YouSaid · the spoken record

Grant Sanderson

lines on the record: 142
first: 2020-08-23
most recent: 2020-08-23
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“I think so. I think there's a ton of challenges there, right? Like radiation being kind of the biggest one. And I think there's a ton of people who look at that and say, why? Why would you want to do that? Let's let the robots do the science for us. But I think there's enough people who are genuinely inspired about broadening the worlds that we've touched. Or people who think about things like backing up the light of consciousness with super long-term versions of Terraforming. As long as there's a.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, it is. I think it's great. The idea of seeing it basically done by smaller entities instead of by governments. I mean, it's a heavy collaboration between SpaceX and NASA in this case. But moving in the direction of not necessarily requiring an entire country and its government to make it happen, but that you can have. Something closer to a single company doing it. We're not there yet because it's not like they're unilaterally saying we're distributing people up into space. It's just a sign that we're able to do more powerful things with smaller groups of people. I find that inspiring. I hope we see people land on Mars in my lifetime”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, kind of there is like there's, I think it's fairly interesting to see when innovations in one field allow for innovations in another, like the advent of computing. Seems like a prerequisite for the advent of chaos theory. You have this truth about physics and the world that in theory could be known, you could find Lorenz's equations without computers. But in practice, it was just never going to be analyzed that way unless you were doing like a bunch of simulations and that you could computationally see these models. So it's like physics allowed for computers, computers allowed for better physics. you know watch rinse and repeat that self-proportionality That's exponential. So I think I wouldn't. Think it's too far to say that that's a law of some kind”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Is true, a good book to read if you want that sense is peak, which essentially talks about peak performance in a lot of different ways, like chess, London cab drivers, how many push-ups people can do, short-term memory tasks. And it's meant to be like a concrete manifesto about deliberate practice and such, but the one sensation you come out with is, wow, no matter how good people are at something, they can get better and like way better than we think they could. I don't know if that's actually related to exponential growth, but I do think it's a true phenomenon that's interesting.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a good point. It might not actually be an example of exponentials because of something which grows in proportion to itself, but instead it's almost like a benchmark that was set out that everyone's been pressured to meet. And it's like all these innovations in micro inventions along the way rather than some consistent sit back and just let the lily ped grow across the lake phenomenon.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Saturation that would break down as you do anything that skews what that proportionality constant is, you can make it maybe not break down as being an exponential, but it can seriously slow what that exponential rate is.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“The amount that you have so that the software you write enables you to write more software. And I think we see this with the internet, like the advent of the internet makes it faster to learn things, which makes it faster to create new things. I think this is oftentimes why investment will grow exponentially, that the more resources a company has, if it knows how to use them well, the more it can actually grow. So I mean, you know, you reference Elon Musk. I think He seems to really be into vertically integrating his companies. I think a big part of that is because you have the sense what you want is to make sure that the things that you develop, you have ownership of in that they enable further development of the adjacent parts, right? So it's not just this, you see a curve and you're blindly drawing a line through it. What's much more interesting is to ask when do you have this proportional growth property? Because then you can also recognize when it breaks down, like in an epidemic, as you approach.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, so I think it's, it can be very inspiring to note when something, like Moore's Law is another great example where you have this exponential pattern that holds shockingly well and it enables just better lives to be led. I think the people who took Moore's law seriously in the 60s were seeing that, wow, it's not going to be too long before these giant computers that are either batch processing or timeshared, you could actually have one small enough to put on your desk on top of your desk and you could do things. And if they took it seriously, like you have people predicting smartphones like a long time ago. And it's only out of kind of this, I don't want to say faith in exponentials, but an understanding that that's what's happening. What's more interesting, I think, is to really understand why exponential growth happens and that the mechanism behind it is when the rate of change is proportional to the thing in and of itself. So the reason that technology would grow exponentially is only going to be if the rate of progress is proportional.