Podcast - People I (mostly) admire Flashcards
EPISODE 121: Exploring Physics, from Eggshells to Oceans
Physicist Helen Czerski loves to explain how the world works. She talks with Steve about studying bubbles, setting off explosives, and how ocean waves have changed the course of history.
If you understand a little bit of the framework, the critical thing you can do is you can ask the right question. And that’s what this is all about, really. There’s too much in the world to know. We don’t all know the answers to everything. Having a framework for thinking about the world — it is brilliant for satisfying curiosity, but it’s also phenomenally important for being able to ask the right questions so that we can make judgments about the complicated world around us.
You often don’t need a gigantic amount of knowledge, but you do need a framework for reality. And that’s the thing that lets you ask the right question.
Dec 2023
31/03/24
EPISODE 114: Is Perfectionism Ruining Your Life?
Psychologist Thomas Curran argues that perfectionism isn’t about high standards — it’s about never being enough. He explains how the drive to be perfect is harming education, the economy, and our mental health.
When you look at the relationship between perfectionism and performance, you find very weak to non-existent associations. And not only do you not get the success benefits of these perfectionistic drives, but also you get a lot of significant mental distress and things like anxiety, depression, low mood, self harm, and, in extreme cases, thoughts of suicide. All these things are positively correlated with perfectionistic tendencies.
Perfectionism can be expressed in many different ways. The first way is through what’s called “self-oriented perfectionism,” and that’s an intense internal drive and need to be perfect, and nothing but perfect. The approach is a very simple paper and pencil questionnaire. There’s a psychological measure called the Multidimensional Perfectionism Scale, which taps into self-oriented perfectionistic tendencies. It’s useful, I think, to think about these tendencies as a spectrum.
The socially-prescribed perfectionism is very much perfectionism rooted in perceptions of the outside world and people in our social circles, but also people more generally. And it’s this idea that everybody expects me to be perfect — so not only do I expect myself to be perfect, but when I look out into the world I see people watching, judging, waiting to pounce if I’ve shown a shortcoming, a flaw, or I failed. And so socially-prescribed perfectionists are very hypersensitive to other people’s validation and approval. And they’re very keen to conceal and disguise their shortcomings from the world because, essentially, the moment they reveal any weakness, they feel like they’re being judged. So socially-prescribed perfectionism is very much rooted in perfectionism coming from the outside.
I’m not a person who spends hours on social media. Actually there’s a lot of circumstantial evidence that social media is certainly a culprit because the trend for socially-prescribed perfectionism really starts to inflect around 2007, 2008, which just so happens to be the time Apple released the first iPhone and the social media platforms came into our lives 24/7. I don’t think there’s any doubt that the images and moving pictures of perfection and perfect lives and lifestyles that are projected to us all the time are having an impact on levels of social expectations to be perfect.
But one of the things I try to do in the book is zoom out a little bit and try to ask the deeper question about, “Why is it social media platforms create algorithms that push those images and moving pictures into our lives 24/7? What’s the incentive structures?” And the incentives, of course, is that it’s profitable to keep people online, engaged, keep sucking in our attention and, importantly, our spending through the deployment of targeted ads, which is the major revenue stream for these platforms. So that aura of discontent, that aura of not being quite perfect enough, and “here’s a material solution, a targeted ad, to bridge that gap again between the kind of imperfect person that we think we are and this perfect person we think we should be based on what we see on social media”.
Sep 2023
29/03/24
I can believe your story that social media has these adverse psychological impacts on people. But if social media is so destructive, why do you think people so eagerly and actively engage with it? Do you think they’re miscalculating the cost and benefits? They’re just making a mistake and they’re destroying themselves because they’re short-sighted? Or do you think maybe the answer is the other benefits of social media are so great — the enjoyment, the fun, the being part of society are so great — that people are willing to take the bad with the good?
I think there’s certainly an element of social media use that is for the right reasons — bringing about community, sharing interest, sharing information. I use certain social media platforms to learn about the world, politics, economics, and all the rest of it. And it’s actually helped me understand these things in a much clearer way than perhaps I would have been able to from the mainstream media. But I don’t think that there’s any doubt that there are also problems with social media, problems that are linked to what social media platforms need to do to grow. And the vast majority of their profit, of course, comes from targeted advertising that’s linked to material solutions to perceived problems or holes that people have in their lives.
