1 00:00:00,640 --> 00:00:02,680 Speaker 1: I'll get a team. Welcome to another installment of your 2 00:00:02,720 --> 00:00:06,359 Speaker 1: favorite show, with your favorite people. David, John Ken, Patrick Gillespie, 3 00:00:06,400 --> 00:00:12,320 Speaker 1: Tiffany and Margaret, Woman of Veronica. Two dogs, cook cloak cookie. 4 00:00:11,920 --> 00:00:12,840 Speaker 2: Good they two dogs? 5 00:00:13,360 --> 00:00:14,040 Speaker 1: Are you all right? 6 00:00:14,520 --> 00:00:15,040 Speaker 2: I'm great? 7 00:00:15,120 --> 00:00:16,239 Speaker 3: Thanks great? 8 00:00:16,560 --> 00:00:20,720 Speaker 1: Great, dog's good, dog good, cat good, motorbike good. 9 00:00:20,960 --> 00:00:21,880 Speaker 2: Everything's good. 10 00:00:22,560 --> 00:00:24,680 Speaker 1: To get rid of the cat, the motorbike or the dog. 11 00:00:24,720 --> 00:00:25,560 Speaker 1: What would go first? 12 00:00:25,560 --> 00:00:28,880 Speaker 2: One of them? None of them? We all live homeless 13 00:00:28,920 --> 00:00:31,320 Speaker 2: on food stamps. If we had two meat, the cat, 14 00:00:31,400 --> 00:00:32,560 Speaker 2: the dog and the motorbike. 15 00:00:32,800 --> 00:00:34,200 Speaker 1: A food stamp still a thing. 16 00:00:34,520 --> 00:00:34,919 Speaker 3: I don't know. 17 00:00:35,360 --> 00:00:37,959 Speaker 4: They never were in Australia, but I think they are 18 00:00:38,000 --> 00:00:38,600 Speaker 4: in the US. 19 00:00:38,680 --> 00:00:43,840 Speaker 1: Yeah. I think you've been watching too many Disney movies. Tiff. Hello, 20 00:00:44,479 --> 00:00:46,000 Speaker 1: Professor Gillespie, how are you? 21 00:00:46,520 --> 00:00:46,720 Speaker 2: Yeah? 22 00:00:46,840 --> 00:00:47,760 Speaker 3: Good? Good to be here. 23 00:00:47,920 --> 00:00:49,840 Speaker 4: I got to ask that last time we talked, Tiff 24 00:00:49,840 --> 00:00:53,159 Speaker 4: had bought an accessory for her bike to make it 25 00:00:53,360 --> 00:00:54,400 Speaker 4: really really noisy. 26 00:00:54,720 --> 00:00:56,160 Speaker 3: Has that being now fitted? Tiff? 27 00:00:56,320 --> 00:00:59,640 Speaker 2: It's been fitted. It's so good. I am so obsessed. 28 00:00:59,720 --> 00:01:02,680 Speaker 2: Now you've got no idea, David. We'll talk about it later. 29 00:01:03,120 --> 00:01:05,600 Speaker 1: No, talk about it, talk about it now, because I 30 00:01:05,680 --> 00:01:07,480 Speaker 1: want people to know what I have to contend with. 31 00:01:08,560 --> 00:01:12,160 Speaker 4: So you're frustrated at writing around on the equivalent of 32 00:01:12,160 --> 00:01:14,199 Speaker 4: a singer sewing machine, and now it's a singer sewing 33 00:01:14,240 --> 00:01:16,440 Speaker 4: machine that sounds like a Harley is it? 34 00:01:16,440 --> 00:01:17,240 Speaker 3: It really is? 35 00:01:17,319 --> 00:01:21,280 Speaker 2: And it's like I can't even describe. It has transformed 36 00:01:21,319 --> 00:01:24,200 Speaker 2: my relationship with the bike. I cannot ride it enough. 37 00:01:25,640 --> 00:01:29,399 Speaker 1: There aren't too many women that I know that are 38 00:01:30,280 --> 00:01:32,720 Speaker 1: I mean, in fact, you might be the only one 39 00:01:33,000 --> 00:01:38,440 Speaker 1: that's totally enamored with a motorbike that's loud and obnoxious. 40 00:01:39,160 --> 00:01:40,399 Speaker 2: So how good is it? 41 00:01:40,440 --> 00:01:43,559 Speaker 1: Though? It is good? It is good. It does sound 42 00:01:43,640 --> 00:01:45,760 Speaker 1: I mean, but I'm a bogan. 43 00:01:45,920 --> 00:01:48,880 Speaker 4: So you're going to have to get a sound clip 44 00:01:49,040 --> 00:01:51,120 Speaker 4: and edit it in here, Tiff, so that people know 45 00:01:51,160 --> 00:01:52,000 Speaker 4: what we're talking about. 46 00:01:52,240 --> 00:01:54,440 Speaker 2: I'll go give it a few revs. Maybe next week 47 00:01:54,600 --> 00:01:56,279 Speaker 2: I'll get out on it, give it a few revs, 48 00:01:56,280 --> 00:01:57,640 Speaker 2: and pop it in for the listeners. 49 00:01:57,840 --> 00:01:59,840 Speaker 1: You could just go out record it to your phone. 50 00:02:00,000 --> 00:02:02,360 Speaker 1: Could you do that? Could you literally record it on 51 00:02:02,400 --> 00:02:05,200 Speaker 1: your phone and insert it into this episode. 52 00:02:05,320 --> 00:02:05,880 Speaker 3: I could do that. 53 00:02:06,040 --> 00:02:20,840 Speaker 1: I could do it, Tiff. Let's hear your motorbike now, boom, 54 00:02:21,040 --> 00:02:23,760 Speaker 1: And that was TIFF's motorbike. Everyone I've just created some 55 00:02:23,800 --> 00:02:29,079 Speaker 1: more work for every woman listens to the show will 56 00:02:29,080 --> 00:02:31,160 Speaker 1: be like, not every woman, but a lot will be like, 57 00:02:31,560 --> 00:02:32,200 Speaker 1: I don't get it. 58 00:02:33,800 --> 00:02:35,960 Speaker 2: I didn't get it. 59 00:02:35,600 --> 00:02:39,240 Speaker 1: What didn't you get? What do you get now? Sorry, David, 60 00:02:39,280 --> 00:02:42,640 Speaker 1: this is really this is very inappropriate. But what do 61 00:02:42,680 --> 00:02:44,280 Speaker 1: you get now that you didn't get? 62 00:02:44,800 --> 00:02:47,239 Speaker 2: Well, I didn't get how much it was going to 63 00:02:47,440 --> 00:02:52,760 Speaker 2: change the entire riding experience, not just the ah Everyone's 64 00:02:52,800 --> 00:02:55,519 Speaker 2: back bokes louder than mine. And I'm on a Junomi 65 00:02:55,840 --> 00:03:01,600 Speaker 2: sewing machine. Well, I go the long way home. I 66 00:03:01,840 --> 00:03:04,519 Speaker 2: couldn't be bothered getting it, getting it unpacked, to bring 67 00:03:04,560 --> 00:03:07,880 Speaker 2: it fifteen minutes to the gym before. Now, I, oh, 68 00:03:08,560 --> 00:03:12,080 Speaker 2: I don't know something, something happens. It's very visceral. It's 69 00:03:12,360 --> 00:03:14,640 Speaker 2: it's the sound, it's the feeling. Do you know what. 70 00:03:14,680 --> 00:03:18,120 Speaker 2: I've got a comms unit the communications for other writers, 71 00:03:18,200 --> 00:03:20,200 Speaker 2: and I don't like it because it takes me away 72 00:03:20,200 --> 00:03:24,400 Speaker 2: from the presence of the sensation of the whole bike experience. 