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, exactly. So even when you know the fact and you do the division, it's like. Wow, so you've gone like that whole time, and then day 49 it's only covering half, and then after that it gets the whole thing But I think you can make that even more visceral if rather than going one day before you say, How long until it's covered 1% of the lake? And so what would that be? How many times you have to double to get over 100? Like seven, six and a half times, something like that. At that point, you're looking at 43, 44 days into it. You're not even at 1% of the lake. So you've experienced 44 out of 50 days. And you're like, yeah, that's lily bad. It's just 1% of the lake. But then next thing you know, it's the entire lake.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“So you have a good instinct for exponential growth So, I think a lot of, like, the knee-jerk reaction is sometimes to think that it's like half the amount of time, or to at least be like surprised that. Like after 49 days, you've only covered half of it”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Know the other classic example for gauging someone's intuitive understanding of exponential growth is I've got like a lily pad on a on a lake, really big lake, like Lake Michigan. And that lily pad replicates. It doubles one day, and then it doubles the next day, and it doubles the next day. And after 50 days, it actually is going to cover the entire lake. So after how many days does it cover half the lake?”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Heard this is like an old proverb where someone, the king offered him a gift and he said, the only gift I would like, very modest, give me a single grain of rice for the first chessboard and then two grains of rice for the next square. Twice that for the next square and just continue on. That's my only modest ask, your sire. And it's all more grains of rice than there are anything in the world by the time you get to the end.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Supposed But I think that probably only really goes for small numbers because the real counterintuitive thing about exponential growth is like as the numbers start to get big. So I bet if you took that same setup and you asked them, oh, if I keep tripling the size of this rock pile, seven times, how big will it be? I bet it would be surprisingly big even to like an. Society without numeracy. And that's the side of it that I think is pretty counterintuitive to us, but that you can basically train into people. Like, I think computer scientists and physicists, when they're looking at the early numbers of COVID, they were the ones thinking like, oh God, this is following an exact exponential curve. And I heard that from a number of people. And almost all of them are like techies in some capacity, probably just because I live in the Bay Area.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, if you have one friend versus a hundred friends, what's in between that? Yeah, 10 friends seems like the social status in between those two states. So that's like deeply intuitive to us to think logarithmically like that. And for some reason, we kind of train it out of ourselves to start thinking linearly about things.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“It's funny. I think it's extremely intuitive to humans. And then we train it out of ourselves such that it's then really not intuitive. And then I think it can become intuitive again when you study a technical field. So what I mean by that is, have you ever heard of these studies where in a setting where you're studying a group that has been disassociated from a lot of modern society and you ask what number is between one and nine? And maybe you would ask you, you've got like one rock and you've got nine rocks. You're like, what pile is halfway in between these? And our instinct is usually to say five. That's the number that sits right between one and nine. But sometimes when numeracy and the kind of just basic arithmetic that we have isn't in a society, the natural instinct is three because it's in between in an exponential sense, in a geometric sense that one is three times bigger and then the next one is three times bigger than that. So it's like what's”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“So, like just teaching what that value is and giving some intuitions on how do certain changes in behavior change that value and then what does that imply for exponential growth? I think those are general enough lessons and they're like resilient to all of the chaoses of the world that it's still like valid to take from the video.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“So, R0 is if you are infectious and you're in a population which is completely susceptible, what's the average number of people that you're going to infect during your infectiousness? So certainly during the beginning of an epidemic, this basically gives you kind of the exponential growth rate. Like if every person infects two others, you've got that 1, 2, 4, 8 exponential growth pattern. As it goes on, and let's say it's something endemic where you've got like a ton of people who have had it and are recovered, then the R0 value doesn't tell you that as directly because a lot of the people you interact with aren't susceptible. But in the early phases, it does. And this is like the fundamental constant that it seems like epidemiologists look at and the whole goal is to get that down if you can get it below one, then it's no longer epidemic. If it's equal to one, then it's endemic. And it's above one, then your epidemic.