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So what happens in a world where people don't have to work and the the utopians go, well, that's the best of all possible worlds, good people are free to pursue their dreams, whatever.

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But imagine the person who loses their job. They don't go cartwheeling down the street going, great. Now I can write that novel.

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Hey, guys, this is part two of my amazing conversation with Adam Robinson. We split it up into two episodes because the conversation was about four hours. You're not missing anything by starting here. But I'd highly recommend you go back and listen to part one. I mean, why wouldn't you? Adam is phenomenal and our conversation is fascinating. Anyway, here's part two with Adam Robinson.

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Before we get started, here's a quick word from our sponsor, Farnam Street is sponsored by Medlab. For a decade, Medlab has helped some of the world's top companies and entrepreneurs build products that millions of people use every day. You probably didn't realize that at the time, but odds are you've used an app that they've helped design or build apps like Slack, Coinbase, Facebook Messenger, Oculus, Lonely Planet and so many more. Medlab wants to bring the unique design philosophy to your project.

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Let them take your brainstorm and turn it into the next billion dollar app from ideas sketched on the back of a napkin to a final ship product. Check them out at Medlab Dutko. That's Medlab Dutko. And when you get in touch, tell them chainsawing you. Which are the habits that work well in school and life and which are the habits that work well in school and then don't work well in life?

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Well, if you think about it, that's such a good question. If you think about it in school, regraded, regraded and the expectations are set by someone else. By the way, this is this is a really cool study I did once on school. And then we'll get to the other part of your question. I was giving a talk in Sacramento some years back to 500 teachers, and I said grading is subjective.

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And they got really kind of like their grumble grumble. Right.

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And I said, but you don't have to take my word for that. My teachers think they're grading is objective.

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Right. And said, oh, no. Like we tell the students exactly what we expect and we're very objective or our grading.

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And I said, look, you don't have to take my word for it. Let's do an experiment. So I. I took out a box. I was in a big gymnasium with five hundred teachers and it put a big box on the table. And with great fanfare, I held up a bunch of sample tests and I said I had students read an answer to the question, is there evidence of global warming? And I've got hundreds of tests here.

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I'd like to pass it out to you guys and you grade it.

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And and as an afterthought, I said put down an Emerin F if you're a male or female teacher. Now, unbeknownst to the teachers, they were all grading exactly the same essay. But on some of the on half the tests, I had a female's name at the top and then some a male name at the top. On half the tests I had the paragraphs clearly indented and on half not. And in half the tests were printed and half script.

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And I gave them and I said graded on an eight F scale f being failing and and put an M or an F if you're and I said, I know that you're all not environmental scientist, but we're all adults.

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We know something about global warming, just kind of just grade it intuitively. Right. And then over their lunch break, I fed all the numbers into a stat program and I said I said, guys, remember, I said grading is subjective.

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The number one positive factor on on your essay was a female name at the top of the paper. It was worth an extra quarter of a grade on A equals 4.0. Big with 3.0 equals 3.0. It was worth a quarter of a grade. It was worth a full half a grade. Half a grade.

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Mind you, if a male teacher was grading the female so male teachers graded female students a half a grade easier than they graded male teachers, both teachers, male and female, graded the opposite sex more leniently. Both male and female teachers graded boys more harshly. Mind you, this wasn't a science test. Not like, you know, some other like this is this is an actual science paper, right? You have to, like, provide evidence and stuff.

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Um, and it was worth three, almost three fourths of a letter grade when you added in the paragraph indenting printing or script was not a factor.

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So if a male student was created by a male teacher and forgot to indent his paragraphs, his letter grade dropped three fourths of a point seven three of a grade, almost a full grade from content from exactly the same content.

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So how do you measure how well students are doing? Right. And how do you measure how well the teachers are doing? Right. Anyway, to go back to your question, sorry, I couldn't resist that.

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So skills in life are following instructions, doing what's expected and in life, I don't think there are many assignments like that.

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You know, even when your boss says do X, you don't really mean just do X like you're not a robot.

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We're not I mean, I, I imagine certain jobs are like that.

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Like, if you're flipping burgers, you could almost make the argument that increasingly jobs are becoming procedural and the procedures are the mechanism by which you assign people tasks.

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If this situation you do this, if this you do this right, it's that maybe that's certainly where jobs are going and it's certainly where A.I. and robotics are taking things right.

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In another, we can argue about whether it's 20 years or 30 or 40 or 50 some time in the next few decades, an algorithm or a robot powered by our thumbs is going to do whatever you do better, faster, more reliably in every cheaper.

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And that that isn't terrifying people in that scare you? It scares me for the world that it scares me because people define themselves largely in terms of what they're able to contribute economically.

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And so what happens in a world where people don't have to work and the the utopians go, well, that's the best of all possible worlds.

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Good people are free to pursue their dreams, whatever.

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But yeah, but I don't think psychologically, if you're not contributing to society, there's an impact on you as a person. Devastating. Imagine the person who loses their job.

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They don't go cartwheeling down the street going great.

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Now I can write that novel that they're devastated. Now imagine everyone's out of work. And so so that's a that's a future we really have to to to prepare for.

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The Silicon Valley has begun to prepare the world for that floating notions of universal basic income.

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Right. That's largely being floated out of Silicon Valley because they see where it's going. They see what the technology is going.

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And even in creative fields, AI is such a terrifying threat. Stephen Hawking gave mankind a one in 20 shot of surviving A.I., a one in 20 shot. And you can do the math. That means a 19 20 shot. We don't survive. I that's how serious it is.

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And Stephen Hawking, no slouch in the IQ department.

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What is it about that the A.I. that scares us so much or threatens humanity in this case?

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OK, so the real threat with A.I. first, let's talk about how fast AI progresses.

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So right now there is no AI, it's not really AI, it's machine learning, pattern recognition, it's not really AI.

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What people think of AI is an intelligence that left on its own can learn. Right. So let's talk about a domain. I know something about chess. Right? So I'm a real chess master with a life title. I'm pretty darn good at chess.

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So I understand the implications of this with A.I..

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So the best human chess player in the world is a twenty seven year old Norwegian named Magnus Carlsen. He has a chess rating of twenty eight twenty so two thousand eight hundred and twenty ish, give or take, something like that.

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The best computer software that had been trained on the best human games software trained on the best human games, has a chess rating now of about thirty three hundred would crush Magnus Carlsen in a match.

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And that's software you can get on your iPhone. On your iPhone. Yeah, yeah, yeah. Maybe a little more. Maybe on your laptop. But yes, your iPhone software is paid like twenty six hundred. You're your laptop. Yeah. It'll crush Magnus Carlson in one hundred game match. Carlson would be lucky to get five games out of one hundred like it. Just be a wipe out. And he's the human champion. Right. OK, that was the best software trained on human games.

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Google's company Deep Minds said what if we don't train it on human games?

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What if we just have the software play itself?

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So they gave us to give it rules. And that was just the rules of the game. Nothing else. Just play yourself.