73 00:03:25,240 --> 00:03:27,080 Speaker 3: Yeah, I know what, I know what you're talking about. 74 00:03:27,200 --> 00:03:34,160 Speaker 4: You know my first car was a Gemini, Yeah, which 75 00:03:34,280 --> 00:03:36,160 Speaker 4: was a you know, it's a single sewing routine with 76 00:03:36,200 --> 00:03:41,440 Speaker 4: four wheels. And it it became a lot faster and 77 00:03:41,520 --> 00:03:44,120 Speaker 4: better when I went and bought some speech stripes and 78 00:03:44,160 --> 00:03:46,640 Speaker 4: put you stuck them down the side of it. 79 00:03:46,640 --> 00:03:52,240 Speaker 1: It's it's it's an indictment on you that you owned 80 00:03:52,240 --> 00:03:55,800 Speaker 1: a Gemini and but that you are brave enough to 81 00:03:55,920 --> 00:03:58,440 Speaker 1: say that you are under Gemini a long time ago. 82 00:03:59,240 --> 00:04:04,000 Speaker 1: Although back the day they were they were kind of okay. 83 00:04:04,040 --> 00:04:06,120 Speaker 4: It wasn't that kind of Gemini. It was a blue 84 00:04:06,200 --> 00:04:09,800 Speaker 4: nurse's car. So it was a lovely hell blue. 85 00:04:10,480 --> 00:04:14,440 Speaker 1: That kind of baby powder blue. All right, Now, we're 86 00:04:14,440 --> 00:04:17,600 Speaker 1: not here to talk about bloody motorbikes or forty year 87 00:04:17,600 --> 00:04:21,080 Speaker 1: old Geminis. We're here to talk about the things that 88 00:04:21,120 --> 00:04:24,119 Speaker 1: you've been sticking your nose into, because you fucking stick 89 00:04:24,160 --> 00:04:25,680 Speaker 1: your nose in a lot of places. 90 00:04:25,760 --> 00:04:26,200 Speaker 4: You do. 91 00:04:26,839 --> 00:04:30,360 Speaker 1: You do open doors that nobody asked you to open. 92 00:04:30,480 --> 00:04:33,840 Speaker 1: Let's just say that. But you do it with gay 93 00:04:33,880 --> 00:04:38,039 Speaker 1: abandon You just you just throw it open and just 94 00:04:38,320 --> 00:04:42,440 Speaker 1: cartwheel in there. And so one of that. By the way, everyone, 95 00:04:43,000 --> 00:04:46,000 Speaker 1: David who please help him get some more followers on 96 00:04:46,080 --> 00:04:49,640 Speaker 1: buddy LinkedIn. He's up to two thousand, but my nana's 97 00:04:49,680 --> 00:04:51,720 Speaker 1: on two and a half thousand and she's dead. So 98 00:04:52,200 --> 00:04:53,920 Speaker 1: if you could give him, if you could give him 99 00:04:53,920 --> 00:04:56,800 Speaker 1: a chop out, follow him on LinkedIn. And also, if 100 00:04:56,800 --> 00:04:58,800 Speaker 1: you're not a member or if you're not following on 101 00:04:59,120 --> 00:05:04,080 Speaker 1: the You Project podcast facebook page where he posts every 102 00:05:04,160 --> 00:05:06,560 Speaker 1: second or third one of his articles, jump in there. 103 00:05:07,200 --> 00:05:09,920 Speaker 1: Also follow on there. But you put up something this 104 00:05:10,000 --> 00:05:14,640 Speaker 1: week around an AI kind of let's call it a 105 00:05:15,680 --> 00:05:18,320 Speaker 1: resource that was developed. Do you want to I won't. 106 00:05:18,440 --> 00:05:21,800 Speaker 1: I won't spoil the I won't spoil the show. Tell 107 00:05:21,880 --> 00:05:23,800 Speaker 1: us what was developed and what it's for. 108 00:05:24,560 --> 00:05:24,880 Speaker 3: Okay. 109 00:05:24,960 --> 00:05:30,560 Speaker 4: So O two, which is a British telecommunications company, decided 110 00:05:30,640 --> 00:05:35,119 Speaker 4: that rather than just blocking scammer numbers as they're coming 111 00:05:35,160 --> 00:05:39,679 Speaker 4: through their servers, they would prefer to waste the scammer's time, 112 00:05:40,080 --> 00:05:44,279 Speaker 4: figuring that that's actually a more powerful this incentive to 113 00:05:44,520 --> 00:05:47,800 Speaker 4: phone scammers because just blocking their numbers just means that 114 00:05:47,560 --> 00:05:50,800 Speaker 4: the computer moves on to the next number, and you know, 115 00:05:51,120 --> 00:05:54,479 Speaker 4: they haven't really harmed the scammer at all. So what 116 00:05:54,560 --> 00:05:59,080 Speaker 4: they developed was an AI that imitates your grant and 117 00:05:59,320 --> 00:06:04,000 Speaker 4: this AI will have a really lengthy conversation with anyone 118 00:06:04,200 --> 00:06:10,680 Speaker 4: that rings them, and it's really interesting. I think you've 119 00:06:10,680 --> 00:06:13,640 Speaker 4: got some audio here, Tiff, and I whack it in 120 00:06:13,720 --> 00:06:16,440 Speaker 4: because listening to this is your You're now going to 121 00:06:16,480 --> 00:06:20,520 Speaker 4: listen to the actual AI having a real conversation with 122 00:06:20,640 --> 00:06:22,080 Speaker 4: real phone scammers. 123 00:06:23,120 --> 00:06:27,760 Speaker 5: So w is than a dot And then DoD, I 124 00:06:27,800 --> 00:06:30,080 Speaker 5: think you're perfectly bothering people, right. 125 00:06:30,360 --> 00:06:38,000 Speaker 6: I'm just trying to have a little chat. Gosh, how 126 00:06:38,080 --> 00:06:43,640 Speaker 6: time flying. It's showing me a picture of my cat Fluffy. 127 00:06:44,080 --> 00:06:46,880 Speaker 1: It's showing you the picture of your cat Fluffy. It's 128 00:06:46,920 --> 00:06:48,400 Speaker 1: not calling med stupid. 129 00:06:49,320 --> 00:06:52,719 Speaker 6: Got it, dear, because while they're busy talking to me, 130 00:06:53,400 --> 00:06:56,680 Speaker 6: they can't be scamming you. And let's face it, Dear, 131 00:06:57,400 --> 00:06:59,240 Speaker 6: I've got all the time in the world. 