, seven, ten thirty Sandy and Hampton, Hampton, 320 00:20:40,359 --> 00:20:41,040 Speaker 2: New Hampshire. 321 00:20:41,960 --> 00:20:44,480 Speaker 4: Yep, Hi Sandy, Hi, Hi. 322 00:20:45,119 --> 00:20:48,600 Speaker 5: So I think that, hey, I is gonna be the 323 00:20:48,680 --> 00:20:50,320 Speaker 5: death of criticalking. 324 00:20:50,760 --> 00:20:52,359 Speaker 2: Oh yes, I already. 325 00:20:52,880 --> 00:20:56,879 Speaker 5: I already see it with just compute as people get 326 00:20:56,960 --> 00:21:00,640 Speaker 5: tied up and I'm anur. Don't think for the steps 327 00:21:00,640 --> 00:21:05,280 Speaker 5: to figure something out on a computer. But AI, they're 328 00:21:05,280 --> 00:21:06,280 Speaker 5: just going to have to talk. 329 00:21:08,240 --> 00:21:09,800 Speaker 2: Yes, yeah, yeah, I agree. 330 00:21:12,760 --> 00:21:17,240 Speaker 6: And I can give you an example. When one of 331 00:21:17,280 --> 00:21:22,720 Speaker 6: my twins was in sixth grade, they have the National testing. 332 00:21:22,320 --> 00:21:24,120 Speaker 1: Fun and. 333 00:21:25,560 --> 00:21:27,720 Speaker 6: The school bought me because. 334 00:21:28,320 --> 00:21:31,480 Speaker 5: To ask if he got hold of a test because 335 00:21:31,520 --> 00:21:33,880 Speaker 5: he got the only algebra question right. 336 00:21:35,119 --> 00:21:37,840 Speaker 6: Uh, the test the only one in the whole school. 337 00:21:38,160 --> 00:21:39,119 Speaker 6: And he hadn't been. 338 00:21:39,000 --> 00:21:42,520 Speaker 5: Taught algebra yet. And I asked the teachers, did you 339 00:21:42,600 --> 00:21:47,040 Speaker 5: think to ask him? And his answer to the teachers 340 00:21:47,320 --> 00:21:52,440 Speaker 5: was that he knows order operations. He figured out algebra 341 00:21:52,520 --> 00:21:56,000 Speaker 5: on his own. But that's because he had an interest 342 00:21:56,160 --> 00:22:03,000 Speaker 5: in and learned all the different fines. And even the 343 00:22:03,080 --> 00:22:06,440 Speaker 5: teachers couldn't get into his homework. So I told for English, 344 00:22:06,960 --> 00:22:08,920 Speaker 5: I said, well, we wants to use learn how to 345 00:22:09,040 --> 00:22:11,200 Speaker 5: use square roots like. 346 00:22:11,200 --> 00:22:13,600 Speaker 6: In second grade. Oh, he's too young to that. I'm like, no, 347 00:22:13,720 --> 00:22:14,399 Speaker 6: he wants to know. 348 00:22:14,480 --> 00:22:16,159 Speaker 5: If he wanted to do his homework, tell him to 349 00:22:16,200 --> 00:22:17,160 Speaker 5: his homework from nths. 350 00:22:17,240 --> 00:22:18,400 Speaker 6: Then they teach him about that. 351 00:22:19,160 --> 00:22:24,560 Speaker 5: So he learned everything, and he can do everything without 352 00:22:24,600 --> 00:22:28,040 Speaker 5: a calculator, although he uses he can't use a calculator. 353 00:22:28,080 --> 00:22:28,240 Speaker 4: Now. 354 00:22:28,960 --> 00:22:33,400 Speaker 5: Wow, And and like when I was a kid in school, 355 00:22:33,560 --> 00:22:37,560 Speaker 5: we weren't allowed to use calculators until we understood it 356 00:22:38,160 --> 00:22:40,720 Speaker 5: weren't problems out on our own using our brain. 357 00:22:41,359 --> 00:22:44,919 Speaker 2: Wow, this kid seems super smart. I mean even in 358 00:22:45,000 --> 00:22:48,240 Speaker 2: high school. I think I cried because it's not in 359 00:22:48,320 --> 00:22:51,440 Speaker 2: high school. But somewhere along the line, I didn't understand, 360 00:22:51,640 --> 00:22:55,560 Speaker 2: you know, the order of operations this because it's in 361 00:22:55,600 --> 00:22:58,400 Speaker 2: the parentheses cod of first or last or I don't 362 00:22:58,440 --> 00:23:03,679 Speaker 2: even all that. Oh my god, I was. I was 363 00:23:03,760 --> 00:23:07,200 Speaker 2: bad at math, and and here I am. I don't 364 00:23:07,520 --> 00:23:09,640 Speaker 2: This job doesn't require a lot of math. 365 00:23:11,200 --> 00:23:17,040 Speaker 6: Well, it's just critical thinking and a little like the 366 00:23:17,280 --> 00:23:22,000 Speaker 6: maths is how yah? Were remember the quorder of operations? 