Speaking to young people, I think they know exactly how these platforms work and the algorithms are geared to keeping them online as much as possible. And yet they still use them despite that because of the reasons you described. They find a sense of community, they can hang out with their friends, they can chat to their friends. I am absolutely of the opinion that young people, and people in general, are using social media because there’s a genuine use value for it. All I’m saying is that perfectionism slots into a broader economy where the imperative is for us to do more, have more, be more. And social media is just one part of that.
When you are a professor at the London School of Economics and the people that come into your classroom are really the 1% of students, the highest of the highest achievers. You can tell that they’ve come through a very brutal process through school where they’ve been tested all the time, compared, ranked, and had placed on them, and placed on themselves, excessive expectations to achieve and continue to achieve at every possible assessment. And so they get there almost a dazed boot-camp survivor. And they’re wracked with that tension still, and they have this intense need and burning desire to continue to excel. Of course, now they’re in a situation where they’re amongst the elite. I see a lot of issues in students who find it really difficult once they’re in amongst the very best achievers to continue to feel like they’re achieving. There’s no doubt that comes from an excessively competitive and pressurized education system.
I don’t really understand why but clearly the competitiveness of elite university admissions seems to have gone up dramatically over the last 40-50 years and it’s clear that the kids are highly attuned to it. So how would you change the educational system to try to lessen the forces that are pushing kids towards perfectionism?
You have to open up higher education, increase the access to it, increase the number of places. Daniel Markovits actually wrote a book called The Meritocracy Trap where he dealt with these issues in far more detail than I did. And the conclusion from his book was really that we need to open up access to education and, of course, that’s going to cost money. But that will, lessen the competition, i. e. make it easier for people to access higher education, even elite higher education. But we should see this actually as an investment, not a drain. A highly-educated population is one that can generate a hell of a lot of economic activity, is creative, is innovative, is entrepreneurial, can lift people out of hardship and poverty so that they’re contributing to society in meaningful ways that are aligned with their interests and skill sets. This is what education can do. And I know it sounds utopian. I know it sounds idealistic. I get that. But I really do think if you want to change the education system, you have to open up access and you have to make big investments in it.
So if you’re a parent and you don’t want to burden your kids with a lifelong suffering of perfectionism, what should you do?
Well, at the moment, and as I talk about extensively in my book, parents don’t have a choice in this matter. The pressures are the way they are because it’s really important these days for young people to do well in school because the college premium still exists, but it exists for a narrow and elite set of professions — finance, law, medicine. You could probably add tech to that list too. So that’s a lot of pressure. However, that said, there are things that we can do as parents to try to mitigate some of that pressure. The biggest one is unconditional love, support, and validation for our children.
I will say in my own experience, when I’ve spoken to big groups, in public lectures, one thing you sense from the audience, just by looking at the audience, is that women are much more engaged and seem much more open minded to new ideas.
Women are used to having to defend themselves and are used to not being believed, so maybe that makes them more open minded to other people too.
EPISODE 126: How to Have Great Conversation
3 types of conversations:
1. Practical: the other person is looking for solutions, wants to be helped
2. Emotional: wants to be hugged, not looking for solutions!
3. Social: chit-chat, wants to be heard
March 2024
29/03/24
EPISODE 117: Nate Silver Says We’re Bad at Making Predictions
LEVITT: So let’s start with the topic you’re most famous for, and that’s predicting election outcomes. In 2008, your first foray into political prediction, you correctly predicted 49 out of 50 states in the Electoral College. And then you, against all odds, did even better in 2012, getting every single state right.
LEVITT: So you say we’re bad at predictions, and then you get into what you think the main reasons are that we fail. One reason you raise is that we focus on those signals that tell the story about the world as we would like it to be, not how it really is.
SILVER: The issue is that all those states are correlated. They have the same, basically like white, working-class voter base, so when Trump does better than expected in Wisconsin, he’ll probably also do better in other midwestern states, Michigan, Ohio, Pennsylvania, and so forth. And so our model realized that these things are highly correlated, that being up a tiny bit in a lot of states is actually not all that good, because if it’s a uniform swing of the direction, then all of a sudden you lose all these states by a point, two points, instead of winning them by a point or two points. And that’s basically what happened, is that Clinton’s support was overestimated in the upper Midwest, and that’s a critical reach in the electoral college. And then you get Donald Trump as president.