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, because I don't want to pretend like I'm an epidemiologist. Like, we have a ton of armchair epidemiologists. The spirit of that was more like, can we through a little bit of play draw reasonable-ish conclusions and also just get ourselves in a position where we can judge the validity of a model, I think people should look at that and they should criticize it. They should point to all the ways that it's wrong because it's definitely naive and the way that it's set up. But to say what lessons from that hold, like thinking about the R0 value and what that represents and what it imply.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, you have facts that Like actually adds value to people's lives through the predictions that it makes. But that line isn't always drawn because you have to get a little bit technical in order to properly draw that line out. And often I think popularized forms of media just shy away from being a little too technical.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“And just it's not an intuitive axiom system in comparison to other fields of math. So you as the student really have to walk through mud to get there. And you're constantly confused about how this relates to the beautiful things about coffee mugs and movius strips and such. And it takes a really long time to actually see topology in the way that mathematicians see topology. But I don't think it needs to take that time. I think there's this is making me feel like I need to make more videos on the topic because I think of a lot of people.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Constructing mappings between them translates into non-trivial facts about other parts of math. And that, I just, I don't think that's actually popularized. I don't even think it's emphasized well enough when you're starting to take a topology class because you kind of have these two problems. It's like either it's too squishy. You're just talking about coffee mugs and donuts, or it's a little bit too rigor-first, and you're talking about the axiom systems with open sets and an open set is not the opposite of closed set. So sorry about that, everyone. We have a notion of clopen sets for ones that are both at the same time.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“There's all sorts of things you can be interested in with random imaginative manipulations of things. Is that really what mathematicians are into? And the short answer is not really. It's not as if someone was sitting there thinking like, I wonder what the properties of clay are if I add some arbitrary rules about what when I can't cut it and when I can't glue it. Instead there's a ton of pieces of math that can actually be equivalent to like these very general structures that's like geometry, except you don't have exact distances. You just want to maintain a notion of closeness. And once you get it to those general structures,”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“My hope with that is I feel like topology is, I don't want to say it's taught wrong, but I do think sometimes it's popularized in the wrong way where you'll hear these things of people saying, oh, topologists, they're very interested in surfaces that you can bend and stretch, but you can't cut or glue. Are they? Why?”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“I hope that's not exactly how I phrase it because I think my hope would be is that I leave it to you to think about why you would expect that to be true and then to want to know what aspects of Amobius strip you want to formalize such that you can prove that intuition that you have because at some point now you're starting to invent algebraic topology if you have these vague instincts like I want to get this Mobius strip I want to fit it such that it's all above the plane, but its boundary sits exactly on the plane. I don't think I can do that without crossing itself, but that feels really vague. How do I formalize it? And as you're starting to formalize that, that's what's going to get you to try to come up with a definition for what it means to be orientable or non-orientable. And like once you have that motivation, a lot of the otherwise arbitrary things that are sitting at the very beginning of a topology textbook start to make a little more sense.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Wish I could say that wasn't a function of laziness, right? And that's like you've worked so hard on making the 20 minutes already to extend it out even further would take more time.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“But I think a meaningful part of the value to add is not just the technology, but to give the story around it as well. And that's kind of my job. It's not just to make the visuals that someone will look at. It's to be the one to decide what's the interesting thing to walk through here. And even though there's lots of other interesting paths that one could take, that can be kind of daunting when you're just sitting there in a sandbox and you're given this tool with like five different sliders and you're told to like play and discover things like where do you do? What do you start? What are my hypotheses? What should I be asking? Like a little bit of guidance in that direction can be what actually sparks curiosity to make someone want to imagine more about it.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, because the thing is, a way that you could do that project is you make the model and then you put it out and you say, here's a thing for the world to play with. Like, come to my website where you interact with this thing. And people did sort of remake it in a JavaScript way so that you can go to that website and you can test your own hypotheses.