132 00:07:00,360 --> 00:07:02,000 Speaker 3: So you know, she's got a bit of a sense 133 00:07:02,000 --> 00:07:02,720 Speaker 3: of humor as well. 134 00:07:02,760 --> 00:07:05,240 Speaker 4: You know that one fellow saying has been nearly an hour, 135 00:07:05,279 --> 00:07:10,400 Speaker 4: and she's saying, oh, how time flies. So it's an 136 00:07:10,440 --> 00:07:14,320 Speaker 4: interesting approach to the problem in that they figured, well, 137 00:07:15,000 --> 00:07:17,000 Speaker 4: we haven't just got one grannie we can put on 138 00:07:17,040 --> 00:07:19,800 Speaker 4: the phone. You know, got one AI grannie, You've got 139 00:07:19,840 --> 00:07:24,360 Speaker 4: a billion of them, so you know, run them all simultaneously. 140 00:07:24,480 --> 00:07:27,080 Speaker 4: And so that's their plan, is to just waste the 141 00:07:27,120 --> 00:07:32,200 Speaker 4: scammer's time and clog up their calling networks with conversations 142 00:07:32,240 --> 00:07:34,560 Speaker 4: with not very real grannies. 143 00:07:34,840 --> 00:07:39,120 Speaker 1: Is is it? It must be bloody great AI. If 144 00:07:39,200 --> 00:07:42,240 Speaker 1: they can keep someone on the phone for an hour 145 00:07:43,320 --> 00:07:46,800 Speaker 1: thinking that they're actually having a real conversation with the 146 00:07:46,840 --> 00:07:47,960 Speaker 1: real person. 147 00:07:48,080 --> 00:07:50,920 Speaker 4: Well, they've got a strong motivation to stay on the phone. 148 00:07:50,960 --> 00:07:55,600 Speaker 4: So you're remembering that, you know, it's a fairly unrewarding job, 149 00:07:55,640 --> 00:07:58,360 Speaker 4: I guess, in the sense that you know, ninety nine 150 00:07:58,400 --> 00:07:59,920 Speaker 4: percent of people have to speak hang up with it 151 00:08:00,040 --> 00:08:04,000 Speaker 4: in the first second. So they want to stay on 152 00:08:04,040 --> 00:08:06,160 Speaker 4: the phone. They want to think that they're making progress. 153 00:08:06,160 --> 00:08:07,800 Speaker 4: They want to talk to someone and see if they 154 00:08:07,800 --> 00:08:10,400 Speaker 4: can get them too interested in what they're doing. And 155 00:08:10,520 --> 00:08:12,760 Speaker 4: the interesting thing about an AI is that it can adapt. 156 00:08:12,960 --> 00:08:15,560 Speaker 4: It can have a genuine conversation with you. It can 157 00:08:15,600 --> 00:08:17,520 Speaker 4: talk to you about what you've said, it can listen 158 00:08:17,520 --> 00:08:19,800 Speaker 4: to what you've said, it can engage in it. And 159 00:08:19,840 --> 00:08:24,680 Speaker 4: this one has been told programmed to keep the conversation 160 00:08:24,880 --> 00:08:29,280 Speaker 4: going whatever it takes. And as you heard from those examples, 161 00:08:29,360 --> 00:08:33,880 Speaker 4: it was managing to do it for an hour, which now, look, 162 00:08:34,080 --> 00:08:36,439 Speaker 4: I suspect this is a fairly short term solution and 163 00:08:36,520 --> 00:08:39,720 Speaker 4: that you know pretty soon these scammers will be AIS themselves, 164 00:08:39,760 --> 00:08:42,360 Speaker 4: and soe AI is talking to our eyes and no 165 00:08:42,360 --> 00:08:44,439 Speaker 4: one will waste anyone's time. But I guess a few 166 00:08:44,720 --> 00:08:48,080 Speaker 4: within megacycles of computing time might go down the drain. 167 00:08:48,240 --> 00:08:52,559 Speaker 4: But it's an interesting use of AI at the moment, 168 00:08:52,600 --> 00:08:56,240 Speaker 4: and it shows how far it's gone that you could 169 00:08:56,240 --> 00:08:59,360 Speaker 4: be talking to what you think as a person in 170 00:08:59,440 --> 00:09:01,880 Speaker 4: real time and in fact you're. 171 00:09:01,800 --> 00:09:08,560 Speaker 1: Not getting It's getting harder and harder to identify what 172 00:09:08,679 --> 00:09:10,720 Speaker 1: is real and what is not. Like I got sent 173 00:09:10,840 --> 00:09:14,520 Speaker 1: something today through one of the speaking agencies that I 174 00:09:14,559 --> 00:09:18,200 Speaker 1: do some work for, and I didn't open it, but 175 00:09:18,520 --> 00:09:22,240 Speaker 1: it looked legit. It was from there, had there had 176 00:09:22,280 --> 00:09:25,320 Speaker 1: everything that looks like the other emails I get from them, 177 00:09:26,000 --> 00:09:29,800 Speaker 1: but it had a file to open, which didn't make sense, 178 00:09:30,600 --> 00:09:32,600 Speaker 1: so I didn't open it. And I'm nine to nine 179 00:09:32,640 --> 00:09:36,120 Speaker 1: point nine, I'm sent. Sure it was a virus or something, 180 00:09:36,200 --> 00:09:40,400 Speaker 1: but I just nearly automatically opened it because I've opened 181 00:09:40,400 --> 00:09:43,600 Speaker 1: things from them many times and it just came in 182 00:09:43,640 --> 00:09:45,720 Speaker 1: and it's like, oh, there's a file here, blah blah 183 00:09:45,720 --> 00:09:48,360 Speaker 1: blah about that, and then I read what it was about, 184 00:09:48,400 --> 00:09:51,160 Speaker 1: and I'm like, what what is What is that, but 185 00:09:51,280 --> 00:09:55,240 Speaker 1: you I think if you're not ever vigilant, you know 186 00:09:55,280 --> 00:09:58,400 Speaker 1: we're going to get in trouble because the quality. I mean, 187 00:09:58,440 --> 00:10:03,280 Speaker 1: there's also some ridiculously terrible kind of you know, scams, 188 00:10:03,320 --> 00:10:05,640 Speaker 1: but some of them are pretty high level, aren't they. 189 00:10:06,440 --> 00:10:09,640 Speaker 4: Yeah, But I guess on the flip side of this 190 00:10:09,840 --> 00:10:12,840 Speaker 4: is it's worth thinking about where it could be useful. 