367 00:23:23,240 --> 00:23:29,080 Speaker 2: Yeah? Yeah, well I agree with you one hundred percent absolutely. 368 00:23:29,440 --> 00:23:29,520 Speaker 1: And. 369 00:23:31,160 --> 00:23:35,040 Speaker 6: Cal people just don't even half of them don't even 370 00:23:35,040 --> 00:23:36,680 Speaker 6: know how to use it do a definite decimal. 371 00:23:38,280 --> 00:23:41,320 Speaker 2: Well, I did used to know how to do it, 372 00:23:41,359 --> 00:23:43,080 Speaker 2: and I know it a little bit. I mean I 373 00:23:43,160 --> 00:23:46,440 Speaker 2: kind of get it. I could figure it out again, jeez, 374 00:23:46,960 --> 00:23:50,880 Speaker 2: you know, I'm exposing myself as as pretty uh ignorant 375 00:23:50,920 --> 00:23:53,640 Speaker 2: on a bunch of stuff here. I should probably keep 376 00:23:53,680 --> 00:23:54,480 Speaker 2: it to myself. 377 00:23:55,480 --> 00:23:57,880 Speaker 4: You get the basic sund Yeah, I mean I did 378 00:23:58,000 --> 00:23:58,280 Speaker 4: learn it. 379 00:23:58,320 --> 00:24:02,840 Speaker 2: I forgot it. I learned it what fifty forty years ago? 380 00:24:04,119 --> 00:24:04,440 Speaker 2: You kid? 381 00:24:04,520 --> 00:24:09,080 Speaker 5: You could recall it if there was any MP that 382 00:24:09,200 --> 00:24:10,920 Speaker 5: knocked out all of the electronics. 383 00:24:10,920 --> 00:24:12,960 Speaker 2: Oh yeah, if I go to the library and I 384 00:24:12,960 --> 00:24:14,920 Speaker 2: pull out the little jar and I see the card 385 00:24:14,960 --> 00:24:16,680 Speaker 2: and the number I find, I can find the book. 386 00:24:16,800 --> 00:24:20,399 Speaker 5: Yes, yeah, so there's some people that can't do that. 387 00:24:21,000 --> 00:24:24,800 Speaker 5: There's some people that and if AI. 388 00:24:24,640 --> 00:24:26,639 Speaker 6: Goes down, then something happens. 389 00:24:27,119 --> 00:24:28,600 Speaker 5: People are not going to know how to cook. 390 00:24:28,840 --> 00:24:31,520 Speaker 6: They're not going to how to plan their meals because. 391 00:24:31,320 --> 00:24:34,480 Speaker 5: People can look at the refrigermators now and have ask 392 00:24:34,600 --> 00:24:37,440 Speaker 5: what can I make from supper by looking at what's 393 00:24:37,440 --> 00:24:38,040 Speaker 5: in the fridge. 394 00:24:38,160 --> 00:24:39,360 Speaker 2: Yeah, that's right. 395 00:24:40,400 --> 00:24:42,640 Speaker 6: They don't even make those of the decisions though. 396 00:24:43,640 --> 00:24:45,520 Speaker 2: By the way, let me share what I made some 397 00:24:45,600 --> 00:24:48,800 Speaker 2: soup today in just that way without AI. Maybe I 398 00:24:48,880 --> 00:24:52,280 Speaker 2: need AI. Let me tell you my ingredients and then 399 00:24:52,320 --> 00:24:55,800 Speaker 2: I'll tell you if it was good. It was carrots. 400 00:24:55,920 --> 00:24:57,760 Speaker 2: I had a whole bag of carrots, got to use 401 00:24:57,800 --> 00:25:02,760 Speaker 2: them up, had onion, chopped up onion. I had half 402 00:25:02,800 --> 00:25:06,440 Speaker 2: of a thing of sun dried tomatoes. I don't don't 403 00:25:06,480 --> 00:25:09,400 Speaker 2: have a recipe or anything. I'm just taking whatever I got, 404 00:25:09,720 --> 00:25:12,520 Speaker 2: sticking it in a pot. I had two things of 405 00:25:12,680 --> 00:25:17,000 Speaker 2: chicken broth. I had two cans of boiled oysters. I 406 00:25:17,000 --> 00:25:22,199 Speaker 2: had one can of salmon like in tuna, but it 407 00:25:22,240 --> 00:25:30,880 Speaker 2: was salmon and two bay leaves. And I had no 408 00:25:30,920 --> 00:25:33,320 Speaker 2: idea if it would be good, and it was. It 409 00:25:33,359 --> 00:25:36,119 Speaker 2: was surprisingly not so bad. But you know, if you 410 00:25:36,200 --> 00:25:38,920 Speaker 2: just stick a bunch of stuff in chicken broth, most 411 00:25:38,960 --> 00:25:40,399 Speaker 2: of the time it turns out okay. 412 00:25:42,520 --> 00:25:42,840 Speaker 6: Yeah. 413 00:25:43,359 --> 00:25:44,879 Speaker 5: That's the fun of cooking. 