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And in four hours, that program leapfrogged all existing human knowledge of the game. And as a chess rating of hard to estimate, like thirty six thirty seven hundred compared with thirty three hundred it that program, mind you, it had no human intervention, they just gave it the rules.

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And this is what was fascinating about it. It recreated all human knowledge of the game on its own.

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Imagine giving a computer the rules of syntax and it creates the English language and Russian and Japanese and just on its own, it's like the story of if you had enough monkeys of it eventually.

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Exactly. But now so but also with enough monkeys, you create a lot of gibberish.

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But this could actually create. Replicated all human knowledge there, any existence of that human knowledge without any knowledge of the game, and because after all, human beings are pretty smart. But then this is two interesting things about this Google, which is to say Deep Mind only released 10 of the 100 games. This this computer program played a match against the reigning software.

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Thirty three hundred it beat it's seventy two to twenty eight in a one hundred game match with no losses.

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The twenty eight draws exactly fifty six tries. OK, right.

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And so the reason is that even if God were playing the software, it's not going to God's not going to win every game because it's like tic tac toe.

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If you don't make a mistake it's raw. Yeah right. You can't. So there's a limit actually on how God's chess is probably like thirty nine hundred.

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I'm making that up. I'm being goofy now.

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So.

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So what was fascinating, they only released ten of the games and this is what was interesting and so scary is that the chess program when you looked at the games and again I'm in a position to assess the value of the moves, how good they are.

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When I looked at the games, I went, wow, this is this computer so good. But then the computer would make alien chess moves, surprising moves, not just surprising, just like like you just go, what? Why is it doing that?

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It doesn't make any sense. Makes no sense. So get this.

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So for example, it is a rule of thumb with human chess players that you should even if you don't know about the game, you can sort of appreciate this.

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Your king is the most valuable piece. You should tuck your king away in the corner and keep it well protected.

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In this one game. The chess computer is called Alpha Zero. Go sorry, Alpha Zero. Chess marched the king smack into the middle of the board. Imagine your Napoleon leading your troops and you yourself go right into the center of the battle and bullets are whizzing by you and nothing touches you.

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And you go, how can the computer get away with this? Like, there's no way that's got to lose.

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Like and it won the game like five, ten years later wins the game. You go, how the heck did it do that? And so it played alien moves.

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So not only did it recreate human knowledge of the game, it then created alien chess. Now, imagine that computers unleashed not in the domain of chess, but in other domains.

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It'll totally recreate everything we know and then go beyond it in ours. So the scary thing with A.I. is this, that it will become exponentially smarter than we are in minutes. So imagine the following scenario. Let's say the current reigning A.I. has an IQ of say, fifty, right. Let's say I'm just picking a number out of the air.

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Eventually I'll get to like one fifty.

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It'll be as smart as a really smart human being, like, really super smart. Then the computer will go, oh, wait a second, I can do a better job of programming myself.

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It'll then reprogram itself. Now it has an IQ of 200 and then goes, wait a second, I can do much better than that.

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And it reprogrammed itself again. Now it's got an IQ of three hundred.

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It goes, oh, I was a dummy two minutes ago and it will keep reprogramming itself with alien A.I. and I say alien because we won't recognize it. We won't be able to even follow what it's doing and it'll control everything. That's the problem.

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You know, there's the Terminator scenario, like, you know, these machines are going to destroy us and blah, blah, blah.

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No, they're not. They're just not going to care about us. And it's a bit like the genie when you you get three wishes and the genie, I'll give you three wishes and then it'll say goodbye and it'll control.

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So an alien intelligence will control everything.

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You won't be able to cut it out of the. And women don't even understand it. Oh, you won't understand it right now. There are mathematical proofs done by computers that human beings can't understand. The best mathematicians in the world don't understand those proofs and all they can say is we can't find a mistake in it. It's probably right, but we're not real sure. We can't follow it. And the reasoning will be so convoluted but human, there's no way you could even explain it.

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So even now, the computer Watson, IBM's Watson can come up with a medical diagnosis, the challenge of which is to explain the diagnosis to the doctor, because the doctor can't follow the reasoning of the neural net and all the accurate diagnosis they wouldn't have come to through a convoluted sort of mechanism of here's how we arrived at that.

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Right. You couldn't they couldn't explain it because it's in an embedded neural net. And there's with all kinds of feedback loops, you wouldn't be able to describe it so that any human being could understand it.

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It's not a bonus. I mean, it's out of the understanding if we're creating more accurate diagnoses and we're sure.

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But now what happens when those machines are controlling not just diagnoses, but they're actually.

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Controlling the surgeries and controlling weather patterns and controlling tanks because they'll think that they know best and then automatic, they will know best.

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The algorithms are just this is the thing about algorithms. And I it is optimizing unforgiving and relentless because it continues to optimize.

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And whatever values you put into into the eye, it will just execute that with with unstoppable efficiency.

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So you got an A.I. in the 1990s the way before any of this stuff. How did you get started? Word of that.

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Right. So I was interested in so I saw my interest in the Princeton Review in the late 80s, late 1980s, early 1990s, and I got into A.I. and because I was interested in intelligence and thinking. Right. And the problem with AI back then and even if you had asked a computer scientist, you know, what are you into, he or she would not have said A.I. because the problem then was the speed of the computer's hardware limitation, the hardware limitations.

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So when I wanted to run a neural net, remember, remember Pentium chips. Yeah, right.

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So I literally set up the neural net and hit enter on my computer and walk away from it for a week and a week later. Maybe I'd get an answer. And if the result was good to go, great, good model. And if it wasn't, I'd have to tweak the variables and hit enter again and then walk away from the computer and and so now I is done. You know, you can run the neural net, I can minutes or something and soon they'll have quantum computing.

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That's really scary. And what is quantum computing just for people.

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So, so right. So quantum computing is when you use actually subatomic quantum states to to do your computations.

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And I myself don't understand the physics and the math of it, but evidently you'll be able to do things pretty much instantaneously. So, for example, any thing that's encrypted with quantum computing, you'll be able to crack that, whatever the encryption is in, I don't know, seconds or minutes. So quantum computing is scary because there are no secrets now. Now you can't encrypt, you can't hide things. And I know hiding sounds bad, but sometimes you want things hidden.

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You don't want your Social Security number hackable and other things or government secrets. So. So, yeah, quantum quantum computing is is scary.

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And and the speed with which it will accelerate A.I. is also how will it accelerate just based on the power of the our right and sheer power in the same way that things that used to take me a week on my computer on Pentium chips back in the early 1990s. Now take seconds. And how much in seconds? Take a millisecond.

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Right. And and it's a parabolic leap. Parabolic leap.

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And which fundamentally changes reality. It's not just we're doing things faster. It's you now. You're doing things fundamentally differently. You make when information can't be protected, imagine anyone can know anything about everything.

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That's pretty scary.

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I want to come back to something you said about the computers, A.I. and chess. And so the first version of the program you mentioned had learned chess from the human games. There is data available. The second version of the program didn't used it. What's the role in data today in terms of machine learning and A.I.? And then what do you see that role as in the future?