Oct 2023
31/03/24
LEVITT: So the point you just made is that it’s not that they’re 50 independent shocks, with each state getting some random draw. It’s that there’s a set of shocks, a small number, and they trickle across states. Is that why you think other people’s model gave a higher percentage chance to Clinton? Because essentially they were getting the standard errors wrong?
SILVER: That’s the main reason. The Huffington Post, for example, had a model that had Clinton with a 99 percent chance of winning. There was a model at Princeton that was like 99-point-some percent. If you remove the part of your model that says that these states are correlated and not independent, then you’ll get a really over-confident answer. There are some other subtle things, too. Our model had priced in the fact that in 2016, you had a big third party vote. You had a lot of undecided voters, so there were more votes up for grabs than typically. A lot of people who say they’re going to vote third party actually wind up, under the pressure of the ballot booth, picking one of the two major parties. But the main thing was just that you cannot treat this as 50 independent contests. It’s the same two people on the ballot in every state. And I grew up in Michigan. People in Michigan are not that different than people from Wisconsin or Ohio, despite rooting for different football teams and so forth.
LEVITT: Yeah, exactly. You got back in form in 2020, right? You got 48 out of 50 states. And I have to say, I was surprised to see, reading your new Substack account, that you’re not sure whether you’ll even cover the 2024 election. And I’ll believe that when I see it, because the demand for your forecast is going to be intense.
SILVER: The issue is that people look at me as some avatar for — I don’t even know what anymore, right? But there’s a lot of pressure to convey information to people that are not in a mood for rigorous analysis necessarily at all. You have people who feel very strongly emotionally about this election. But I think people have trouble grasping the idea that an election is one event drawn from a larger sequence — a reference class, is the nerdy way to put it — and parties don’t want you to believe that elections are probabilistic. They want you to think that our guy is the righteous guy to inhabit the White House and that you as the voter control this process. But yeah, it’s a little bit of oil and water as far as what the audience wants versus what a probabilistic forecast can really provide.
LEVITT: I don’t think I’d be exaggerating if I said that you are the number one celebrity data scientist in America — that if we polled a representative group of Americans and we asked them to name a data scientist, your name would come up more than any other. And I love that for two reasons. The first is because I have great admiration for what you do with data. And the second reason is that when it comes to data science, I think you’re essentially self taught. You don’t have any fancy credentials like a Ph.D. You didn’t even major in the right subject in college. You’re an economics major in college, whereas the kind of people who get hired as data scientists at fancy tech firms, they tend to be statisticians or computer scientists by training.
And I’ve always argued that the most important determinant of a great data scientist isn’t knowing lots of complicated techniques. It’s having common sense and curiosity, a knack for asking good questions, and the ability to tell a good story with data. Your success, I think, should be an inspiration to every budding data scientist who fits that bill. So that’s my explanation for your success. What do you think the secret is to your success?
SILVER: I mean, it still is a little bizarre. First of all, let me say one thing. I do think actually the fact that I was an economics graduate at University of Chicago, by the way, is worth pointing out because I think economists are good at framing questions that can be answered rigorously, ideally with data.
SILVER: I also have a lot of hands-on experience in weird ways, from playing poker, from building fancy models for fantasy baseball, and things like that. It’s weird to be someone who’s not terribly quote unquote “political.” You know, to be very caught up in elections and then people are making inferences about your political preferences based on what your forecast says — that’s been a little bit of a weird journey. I think being in the right place at the right time, too. I mean, like, interest in American elections increased vastly beginning in 2008 with the rise of Barack Obama. We certainly have had a series of very close and dramatic and interesting elections, right? Any country that can elect both Obama and Donald Trump back to back is a complicated country. It’s been interesting having a front row seat at this very confusing time, in some ways, to be an American.
LEVITT: Let’s take an example of something you’ve done recently, which I think is fascinating from the perspective of this conversation we’re having. It’s in your Substack, the Silver Bulletin. And it’s about Covid-19. And I thought the results themselves were really interesting, but what I especially liked is the way you talked about the results. Could you just lay out the question you were trying to shed light on about Covid, your empirical strategy, and your findings?