414 00:25:44,960 --> 00:25:50,159 Speaker 6: It's chemistry and you learn what works together what doesn't. 415 00:25:50,280 --> 00:25:53,520 Speaker 2: I did learn that sun dried tomatoes are so strong 416 00:25:53,560 --> 00:25:56,760 Speaker 2: in their flavor that it kind of overpowered everything else. 417 00:25:57,000 --> 00:25:59,520 Speaker 2: So I would actually do that again because it kind 418 00:25:59,520 --> 00:26:02,199 Speaker 2: of had a uster be flavor. It was very healthy, 419 00:26:02,400 --> 00:26:06,320 Speaker 2: but it just had too much of the tomato, the 420 00:26:06,480 --> 00:26:09,119 Speaker 2: sun dried tomato taste. I hear what you're saying, Sandy, 421 00:26:09,160 --> 00:26:12,160 Speaker 2: Thank you. I agree one hundred percent. Thanks. And now 422 00:26:12,240 --> 00:26:16,280 Speaker 2: it's Mike in Cambridge. Call you, Mike, Hello, how are you? 423 00:26:16,400 --> 00:26:17,800 Speaker 2: I am super well? Thank you. 424 00:26:18,280 --> 00:26:20,359 Speaker 4: I love you. I love your topic tonight about the 425 00:26:20,400 --> 00:26:23,239 Speaker 4: AI and the and the computers and whether or not 426 00:26:23,320 --> 00:26:28,040 Speaker 4: we should embrace it or be fearful. It's and so 427 00:26:28,080 --> 00:26:29,760 Speaker 4: I just I just want to weigh in with it 428 00:26:30,000 --> 00:26:33,080 Speaker 4: slightly different way to look at it. I don't think 429 00:26:33,160 --> 00:26:35,600 Speaker 4: we need to do one or the other as if 430 00:26:35,600 --> 00:26:38,439 Speaker 4: they're the only two choices. I think in different parts 431 00:26:38,440 --> 00:26:40,880 Speaker 4: of our lives we can, we can embrace it. So 432 00:26:40,920 --> 00:26:43,679 Speaker 4: certainly in the workplace, that's the way now that we 433 00:26:43,760 --> 00:26:46,520 Speaker 4: have to communicate with our co workers or if we're 434 00:26:46,560 --> 00:26:50,320 Speaker 4: talking to other companies in the course of business. Certainly 435 00:26:50,440 --> 00:26:52,440 Speaker 4: you have to know what you're doing with the AI 436 00:26:52,560 --> 00:26:54,280 Speaker 4: and with the computer in order to be able to 437 00:26:54,320 --> 00:26:57,840 Speaker 4: do your job. And when you're making purchases, it's easy 438 00:26:57,880 --> 00:27:00,439 Speaker 4: to use the computer to do searches of like if 439 00:27:00,480 --> 00:27:03,040 Speaker 4: you want to buy a particular item, you can compare 440 00:27:03,080 --> 00:27:05,560 Speaker 4: the price, you can see who has it in stock, 441 00:27:05,720 --> 00:27:07,280 Speaker 4: or whether you want to have it deliver it to 442 00:27:07,359 --> 00:27:09,960 Speaker 4: your home. So sure, I think the AI and I 443 00:27:10,000 --> 00:27:12,720 Speaker 4: think the use of the computer is the way to go. 444 00:27:13,240 --> 00:27:15,560 Speaker 4: But with family and friends, I think we need to 445 00:27:15,640 --> 00:27:19,280 Speaker 4: hold on to some aspects of communication from the past. 446 00:27:19,760 --> 00:27:22,040 Speaker 4: I think we should regularly with our children, and with 447 00:27:22,080 --> 00:27:25,600 Speaker 4: our spouses and with our friends, we should have conversations 448 00:27:25,640 --> 00:27:28,840 Speaker 4: either in person and when that's not possible, by