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Is are those data sets going to be proprietary or will some guy in his garage be able to compete with Google on search?

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So funny you should say that. So that's a really profound question that goes to who owns the data. Right. So so when you log into Google, when you use Google or you use Facebook, Google and Facebook are gathering data.

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Right. When you buy something on Amazon, when you look at something and don't buy it, Amazon is gathering data and it's able to use that data and monetize the data.

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Right. And so the question is why even, by the way, when you don't use something, that's a data point. If Shane hasn't logged into Google for three days, Google has learned something about Shane.

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It right. So it's remarkable how much data they have. Oh, it's remarkable. And so there was a woman, a female journalist in the United States, get this, in the United States, you don't own your data. Google does or Facebook. It's their corporate data. But in Europe, the individual owns his or her data. So a female journalist asked Tinder, the dating app, give me all the data you have on me. And they gave her.

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I forget whether it was seven or eight hundred pages of unique information. So not 700 duplicate press seven and. They had 700 pages worth of information about her, like I'm just making this up. They knew her first grade transcript. You know, they they knew what she ate for lunch five years ago, like they knew so much about her. And she was horrified that they were like, how could you even get this data?

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Like some kind of data? You kind of can figure out how they would get. But remember, the value of your data is when they cross-reference it with other people. Yeah, right.

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The value emerges not just because Shane is using Google, but lots of other people are and they're able to to to do use statistics and machine learning to find patterns so that they uncover things about Shane that even Shane didn't know. There was a famous case about five or six years ago of Target. You know, this where target the pregnancy.

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Yes. Yeah, right.

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So Target's algorithms knew that a young woman was pregnant because she shifted from unscented products from presented to unscented.

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They knew before she knew. Right. She didn't know she was pregnant.

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So the algorithm, by the way, the algorithm I'm using air quotes. Dout didn't know that she was pregnant.

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They just knew that she would tend to buy baby products six months later. So let's start hitting her up now.

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The computer doesn't, as it were, no pregnancy.

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It just knows that when a woman of a certain age shifts from scented products to unscented X, months later she's buying baby products.

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So let's get her up right now. And so. So, yes, Google and Facebook and Amazon know things about you that you don't even know for sure. And is that a cumulative advantage?

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Like can that ever be?

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I don't know how it can be. You could ever compete with that. You'd need to find some other way of gathering the data.

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Should data be a public good?

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Well, you know, that's a political thing. You know, it may be.

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There's Aristotle Onassis, who was once one of the wealthiest men in the world, said the secret to business is knowing something that others don't.

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Well, that's certainly the secret to Google and Facebook and Amazon and Netflix. They know everything about you and and they can leverage that.

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I'm not making a political statement now, but it's something that needs to be examined and and, well, it's almost anti-competitive in a way.

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It's hugely anti-competitive. Right. There's no way anyone could ever compete with that. They know exactly what you'll buy and won't buy. They know what price points to offer it to you at. How can someone compete with that?

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And and so the problem is on an anti-competitive level is we're in a global economy.

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And so soon as one country defects from these policy. Exactly right.

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A country will offer its own companies a competitive advantage globally.

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So good use that data for sure. So it's it's tough to argue for anti these antitrust laws because you put yourself at a globally disadvantage.

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That's exactly right. So it's tough. It's got to be these are really tough questions we need to work out now.

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And again, one of the problems with algorithms is they just get embedded into our daily lives and then they execute with relentless, ferocious, unforgiving efficiency 24/7.

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And we don't even know why they're doing the things they're doing. Again, human beings can't understand the algorithms are too complex.

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They just execute. So so we have some hard choices as a as a not just as a country, but as a as a planet. We've got to come together on these things. Yeah.

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I want to come back to chess a little bit. Sure. I know when you were a kid, how did you learn chess? You went to school, you played this guy at lunch, you got beat.

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So my father taught me how when I was a boy, I had to be careful of the questions I asked my father because I remember one day he was playing chess with a friend. I was all of six and a half and I said, Can I watch? And he said, Yeah, sure you can. But I had to learn the rules of the game. Right. And so. So that's all I knew about chess. I just knew the rules.

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I wasn't interested at all. And then in high school freshman year, first day of high school, a kid in homeroom had a little magnetic chess set me turned around in homeroom. We had twenty minutes to kill. And he said, you know how to play chess.

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And I said, yeah, I know how to play very proudly. Like, it's like saying I know how to play baseball when you've swung a bat once.

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Right. And so so he beat me in, you know, four or five moves and I got kind of pissed and I thought, OK, I'm going to beat him the next day. And he beat me every day that week.

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And I just thought he was so smug about it. I just I was a swimmer and that's really all I cared about was swimming competitive swimmer. Right, four or five hours a day. And I thought, I just want to beat this kid. I just want to by the end of the year, I was my little goal. So I went to the books. And I got a book of Bobby Fischer's games and called My 60 Memorable Games, and I I played over those games every day after swim practice when I got home.

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And by playing over them, you mean mentally or physically you're.

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Oh, I set up a chess board and his book would play, you know, he would list the moves of his games and with annotations, he would explain why he did and did not. You know, that was a good move. Here's why that was a bad move. That's why. So I played over these games over and over until I knew them by heart. And I thought, wait a second, he's been playing chess at that point of 69.

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He'd been playing for, you know, 20 plus years and I thought was more than 60 memorable games.

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I want to get every game he has. Bobby Fischer, Bobby Fischer. Right. This is before he won the world championship. So I went to the library and I got every old chess magazine. I went to downtown Chicago on a train. I was, what, 13? And I went to the library.

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People don't remember what libraries were and in this day at the Internet.

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But I and I spent weeks going there, going through every back issue of every chess magazine globally to find Fischer games. And if there was a Fischer game, that's all I wrote down. I didn't copy down anybody else's game. So it was just my little mania. And so at the end of the year, it actually did. In a couple of months, I had about 700 of his games in a little playbook that I had a little notebook and I by hand had written down all these games.

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And I play them over and over and over and over. And he was the most famous chess player in the world. But he he hadn't yet won the world championship. He was twenty seven, twenty twenty six, twenty six at this point twenty six. Twenty seven.

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And and by the end of the year I had beaten that kid, the kid with the smug smile cause.

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Yeah. And then and then a couple of years later I was one of the best high school players in the country and and my my team this is from Evanston, Illinois. We sent a team to New York and we finished second place that year. We we won the next year. And just by chance on Easter Sunday. And I was, what, sixteen, I was walking with my mother and near Macy's, those of, you know, New York, it's on 34th and Broadway.

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I across the street I spot Bobby Fischer, which is like he was the most reclusive person in the world.

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And I said, Mama, I'll see you later. That's that's my hero over there. And I ran across, like, dodging traffic to get to him out of a crowd. He was enormous. Six, three. Yeah. Like but even still to spot him in a crowd, like, what are the odds that and and he just ran up to him and I said, Mr. Fischer, Mr. Fischer, you know, in 1962 when you played Richard Skiing and the Sicilian Matchplay Play Panicking three and Move six, like what was that about?