SILVER: It was a Friday afternoon and kind of what inspires me to write particular posts I’m never quite certain of. But what I thought was a relatively straightforward finding — that until the introduction of vaccines, so early 2021, basically there was no relationship to speak of between the political orientation of a state and how many people were dying from Covid. So you had, for example, some blue states like New York, New Jersey, Massachusetts that had very high death rates. You also had some red states, Arizona, the Dakotas, and whatnot, that have very high death rates from Covid. Not much of a correlation. And then, once you can get vaccinated, you see pretty strong correlations. The top of the list of Covid deaths is almost all red states. The bottom of the list is almost all blue states. Not perfect, but having looked at lots of data sets when it comes to American politics, you know when you see the red states and the blue states lined up in a particular way. And the reason here is not because, like, Covid targeted Republicans, but because Republicans were quite a bit less likely to get vaccinated.
SILVER: So if you just lump all red states and all blue states together, meaning based on how they voted in 2020 and 2016, then the red states are about 35 percent higher. The very red states, like West Virginia, it’s a larger gap than that potentially. But yeah, if you just put them into groups, then about 35 percent higher.
SILVER: Yeah, this was a Yale study. I think it was a collaboration between the management school and public health school. And they actually are going to the individual level data. They’re looking up voter registration records in Ohio and Florida and running a search of people who died during that period. And they found the same thing — that up until January or February 2021, neither party’s registered voters have higher excess deaths. And then once vaccines are available, then Republicans do. And the good thing about this is, A, they have individual level records, B, because they’re confining this analysis to individual states, Florida and Ohio, then there’s less, like, regional luck of the draw and where Covid kind of happens to land or where a particular strain might have more effect. So they’re controlling for a lot of the things that my kind of quick and dirty analysis didn’t do and find the same thing. And that’s, again, as a researcher, when you start to say, okay, here are two pretty different methods and they have a similar result, one’s more involved, one’s simple. That starts to be pretty robust more often than not.
LEVITT: The other thing that I found really interesting, and it’s a little bit behind the scenes, is the fact that there wasn’t a difference between the red and blue states before the vaccine. And presumably if the Republicans didn’t like the vaccines, they also didn’t like a lot of the other policies we were doing to try to fight Covid, like restricting social contact. The implication is that maybe those other policies weren’t working very well, which is interesting because we just don’t talk about that as much as we should, thinking about future epidemics and what maybe we should be doing.
LEVITT: Here’s what I found entertaining as an outsider watching you do this, but I find incredibly frustrating when I’m the one being attacked, is that **there’s a real imbalance between the amount of work it takes to produce a thoughtful data-driven piece that makes a sensible point about the world, but then to criticize empirical work takes no time at all, right? **That drives me crazy, that mismatch between how hard it is to produce and how much people can sway readers just by criticizing you without any support.
SILVER: A big culprit in this is also Twitter, or I guess it’s now called X officially. The fact that you can take a position and write a pithy tweet in a minute or 20 seconds I think makes this issue worse and leads to a lot more tribal rivalry and kind of dunking on people. I have kind of quite self consciously pivoted away from Twitter toward Substack, toward my newsletter there, for many reasons. One is that you can get subscribers, including paying subscribers. And so at least if you’re the subject of some annoying controversy, you can make a little bit of money off it now. But also to be able to control the tone and say, “Look, I’m gonna take four hours with this subject and not four minutes.”
LEVITT: So I want to go back to the book you wrote. It’s called The Signal and the Noise. And knowing we’d be talking, I went back and I took a look at it for the first time since right after it came out. And I have to say it’s really an awesome book. You make a lot of simple but I think important points. So to me, the most succinct summary of the book is this. You wrote: “We need to stop and admit it: we have a prediction problem. We love to predict things, and we aren’t very good at it.”