voice 449 00:27:28,920 --> 00:27:31,920 Speaker 4: on the telephone, because I think that remains the best 450 00:27:31,920 --> 00:27:35,080 Speaker 4: way to communicate and to show how you feel while 451 00:27:35,080 --> 00:27:37,919 Speaker 4: you're communicating. So I think we can embrace it in 452 00:27:37,960 --> 00:27:40,240 Speaker 4: the workplace because we need to or we won't be 453 00:27:40,280 --> 00:27:42,679 Speaker 4: able to do our jobs, but we need to be 454 00:27:42,720 --> 00:27:45,920 Speaker 4: a little more weary, not necessarily fearful, but we need 455 00:27:45,960 --> 00:27:49,320 Speaker 4: to be aware of the right as we increase the 456 00:27:49,400 --> 00:27:53,800 Speaker 4: dependence on these devices, that we don't lose contact with 457 00:27:53,920 --> 00:27:57,840 Speaker 4: people and the opportunities that we have to communicate face 458 00:27:57,880 --> 00:28:01,200 Speaker 4: to face, ideally and when that's not possible, at least 459 00:28:01,440 --> 00:28:05,880 Speaker 4: talk by voice. Talk on the telephone. Either you can 460 00:28:05,920 --> 00:28:08,560 Speaker 4: hear the feeling, you can hear the pause, you can 461 00:28:09,040 --> 00:28:11,600 Speaker 4: you can hear the tone of voice of the person 462 00:28:11,720 --> 00:28:13,840 Speaker 4: you're talking to. So I think we can do both 463 00:28:13,840 --> 00:28:14,640 Speaker 4: at the same time. 464 00:28:16,560 --> 00:28:19,240 Speaker 2: I know you I don't know if you heard this, 465 00:28:19,400 --> 00:28:22,640 Speaker 2: but Michael Caine and Matthew mcconnoughey have both made deals 466 00:28:22,840 --> 00:28:28,720 Speaker 2: to have their voices AI copied, so if they called 467 00:28:28,760 --> 00:28:32,600 Speaker 2: you up on the phone, you would think we was them. 468 00:28:32,760 --> 00:28:35,640 Speaker 2: And here's here's something to watch out for as that 469 00:28:35,680 --> 00:28:40,800 Speaker 2: becomes more, uh, higher quality and more prevalent. There's a 470 00:28:40,840 --> 00:28:46,320 Speaker 2: scam that's pretty common where people will somehow get information about, 471 00:28:46,600 --> 00:28:49,280 Speaker 2: say a grandmother, and know that they have a grandson 472 00:28:49,360 --> 00:28:52,880 Speaker 2: named little Jimmy, and one of the scams, they'll call 473 00:28:52,960 --> 00:28:57,600 Speaker 2: up and say little Jimmy's been in an accident, we 474 00:28:57,640 --> 00:29:01,680 Speaker 2: need you to send money right away. And that's you 475 00:29:01,720 --> 00:29:04,080 Speaker 2: know people that fools people all the time. Yeah, but 476 00:29:04,160 --> 00:29:07,720 Speaker 2: with AI, they can get a hold of some sort 477 00:29:07,760 --> 00:29:12,960 Speaker 2: of voice print of little Jimmy. Well then then Grammy's 478 00:29:13,000 --> 00:29:16,560 Speaker 2: gonna completely believe it and send all her money. It's 479 00:29:16,600 --> 00:29:18,160 Speaker 2: it's kind of kind. 480 00:29:17,960 --> 00:29:20,240 Speaker 4: Of I know, I know, it's it's kind of scary. 481 00:29:20,240 --> 00:29:23,200 Speaker 4: I mean, at least we have the caller ID that 482 00:29:23,240 --> 00:29:25,280 Speaker 4: can kind of help us a little bit to be 483 00:29:25,360 --> 00:29:28,080 Speaker 4: sure of who's really calling us. If we see, uh, 484 00:29:28,160 --> 00:29:30,000 Speaker 4: you know, you see your wife's name or phone number 485 00:29:30,120 --> 00:29:33,960 Speaker 4: jump up on your phone, chances chances are better that 486 00:29:34,040 --> 00:29:36,240 Speaker 4: you you're really talking to your wife. But if it 487 00:29:36,240 --> 00:29:38,880 Speaker 4: comes from an unknown number, sure, we're gonna have to 488 00:29:38,880 --> 00:29:39,760 Speaker 4: be careful about that. 489 00:29:40,080 --> 00:29:43,040 