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Like, because I knew all of his games by heart and it didn't occur to me that he would like to get lost, kid. Right. And he was a famous recluse, like just paranoid about everybody. But here's the strange little kid knows all of his games and he just became a mentor. He was, you know, and again, this was in the two years leading up to his winning the world championship.

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And he spent time with him before the. Yeah, I got I got a chance to spend time with him at the there used to be a resort called Grossinger's in the Catskills. If you saw the movie Dirty Dancing with Patrick Swayze back in the day, it was where.

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Muhammad Ali had trained, I guess he was I don't know if it was Muhammad Ali had trained for one of his boxing matches there. So they offered Fisher a chance to train for the Spassky match, the world championship. And I got to spend two weeks with them watching him prepare for the world championship, which was so cool.

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What did you learn from watching him prepare? Hmm? Well, this is interesting. Fischer Fischer woke up at 11 or 12 in the morning and went to sleep at three or four at night. So you learn that for him, that was the optimal time of day, right? Time of day and night where he functioned the best. And so you learn to find out, like, how you function the best.

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We all do that, right?

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Like, I know there are certain times of the day, like the first 30 minutes when I wake up or where I get the most creative ideas or an after 10:00 at night for me so each of us learn.

[00:29:27]

So I learned the importance of that. I learned the importance of of of pretty much a single minded focus. That's all he did, which is one of his tragedies. He said famously, once, all I ever want to do ever is play chess. And that's great. But when you win the world championship now, what do you do? And, you know, he he sadly lost his mind to paranoia over the years. And that was a real shame.

[00:29:56]

Paranoia's tough because you don't trust anybody. And after a while, he didn't even trust me. He just fell out of touch with with the world. But on a positive note, I learned the importance of focus of knowing your opponent. He he had a game of red book of his opponents games. That's all he did. He just played over those games over and over. And and I could tell which is the same way you learn. Right, exactly.

[00:30:23]

I just played over phishers games.

[00:30:26]

Now, the thing is, I played people other than Fischer. So so it's not the optimal way I should have studied other people.

[00:30:32]

I should have, and so did Fischer. But when he was preparing for the world championship match, that's all he did.

[00:30:38]

Didn't he do like a bait and switch because he assumed. Yes. Yes. So his whole life. So mind you, he was a world class chess player.

[00:30:46]

Actually, let me talk about Fischer, because Fischer was unique in the world in the following way. Fischer learned the game of chess at the age of six. And by the time he was eleven, sorry, by the time he was twelve, he was a good twelve year old chess player, not a great twelve year old chess player.

[00:31:05]

He was a good twelve year old player. That's six years later, right after six years of playing chess, Bobby Fischer was good at the age of 13. A year later, he played a game that some people say was the game of the century as a thirteen year old. So one year later, he goes from good to playing a game. People today still play over going, oh, my God, like a thirteen year old. Play that game at the age of 14.

[00:31:27]

He was the US champion, mind you, at twelve he was merely a good twelve year old. At thirteen, he played a game that some people say is one of the best ever. At fourteen, he was the US champion.

[00:31:36]

At fifteen, he was one of the top eight players in the world. What happened exactly? And he did that on his own. Imagine no coach, no coach, no nothing. Now, that's not entirely true.

[00:31:48]

He had a coach, but the coach had a chess rating of like two thousand. OK, like not really a coach.

[00:31:53]

It be like a decent player. It'd be like you pick up a tennis racket and your coach is the best player in your neighborhood. OK, that's not going to get you too far. It right.

[00:32:07]

It'll get you the rudiments. Fisher did that entirely on his own and no one in the history of the game has improved like that, not just that game in any field on his own. And how to do it? Well, he was a genius and he was very simple and he played the same series of openings.

[00:32:30]

You know, Tim asked me that question, Tim. First, he said what was unique about him.

[00:32:35]

And I said in a childlike simplicity, he really did. He looked when you looked at his games, it was like a child playing them.

[00:32:42]

And and everyone else had really complicated games. And fishers were always very simple. And the thing about Fisher, oh, this is something really about Fisher. He always played to win. He didn't mind losing, actually. He hated losing, but he wanted to win more.

[00:33:00]

And so there were games where anyone else would have accepted a draw that Fisher would play to win and in doing so, sometimes lost. And I think there are two ways to live your life. You can play to win.

[00:33:12]

No one wants to lose. You can play to win, or you can play not to lose. And Fisher always played to win. And you knew, even if in a losing position, he was looking for a way to win. He was not giving up. Not till you actually totally beat him.

[00:33:28]

Not that that happened too often. But so there was a ferocious will to win, that was something also about Fisher, and he did it entirely on his own against the Russians who were determined to beat him as a group, as a bloc. Imagine a kid in Russia taking up the game of baseball and becoming the best baseball player in history on his own.

[00:33:49]

This was during the Cold War, right? During the Cold War. Politics, things going on, so many political things. And he had the ultimate bait and switch with with Spassky.

[00:33:57]

Yeah, he always played John King for always played the same opening's. Imagine always coming out with a right hook and then in the world championship, switching up and playing something different.

[00:34:07]

And Spassky had been going over his games just like he was going over Spassky.

[00:34:10]

And then all of a sudden something totally different doesn't look the same.

[00:34:14]

Now, Fischer was a total bait and switch and Fischer set up the Russians for me, just figured this out, 10 for 16, 17 years, played exactly the same moves.

[00:34:26]

So everyone knew exactly what he was going to do. And in the world championship, only he did something different, which must have been unnerving.

[00:34:33]

If you're on the other side, you wouldn't know what to do. You would have been flabbergasted. Like all the preparation I did for Fischer is useless. I thought I was playing one person. I'm now playing someone else. So. So that was. Yeah. An incredible bait and switch that Fischer pulled. Yeah.

[00:34:51]

Want to come back to a little bit to tie this to I a little bit. But I want to come back to the role of information in decision making sure computers can handle way more information than we can as humans.

[00:35:02]

You had talked about the Paul Slovak study. Yes.

[00:35:06]

On the phone with me. Can you elaborate a little bit?

[00:35:09]

Sure. So human beings have a limited processing ability, right?

[00:35:14]

Our brains are have very little what's known as working memory.

[00:35:20]

We can't maintain a whole lot of information in our head at any one point. And so because of that limited processing ability, we we have a hard time with too much information. And so so we like to think that the more information we get I mean, using air quotes now, the better informed we are and we'll make better decisions. But that's not true. And a seminal study was done by by a psychologist named Paul Slovic back in nineteen seventy four.

[00:35:50]

So Paul Slovic gets eight horse handicappers into a room and he says, I'm going to see how good you guys are. Right. And so he says we're going to spend today handicapping horse races, which is to say, predicting the winner of a horse race. And this had been races that had been run over the last few decades that Slovic had gotten the stats on and deleted the names of all the horses, because if you knew the name of the horse, give you a nedret.

[00:36:14]

So all you saw was numbers. It's all you saw.