SILVER: Yeah, that’s the thesis of the book, basically. So go out and buy it! I think the irony — it comes up a little bit with the Trump in 2016 prediction, I’d call it a forecast, technically — but like people really demand certainty, right? They assume that if someone’s an expert, they must know all the answers with a high degree of confidence, when there are times, like in 2016, where the right answer is: be less certain. That’s a kind of hard message to sell, and it’s not getting any easier in the kind of days of Twitter and other social media, where people have access to a stylized interpretation of facts that flatter their political and other preferences. But human beings in so many domains have failed, including economics, right? Economics is notorious for challenges in predicting macroeconomic conditions. Problems that we thought were solved, like inflation, obviously weren’t in the past couple of years. So there really aren’t very many examples of successful predictions. Exceptions include weather forecasting. Twenty-five or 30 years ago, weather forecasting was literally a joke, but also there was very little predictive power more than a couple of days out. And now they can precisely say, next Tuesday at 3 p. m., 80 percent chance of rain. It’s quite useful. So what makes weather forecasters good are a couple of things. One is they do actually have physical models of the world. It’s not just purely statistical. That helps a lot. And also they have a lot of practice, where if you make forecasts every day, 24 hours a day, of temperature and wind and pressure and all these other variables, then experience really helps. You get a lot better calibrated if you get a lot of feedback knowing when you’re right and when you’re wrong.
LEVITT: So you say we’re bad at predictions, and then you get into what you think the main reasons are that we fail. One reason you raise is that we focus on those signals that tell the story about the world as we would like it to be, not how it really is.
SILVER: This is especially true if you cover politics for a living and cover elections and polling. I can guarantee you that like, in October, 2024, you’ll have Republicans making these grand claims that Trump is going to win based on the data and Democrats are saying the same thing about Biden and it’s kind of funny how people like just don’t have an awareness of, like, how much confirmation bias they have. It’s like they might intellectually understand in some abstract way that like, confirmation bias exists, but partisan political preferences train you to see everything in a blinkered, partisan way. There’s no particular reason that your view on marginal tax rate should correlate with your view on abortion, for example, or like transgender rights or something like that. But parties try to get people to form coalitions by agreeing on a bunch of unrelated stuff. And it’s almost like a recipe toward confirmation bias. I think about the game theory of politics a little bit, right? It’s not a coincidence that most presidential elections are about 50-50. The parties are very efficient in some ways at forming coalitions. But that means they’re taking complicated human affairs and complicated people and voters and smooshing them all down into one dimension. And so that’s a recipe for being yelled at if you have heretical, complicated political views.
LEVITT: Let’s talk about your new book. You’ve been at it for a long time. I can see talk about it online going back at least to 2021. This must be some book you’re working on.
SILVER: Yeah, so the subject of the book is gambling and risk, which is an ambitious subject. It starts out literally in the world of capital G gambling, so the first two chapters are about poker, and there’s a chapter about the history of Las Vegas, the history of casino gambling, there’s a chapter on sports betting. That’s the first half. Then there’s chapters on venture capital and the cryptocurrency bubble and collapse. There’s a chapter at the end about economic progress and capitalism. There’s actually a lot of economics in the book, I think, in different ways. So it’s a very ambitious book that I hope will provide interest on every page.
LEVITT: You have built a life around analyzing data. And what I find so shocking in the modern world is how little training and exposure the typical person gets in a school setting to data-related things. Have you thought at all about the teaching of data science or data analysis and how we might do it to middle school kids or high school kids?
SILVER: First of all, I think there should be statistics and probability and kind of logical thinking classes taught from a relatively early age, I would say. And then for some reason, still with math education in the U.S., there’s still sometimes too much of an emphasis on the technical side of things, and not as much on, like, problem-solving, logical quote unquote “rational thinking” skills. One thing I will do — sometimes I’ll be asked to judge student research paper competitions, so they’re trying to solve some sports problem or some election modeling problem, and almost invariably the people use way too many fancy techniques and aren’t spending enough time asking basic questions of the data, or thinking about confounding variables, or figuring out like what a more robust strategy is for answering a question — all the things you were talking about before. I haven’t thought about what the curriculum would be, but a combination of statistics, but really logical thinking I think would benefit the students of the United States.
My advice, if you want to learn how to analyze data yourself, is to find a question you care about, get your hands on some data, and try to figure out the answers. There is no substitute for that kind of real world experience. The second best thing you can do to learn about analyzing data, short of doing it yourself, is to read what Nate Silver has to say, either in his outstanding book The Signal and the Noise, or in his new Substack, entitled Silver Bulletin.
