1 00:00:01,100 --> 00:00:04,490 S1: Welcome to Unsupervised Learning, a security, AI and meaning focused 2 00:00:04,490 --> 00:00:07,370 S1: podcast that looks at how best to thrive as humans 3 00:00:07,370 --> 00:00:11,570 S1: in a post AI world. It combines original ideas, analysis, 4 00:00:11,570 --> 00:00:14,810 S1: and mental models to bring not just the news, but 5 00:00:14,810 --> 00:00:21,709 S1: why it matters and how to respond. All right. Welcome 6 00:00:21,710 --> 00:00:25,040 S1: to unsupervised learning. This is Daniel. Hope you're doing well. 7 00:00:25,040 --> 00:00:28,550 S1: So we added a new fabric pattern called explained Term's 8 00:00:28,550 --> 00:00:31,730 S1: which takes an input, finds the difficult terms and gives 9 00:00:31,730 --> 00:00:35,059 S1: a definition. And I got this put together because I 10 00:00:35,060 --> 00:00:40,400 S1: was watching a really complex machine learning AI video with Daksh, 11 00:00:40,400 --> 00:00:43,610 S1: and I forget who the people were. Oh, it was 12 00:00:43,610 --> 00:00:46,610 S1: two different guests, one from anthropic and maybe another one 13 00:00:46,610 --> 00:00:49,010 S1: from DeepMind. Not sure, but they went into some pretty 14 00:00:49,010 --> 00:00:51,140 S1: heavy stuff, and there was a bunch of terms that 15 00:00:51,140 --> 00:00:53,150 S1: I just heard them talking about, and I wished I 16 00:00:53,150 --> 00:00:55,100 S1: could just go and look them up. So I went 17 00:00:55,100 --> 00:00:58,790 S1: and created this pattern for that. Had a fantastic weekend 18 00:00:58,790 --> 00:01:02,450 S1: at EDC, the annual pilgrimage for a bunch of friends 19 00:01:02,450 --> 00:01:05,450 S1: and I. And yeah, it was fabulous. Just great music, 20 00:01:05,450 --> 00:01:09,080 S1: great companionship, just amazing. And I would say if you 21 00:01:09,080 --> 00:01:12,020 S1: don't have friends that you go and do something with, 22 00:01:12,020 --> 00:01:15,560 S1: like every year, you want to definitely do that. It's, uh, 23 00:01:15,560 --> 00:01:19,069 S1: it's really valuable to have something to look forward to 24 00:01:19,069 --> 00:01:21,560 S1: and just have that time with friends. And it's like, 25 00:01:21,560 --> 00:01:23,900 S1: it helps you get through the rest of the year, 26 00:01:23,900 --> 00:01:27,260 S1: which I haven't been having that much trouble doing that lately, 27 00:01:27,260 --> 00:01:30,950 S1: but it's, uh, it's something that everyone should be doing. Like, 28 00:01:30,950 --> 00:01:33,530 S1: Tim Ferriss talked about this and it got me thinking 29 00:01:33,530 --> 00:01:36,500 S1: about it. And luckily I already have a couple of these. Basically, 30 00:01:36,500 --> 00:01:38,390 S1: one is like Defcon black hat, and the other one 31 00:01:38,390 --> 00:01:41,929 S1: is EDC, and we're thinking about sprinkling a few smaller 32 00:01:41,930 --> 00:01:44,570 S1: ones in there as well. But something to do if 33 00:01:44,569 --> 00:01:46,700 S1: you don't have these already and it could be local. 34 00:01:46,700 --> 00:01:50,120 S1: It could just be like LAN parties or whatever, just 35 00:01:50,120 --> 00:01:52,790 S1: small things. Thinking about writing a short book about what 36 00:01:52,790 --> 00:01:56,270 S1: people will need to become antifragile. In the world of AI, 37 00:01:56,300 --> 00:01:58,310 S1: a lot of people are asking me this like, what 38 00:01:58,310 --> 00:02:00,080 S1: do I need to learn? What are the skills I 39 00:02:00,080 --> 00:02:03,800 S1: have to master to be resilient to AI? And a 40 00:02:03,800 --> 00:02:07,700 S1: working title for this is something like human 3.0 Skills 41 00:02:07,700 --> 00:02:11,179 S1: and Mental Frames Required to thrive in a post AI world. 