[00:36:17]

And so he said, we're going to handicap forty horse races and we're going to do so in four rounds, ten races each. And in the first round, I'm going to give each of you horse bettors, handicappers any five pieces of information you want. So like you might want the weight of the jockey. But that guy next to you, the other handicapper, he doesn't really care about the weight of the jockey. He wants some other variables.

[00:36:40]

So each of you, whatever, five pieces you want it.

[00:36:42]

And that guy wanted in each of the horse handicappers wanted they got. And at the end of the the first round, with five pieces of information, they were seventeen percent accurate, which is seventeen or nineteen. Hold on. Nineteen percent accurate, which is pretty good. It's almost twice as good as chance. Right. Apology. It's been a while since I looked at the study. Seventeen or nineteen percent anyway.

[00:37:07]

Almost twice as good as chance which is pretty darn good with five pieces of information I say twice as good as chance. There were ten horses in every race so we would expect ten percent accuracy. Just blind guessing. Just one in ten. Yeah. You got a ten percent chance of getting the horse right. So if you're betting nineteen percent, you've almost doubled your your results. That's pretty good. And they also asked each of the bettors to rate how confident they are they are in their predictions and they were.

[00:37:36]

Oh I remember what it was.

[00:37:37]

That's why I was confusing the two. They were seventeen percent accurate. They were nineteen percent confident that was it.

[00:37:44]

So almost identical confidence and accuracy, round one, round one with five pieces of information.

[00:37:51]

Round two, they were given ten pieces of information, then twenty and in round four they had forty pieces of information and there were ten races. So this was statistically valid results. Right.

[00:38:06]

Their accuracy was still only 17 percent with forty pieces of information, but their confidence almost doubled to thirty one percent.

[00:38:15]

It went from nineteen percent to thirty one percent. So they are now almost twice as confident as they ought to be, which is to say they're overconfident. So all the extra information did was just feed their confirmation bias.

[00:38:28]

They had already made a. Decision based on five pieces of information and all the new information did, was make them more confident in a decision they already made. Right. Which is what, by the way, in my investment analysis, when I look at global markets, I reduce our global markets down to a handful of variables like copper versus gold or investment grade bonds, forces versus corporate bonds. Right now, Kudayev ratio. I told you about that.

[00:38:55]

Those are my two of my really key variables to predict not just the stock market, but interest rates.

[00:39:02]

And so the goal is to reduce complexity to a few pieces of information that you can follow.

[00:39:10]

And reason with the trouble with too much information is you can't reason with it and you don't know how things interact, whereas if you have the few key variables, you can figure out how they interact.

[00:39:21]

That's exactly right. That's such a good point chain. You can figure out how they can interact and you can figure out when you're wrong.

[00:39:28]

Right. Right. So one of my favorite investment questions is what would I need to see to change my view? And if you're dealing with dozens of variables, there's no way that you can change your view on like there's just too much information to keep in mind, right. The way they all interact.

[00:39:45]

But if you're dealing with three or four or five variables, I can say I'm bullish on the US stock market until I see this one variable declined by more than three percent, in which case then I'm bearish because now I know I've gotten the feedback I need on the variables.

[00:40:00]

I understand like I have a hypothesis that distilling things down to the the essence or the the key variables, not all the variables, but the ones that are probably going to govern the outcome.

[00:40:12]

Sure also helps you with information filtering, because now you don't have to pay attention to all of these other things coming at you. You don't have to read every press release. You don't have to.

[00:40:22]

Yes, exactly. So you had asked me earlier before we started this, you know, like, how long do I look at markets and I size up global markets and really in in 15 seconds or 30 seconds because I know that.

[00:40:35]

And by the way, when I say global markets, I mean all global markets, including currencies, bonds and current commodities, is that say I look at every single currency, but I look at most of the major stock markets and commodities and the major currency pairs and interest rates and 15 to 30 seconds, because I know what I'm looking for and I'm only looking for changes that are unexpected because I've already formed a view.

[00:40:58]

Right. I know how the market should behave and now I'm just looking for deviations. So I just have to glance and see if there's a deviation. It's kind of like looking at a imagine a classroom of kids and a teacher just scans every day and just looking for as a kid missing or is there a new kid present and then got to pay attention. And so I'm able to do that in global markets because I know what I'm looking for, which gives me a reasoning edge also.

[00:41:25]

How does that give you a reasoning edge? There's a saying in Zen Buddhism, the beginner mind sees many possibilities. The experts is only a few.

[00:41:36]

Think about driving. Think about when you learned how to drive SHEIN when you first got behind the wheel.

[00:41:41]

It's terrifying. You have no idea where to like. What do you look at there?

[00:41:46]

People walking around, there's stoplights, there are other cars and still millions of inputs, millions of inputs, and you have no idea what to look at. And the process of becoming a skilled driver, that all becomes automated. Right?

[00:42:01]

You don't have to look for very much at all, you know, exactly what's relevant and what's not.

[00:42:06]

And so I think that's true with all experts in all fields, is you learn what not to pay attention to so that you can give more attention to what's important. Right.

[00:42:18]

Otherwise, the person is like looking at a million different things. I actually don't understand that about the investing world. I look at three or four or five things to understand global markets.

[00:42:29]

I don't understand people who look at literally dozens to hundreds to thousands of things.

[00:42:34]

This statistic, that government report, that annual report, how in the world can you possibly form a view of the world that you can taste like which variable is having which effect and how would you know? You can't. You can't.

[00:42:51]

But if you're consistently wrong and you have few variables and you know, those are the variables, or at least one of them is one of them is a skew or your reasoning is a skew and like modify and find out what works and that that allows you the feedback to actually get better over time.

[00:43:06]

Right. Is getting better. Might just keep you in the same relative position because the world is always changing.

[00:43:11]

But hopefully you can actually accelerate that and break out of I think you can any individual who approaches his or her life in any domain can get better. You know what? I'm going to tell you something from chess. This is an interesting rule.

[00:43:27]

Fischer played a very limited. Opening repertoire, which is to say he played very few openings and got really good at those openings before he did anything else. So the thing is, instead of doing lots of different things, get good at a couple. So, for example, if I were a guitar teacher, I'm just making this up.

[00:43:45]

I'm not if I were, I would have a student gain mastery over a few songs and get really good at those and then slowly build on that expertise to introduce new songs. Instead, what we do is we try to teach a student dozens of songs and you don't really get any good at any.

[00:44:05]

Right. That's the difference between the U.S. education system, by the way, and Oxford and Cambridge.

[00:44:11]

Tell me more about that. Sure.

[00:44:12]

So the idea of a liberal arts education is this. If you learn a lot, sorry, if you learn a little about a lot of different subjects, you'll be able to sort of piece it all together, it being an air quotes. Right.

[00:44:27]

If you learn a little bit about a lot of things, you'll be able to generalize and kind of think about anything. Well, no, you won't, because you've never learned how to think. At Oxford and Cambridge, the belief is learn one thing really well and then you can learn anything else really well until you've gone into depth, until you've actually done something, learned and mastered it and gone through the process of learning, you probably don't really understand how to learn.