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Interactive for yourself, and you decide what the best narrative to spin is. As a more concrete example, like my process with, I made this video about SIR models for epidemics. And it's like this agent-based bottling thing where you tweak some things about how the epidemic spreads and you want to see how that affects its evolution. My format for making that was very different than others where rather than scripting it ahead of time, I just made the playground and then I played a bunch and then I saw what stories there were to tell within that.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, well, so what's interesting is you're saying that, and the videos are non interactive in the sense that there's a play button and a pause button, and you could ask like, hey, while you're programming these things, why don't you program it into an interactable version, make it a Jupyter notebook that people can play with, which I should do, and that would be better. I think the thing about interactives, though, is most people consuming them just sort of consume what the author had in mind. And that's kind of what they want. I have a ton of friends who make interactive explanations. And when you look into the analytics of how people use them, there's a small sliver that genuinely use it as a playground to have experiments. And maybe that small sliver is actually who you're targeting and the rest don't matter. But most people consume it just as a piece of well-constructed literature that maybe you tweak with the example a little bit to see what it's getting at. But in that way, I do think like a video can get most of the benefits of the interactive, like the interactive app as long as you make the”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Is a risk that the stuff that I do also fits that same bill, where at best it's giving this kind of intellectual candy on giving a glimpse of feeling like you understand something. But unless you do something active, like reinventing it yourself, like doing problems to solidify it, even things like space repetition memory to just make sure that you have like the building blocks of what do all the terms mean unless you're doing something like that, it's not actually going to stick. The very same thing that's so admirable about Feynman's lectures, which is how damn sad. Reveal a little bit of the flaw that we should, as educators, all look out for, which is that that does not correlate with long-term learning.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“His teaching style is interesting because people have described the Feynman effect where while you're watching his lectures or while you're reading his lectures, everything makes such perfect sense. So as an entertainment session, it's wonderful because it gives you this intellectual satisfaction that you don't get from anywhere else that you finally understand it. But the Feynman effect is that you can't really recall what it is that gave you that insight even a week later. And this is true of a lot of books and a lot of lectures where the retention is never quite what we hope it is.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Everything is either trivial or impossible. And it's like a shockingly thin line between the two where you can find something that's totally impenetrable. And then after you get a feel for it, it's like, oh yeah, that whole subject is actually trivial in some way. So maybe that's what goes on. And every researcher is just on the other end of that hump and it feels like it's so far away, but one step actually gets them there.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“I do think you're being a little bit more generous than is necessarily. And I promise that's not even false humility because I sometimes think when I research a video, I'll learn like 10 times as much as I need for the video itself. And it ends up feeling kind of elementary. So I have a sense of just how far away the stuff that I cover is from the actual depth.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Like these two things end up being related to very different fields, like some of them more complex analysis, some of them more like algebraic geometry. And then when you just go out so far as to take those adjacent fields, place one PhD student into a seminar of another ones. They don't understand what the other one's saying at all. Like you take the complex analysis specialist inside the algebraic geometry seminar. They're as lost as URI would be. But I think going around and like trying to have some sense of what this big picture is certainly has personal value for me. I don't know if I would ever make new contributions in those fields, but I do think I could make new expositional contributions where there's kind of a notion of things that are known but haven't been explained very well.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“It is that you've got two types of people, or especially two types of researchers there's the fox that knows many different things and then the hedgehog that knows one thing very deeply. So like von Neumann would have been a fox. He's someone who knows many different things, just very foundational a lot of different fields. Einstein would have been more of a hedgehog, thinking really deeply about one particular thing. And both are very necessary for making progress. So between those two, I would definitely see myself as like the fox where I'll try to get my paws in like a whole bunch of different things. And at the moment, I just think I don't know enough of anything to make a significant contribution to any of them. But I do see value in having a decently deep understanding of a wide variety of things. Like most people who know computer science really deeply don't necessarily know physics very deeply or many of the aspect like different fields in math even let's say you have like an analytic number theory versus an algebraic number theory.