The following is a conversation with Luiz and Joao Batala, brothers and cofounders of Fermat's

library, which is an incredible platform for annotating papers. Is there right on the Fermat's

library website? Justice Pierre de Fermat scribbled his famous last theorem in the margins.

Professional scientists, academics, and citizen scientists can annotate equations, figures,

ideas, and write in the margins. Fermat's library is also a really good Twitter account to follow.

I highly recommend it. They post little visual factoids and explorations that reveal the beauty of

mathematics. I love it. Quick mention of our sponsors. Skiff, Simply Safe, Indeed, Nutsuite,

and FourSigmatic. Check them out in the description to support this podcast.

As a side note, let me say a few words about the dissemination of scientific ideas.

I believe that all scientific articles should be freely accessible to the public.

They currently are not. In one analysis I saw, more than 70% of published research articles are

behind a paywall. In case you don't know, the funders of the research, whether that's government

or industry, aren't the ones putting up the paywall. The journals are the ones putting up the

paywall, while using unpaid labor from researchers for the peer review process.

Where is all that money from the paywall going? In this digital age, the costs here should be

minimal. This cost can easily be covered through donation, advertisement, or public funding of

science. The benefit versus the cost of all papers being free to read is obvious, and the fact that

they're not free goes against everything science should stand for, which is the free dissemination

of ideas that educate and inspire. Science cannot be a gated institution. The more people can freely

learn and collaborate on ideas, the more problems we can solve in the world together, and the faster

we can drive old ideas out and bring new, better ideas in. Science is beautiful and powerful,

and its dissemination in this digital age should be free. This is the Lex Friedman podcast,

and here's my conversation with Louise and Joao Batalla.

Louise, you suggested an interesting idea. Imagine if most papers had a backstory section,

the same way that they have an abstract. Knowing more about how the authors ended up

working on a paper can be extremely insightful, and then you went on to give a backstory for

the Feynman QED paper. This is all on the tweet, by the way. We're doing tweet analysis today.

Okay. How much of the human backstory do you think is important in understanding

the idea itself that's presented in the paper or in general?

I think this gives way more context to the work of scientists. A lot of people have this

almost kind of romantic misconception that the way a lot of scientists work is almost as the sum

of eureka moments where all of a sudden they sit down and start writing two papers in a row,

and the papers are usually isolated. When you actually look at it, the papers are

chapters of a way more complex story. The Feynman QED paper is a good example. Feynman was actually

going through a pretty dark phase before writing that paper. He lost enthusiasm with physics and

doing physics problems. There was one time when he was in the cafeteria of Cornell and he saw a

guy that was throwing plates in the air. He noticed that when the plate was in the air,

there were two movements there. The plate was wobbling, but he also noticed that the Cornell

symbol was rotating. He was able to figure out the equations of motions, the equations of motions

of those plates. That led him to kind of think a little bit about electron orbits in relativity,

which led to the paper about quantum electrodynamics. That kind of reignited his interest in physics

and ended up publishing the paper that led to his Nobel Prize, basically. I think there are a lot

of really interesting backstories about papers that readers never get to know. For instance,

we did a couple of months ago an AMA around a pretty famous paper, The Gams Paper with Ian

Goodfellow. We did an AMA where everyone could ask questions about the paper and Ian was

responding to those questions. He was also telling the story of how he got the idea for that paper

in a bar. There was also an interesting backstory. I also read a book by Cedric Vellani. Cedric

Vellani is this mathematician, a Fields Medalist. In his book, he tries to explain how he got from

a PhD student to the Fields Medal. He tries to be as descriptive as possible about every single

step how he got to the Fields Medal. It's interesting also to see just the amount of

random interactions and discussions with other researchers sometimes over coffee and how it

led to fundamental breakthroughs and some of his most important papers. I think it's super

interesting to have that context of the backstory. Well, the Ian Goodfellow story is kind of interesting

and perhaps that's true for Feynman as well. I don't know if it's romanticizing the thing, but

it seems like just a few little insights and a little bit of work does most of the leap required.

Do you have a sense for a lot of the stuff you've looked at just looking back through history?

It wasn't necessarily the grind of like Andrew Wiles or the Fermat's last theorem,

for example. It was more like a brilliant moment of insight. In fact, Ian Goodfellow

has a kind of sadness to him almost in that at that time in machine learning,

like at that time, especially in for GANs, you could code something up really quickly in a single

machine and almost do the invention, go from idea to experimental validation in like a single night,

a single person could do it. And now there's kind of a sadness that a lot of the breakthroughs you

might have in machine learning kind of require large scale experiments. So it was almost like

the early days. So I wonder how many low hanging fruit there are in science and mathematics and

even engineering where it's like you could do that little experiment quickly. Like you have an

insight in a bar. Why is it always a bar? But you have an insight at a bar and then just implement

and the world changes. It's a good point. I think it also depends a lot on the maturity of the field.