Speaker 2: You know what, I don't even answer unknown numbers anymore, 490 00:29:43,040 --> 00:29:47,680 Speaker 2: do you. No, it's just a number. I have no idea. 491 00:29:47,960 --> 00:29:50,120 Speaker 4: Well, but what they do now is they'll they'll actually 492 00:29:50,120 --> 00:29:52,800 Speaker 4: come up with with your area code and even maybe 493 00:29:52,840 --> 00:29:55,920 Speaker 4: in exchange that's that's close to your neighborhood. And it 494 00:29:56,000 --> 00:29:57,760 Speaker 4: might not be a number, but you might think it's 495 00:29:57,880 --> 00:29:59,720 Speaker 4: a friend that you don't know because it looks like 496 00:29:59,720 --> 00:30:03,120 Speaker 4: it's a local call. And so that's unfortunate that the 497 00:30:03,640 --> 00:30:08,840 Speaker 4: communications the telephone companies are allowing the marketers to use 498 00:30:08,960 --> 00:30:13,040 Speaker 4: tactics to be deceptive, and it's unfortunate that the phone 499 00:30:13,040 --> 00:30:15,880 Speaker 4: companies are playing right into it because of the profits 500 00:30:15,920 --> 00:30:17,440 Speaker 4: that they get from those businesses. 501 00:30:17,680 --> 00:30:19,720 Speaker 2: One thing that's kind of good is if you see 502 00:30:19,720 --> 00:30:22,960 Speaker 2: a number pop up, it's calls and you just look 503 00:30:23,000 --> 00:30:25,440 Speaker 2: at it. Now you get a text version of what 504 00:30:25,480 --> 00:30:28,560 Speaker 2: they're saying. So if they're leave a message and it 505 00:30:28,640 --> 00:30:32,960 Speaker 2: can it says, HI, this is your brother Timmy. 506 00:30:33,560 --> 00:30:36,080 Speaker 4: Yeah, yeah, then you can go, oh. 507 00:30:35,920 --> 00:30:38,600 Speaker 2: Well, well, actually not Timmy, because you probably see his 508 00:30:38,680 --> 00:30:41,400 Speaker 2: name pop up. But you can get a hint from 509 00:30:41,440 --> 00:30:44,560 Speaker 2: the text message they're leaving whether or not you should 510 00:30:44,560 --> 00:30:47,320 Speaker 2: actually answer or not. But I don't because you know 511 00:30:47,440 --> 00:30:50,560 Speaker 2: those telemarketers. I don't want to have to talk to them, 512 00:30:50,600 --> 00:30:53,280 Speaker 2: So I just wait. If they leave a message, if 513 00:30:53,280 --> 00:30:54,920 Speaker 2: it's somebody legit, I call them back. 514 00:30:56,680 --> 00:30:59,760 Speaker 4: I really do, so, Mike, what do you do for 515 00:30:59,760 --> 00:31:03,080 Speaker 4: little I really, I really would call them back. I'm 516 00:31:03,120 --> 00:31:05,160 Speaker 4: annoyed right from the get goal that they bothered me 517 00:31:05,600 --> 00:31:07,560 Speaker 4: with something in the first place. So when i'm when 518 00:31:07,600 --> 00:31:10,840 Speaker 4: I when I make purchases it's because I want to 519 00:31:10,880 --> 00:31:14,000 Speaker 4: buy something before somebody's trying to sell me that thing. 520 00:31:14,560 --> 00:31:16,560 Speaker 2: Hey, let me ease into the next topic. When you 521 00:31:16,560 --> 00:31:18,840 Speaker 2: make purchases online? We were just talking about this is 522 00:31:18,880 --> 00:31:21,480 Speaker 2: going to be the ten o'clock topic. Do you think 523 00:31:21,480 --> 00:31:27,720 Speaker 2: it's okay to purchase something, say, on Amazon anyone but 524 00:31:27,760 --> 00:31:31,360 Speaker 2: for the future, for the purposes of concise speech here 525 00:31:31,400 --> 00:31:34,240 Speaker 2: from now and I'll be saying Amazon, is it okay 526 00:31:34,280 --> 00:31:37,880 Speaker 2: to purchase something on Amazon knowing you're going to use 527 00:31:37,920 --> 00:31:40,760 Speaker 2: it and send it back? Say you're going to you're 528 00:31:40,760 --> 00:31:43,200 Speaker 2: going to know, a Halloween party, and you know that 529 00:31:43,280 --> 00:31:45,760 Speaker 2: you got a Halloween costume you're going to use one time. 530 00:31:46,200 --> 00:31:50,240 Speaker 2: Is it morally wrong to order a Halloween costume in 531 00:31:50,680 --> 00:31:54,160 Speaker 2: this case, wear it and send it back knowing you're 532 00:31:54,200 --> 00:31:55,160 Speaker 2: going to send it back. 533 00:31:56,120 --> 00:31:58,520 Speaker 4: But you know, I don't think this is it's it's 534 00:31:58,560 --> 00:32:01,720 Speaker 4: a new issue as far as you know online purchases. 