[00:44:53]

You don't understand how to learn.

[00:44:54]

Right, because you've only stated a really superficial level in the United States. You don't really begin to specialize in to go into depth until graduate school.

[00:45:03]

I don't know why they they do that instead.

[00:45:05]

It would be much better if you if you learned one or two subjects really well and then branch out from there. Right. I think US schools would be much better off if they focused on teaching students simply how to read well and write well and rudimentary mathematics.

[00:45:23]

Get that down for the first eight years, make sure they're really good at that and then introduce a subject right.

[00:45:30]

One at a time, Kinnon at a time. Get get good at that.

[00:45:34]

Right. How much how much do you remember from high school chemistry.

[00:45:38]

I mean, I barely remember anything much at all. Right. Not much at all. Really a waste of how many hours of my life. Yeah, right. There are lots of in college too.

[00:45:47]

Like you don't really get a chance to learn much and ultimately it's it's wasted time. Learn one thing. Well then you can learn anything. Well what is the process for learning.

[00:46:01]

Is there a process. Well I think yes there is. How do we learn. Right. So it's such a good question.

[00:46:09]

I wrote a book Wow.

[00:46:11]

25 years ago called What Smart Students Know. And I, I did the following. I realized that, by the way, I'm not plugging the book. I am not. Absolutely not, because I can summarize it now. And I wish I had the time to rewrite the book.

[00:46:25]

But what I did was this. No one ever shows us how to learn ever. Nowhere in school. For example, imagine Shein in French class French one on one, your first French class. Your teacher said, everyone, you're going to have to learn a lot of vocabulary in this class.

[00:46:42]

So before I teach you any words, I'm going to teach you a way to remember vocabulary.

[00:46:46]

They never do that. They just go.

[00:46:48]

We're going to have a quiz on these 30 words on Monday. Good luck. Right.

[00:46:52]

But they don't teach us how to learn actually or remember things like, for example, they don't tell students if you want to remember anything, create a picture, a pattern, a story or rhyme out of it.

[00:47:03]

All the monarchs come back to picture pattern, story or rhyme, but they don't tell us that. So we struggle.

[00:47:09]

We create flash cards which are totally ineffective, and we keep rereading our notes.

[00:47:17]

So I wrote what smart students know.

[00:47:19]

I, I gave students a page from a geology textbook like a sample page, and I spent the next two hundred pages showing how to actually what it would mean to learn that.

[00:47:28]

Like, I actually it's amazing.

[00:47:30]

It's really cool, right? I mean, not that you would do that depth, but like what does it mean to learn that page of information?

[00:47:37]

Like here's everything that you would actually need to do.

[00:47:39]

By the way, if I told you all the steps it would take you to tie your shoe.

[00:47:43]

It'd be much harder than just watch this. Yeah, right. So so the secret to learning anything, is this anything? I'm glad you asked that question. Rehearsing if you want to get good at football, play football. If you want to get good at playing the guitar, play the guitar. If you want to get good at chess, you've got to play chess. Now, you want to break that down to certain skills and rehearse each one of them.

[00:48:09]

So you see people playing pickup, basketball or tennis and they haven't broken it down to skills and they're just out there playing.

[00:48:17]

Right. You want to break the domain down to sub skills and then rehearse each one.

[00:48:23]

Now, the reason I use rehearse is you want to do exactly what you would do in the. Game, right? So, for example, if you want to get good at taking tests, you have to take tests, which is to say the following, let's deconstruct that.

[00:48:39]

So I'm going to in the next two minutes, summarize everything there is to know about learning a subject. And it's this you rehearse whatever you are required to do on the test.

[00:48:49]

So think about a test on a test.

[00:48:52]

You are asked questions you've never seen before and you have to search your memory for the relevant information.

[00:49:00]

So step one, read the questions. Step two, search brain for for relevant information. Step three, collate that information into an answer. Right answer. You have to rehearse each one of those steps to do well in those subjects. Right.

[00:49:18]

So what that means is you need the way to prepare for a test.

[00:49:21]

I hate the word prepare or study because here's what most people mean by the word prepare for or study for a test. Reread my notes. If you think back to when you were in high school, in college, I looked at most students and what they would do is they would highlight their textbooks and take lots of notes and then reread their high lightings and reread their notes.

[00:49:41]

But that's not rehearsing a skill no one tests you on. How will you highlight, right? No one tests you.

[00:49:47]

And on rereading your notes because on the test you're not rereading anything. You're seeing an entirely new situation. So the way to get good at any subject and any domain is to rehearse the skills that you're actually required to do.

[00:50:03]

So practice questions, practice questions that you've never seen before, and you then have to search your memory for the relevant information. By the way, it helps.

[00:50:13]

What I would do in college is I would get textbooks the teacher wasn't using and I would see what questions were asked there.

[00:50:20]

So I'd really get questions I'd never seen before, even from teachers, teachers, authors, textbook authors that weren't my professor and weren't the authors of the textbook I was reading.

[00:50:32]

So I'd really gets stumped with questions. Right. And so because that's going to happen in the actual game. So, for example, imagine you're a basketball coach, right? And you want to train your basketball players at certain points.

[00:50:47]

Your key player is going to be out of action, right. Fouled out or injured. Right. I would have them play basketball games where take out one of the players and you're now playing with four, right, or one of you like, I would try to find a way to make their arm a little, like, wrap it up or something. It was a little constrained. Like, you've got a muscle sore. Now you're playing the game now.

[00:51:10]

Practice now, practice now, rehearse, rehearse, write. So rehearse under varying conditions. But a key the key to learning any skill, really, if there's anything I said today like that was super important. The key to learning any skill. Rehearse it. Break it down into sub skills and rehearse each of those skills.

[00:51:29]

If you're doing something other than that, you're wasting your time rereading your notes. Waste time. You want to get good at a job interview. Have someone asked you questions? Someone who doesn't know you ask you questions. Right.

[00:51:43]

And then grade your feedback and listen to it and exactly right.

[00:51:46]

And then exactly right with the feedback chain then, OK, I spoke too fast when I was coaching a young woman. She had a job interview coming up. And whenever I spoke, she she did the following here. Talk to me, talk to me right now and I'm going to pretend to be her listening.

[00:52:07]

So talk to me right now. She'd just say anything. What I want you to do is.

[00:52:10]

Uh huh, uh huh. Go to huh uh huh. She would say aha so quickly.

[00:52:16]

And I said, Are you aware that you are signaling that you're not listening to the other person?

[00:52:22]

And she was dumbfounded and she'd gone to Columbia University.

[00:52:26]

I mean, you know, a good school. Right. Great school. She said no one's ever told me that before. I said, you say aha.

[00:52:32]

So quickly, there's no way you heard what I asked you or said to you. And all your signaling is you're not listening to me. I already don't like you. And I liked her. I mean, I was right.

[00:52:42]

Yeah. Because a mentor. Right. And so.