EPISODE 118: “My God, This Is a Transformative Power”
My research has always been somewhat interdisciplinary. And I noticed that there was one psychologist called Irving Biedermann — in the 1980s, he wrote a paper and had a back-of-envelope estimation of how many objects babies see and he puts that estimate to 30,000.
LI: ImageNet is a dual purpose data set. The first purpose: for solving the fundamental computer vision problem of object recognition, which is fundamental to human intelligence, it’s fundamental to A.I. At this point you have to go beyond 20 objects so to define the objects and to provide the researchers with the very, very large training set that the field has never even dreamed of. And the second purpose is to create a benchmark that measures progress, so that we can actually invite international researcher community to participate.
LI: Here’s a lot of credit to Professor Geoff Hinton. His group has been leading neural network research for a long time, and I think just like our conviction to ImageNet, he has had long conviction to neural network because he believes it is a elegant way of taking data as they are, the raw data, and it is a way to learn patterns of the world and learn to do tasks like recognizing objects or digits. And I think what they were missing from their side is recognizing the importance of data, as well as, you know, needing the computing power.
LEVITT: Is it fair to say that neural nets have more or less won the day since then? That this was a pivotal moment, because in computer vision ever since, neural nets have been the center of how people think about the world — is that true?
LI: Yes, I think it’s fair to say, compared to other families of algorithms,** neural network, its ability to scale in the model capacity really has won the day**. And now we know that with data, with the high capacity neural network models, we can do a lot of things we could not have imagined.
LI: Yeah, Steve, this is also a growth on my own, right? I entered the field — like we said earlier, I had no idea whether people believed in A.I. or not. I didn’t care. I just thought it was fun. I’m a scientist and I go after curiosity and that’s what my curiosity led me to. And then it started to dawn on me, “My god, this is a transformative power. This is the driver of the next industrial revolution-scale societal change.” And then I went to Google on a sabbatical in 2017 and 2018. And that sabbatical, as a chief scientist of A.I. there, led me to realize every business will be impacted — because I was working on Google Cloud, and we talked to Japanese cucumber farmers, we talked to insurance, we talked to hospital leaders, we talked to, of course, software engineers. Every single business will be needing A.I. at some point.
And that’s when I realized it is a responsibility of my generation of technologists who created all this to ensure that this technology is here to benefit humanity, not to destroy humanity. Of course, every tool is a double edged sword. I’m not naive or blind. Just because I wish this is benevolent doesn’t mean it will always remain benevolent. This is why we need to work. And this is why my own aperture has expanded. At the core, I’m still a technologist. I’m still in the lab with my students making the next generation A.I. tech. But I’ve expanded to leading the Stanford Human-Centered A.I. Institute, to having a voice, talking to policymakers, and talking to the greater public because I think it’s important we collectively get this as right as we can.
LI: Every piece of technology has a way of being used by bad actors or adversarials, and also its unintended consequences, even if you’re using it as a so-called good actor. Civilization has been around for thousands of years. I don’t think we’ve solved all this problem, but we’ve always grappled with this problem, and we have to grapple with this problem. So all the potential bad use of A.I., we need a multi-dimensional approach. Some is laws, right? We have regulatory framework. Some is social norms. Even outside of law, there are norms we have to collectively ensure. And some is partnership. You know, think about nuclear. There were treaties and there are collective recognition that we cannot totally race to the bottom. And some are just defense systems. You just have to have defense systems like cyber security and other measures that are constantly on the lookout.
LEVITT: It’s interesting that so much of the A.I. databases and tools are publicly available. And that’s definitely good for democratizing access to A.I., but my hunch is that it’s bad for constraining the bad actors, right? If a terrorist group had to build a vision training data set with millions of entries and develop the algorithm from scratch, they wouldn’t be able to do it. But so much of the capability now, it seems, in the A.I. world is off-the-shelf. And that makes this problem you’re talking about — how do you make A.I. used primarily for good? — a more difficult challenge. Is that right?
LI: Yes, but it’s more nuanced. You have raised a topic that is very much an ongoing discussion. Right now, as you and I speak, so many people, including governments, have entered this discussion, and we’ll see how this play out. I can say that I’m noticing, Steve, in a short period of time, less than a year, the alertness, awareness, and efforts about A.I. governance has drastically increased. Even the private companies are actively participating in discussions of regulation and self-regulation and other measures. So these are changing landscapes as we speak.