535 00:32:01,880 --> 00:32:05,280 Speaker 4: But even when people were making retail purchases at Their's 536 00:32:05,440 --> 00:32:08,560 Speaker 4: or Bradley's or all those stores that have gone gone 537 00:32:08,600 --> 00:32:11,840 Speaker 4: past us now, people would buy something like a like 538 00:32:11,880 --> 00:32:14,280 Speaker 4: a maybe a battery charge, or they'd go home and 539 00:32:14,320 --> 00:32:16,480 Speaker 4: charge up their battery and then they'd take the charger 540 00:32:16,760 --> 00:32:19,200 Speaker 4: back to the store. And so this is a This 541 00:32:19,880 --> 00:32:22,880 Speaker 4: isn't something new. This has been going on since probably 542 00:32:23,240 --> 00:32:25,840 Speaker 4: people have been bothering with each other. Well, no, I 543 00:32:25,840 --> 00:32:27,640 Speaker 4: think it's wrong. Of course it's wrong. 544 00:32:27,720 --> 00:32:30,640 Speaker 2: Okay, hood one, you're right. And Finally's Basement had a 545 00:32:30,720 --> 00:32:34,240 Speaker 2: very generous return policy. And I heard the story someone 546 00:32:34,240 --> 00:32:37,040 Speaker 2: told me about that. I actually tried. I actually tried 547 00:32:37,080 --> 00:32:40,320 Speaker 2: to do that at Finaling's basement. But I didn't plan 548 00:32:40,440 --> 00:32:43,680 Speaker 2: on no, no, I know, but I wore it out 549 00:32:43,720 --> 00:32:45,680 Speaker 2: a little bit, and I took it back and go, 550 00:32:45,880 --> 00:32:48,600 Speaker 2: you wore it. Somehow they knew I wore it, and 551 00:32:48,640 --> 00:32:50,800 Speaker 2: I just I didn't even wear it anywhere. I just 552 00:32:50,880 --> 00:32:53,440 Speaker 2: kind of wore it to the store. But they they said, 553 00:32:53,440 --> 00:32:55,160 Speaker 2: see this, Yeah, that's how we know. 554 00:32:55,280 --> 00:32:57,680 Speaker 4: Oh yeah, well you know I bought something. I bought 555 00:32:57,680 --> 00:32:59,720 Speaker 4: something in the store a couple of weeks ago, and 556 00:32:59,760 --> 00:33:02,160 Speaker 4: it was an item that somebody might be tempted to 557 00:33:02,480 --> 00:33:04,240 Speaker 4: bring it home and use it because you need it, 558 00:33:04,280 --> 00:33:06,520 Speaker 4: and then bring it back. And what some of the 559 00:33:06,560 --> 00:33:08,960 Speaker 4: retailers are doing now is that if you're gonna, yes, 560 00:33:09,000 --> 00:33:11,800 Speaker 4: you can return the item. But they have the what 561 00:33:11,840 --> 00:33:15,400 Speaker 4: they call a restocking fee. And then that's to discourage 562 00:33:15,400 --> 00:33:17,880 Speaker 4: people from you know, buying something and bringing it back 563 00:33:17,920 --> 00:33:20,960 Speaker 4: to two days later after they used it and get 564 00:33:21,000 --> 00:33:23,240 Speaker 4: all of their money back. And so sometimes that I 565 00:33:23,320 --> 00:33:27,000 Speaker 4: think the restocking fee discourages at least some of it. 566 00:33:27,000 --> 00:33:28,840 Speaker 2: Anyway, Okay, but yeah, that's wrong. 