42 00:02:11,180 --> 00:02:15,320 S1: And I've actually forgot to look at this. I haven't 43 00:02:15,320 --> 00:02:18,440 S1: looked at that, but we'll take a look at it afterwards. So, uh, 44 00:02:18,440 --> 00:02:22,130 S1: got a post here that, uh, was from a while back, 45 00:02:22,130 --> 00:02:24,890 S1: like May of 2023, where I talked about the attack 46 00:02:24,889 --> 00:02:28,310 S1: surface map. Not sure why this is not linking. There 47 00:02:28,310 --> 00:02:31,040 S1: it goes. Yeah. So this is the attack surface map 48 00:02:31,040 --> 00:02:35,150 S1: I put together in May of 23, I think so 49 00:02:35,150 --> 00:02:38,150 S1: about a year ago, and it turns out to be 50 00:02:38,150 --> 00:02:42,830 S1: pretty useful. Still, I think it's still spot on. I'm 51 00:02:42,830 --> 00:02:45,320 S1: not sure what I would modify from this, but worth 52 00:02:45,320 --> 00:02:48,200 S1: taking a look at if you haven't seen it. Okay, security, 53 00:02:48,200 --> 00:02:51,650 S1: see what we got here? So the FBI seized a 54 00:02:51,650 --> 00:02:55,339 S1: bunch of hacking, uh, breach forms again, Chinese brain drain. 55 00:02:55,340 --> 00:02:58,550 S1: So Microsoft is moving nearly 10% of its China based 56 00:02:58,550 --> 00:03:01,730 S1: tech talent out of the country. Yeah, they're talking about 57 00:03:01,730 --> 00:03:05,419 S1: brain drain here. And I personally am pretty happy about this. 58 00:03:05,419 --> 00:03:07,820 S1: I think I feel like the US is in a 59 00:03:07,820 --> 00:03:10,700 S1: good state right now. It's in a good place to 60 00:03:10,700 --> 00:03:14,600 S1: win because Europe is suffering from all sorts of missing 61 00:03:14,600 --> 00:03:19,310 S1: innovation because of regulations. They just love regulating things, regulating 62 00:03:19,310 --> 00:03:22,460 S1: things too much. And that's a problem. So I think 63 00:03:22,460 --> 00:03:25,400 S1: that's like taking them out of the game. China is 64 00:03:25,400 --> 00:03:29,210 S1: being just super aggressive to everyone all the time, hacking everyone, 65 00:03:29,210 --> 00:03:32,150 S1: trying to control everyone. Everyone's trying to control everyone and some, 66 00:03:32,150 --> 00:03:35,690 S1: some sense. And definitely in the US is not innocent 67 00:03:35,690 --> 00:03:39,260 S1: of that. But I would say that our means of 68 00:03:39,260 --> 00:03:44,000 S1: doing that in our goal of doing that is more benign. 69 00:03:44,000 --> 00:03:48,650 S1: And this really leaves the US and Canada to benefit 70 00:03:48,650 --> 00:03:52,010 S1: from this boom in talent and people that are going 71 00:03:52,010 --> 00:03:56,000 S1: to be leaving China. China has so many people, and 72 00:03:56,000 --> 00:03:59,240 S1: a small percentage of those people have a high IQ, 73 00:03:59,240 --> 00:04:01,610 S1: and a small percentage of people in the US have 74 00:04:01,610 --> 00:04:04,520 S1: a high IQ. And guess what? When you have three 75 00:04:04,520 --> 00:04:06,830 S1: times the people, or is it four times the people, 76 00:04:06,830 --> 00:04:09,200 S1: I can't remember. But when you have that many more 77 00:04:09,200 --> 00:04:12,830 S1: people and plus they have a better education system, like 78 00:04:12,830 --> 00:04:17,180 S1: they have so much talent and to have a lot 79 00:04:17,180 --> 00:04:19,909 S1: of that talent go to the US and Canada and 80 00:04:19,910 --> 00:04:22,790 S1: to some degree to Europe. That is a major advantage 81 00:04:22,790 --> 00:04:26,270 S1: for the West. And the other disadvantage that China has 82 00:04:26,270 --> 00:04:29,060 S1: around AI is that I think they don't want to 83 00:04:29,060 --> 00:04:32,330 S1: just turn it on and enable it, because it encourages 84 00:04:32,330 --> 00:04:36,560 S1: people to ask questions. And China doesn't like people asking questions. 