191 00:10:13,200 --> 00:10:16,640 Speaker 4: If you've got AI capable of holding a reasonable conversation 192 00:10:16,720 --> 00:10:19,559 Speaker 4: with you, but also with access to a significant amount 193 00:10:19,600 --> 00:10:24,040 Speaker 4: of data about the organization it works for, then wouldn't 194 00:10:24,080 --> 00:10:27,520 Speaker 4: you rather be talking to an AI like that than 195 00:10:27,600 --> 00:10:30,440 Speaker 4: sitting on hold for two days waiting for your bank 196 00:10:30,520 --> 00:10:32,800 Speaker 4: to talk to you, or for your telcoe to talk 197 00:10:32,840 --> 00:10:35,640 Speaker 4: to you, or for send a link to talk to you, 198 00:10:36,400 --> 00:10:38,560 Speaker 4: which is the alternative at the moment, which is press 199 00:10:38,679 --> 00:10:40,840 Speaker 4: this button and then go on hold for three days. 200 00:10:41,679 --> 00:10:44,960 Speaker 4: Wouldn't you rather be answered every single time, even if 201 00:10:44,960 --> 00:10:47,160 Speaker 4: it's by an AI that you can actually have a 202 00:10:47,200 --> 00:10:50,360 Speaker 4: conversation with and who probably has a pretty good chance 203 00:10:50,400 --> 00:10:52,000 Speaker 4: of giving you the information you're after. 204 00:10:52,559 --> 00:10:55,520 Speaker 1: And also you're not talking to anything that gets pissed 205 00:10:55,559 --> 00:11:00,360 Speaker 1: off or impatient or frustrated or has emotions or is 206 00:11:00,400 --> 00:11:02,640 Speaker 1: going to judge you or you know. 207 00:11:03,120 --> 00:11:06,280 Speaker 4: So in the example we just played the grands, they 208 00:11:06,280 --> 00:11:11,360 Speaker 4: were swearing at her. She's perfectly calm, just keeps the conversation. 209 00:11:11,000 --> 00:11:13,760 Speaker 1: Going, Yeah, yeah, that is, that is. 210 00:11:14,640 --> 00:11:14,880 Speaker 3: Yeah. 211 00:11:14,960 --> 00:11:18,480 Speaker 1: There are a range of applications even with I don't 212 00:11:18,559 --> 00:11:23,400 Speaker 1: use chat GPT for any of my writing or obviously 213 00:11:23,480 --> 00:11:27,320 Speaker 1: for my research because it's irrelevant. But sometimes I'll look 214 00:11:27,400 --> 00:11:31,959 Speaker 1: up something just to see what it says about something 215 00:11:32,120 --> 00:11:34,760 Speaker 1: I already understand, to see if what they bring up 216 00:11:34,840 --> 00:11:37,800 Speaker 1: is similar to what I would write or what I 217 00:11:37,800 --> 00:11:41,679 Speaker 1: would say. And I was thinking the other day about 218 00:11:43,679 --> 00:11:47,480 Speaker 1: what I call in personal intelligence, like how the ability 219 00:11:47,520 --> 00:11:50,120 Speaker 1: to be able to you know, understand others, all that communicates, 220 00:11:50,120 --> 00:11:54,360 Speaker 1: solve problems, work alongside other people, that kind of social intelligence. 221 00:11:54,520 --> 00:11:59,080 Speaker 1: And I thought, I've never heard the term into personal intelligence. 222 00:11:59,160 --> 00:12:02,280 Speaker 1: I wonder if it's actually a term and an area 223 00:12:02,320 --> 00:12:05,960 Speaker 1: of research. And I pulled it up and I said, 224 00:12:06,559 --> 00:12:10,880 Speaker 1: what is inter personal intelligence? On chat GPAT? And it 225 00:12:10,920 --> 00:12:12,880 Speaker 1: brought it up and A Blowke wrote a book Come 226 00:12:12,960 --> 00:12:16,280 Speaker 1: on Boy you forty years ago, whole thesis, whole and 227 00:12:16,679 --> 00:12:18,920 Speaker 1: a guy at a Harvard a psychologist and a professor, 228 00:12:18,960 --> 00:12:22,559 Speaker 1: and yeah, it's one of eight kinds of intelligence he describes. 229 00:12:23,000 --> 00:12:25,080 Speaker 1: But anyways, so it finished, and then at the end, 230 00:12:25,800 --> 00:12:29,280 Speaker 1: unprompted by me chat, GPT goes, I hope you find 231 00:12:29,280 --> 00:12:32,760 Speaker 1: that valuable, Craig, because it's interesting because that intersects with 232 00:12:32,800 --> 00:12:36,000 Speaker 1: your research and probably something you could talk about in 233 00:12:36,040 --> 00:12:39,600 Speaker 1: your workshops and on your podcast. Like it gave me 234 00:12:39,640 --> 00:12:43,240 Speaker 1: a whole paragraph of just general advice around how I 235 00:12:43,280 --> 00:12:47,439 Speaker 1: could potentially use this information in different capacities and context 236 00:12:47,840 --> 00:12:50,040 Speaker 1: and I didn't ask it any of that. I'm like, 237 00:12:50,600 --> 00:12:54,640 Speaker 1: this motherfucker has been studying me, like what it's like, 238 00:12:54,720 --> 00:12:55,440 Speaker 1: it knows me. 239 00:12:56,200 --> 00:12:58,560 Speaker 4: Well, that's a nice segue to the next bit that 240 00:12:58,600 --> 00:13:01,960 Speaker 4: we want to talk about, Craig, which is you would 241 00:13:01,960 --> 00:13:06,559 Speaker 4: have seen the things about Bunnings getting a slap over 242 00:13:06,600 --> 00:13:09,360 Speaker 4: the wrist with a damp piece of lettuce by the 243 00:13:09,400 --> 00:13:12,439 Speaker 4: Privacy Commissioner saying, oh, you'ren aughty folks, you shouldn't have 244 00:13:12,480 --> 00:13:15,600 Speaker 4: done that. And what Bunnings were doing was for three 245 00:13:15,679 --> 00:13:22,520 Speaker 4: years they ran profiling software in their stores without telling 246 00:13:22,520 --> 00:13:27,120 Speaker 4: anyone they were doing it. So this was facial recognition 247 00:13:27,440 --> 00:13:30,200 Speaker 4: and the reason that Bunnings said they were doing it 248 00:13:30,320 --> 00:13:37,040 Speaker 4: was to because they wanted to identify criminals in known shoplifters, 249 00:13:37,080 --> 00:13:41,120 Speaker 4: potential trouble makers, etc. In their stores. They say seventy 250 00:13:41,160 --> 00:13:44,720 Speaker 4: percent of problems in their stores come from a very 251 00:13:44,840 --> 00:13:47,880 Speaker 4: very small group of people that they would use this 252 00:13:47,960 --> 00:13:53,760 Speaker 4: software to identify, and the privacy commissioner said, wow, you 253 00:13:53,840 --> 00:13:56,040 Speaker 4: can't do that. They didn't have a problem with them 254 00:13:56,320 --> 00:13:59,120 Speaker 4: using the software, they had a problem with them doing 255 00:13:59,160 --> 00:14:04,960 Speaker 4: it without telling any one. So and this sort of 256 00:14:05,000 --> 00:14:09,040 Speaker 4: thing is increasingly becoming part of retail. I don't know 257 00:14:09,040 --> 00:14:12,760 Speaker 4: if you've ever paid close attention to the self checkout 258 00:14:13,400 --> 00:14:15,680 Speaker 4: kiosks in the supermarket, but if you have, you would 259 00:14:15,679 --> 00:14:19,440 Speaker 4: have noticed a real time video of yourself operating the machine. 