567 00:33:28,920 --> 00:33:31,200 Speaker 4: That's wrong. I don't I can't imagine you're gonna get 568 00:33:31,200 --> 00:33:32,760 Speaker 4: anbody who's gonna call you and say that it's the 569 00:33:32,840 --> 00:33:33,520 Speaker 4: right thing to do. 570 00:33:33,760 --> 00:33:36,680 Speaker 2: I don't know, you'd be surprised. I might even make 571 00:33:36,680 --> 00:33:39,640 Speaker 2: the case myself. I might take you know, I might 572 00:33:39,680 --> 00:33:42,000 Speaker 2: be the softist and take both sides. 573 00:33:42,800 --> 00:33:42,920 Speaker 4: Uh. 574 00:33:43,320 --> 00:33:48,320 Speaker 2: So you're saying it's morally wrong, might morally wrong. 575 00:33:48,520 --> 00:33:51,080 Speaker 4: Yeah, and in some cases it might be legally wrong. 576 00:33:51,160 --> 00:33:52,920 Speaker 4: It's depending, you know, if you bring it back in 577 00:33:52,960 --> 00:33:56,520 Speaker 4: this you're not adsing, Okay, but I mean, yeah, it's 578 00:33:56,560 --> 00:33:59,000 Speaker 4: wrong on every on every level, I think it's wrong. 579 00:33:59,080 --> 00:34:03,320 Speaker 2: Sure, So if they that way, if they were living commandment, 580 00:34:03,400 --> 00:34:06,520 Speaker 2: if if some if the pope goes up on a 581 00:34:06,560 --> 00:34:10,080 Speaker 2: mountain and gets another tablet gets a tablet, it might 582 00:34:10,200 --> 00:34:15,080 Speaker 2: say thou shalt not buy stuff and return it on purpose. 583 00:34:16,040 --> 00:34:20,160 Speaker 4: Well, but I but I realized that other people are 584 00:34:20,200 --> 00:34:22,239 Speaker 4: going to do that, and I think the price of 585 00:34:22,280 --> 00:34:25,759 Speaker 4: the product is already built into they they know, they 586 00:34:25,800 --> 00:34:28,279 Speaker 4: know how many you know what percentage of the people 587 00:34:28,280 --> 00:34:30,719 Speaker 4: who buy a product. They're going to do exactly what 588 00:34:30,760 --> 00:34:34,560 Speaker 4: your your question is tonight, your moral question. People will 589 00:34:34,600 --> 00:34:37,400 Speaker 4: do that, and that's built into the original price. 590 00:34:37,160 --> 00:34:37,760 Speaker 2: Of the ISOK. 591 00:34:38,320 --> 00:34:40,239 Speaker 4: We're all we're all paying for that. I don't think 592 00:34:40,280 --> 00:34:41,360 Speaker 4: the retail is losing. 593 00:34:41,640 --> 00:34:44,319 Speaker 2: You're so interesting that I need to but I need 594 00:34:44,360 --> 00:34:48,200 Speaker 2: to break. Thanks so much, Mike, take care, Thank you. Yeah, 595 00:34:48,360 --> 00:34:51,680 Speaker 2: Okay moment on WBZ. 596 00:34:52,960 --> 00:34:58,800 Speaker 1: Night Side, Dan Ray on w Boston's news radio rather. 597 00:34:58,719 --> 00:35:00,680 Speaker 2: Jay for Dan tonight on nights. I'm going to finish 598 00:35:00,760 --> 00:35:03,560 Speaker 2: up this topic by giving you an exhaustive list of 599 00:35:05,120 --> 00:35:07,319 Speaker 2: pros and cons of a I pay attention because you're 600 00:35:07,320 --> 00:35:10,400 Speaker 2: probably never going to get as detailed, to listen detailed 601 00:35:10,440 --> 00:35:15,960 Speaker 2: a list as this, pay attention pros, and a lot 602 00:35:15,960 --> 00:35:19,200 Speaker 2: of these that are pros are good for business and 603 00:35:19,320 --> 00:35:21,520 Speaker 2: bad for you. I will, I will grant you that 604 00:35:21,719 --> 00:35:27,080 Speaker 2: efficiency and speed automates repetitive tasks and seconds. Right, that's 605 00:35:27,160 --> 00:35:32,520 Speaker 2: like jobs, gone processes, huge volumes of data faster than humans. 