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think one of my biggest regrets from undergrad is not having built better relationships with the professors I had there. And I think a big part of success in research is that element of like mentorship and people giving you the kind of scaffolded problems to carry along. For my own goals right now, I feel like I'm pretty good at exposing math to others and like want to continue doing that. For my personal learning, I Are you familiar with the Hedgehog Fox dynamic? I think this was either the ancient Greeks came up with it or it was pretended to be something drawn from the ancient Greeks. I don't know who to point it to, but they brought it.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Before I see how this person went at it, I'm really going to try to approach it for myself, no matter what you gain, all sorts of inarticulateable intuitions about that topic which aren't going to be there if you simply go through the proof. For example, you're going to be trying to come up with counterexamples. You're going to try to come up with intuitive examples, all sorts of things where you're populating your brain with data. And the ones that you come up with are likely to be different than the one that the text comes up with. And that lends at a different angle. So that aspect also slowed Feynman down in a lot of respects. I think there was a period when the rest of physics was running away from him. But insofar as it got him to where he was, I kind of resonate with that. I would be nowhere near it because I not like him at all, but it's like a state to aspire to.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I think the things that I've learned best and have the deepest ownership of are the ones that have some element of rediscovery. The problem is that really slows you down. And for my part, it's actually a big fault. This is part of why I'm not an active researcher. I'm not at the depth of the field. A lot of other people are. The stuff that I do learn, I try to learn it really well. But other times you do need to get through at a certain pace. You do need to get to a point of a problem you're trying to solve. So obviously you need to be well equipped to read things without that reinvention component and see how others have done it. But I think if you choose a few core building blocks along the way and you say, I'm really going to try to approach this.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“Answer your actual question like what I like about his way of going at things is this constant desire to reinvent it for himself like when he would”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“He was doing was actually quite deep, very much mathematical. That should go without saying, but I remember reading a book about Feynman in a cafe once. And this woman looked at me and was like, saw that it was about Feynman. She was like, oh, I love him. I read Shirley, you're joking. And she started explaining to me how he was never really a math person. I don't understand how that can possibly be a public perception about any physicist, but for whatever reason that like worked into his aura, that he sort of shoed off math in place of true science. The reality of it is he was deeply in love with math and was much more going in that direction and had a clicking point into seeing that physics was a way to realize that and all the creativity that he could output in that direction was instead poured towards things like fundamental, not even fundamental theories, just emergent phenomena and everything like that.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“But you read this letter, and I can try to pull it up for you if I want. And it's just this absolutely heartfelt letter to his wife saying how much he loves her, even though she's dead and kind of what she means to him, how no woman can ever measure up to her. And it shows you that the Feynman that we've all seen in Shirley you're joking is different from the Feynman in reality. And I think the same kind of goes in his science where he kind of sometimes has this output of being this awks character. Like everyone else is coming in there's with these fancy falutin formulas, but I'm just going to try to whittle it down to its essentials, which is so appealing because we love to see that kind of thing. But when you get into it, like...”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, I think a ton of people like Feynman, he's probably, it's a little bit cliche to say that you like Feynman, right? That's almost like when you don't know what to say about sports and you just point to the Super Bowl or something as something you enjoy watching. But I do actually think there's a layer to Feynman that sits behind the iconography. One thing that just really struck me was this letter that he wrote to his wife two years after she died. So during the Manhattan Project, she had polio. Tragically, she died. They were just young, madly in love. You know, the icon of Feynman is this, almost this mildly sexist womanizing philanderer, at least on the personal side.”
2020-08-23 · Lex Fridman Podcast · #118 – Grant Sanderson: Math, Manim, Neural Networks & Teaching with 3Blue1Brown · IDENTIFIED FROM THE TRANSCRIPT · source