260 00:14:21,000 --> 00:14:25,720 Speaker 4: And there's no guarantees around any of this stuff, you know. 261 00:14:25,960 --> 00:14:27,880 Speaker 4: Bunning said, Look, it was deleted as soon as it 262 00:14:27,960 --> 00:14:30,640 Speaker 4: wasn't a match, like you know, in less than a second, 263 00:14:31,160 --> 00:14:34,200 Speaker 4: your photo was off the system. No one was keeping anything. 264 00:14:34,680 --> 00:14:39,320 Speaker 4: And maybe that's true, it probably is, But the fact 265 00:14:39,360 --> 00:14:41,920 Speaker 4: that it's being recorded at all and is being used 266 00:14:41,960 --> 00:14:48,080 Speaker 4: for identification, I think gives pause for thought. Because profiling 267 00:14:48,120 --> 00:14:51,640 Speaker 4: software like that, which well facial recognition software all of 268 00:14:51,720 --> 00:14:54,400 Speaker 4: us use all the time, use it, probably every morning 269 00:14:54,440 --> 00:14:56,480 Speaker 4: to unlock your phone every five minutes to unlock your 270 00:14:56,480 --> 00:14:59,720 Speaker 4: phone and is using facial recognition software. It's used at 271 00:14:59,720 --> 00:15:04,160 Speaker 4: the airports to you know, get you through customs all 272 00:15:04,160 --> 00:15:07,160 Speaker 4: that sort of stuff. The question then comes, is it 273 00:15:07,240 --> 00:15:10,520 Speaker 4: okay for to just be being used everywhere all the time, 274 00:15:10,680 --> 00:15:12,960 Speaker 4: for us to be able to be identified, particularly in 275 00:15:13,080 --> 00:15:18,080 Speaker 4: places where it can be matched to other information about us, 276 00:15:18,440 --> 00:15:22,240 Speaker 4: like our credit card or our Flyby's number, or our 277 00:15:22,320 --> 00:15:25,800 Speaker 4: rewards number which records every purchase we've ever made, and 278 00:15:25,840 --> 00:15:27,960 Speaker 4: our preferences, you know, whether we happen to like the 279 00:15:28,000 --> 00:15:30,720 Speaker 4: full fat milk or the trim All that kind of 280 00:15:30,760 --> 00:15:34,200 Speaker 4: stuff starts to be then being associated with stuff that 281 00:15:34,240 --> 00:15:39,000 Speaker 4: can be picked out immediately by software that's automatically identifying you. 282 00:15:39,040 --> 00:15:40,960 Speaker 4: Because if you all had a number plate stable to 283 00:15:41,000 --> 00:15:46,080 Speaker 4: your forehead, So how comfortable are we with that? So 284 00:15:46,280 --> 00:15:49,040 Speaker 4: taking the example that you just gave with chat GPT, 285 00:15:49,800 --> 00:15:52,680 Speaker 4: what if you you know, you were browsing the women's 286 00:15:52,680 --> 00:15:58,760 Speaker 4: little Lingerie section at Target as you do, and you know, 287 00:15:58,800 --> 00:16:02,720 Speaker 4: the facial recognitions software says, oh, it's got an interest 288 00:16:02,720 --> 00:16:03,720 Speaker 4: in women's. 289 00:16:03,480 --> 00:16:05,240 Speaker 3: Underwear back again. 290 00:16:07,080 --> 00:16:11,760 Speaker 4: And pops onto Instagram, does a complete search of Instagram. 291 00:16:12,160 --> 00:16:14,560 Speaker 4: You know, the reverse image search of Instagram, which your 292 00:16:14,640 --> 00:16:18,320 Speaker 4: face identifies your Instagram account and then starts popping ads 293 00:16:18,360 --> 00:16:22,720 Speaker 4: for women's underwear in your Instagram. So that's the sort 294 00:16:22,720 --> 00:16:25,960 Speaker 4: of thing that can be used commercially and is all 295 00:16:26,040 --> 00:16:30,080 Speaker 4: doable now with existing technology, And who's to say it isn't. 296 00:16:30,240 --> 00:16:33,360 Speaker 4: I mean, how did chat GPT know so much about you? 297 00:16:33,600 --> 00:16:38,040 Speaker 4: He is a good question to ask, But the question 298 00:16:38,160 --> 00:16:40,680 Speaker 4: is how much of this are we prepared to allow 299 00:16:40,800 --> 00:16:43,920 Speaker 4: have happening. It's all a bit amusing when it's about 300 00:16:43,920 --> 00:16:47,960 Speaker 4: your lingerie shopping habits, but it's less amusing when you 301 00:16:48,120 --> 00:16:50,680 Speaker 4: get a fine in the post for jaywalking at two 302 00:16:50,720 --> 00:16:54,920 Speaker 4: am and Mount Iseres because you know a camera picked 303 00:16:54,960 --> 00:16:58,480 Speaker 4: you up, identified your face, I reported you to authorities 304 00:16:58,520 --> 00:17:01,560 Speaker 4: and sent you a fine. Or when your car, as 305 00:17:01,600 --> 00:17:04,320 Speaker 4: we've talked before, you know, shoots a letter off to 306 00:17:04,440 --> 00:17:07,960 Speaker 4: the to the coppers and says, oh, by the way, 307 00:17:09,119 --> 00:17:11,360 Speaker 4: Harps was doing five k over the limit and he's 308 00:17:11,600 --> 00:17:12,720 Speaker 4: photographic proof of it. 309 00:17:15,119 --> 00:17:18,160 Speaker 1: You know, even what you were saying with at Woolworth, 310 00:17:19,080 --> 00:17:23,480 Speaker 1: I was thinking that because well, where I shop anyway, 311 00:17:23,680 --> 00:17:26,000 Speaker 1: there's I think there's two cameras, so there's one that 312 00:17:26,960 --> 00:17:29,320 Speaker 1: is straight on at my face. But then there's one 313 00:17:29,359 --> 00:17:33,159 Speaker 1: above me where it can see what you're doing. It 314 00:17:33,200 --> 00:17:36,720 Speaker 1: can see you scanning things. So it's above my head 315 00:17:36,760 --> 00:17:40,200 Speaker 1: looking at me, scanning things, looking at me taking things 316 00:17:40,280 --> 00:17:42,800 Speaker 1: out of the basket, scanning them, then putting them into 317 00:17:42,840 --> 00:17:48,399 Speaker 1: my bag. So it's this kind of overhead view and 318 00:17:48,480 --> 00:17:51,880 Speaker 1: I think, I mean, honestly, I guess it doesn't bother 319 00:17:51,960 --> 00:17:56,080 Speaker 1: me because I've never thought about it too much. But no, no, 320 00:17:56,160 --> 00:17:58,320 Speaker 1: I mean, maybe it's there, but I've never seen this 321 00:17:58,480 --> 00:18:00,960 Speaker 1: anywhere where it says, by the way, you're being filmed 322 00:18:01,040 --> 00:18:01,560 Speaker 1: right now. 323 00:18:02,640 --> 00:18:05,560 Speaker 4: Yeah, And that was the problem that the Privacy Commissioner 324 00:18:05,560 --> 00:18:07,159 Speaker 4: had with what Bunning's were doing. Like I said, they 325 00:18:07,160 --> 00:18:09,080 Speaker 4: didn't have a problem with the fact they were doing it. 326 00:18:09,880 --> 00:18:12,280 Speaker 4: The problem was they didn't tell people they were doing 327 00:18:12,320 --> 00:18:15,600 Speaker 4: it and give people the opportunity not to consent. 