85 00:04:36,560 --> 00:04:38,929 S1: They want to feed them a narrative and have that 86 00:04:38,930 --> 00:04:42,890 S1: narrative be the only one and basically control that. And 87 00:04:42,890 --> 00:04:45,230 S1: it's just not a game that they're going to win. 88 00:04:45,230 --> 00:04:48,620 S1: I don't think, well, they could win, they could still win. 89 00:04:48,620 --> 00:04:52,219 S1: But I feel like they're setting themselves up to fail 90 00:04:52,220 --> 00:04:56,420 S1: and for the West to benefit. So what's essentially going 91 00:04:56,420 --> 00:04:58,940 S1: to happen as a result is the world will pull 92 00:04:58,940 --> 00:05:02,120 S1: away from them, and their best minds will brain drain 93 00:05:02,120 --> 00:05:04,190 S1: out of the country. They will go to Europe, they 94 00:05:04,190 --> 00:05:06,230 S1: will go to Canada, and they will go to the US. 95 00:05:06,230 --> 00:05:10,250 S1: Air Force Secretary Frank Kendall flew in an AI controlled 96 00:05:10,250 --> 00:05:14,089 S1: F-16 and basically said, it's just about as good as 97 00:05:14,089 --> 00:05:18,950 S1: experienced human pilots, technology. Everything covered at Google I o got. 98 00:05:19,080 --> 00:05:22,109 S1: A link here, and unfortunately, it's impossible for me to 99 00:05:22,110 --> 00:05:25,080 S1: get excited about this because Google has turned into like 100 00:05:25,080 --> 00:05:27,539 S1: a cool idea lab because they're not really good at 101 00:05:27,540 --> 00:05:32,099 S1: making products. Why? Because they're simply not good at managing products. 102 00:05:32,100 --> 00:05:34,710 S1: They have so many smart engineers, and they have lots 103 00:05:34,710 --> 00:05:37,799 S1: of talent and lots of cool potential ideas, but they 104 00:05:37,800 --> 00:05:40,350 S1: don't have vision and they don't have product management, and 105 00:05:40,350 --> 00:05:42,930 S1: they don't have vision. That's led by product management and 106 00:05:42,930 --> 00:05:45,510 S1: by the leaders. They have basically a throw it at 107 00:05:45,510 --> 00:05:48,720 S1: the wall technique and it's really hurting them. And I 108 00:05:48,720 --> 00:05:51,240 S1: think the only thing that's going to fix it is 109 00:05:51,240 --> 00:05:55,260 S1: like a dramatic overhaul with replacement of the CEO. I 110 00:05:55,260 --> 00:05:57,780 S1: think that's looking more and more likely. Pretty cool idea 111 00:05:57,810 --> 00:06:02,160 S1: about how to avoid the mediocre success trap, because you 112 00:06:02,160 --> 00:06:05,279 S1: basically want a home run or strike out. And this 113 00:06:05,279 --> 00:06:08,550 S1: is talking about not getting a strong signal from an experiment. 114 00:06:08,550 --> 00:06:11,700 S1: This is the context that they're talking about here. Ilya 115 00:06:11,700 --> 00:06:17,610 S1: Sutskever suggests mastering these 30 ML papers to grasp 90% 116 00:06:17,610 --> 00:06:21,479 S1: of crucial machine learning knowledge. I've started going through the list. 117 00:06:21,480 --> 00:06:25,140 S1: I'm not all the way through by any means. MIT 118 00:06:25,140 --> 00:06:29,520 S1: researchers came across three ancient stars, and they're going around 119 00:06:29,520 --> 00:06:32,489 S1: the Milky Way for some reason, and they appear to 120 00:06:32,490 --> 00:06:35,729 S1: be some of the oldest in the entire universe. And 121 00:06:35,730 --> 00:06:38,820 S1: I'm like, how do they randomly end up near us? 122 00:06:38,820 --> 00:06:42,630 S1: A strange and the article didn't really explain it that well, 123 00:06:42,630 --> 00:06:45,630 S1: scientists just found out that whales have their own alphabet 124 00:06:45,630 --> 00:06:48,570 S1: in their songs, and my buddy Mark has been