[00:52:45]

So you need feedback. And she was stunned. She said no one's ever told me that before. And I said, just talk more slowly. Don't say a so quickly. Listen to the person.

[00:52:55]

So let me encapsulate this a little bit. If I'm in school, I'm in university, high school, I'm doing physics.

[00:53:01]

The questions at the end of the chapter, which most people annoy or avoid, and teachers may assign the odd numbers or the even numbers. Right. You should be doing them all and not not looking at your notes, trying to do them.

[00:53:14]

And then if you're stuck, go back and look in your book. Exactly right.

[00:53:18]

If you reread your notes, all you're getting good at is like following following problems you've already seen before. That's not going to help you when you get a new problem, because that's what's going to happen on the test.

[00:53:28]

I'll go one better you want to get really good.

[00:53:31]

Try the sample questions at the end of the chapter before you read the chapter.

[00:53:35]

That's interesting now because what that does is it primes you now, now all of a sudden. Well, how would I? There's no way I can answer those questions.

[00:53:43]

Let's now you're primed as you read the chapter of what's relevant and what's not. And you also prime yourself for the following. What happens when I have incomplete information and what happens if I forget the Pythagorean theorem?

[00:53:57]

How do I answer the question that that's what I did.

[00:54:00]

The Princeton Review, by the way, what happens when you know only two of the five choices? What do you do? Right. Like, there's there's a whole range of things that you can do when you're skilled. Right. So what happens in a what happens is a baseball player, if I've got sweat in my eye and a fly ball is coming at me, what do I do?

[00:54:22]

Right.

[00:54:22]

I mean, you need to rehearse for the unusual as well known optimal conditions, not optimal conditions, because you can't ever count on optimal ever.

[00:54:32]

And if you get them great, you're lucky.

[00:54:34]

So would you organize what's what smart students know differently now or would you take the same approach, which is like here's a page of geology and how would you update?

[00:54:44]

I would ask them to say I'd estimate a breakdown. I'd have I teach them how to teach themselves.

[00:54:50]

I'd say, OK, in a little Socratic way. I go on their test on this material.

[00:54:56]

Will you have seen the questions before? Yes or no? No, I get step them through and get them to discover.

[00:55:03]

Whoa, reading my notes re reading my notes is just a total waste of time. Read like that's all they do. They take notes in class verbatim.

[00:55:11]

Here's a skill on the test. Do you parrot back exactly the teacher's words or do you express them in your own words. Well, of course you express them in your own words. Well then you have to rehearse doing that. Right? Right.

[00:55:24]

So when you take notes in school, they're probably verbatim just because you've got to keep up with the teacher. Right.

[00:55:30]

But then translate those notes into your own words, because that's what you're going to have to do on the test.

[00:55:34]

Don't we read your notes, translate them, because you've got to do that on the test. And if you haven't done it before, you're not going to be able to do it on the test.

[00:55:43]

That's really fascinating. How does that carry over to adults then, who might be working in an organization or need to acquire new skills or. Well, to figure out what it is you're required to do and then and rehearse that skill, like let's say it's presentations, you've got to give presentations to clients, right, then you're going to have to rehearse that.

[00:56:05]

What if the skills more murky? What if it's like managing people? Oh, how do we go about learning to manage people? How do we get the feedback? That's such a good question.

[00:56:14]

I have to think about that. That's that's a challenging one, because after all, you're going to have to teach yourself unless you're being coached.

[00:56:23]

Right. Here's the way to think about it. If you're playing tennis by yourself, do you think you, Shein, are going to be able to correct your tennis strokes?

[00:56:31]

I don't think so.

[00:56:32]

Right, because you're not going to be able to see what you did wrong.

[00:56:36]

Right. You're not going to get out of the system. You're part of it. Right. You're embedded within it. It's like a fish, like not knowing that it's surrounded by water because it doesn't know non water. Right.

[00:56:45]

So Fisher and Chess was able to do that because he saw the results of his game. He could play over the games he got up.

[00:56:51]

That was a mistake and never doing that again. Right. Right.

[00:56:55]

But when you're dealing with people, how do you know, like what results am I getting? And, for example, people could smile. You could be getting totally the wrong feedback. They may be smiling because they don't want to upset you. Right. Here's an example. When I want to give a talk and I want to rehearse for that talk, I need to do it in front of people who don't know me.

[00:57:15]

If they're my friends, they're going to be smiling and nodding their heads, like just because they know me right.

[00:57:22]

When I have to make a pitch, it's going to be to someone who doesn't know me. Right.

[00:57:25]

Right. So I need that feedback. Right. Here's something.

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When I want to find out whether I know something, I explain it to people that I barely know. Like I could be sitting next to someone at a restaurant going, this is a new book I'm writing. And like, would you like to listen? And, you know, if I can engage that person who's a stranger and get them and then get them interested, then I know.

[00:57:45]

OK, good. Now I'm getting some feedback. Now, that's useful information, right? I mean, I gave you a copy of my book.

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Right. And I have given other friends copies of my book to get feedback right.

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And if they said, oh, I'm you know, this one chapter is a little long, like, then it's useful.

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And by the way, I've given it to people who who like who know me, but they're not friend friends like they don't that well, like you didn't know me that well, really.

[00:58:09]

And so I use that feedback to to revise. But your question is a good one in everyday life. How do we get feedback? Boy, I think sometimes you just have to ask. Here's a question I wanted to ask friends. What do people know about Adam that Adam doesn't know about Adam? That's a great question.

[00:58:29]

Have you have you. Assa Yeah. I just started to ask, you know, and be brutal. Right? Like, what does Adam know about what does everyone know about Adam that Adam doesn't know?

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And like, OK, I can be prepared for the answers and be honest, really.

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If you want it to be scientific about that, you could submit it to 30 friends and give them some like online forum where like survey monkey or something where they didn't.

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You just like, OK, by the way, someone told me it was really good advice regarding writing.

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He said, Adam, if one person tells you something, that's that person's opinion. But if three people tell you the same thing, you've really got to listen.

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And I think that's that's good advice. That's really good advice.

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It's hard because sometimes your friends, they don't want to hear and using air quotes hurt your feelings, but they're hurting you by not being honest with you in a way.

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Yeah. You know, yeah. I think that's. Boy, that would be a great website, you know, someone could start a website here, someone out there listening to Shane's brilliant blog and podcast.

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Here's an idea. You create a website where people can write feedback for people and it's submitted to the person anonymously.

[00:59:40]

I just thought you ought to know. Yeah. Like that. Be really cool.

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That be a great service for people and like, oh, wow.

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I didn't realize, you know, I cut people off. Right.

[00:59:50]

I talk to now you can start seeing things about yourself that you're blind to, totally blind to, don't want to bring up because of social norms or they're your friends or.

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Yeah.

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Or they just don't think someone and I know young man, brilliant. And he's so brilliant. He almost comes across as too slick.

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And I was talking to someone about him who knows the same young man and and they said, well, Adam, you should tell him. And it didn't occur to me to tell him that. And I and I said to him, I said, by the way, you might want to stumble occasionally, right.