328 00:18:16,240 --> 00:18:19,520 Speaker 1: So what would be then if they if they told everyone? 329 00:18:19,720 --> 00:18:23,240 Speaker 1: I mean, what would you do tif if they went, hey, 330 00:18:23,600 --> 00:18:25,880 Speaker 1: if you shop here, we're going to film you, would 331 00:18:25,920 --> 00:18:31,000 Speaker 1: you still shop there? I mean probably, I guess. I 332 00:18:31,040 --> 00:18:33,679 Speaker 1: get like, I know I'm being filmed, but then do 333 00:18:33,720 --> 00:18:36,639 Speaker 1: we know what they'd do with that footage? David or 334 00:18:36,640 --> 00:18:37,359 Speaker 1: As the. 335 00:18:37,440 --> 00:18:39,680 Speaker 3: Big problem isn't it. No, we don't. 336 00:18:40,480 --> 00:18:42,360 Speaker 4: We know what they say they do with it when 337 00:18:42,400 --> 00:18:45,760 Speaker 4: the Privacy Commissioner hauls them up before them. But do 338 00:18:45,840 --> 00:18:48,159 Speaker 4: we really know what they'd do with it and what 339 00:18:48,240 --> 00:18:50,600 Speaker 4: guarantees we got. And last time when we talked about 340 00:18:50,600 --> 00:18:54,440 Speaker 4: this with the cars, and this data was being sent 341 00:18:54,480 --> 00:18:57,679 Speaker 4: off to Chinese and South Korean companies, and there was 342 00:18:57,720 --> 00:18:59,840 Speaker 4: no explanation as to what was being done with it 343 00:19:00,119 --> 00:19:03,240 Speaker 4: other than it was being used to train AI's well 344 00:19:03,440 --> 00:19:07,240 Speaker 4: to do what you know, there's just no real detail 345 00:19:07,280 --> 00:19:09,480 Speaker 4: about this stuff, and I don't think anyone can with 346 00:19:09,680 --> 00:19:12,679 Speaker 4: confidence say they know what's being done. I guess that 347 00:19:12,960 --> 00:19:15,080 Speaker 4: coming back to the question you asked about, would Tiff 348 00:19:15,119 --> 00:19:18,160 Speaker 4: still go into Bunnings, Well, she might, but I reckon 349 00:19:18,200 --> 00:19:20,679 Speaker 4: there'd probably be a roaring trade in those face masks 350 00:19:20,680 --> 00:19:23,720 Speaker 4: things you can buy that make it really, really difficult 351 00:19:24,000 --> 00:19:28,960 Speaker 4: for these detection softwares to work. So there's these ones 352 00:19:28,960 --> 00:19:32,000 Speaker 4: that they sort of blur your features. They're still basically 353 00:19:32,040 --> 00:19:35,000 Speaker 4: see through, but they blur your features and the software 354 00:19:35,040 --> 00:19:38,560 Speaker 4: struggles with it. They were very very popular in Hong Kong, 355 00:19:38,600 --> 00:19:42,000 Speaker 4: by the way, during the democracy riots because they knew 356 00:19:42,040 --> 00:19:46,480 Speaker 4: that this technology was being used there, and so they 357 00:19:46,680 --> 00:19:49,000 Speaker 4: sold a lot of those masks. You might see a 358 00:19:49,000 --> 00:19:51,720 Speaker 4: lot of that sort of thing happening if people are 359 00:19:52,000 --> 00:19:56,000 Speaker 4: being told what's going on and deciding, actually, I'd really 360 00:19:56,080 --> 00:19:58,760 Speaker 4: rather not be identified. You know, when I go in 361 00:19:58,800 --> 00:20:01,320 Speaker 4: and buy a lawnmower. 362 00:20:00,520 --> 00:20:04,360 Speaker 1: I wonder if in some not too distant dystopian future 363 00:20:05,359 --> 00:20:09,719 Speaker 1: they are selling So they're filming us and then selling 364 00:20:09,760 --> 00:20:14,359 Speaker 1: that too. I mean, unless there was some commercial upside 365 00:20:14,480 --> 00:20:18,160 Speaker 1: for them, there'd be no reason for them to record 366 00:20:18,200 --> 00:20:19,200 Speaker 1: it unless they. 367 00:20:19,200 --> 00:20:21,680 Speaker 3: Were going to there's a lot of commercial upside. 368 00:20:21,800 --> 00:20:25,040 Speaker 4: So just just the example I gave you before, where 369 00:20:25,920 --> 00:20:28,480 Speaker 4: you know, if it noticed that you're hanging around in 370 00:20:28,520 --> 00:20:30,480 Speaker 4: the lingerer section, then that day not. 371 00:20:30,640 --> 00:20:33,360 Speaker 1: Just stop saying that. Please, could you find another example, 372 00:20:33,520 --> 00:20:38,320 Speaker 1: another example? Could it not be the produce section of something? 373 00:20:38,720 --> 00:20:42,040 Speaker 4: To be really really clear, listeners, I have no evidence 374 00:20:43,160 --> 00:20:45,080 Speaker 4: to hang around in the lingerie section. 375 00:20:45,840 --> 00:20:47,360 Speaker 3: It is just a hopothetical. 376 00:20:47,720 --> 00:20:49,040 Speaker 1: But target do though. 377 00:20:49,520 --> 00:20:55,119 Speaker 4: But but I guess there's an instant commercial upside to that, 378 00:20:55,600 --> 00:20:58,760 Speaker 4: which is that information is valuable to an advertiser of 379 00:20:58,880 --> 00:21:04,359 Speaker 4: lingerie products, and there's other commercial applications as well, because 380 00:21:04,359 --> 00:21:07,000 Speaker 4: then they've got that information linked to the other things 381 00:21:07,040 --> 00:21:10,879 Speaker 4: that you purchase, So you may never purchase lingerie, but 382 00:21:11,560 --> 00:21:14,240 Speaker 4: it now knows something about you that it didn't know before, 383 00:21:14,520 --> 00:21:16,959 Speaker 4: which it would have only gotten from your purchase history, 384 00:21:17,440 --> 00:21:20,359 Speaker 4: and so it can cater the advertising to you, It 385 00:21:20,400 --> 00:21:23,440 Speaker 4: can target things to you, it can sell that targeting information. 