606 00:35:32,520 --> 00:35:36,360 Speaker 2: More jobs, cuts down administrative work and feels like healthcare, 607 00:35:36,600 --> 00:35:40,279 Speaker 2: law and finance. More Jobs Gone good for business, bad 608 00:35:40,320 --> 00:35:43,239 Speaker 2: for you. If it reduced your cost too, If they 609 00:35:43,239 --> 00:35:45,200 Speaker 2: handed down the savings to you, that'd be good, But 610 00:35:45,680 --> 00:35:49,600 Speaker 2: what are the chances of that cost savings reduces labor 611 00:35:49,640 --> 00:35:53,839 Speaker 2: costs for routine and data heavy tasks. Lowers overhead by 612 00:35:53,880 --> 00:35:57,440 Speaker 2: streamlining workflow and reducing human error because there won't be 613 00:35:57,560 --> 00:36:04,000 Speaker 2: humans because they're job is gone. Accuracy and consistency delivers 614 00:36:04,040 --> 00:36:08,720 Speaker 2: precise output when trade correctly minimizes human bias in certain 615 00:36:09,160 --> 00:36:12,680 Speaker 2: analytical tax That's only true depending on how it's programmed. 616 00:36:13,640 --> 00:36:19,160 Speaker 2: Repeat tasks reliably without fatigue. More Jobs Gone provides twenty 617 00:36:19,480 --> 00:36:23,040 Speaker 2: four to seven assistance and customer service bad. That's supposed 618 00:36:23,040 --> 00:36:24,600 Speaker 2: to be a pro, but it's bad for you because 619 00:36:24,680 --> 00:36:27,000 Speaker 2: who wants to talk to AI for customer service, tech 620 00:36:27,040 --> 00:36:33,600 Speaker 2: support or education education. I don't think they mean school. 621 00:36:33,640 --> 00:36:37,640 Speaker 2: I think they mean product use aids individuals with disabilities 622 00:36:37,640 --> 00:36:43,200 Speaker 2: through voice recognition. That's pro, that's legit. Innovation and discovery 623 00:36:43,280 --> 00:36:50,680 Speaker 2: helps researchers solve complex problems, accelerates drug discovery, identifies human parents, 624 00:36:52,000 --> 00:37:00,320 Speaker 2: identifies patterns human off humans often miss recommends personalization, recommends products, 625 00:37:00,320 --> 00:37:03,920 Speaker 2: et cetera to you, we know about that, and improves 626 00:37:04,000 --> 00:37:08,640 Speaker 2: vehicle safety with advanced driver resistance programs systems. That's good. 627 00:37:09,440 --> 00:37:14,560 Speaker 2: Monitors industrial environments for hazards good. Flag cybersecurity threats faster 628 00:37:14,640 --> 00:37:21,040 Speaker 2: than humans. Good. Let's get to some cons. Job displacements, 629 00:37:21,280 --> 00:37:25,080 Speaker 2: automated routine jobs and manufacturing, transportation based office work, and 630 00:37:25,120 --> 00:37:30,160 Speaker 2: customer service. Lots of jobs, Bye bye, precious. Older workers 631 00:37:30,200 --> 00:37:32,880 Speaker 2: are those without technical training, as I have been told, 632 00:37:33,320 --> 00:37:38,560 Speaker 2: telling you get smart, learn the stuff, or you're going 633 00:37:38,600 --> 00:37:44,640 Speaker 2: to be out to pasture. Bias and discrimination as a 634 00:37:44,680 --> 00:37:48,680 Speaker 2: con reflects biases in the data it's trained on, can 635 00:37:48,760 --> 00:37:53,400 Speaker 2: reinforce social inequities if not carefully monitored. Decision making systems 636 00:37:53,400 --> 00:37:59,440 Speaker 2: and housing blending and hiring can produce unfair outcomes. Privacy 637 00:37:59,840 --> 00:38:02,120 Speaker 2: can cons well you already you don't need to be 638 00:38:02,160 --> 00:38:05,720 Speaker 2: a genius to understand that. Can generate convincing false text 639 00:38:05,760 --> 00:38:09,040 Speaker 2: images and videos, makes it harder for the public to 640 00:38:09,080 --> 00:38:14,440 Speaker 2: distinguish truth, enables political manipulation and fraud. These are huge 641 00:38:14,520 --> 00:38:18,520 Speaker 2: bad things. The thing is, it's here, whether you like 642 00:38:18,600 --> 00:38:20,799 Speaker 2: it or not, and you might as well jump on 643 00:38:21,000 --> 00:38:23,560 Speaker 2: and get what you can out of it. Coming up 644 00:38:24,640 --> 00:38:27,959 Speaker 2: Is it morally okay to order stuff knowing you're going 645 00:38:28,000 --> 00:38:29,600 Speaker 2: to return it. It's WZ