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Because you're so polished and so slick that you're not approachable.

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Right.

[01:00:30]

And even there, his response was a slick. Oh, yeah. The pratfall effect. I know that. OK, I'll do that.

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So so I mean, he even though he knew I was going with that. Right. Right. And so, so yeah. I think providing feedback is a is a tough one.

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I'm sure in the comments section there's going to be all kinds of feedback for, for me now and OK, bring it on.

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You know, and as long as it's comes from a position of love, you know, and and I think that's important. I think yeah. I think that's what people know.

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If you if you come up from a place of love and constructive I don't say constructive criticism, but just feedback because we're all blind really to the effect we have on people and not totally blind. We have some idea, but it'd be good to get feedback. And and by the way, if I were an employer to your question and I and you asked about managing people, I, I'd sit down my entire department and say I'm I have not I have an online form.

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I want you to tell me ways I'm I could be improved ways I really suck.

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Yeah. And ideally people would tell you that.

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But then you also get into this like, I don't want to tell my boss this. Right. Like there's a social dynamic to it or a psychological impediment.

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And there's also like you kind of go whatever, you know, you just kind of roll your eyes and go whatever.

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But but I think if it were earnestly submitted, the petition like, hey, I need this feedback, I want to do better for you guys.

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By the way, your bonuses are tied to your performance. So help me make you more money. Yeah.

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And help our company make more money. Help us contribute to the world.

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Please give me some feedback. I found when I was managing people, they would always put their toe through that door, but it wouldn't be a foot. It would be a toe.

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And how you responded to that first sort of here's something you could do better or this criticism or whatever you want to call it, a fact you don't know about yourself.

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How you respond to that is indicative of whether that foot would come in the door and then, you know, the body and the person probing their probing you.

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And most people I know observe this at work. Most people had this default response of like, oh, you don't know what you're talking about or that's not true. Or here's a case where it wasn't true and that's a complete shutdown.

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Oh, yeah. For that other person. And they'll never put that foot through the door with you again.

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And when they by the way, I hear this with investors. I'm trying to to to to convey some information. And their response is to argue against it. Yeah.

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Instead of going. OK, well let me see if that's a valid idea. Let me just explore that. Let me just entertain it. It's so funny.

[01:03:15]

You should talk about this in in the Middle Ages, there was an institution designed by Kings to provide feedback and it was called the court jester.

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And the function of the court jester was not to amuse the king. The function of the court jester was far more serious than that. The court jester was the one person who is empowered by the king always to tell the truth.

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And so the king, who might say I was thinking of invading France and the court jester would go, Ha, good luck with that, buddy.

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Remember the last time you invaded Germany? Right.

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And and so because he was a gesture, the court, the king's court could then laugh at off, but the function of the court jester was actually to the king.

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The truth, I had no idea. Yeah. And and he was empowered to do so because then the king could save face.

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Everyone would laugh, but the king would go, OK, maybe I shouldn't invade France.

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Maybe that's not such a good idea. And so. Right. It wasn't an entertaining function. It was the real function. Tell the truth feedback. How do we get that? It's so hard. It's an. Difficult. Yeah, switching gears just a little bit, maybe your last question, what are some of the biggest learnings that you've gotten from other people that were unexpected?

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Oh, gee, that was an unexpected question. I don't know so much that they were. This is important. We're always teaching on multiple levels.

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So, for example, when there's the direct lesson, but the teachers also saying any number of other things. Right. So, for example, one thing my students always learn from me is I believe they can improve, not just there's the lesson like this is the Pythagorean Theorem. They also have learned lots of things. Adam believes I can raise my score. Raising scores are possible.

[01:05:13]

Right. You're teaching on multiple levels. And and so one thing I've learned just from observation and it's more indirect, is all the multiple ways we influence others. And we're always teaching whether we're aware of it or not. We're always making a statement. And by the way, not just a statement, multiple statements. I was so not really sad, but a couple of Sundays ago, I was walking with a friend through a park in Soho, New York, and there was a little stand that was being set up for the afternoon by some musicians and a mother.

[01:05:50]

And I could tell the mother loved her little son very much.

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The son was, I'm guessing, three and a half or something. I was walking by and the son was wanting to get involved. And as it were, I'm using air quotes, help the musicians set up their equipment and the mother I overheard the mother say to her son, no, no, you can't play music yet, but you can learn how. And I just thought how sad that child is now internalize that he can't play and the only impact he can't play.

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I'm using air quotes now until he learns.

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So the mother, with all the best intentions, was giving him a very bad message.

[01:06:27]

Right. And if you think back to your life, Shane, I certainly know in mine the number of offhand comments that an individual said that positively or negatively impacted me to this day.

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I'll bet you you can think of a handful of statements by this teacher or that adult that that you still remember in a good way or you or negative or in a negative way.

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And it's astonishing that the impact that offhand comments can have on young people. And I know one of the the really important things that I'm I'm really worried about the world and I'm worried about young people in particular and who's the under twenty five generation Gen Z right there been they're inheriting a world that's falling apart and and their brains and attention have been hijacked by technology.

[01:07:18]

And it's I'm deeply worried not just about the world as a whole, but about young people. And I think all of us can.

[01:07:26]

What should we do differently to to stem that or beat it in your mind? Well, for one, see, I'm I'm not sure that there's an answer to that question because I think the adults are so embedded.

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Adults over 25 are so embedded in their in their worldviews.

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I'm not sure that that we're going to change too quickly.

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And on the positive side, young people, the younger generation under 20 GenZE, the youngest generation is free from many of the the labels that everyone over the age of twenty five, like male versus female or Republican versus Democrat or American versus non American, that bedevil political and social and economic discussions of the adults.

[01:08:19]

And so I, I think the youngest generate Gen Z. The youngest generation is more homogenous in a global way than than all prior generations. And I think that's a plus and the minus side. They're inheriting a world where there are few positive voices, few, if any.

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And I think that's what's needed in the world right now. You need Beacon's of people offering positive visions that others can rally behind.

[01:08:46]

And I say positive vision, not us versus them. Positive visions, positive inclusive visions and and maybe young people will provide that maybe maybe a little flip. Maybe we'll get the leadership from Gen Z, maybe.

[01:09:01]

But we certainly need positive voices in the world.

[01:09:04]

I think that's a great place to wrap up because now we can invite everybody listening.

[01:09:08]

We can give them an invitation to the great game. Yay! Thank you so much, Adam. Shane, it was a delight. Thank you so much.

[01:09:19]

Hey, guys, this is Shane again, just a few more things before we wrap up. You can find show notes at Farnam Street blog, dotcom slash podcast. That's fair. And am s t r e t blog. Dotcom slash podcast. You can also find information there on how to get a transcript.

[01:09:39]

And if you'd like to receive a weekly email from me filled with all sorts of brain food, go to Farnam Street blog, dotcom slash newsletter. This is all the good stuff I found on the Web that week that I've read and shared with close friends, books I'm reading and so much more. Thank you for listening.