386 00:21:23,720 --> 00:21:27,560 Speaker 4: I'm not suggesting anyone is doing this, It's just possible. 387 00:21:28,359 --> 00:21:31,200 Speaker 4: And whenever something is possible, there's likely to be someone 388 00:21:31,200 --> 00:21:32,160 Speaker 4: pushing the envelope. 389 00:21:32,800 --> 00:21:35,160 Speaker 1: If you look like you wanted to jump in. 390 00:21:35,240 --> 00:21:37,320 Speaker 2: Well, I feel like, if this, why is this a 391 00:21:37,359 --> 00:21:41,840 Speaker 2: thing around these sorts of things and not people that 392 00:21:41,920 --> 00:21:45,320 Speaker 2: are doing child trafficking and pornography, and like, if this 393 00:21:45,400 --> 00:21:48,520 Speaker 2: sort of stuff exists, is on their better uses than 394 00:21:49,040 --> 00:21:52,719 Speaker 2: trying to catch someone fucking scanning a roma tomato or 395 00:21:52,720 --> 00:21:53,320 Speaker 2: something else. 396 00:21:56,160 --> 00:21:59,879 Speaker 4: It's yeah, it's probably because law enforcement often lags be 397 00:22:00,280 --> 00:22:03,119 Speaker 4: in terms of its access to technology. And then the 398 00:22:03,160 --> 00:22:06,120 Speaker 4: other problem is giving these sorts of capabilities to law 399 00:22:06,160 --> 00:22:09,040 Speaker 4: enforcement is a whole new level as well, in the 400 00:22:09,119 --> 00:22:12,679 Speaker 4: sense that it's people are almost okay with it if 401 00:22:12,760 --> 00:22:14,240 Speaker 4: all it's going to happen is they're going to sell 402 00:22:14,240 --> 00:22:17,200 Speaker 4: you a new pair of shoes with it, but they're 403 00:22:17,280 --> 00:22:19,439 Speaker 4: less okay with it. If someone who can throw you 404 00:22:19,480 --> 00:22:22,000 Speaker 4: in the slammer has access to this information. 405 00:22:23,200 --> 00:22:24,240 Speaker 3: Oh, there is so. 406 00:22:24,240 --> 00:22:26,480 Speaker 1: Much stuff to sort through with this. All right, let's 407 00:22:26,480 --> 00:22:29,919 Speaker 1: do one more before we close the Gillespie door. So 408 00:22:30,000 --> 00:22:34,320 Speaker 1: you wrote an article the other day. Well, there was 409 00:22:34,359 --> 00:22:36,240 Speaker 1: an article that you commented on from the New York 410 00:22:36,320 --> 00:22:40,240 Speaker 1: Times called is being busy good for people with ADHD? 411 00:22:40,840 --> 00:22:41,359 Speaker 3: Yeah? 412 00:22:41,480 --> 00:22:44,560 Speaker 1: What tell us about that? Because I'm I don't know 413 00:22:44,600 --> 00:22:46,320 Speaker 1: the answer. I'm suspecting it might be. 414 00:22:47,040 --> 00:22:51,000 Speaker 4: It is so interesting study, big study done in the 415 00:22:51,080 --> 00:22:56,320 Speaker 4: United States tracking people over sixteen years to see whether 416 00:22:56,359 --> 00:22:59,920 Speaker 4: there're ADHD symptoms, you know, stayed the same, got worse, 417 00:23:00,040 --> 00:23:03,080 Speaker 4: got better, whatever. And what they discovered along the way 418 00:23:03,520 --> 00:23:06,960 Speaker 4: was that it went in and out with adults, and 419 00:23:08,840 --> 00:23:13,639 Speaker 4: that the thing that most accurately predicted the ADHD symptoms 420 00:23:13,720 --> 00:23:16,639 Speaker 4: or the severity of them. And remembering what ADHD is, 421 00:23:16,640 --> 00:23:18,919 Speaker 4: and that the primary symptom is lack of focus, you know, 422 00:23:19,000 --> 00:23:22,520 Speaker 4: lack of an ability to focus, particularly in an adult, 423 00:23:22,520 --> 00:23:25,080 Speaker 4: that can have severe consequences because you can't get anything done. 424 00:23:25,320 --> 00:23:28,520 Speaker 4: You're a bit study at work and in general. And 425 00:23:30,440 --> 00:23:34,119 Speaker 4: so they were tracking this and they found it really 426 00:23:34,880 --> 00:23:38,680 Speaker 4: was affected by how busy you were, So the busier 427 00:23:38,720 --> 00:23:41,520 Speaker 4: you were, the more you had on the more stress 428 00:23:41,560 --> 00:23:44,720 Speaker 4: you were under, which is a little bit paradoxical. The 429 00:23:44,720 --> 00:23:47,560 Speaker 4: more stress you were under, the less likely you were 430 00:23:47,600 --> 00:23:51,600 Speaker 4: to have ADHD symptoms. So and in fact, for many people, 431 00:23:51,680 --> 00:23:53,600 Speaker 4: the more stress they became, the more likely they were 432 00:23:53,600 --> 00:23:58,040 Speaker 4: to be completely in remission from those symptoms. So that's 433 00:23:58,080 --> 00:24:00,360 Speaker 4: an interesting finding and it backs up some other things 434 00:24:00,400 --> 00:24:03,359 Speaker 4: that we've talked about sort of over many episodes, which 435 00:24:03,440 --> 00:24:07,960 Speaker 4: is find a way to generate dopamine if what you 436 00:24:08,040 --> 00:24:11,720 Speaker 4: want to do is address ADHD symptoms. ADHD is a 437 00:24:11,800 --> 00:24:14,760 Speaker 4: lack of dopamine. You do not have enough dopamine to 438 00:24:14,840 --> 00:24:18,000 Speaker 4: keep you focused. And the definition of enough is what 439 00:24:18,000 --> 00:24:20,600 Speaker 4: your brain says is enough, and the level of that 440 00:24:21,040 --> 00:24:24,119 Speaker 4: is dependent on how addicted or stressed you are. The 441 00:24:24,200 --> 00:24:27,359 Speaker 4: higher your addiction or stress levels, the higher it says 442 00:24:27,400 --> 00:24:30,000 Speaker 4: you need. And so even a normal level in someone 443 00:24:30,000 --> 00:24:33,159 Speaker 4: else is not enough to keep you focused. So the 444 00:24:33,240 --> 00:24:36,480 Speaker 4: way ADHD medication works is to give you a dopamine spike. 