working 125 00:06:48,570 --> 00:06:51,479 S1: on deciphering this stuff for years. He's got some real 126 00:06:51,480 --> 00:06:54,720 S1: cool research that he's working on, hopes to be able 127 00:06:54,720 --> 00:06:57,479 S1: to present on it at some sort of marine conference soon. 128 00:06:57,480 --> 00:07:00,960 S1: And this one's about how Swiss parenting styles are so 129 00:07:00,960 --> 00:07:03,539 S1: different from American ones, and they talk about it in 130 00:07:03,540 --> 00:07:06,089 S1: this article. Basically, four year olds being able to walk 131 00:07:06,089 --> 00:07:09,300 S1: to school by themselves in Switzerland. And some will say, well, 132 00:07:09,300 --> 00:07:11,760 S1: that's because it's more safe there. It's not really that 133 00:07:11,760 --> 00:07:14,970 S1: unsafe here either in the US for kids to walk 134 00:07:14,970 --> 00:07:17,730 S1: to school, but people think it is and that's that's 135 00:07:17,730 --> 00:07:20,460 S1: their frame. That's their reality. All right. This one here, 136 00:07:20,460 --> 00:07:23,580 S1: I think the magic amount of therapy for most people, 137 00:07:23,610 --> 00:07:26,970 S1: of course, not all might be some relatively small amount 138 00:07:26,970 --> 00:07:29,760 S1: of therapy that untangles your knots, but doesn't point your 139 00:07:29,760 --> 00:07:32,910 S1: lens permanently on yourself in your troubles. Because I feel 140 00:07:32,910 --> 00:07:36,990 S1: like too much therapy has the approach of like bad chiropractic, 141 00:07:36,990 --> 00:07:39,420 S1: where the goal is they want you to come back 142 00:07:39,420 --> 00:07:43,050 S1: and have more chiropractic visits, right? The goal is not 143 00:07:43,050 --> 00:07:45,300 S1: to fix you where you don't come back. And I 144 00:07:45,300 --> 00:07:48,330 S1: think just so much focus on yourself and your problems. 145 00:07:48,330 --> 00:07:52,770 S1: It's essentially rumination. And rumination is a bad thing that actually, ironically, 146 00:07:52,770 --> 00:07:55,260 S1: you go to therapy to figure out how not to do. 147 00:07:55,260 --> 00:08:00,270 S1: And I'm worried that the therapy industry is basically encouraging rumination. 148 00:08:00,270 --> 00:08:02,380 S1: Recommendation of the week. Make sure you have 1 to 149 00:08:02,380 --> 00:08:04,920 S1: 2 friend trips planned per year, even if it's not 150 00:08:04,920 --> 00:08:09,060 S1: everyone and it's just local. Just anything camping, hiking, whatever, 151 00:08:09,060 --> 00:08:12,420 S1: role playing games, video games, whatever. And maybe you have 152 00:08:12,420 --> 00:08:14,820 S1: a rule of like no tech during that time just 153 00:08:14,820 --> 00:08:19,140 S1: so you can have interactions, live interactions, eat food together, 154 00:08:19,140 --> 00:08:23,940 S1: hang out, even watching TV, just talking, whatever. Super replenishing, 155 00:08:23,940 --> 00:08:26,280 S1: as I just found out from this last trip. And 156 00:08:26,280 --> 00:08:28,890 S1: the aphorism of the week every minute you are angry, 157 00:08:28,890 --> 00:08:33,240 S1: you lose 60s of happiness. Every minute you are angry, 158 00:08:33,240 --> 00:08:38,670 S1: you lose 60s of happiness. Ralph Waldo Emerson. Unsupervised Learning 159 00:08:38,670 --> 00:08:41,309 S1: is produced and edited by Daniel Meisler on a Neumann 160 00:08:41,309 --> 00:08:45,900 S1: U87 AI microphone using Hindenburg. Intro and outro music is 161 00:08:45,900 --> 00:08:49,140 S1: by zombie with the why and to get the text 162 00:08:49,140 --> 00:08:51,300 S1: and links from this episode, sign up for the newsletter 163 00:08:51,300 --> 00:08:56,660 S1: version of the show at Daniel meisler.com/newsletter. We'll see you 164 00:08:56,660 --> 00:08:57,320 S1: next time.