445 00:24:36,560 --> 00:24:39,879 Speaker 4: They're stimulants, so not that people would be prescribing it, 446 00:24:39,920 --> 00:24:41,840 Speaker 4: but you could give someone cocaine. It will give them 447 00:24:41,840 --> 00:24:44,240 Speaker 4: a dopa meane spike, and it will fix their dopamines 448 00:24:44,359 --> 00:24:49,719 Speaker 4: their ADHD symptoms. So the interesting thing is this is 449 00:24:49,880 --> 00:24:54,240 Speaker 4: entirely predictable, which is make someone busy, make them really 450 00:24:54,320 --> 00:24:57,320 Speaker 4: really have to focus, have to stay on task all 451 00:24:57,359 --> 00:25:00,320 Speaker 4: the time, they're having to generate their own dopamine because 452 00:25:00,320 --> 00:25:04,120 Speaker 4: they're so busy. Put them under stress. Stress generates dopamine 453 00:25:04,200 --> 00:25:08,360 Speaker 4: just as well. So make someone busy, time poor, under stress, 454 00:25:08,880 --> 00:25:12,239 Speaker 4: they are likely to not have ADHD symptoms, is what 455 00:25:12,280 --> 00:25:15,800 Speaker 4: this study is saying. And the reason is because of that, 456 00:25:15,840 --> 00:25:17,560 Speaker 4: because they're generating their own dopamine. 457 00:25:17,560 --> 00:25:19,320 Speaker 3: And this lines up with the stuff we've talked. 458 00:25:19,119 --> 00:25:22,160 Speaker 5: About in the past about ice baths, for example, how 459 00:25:22,200 --> 00:25:24,360 Speaker 5: do you address something like this jump in an ice 460 00:25:24,400 --> 00:25:27,159 Speaker 5: bath because an ice bath is a massive dose of 461 00:25:27,200 --> 00:25:30,680 Speaker 5: stress and it gives you a huge dopamine hit. 462 00:25:31,160 --> 00:25:32,520 Speaker 3: So that's what this found. 463 00:25:32,720 --> 00:25:34,960 Speaker 4: The interesting thing is also backed up with some earlier 464 00:25:35,000 --> 00:25:39,879 Speaker 4: studies on students which found, okay, but what they're busy 465 00:25:39,960 --> 00:25:43,520 Speaker 4: doing matters. So you have to be interested in the 466 00:25:43,600 --> 00:25:46,800 Speaker 4: thing you are doing or that's keeping you busy, or 467 00:25:46,840 --> 00:25:49,119 Speaker 4: you won't get any dopamine from it. So being busy 468 00:25:49,240 --> 00:25:53,280 Speaker 4: doing housework, for example, did not have any effect on students' 469 00:25:53,800 --> 00:25:59,560 Speaker 4: ability to focus. But being busy playing sport did oh right. 470 00:26:00,040 --> 00:26:02,720 Speaker 1: It's not just any busy. It's a certain kind of busy. 471 00:26:02,800 --> 00:26:05,240 Speaker 4: It has to be something you want to do. So 472 00:26:05,320 --> 00:26:07,840 Speaker 4: it's got to be something that you enjoy or else 473 00:26:07,880 --> 00:26:10,159 Speaker 4: you're not going to keep up motivation to stay busy 474 00:26:10,200 --> 00:26:13,199 Speaker 4: with it anyway. But also, it's not actually giving you 475 00:26:13,240 --> 00:26:14,480 Speaker 4: an open mean here because you don't want to do 476 00:26:14,520 --> 00:26:15,320 Speaker 4: it in the first place. 477 00:26:15,960 --> 00:26:19,720 Speaker 1: That is very interesting. I wonder if I wonder if 478 00:26:19,800 --> 00:26:23,240 Speaker 1: knowing that in the education system, they could do something 479 00:26:23,280 --> 00:26:24,760 Speaker 1: with that insight. 480 00:26:25,480 --> 00:26:28,080 Speaker 4: I think a lot of teachers know this sort of 481 00:26:29,160 --> 00:26:33,000 Speaker 4: without any official science behind it. They know if I 482 00:26:33,080 --> 00:26:35,199 Speaker 4: want these boys in my class that are bouncing off 483 00:26:35,240 --> 00:26:38,240 Speaker 4: the walls to focus, I'll let them go outside and 484 00:26:38,280 --> 00:26:40,280 Speaker 4: bounce around for a bit and then I'll get more 485 00:26:40,320 --> 00:26:43,320 Speaker 4: out of them. 486 00:26:43,359 --> 00:26:45,479 Speaker 1: Well, t if it looks like you've got some editing 487 00:26:45,560 --> 00:26:48,080 Speaker 1: to do, because you've got to insert you may have 488 00:26:48,200 --> 00:26:51,040 Speaker 1: to reinsert that lady's audio because I don't know how 489 00:26:51,040 --> 00:26:54,359 Speaker 1: good that was, and you have to it was bloody awful, 490 00:26:55,280 --> 00:26:57,240 Speaker 1: and because yeah, that was a bit dodgy, So you 491 00:26:57,320 --> 00:26:59,280 Speaker 1: might have to figure that out. But you're a whiz. 492 00:26:59,280 --> 00:27:03,320 Speaker 1: And then you've got to insert the MT seven with 493 00:27:03,520 --> 00:27:08,359 Speaker 1: the Akropovich System on it, which that means nothing to 494 00:27:08,440 --> 00:27:09,840 Speaker 1: our listeners, but just. 495 00:27:09,760 --> 00:27:12,400 Speaker 2: Trust me, it means something to some of them. 496 00:27:12,400 --> 00:27:15,840 Speaker 1: It means something too much to the twelve Bogans that 497 00:27:15,960 --> 00:27:18,280 Speaker 1: listen to the show. Shout out to the Bogans and 498 00:27:18,320 --> 00:27:22,119 Speaker 1: Galspo's like, what's in Akropovich full system just makes it 499 00:27:22,160 --> 00:27:22,640 Speaker 1: sound good? 500 00:27:22,640 --> 00:27:22,880 Speaker 3: Mate? 501 00:27:22,920 --> 00:27:26,240 Speaker 4: With the baffle out, I'm going to have to do 502 00:27:26,320 --> 00:27:27,680 Speaker 4: something I've never done before. I'm going to have to 503 00:27:27,680 --> 00:27:29,520 Speaker 4: go and listen to one of these episodes so I 504 00:27:29,520 --> 00:27:30,000 Speaker 4: can hear it. 505 00:27:30,480 --> 00:27:32,719 Speaker 1: Yeah, you'll hear it, So it'll be out, not tomorrow, 506 00:27:32,720 --> 00:27:35,240 Speaker 1: the next day. I'm able to say goodbye, affair, but 507 00:27:35,280 --> 00:27:37,320 Speaker 1: as always, we appreciate you. Thank you sir. 508 00:27:37,920 --> 00:27:38,680 Speaker 3: Right out see y