When you look at a field like mathematics, like it's a pretty mature field, a field like machine

learning, it's growing pretty fast. And it's actually pretty interesting. I looked up like the

number of new papers on archive with the keyword machine learning. And like 50% of those papers

have been published in the last 12 months. So you can see just the sense, 50, 50%. So you can see

the magnitude of growth in that field. And so I think like as fields mature, like those types of

moments, I think naturally are less frequent. It's just a consequence of that. The other point

that is interesting about the backstory is that it can really make it more memorable in a way.

And by making it more memorable, it kind of sediments the knowledge more in your mind. I

remember also reading the backstory to Dijkstra's shortest path algorithm, where he came up with it

essentially while he was sitting down at a coffee shop in Amsterdam. And he came up with that

algorithm over 20 minutes. And one interesting aspect is that he didn't have any pen or paper

at the time. And so he had to do it all in his mind. And so there's only so much complexity

that he can handle if you're just thinking about it in your mind. And that when you think about

the simplicity of Dijkstra's shortest path finding algorithm, it's knowing that backstory helps

sediment that algorithm in your mind so that you don't forget about it as easily.

It might be from you that I saw a meme about Dijkstra. It's like he's trying to solve it and

he comes up with some kind of random path. And then it's like my parents aren't home. And then he

does, he figures out the algorithm for the shortest path. It's right through words to convey memes,

but it's hilarious. I don't know if it's in post that we construct stories that romanticize it.

Apparently with Newton, there was no Apple, especially when you're working on problems that

have a physical manifestation or a visual manifestation. It feels like the world could be

an inspiration to you. So it doesn't have to be completely on paper. Like you could be sitting

at a bar and all of a sudden see something and a pattern will spark another pattern and you can

visualize it and rethink a problem in a particular way. Of course, you can also load the math that

you have on paper and always carry that with you. So when you show up to the bar, some little

inspiration could be the thing that changes it. Is there any other people almost on the human side,

whether it's physics with Feynman, Dirac, Einstein, or computer science, touring anybody else,

any backstories that you remember that jump out? Because I'm also referring to

not necessarily these stories where something magical happens, but these are personalities.

They have big egos. Some of them are super friendly. Some of them are self obsessed. Some

of them have anger issues. Some of them, how do I describe Feynman? But he appears to have

appreciation of the beautiful in all its forms. He has a wit and a cleverness and a humor about

him. Does that come into play in terms of the construction of the science?

I think you brought up Newton. Newton is a good example also to think about his back story,

because there's a certain back story of Newton that people always talk about. But then there's a

whole another aspect of him that is also a big part of the person that he was, but he was really

into alchemy. He spent a lot of time thinking about that and writing about it. He took it very

seriously. He was really into Bible interpretation, trying to predict things based on the Bible.

And so there's also a whole back story then. And of course, you need to look at it in the context

that and the time that when Newton lived, but it adds to his personality. And it's important to

also understand those aspects that maybe people are not as proud to teach to little kids, but

it's important. It was part of who he was. And maybe without those, who knows what he would

have done otherwise. Well, the cool thing about alchemy, I don't know how it was viewed at the

time, but it almost like to me symbolizes dreaming of the impossible. Most of the breakthrough ideas

kind of seem impossible until they're actually done. It's like achieving human flight. It's not

completely obvious to me that alchemy is impossible or like putting myself in the mindset of the time.

And perhaps even still, everything that some of the most incredible breakthroughs

are would seem impossible. And I wonder the value of believing, almost like focusing and dreaming

of the impossible such that it is actually is possible in your mind and that in itself manifests

whether the accomplishing that goal or making progress in some unexpected direction. So alchemy

almost symbolizes that for me. I distinctly remember having the same thought of thinking,

you know, when I learned about atoms and that they have protons and electrons, I was like,

okay, to make gold, you just take whatever has an atomic weight below it and then shove another

proton in there, and then you have a bunch of gold. So like, why don't people do that?

It seemed like conceptually is like, you know, this sounds feasible. You might be able to do it.

And you can actually just very, very expensive. Yeah, yeah, exactly. Exactly. So in a sense,

we do have alchemy. And maybe even back then, it wasn't as crazy that he was so into it.

But good people just don't like to talk about that as much. Yeah, but Newton in general was a very

interesting fella. Anybody else come to mind? In terms of people that inspire you, in terms of

people that you just are happy that they have once or still exist on this earth?

I think, I mean, Freeman Dyson for me. Yeah, Freeman Dyson was, I've had a chance to actually

exchange a couple of emails with him. He was probably one of the most humble scientists that

I've ever met. And that had a big impact on me. We're actually trying to convince him to annotate

a paper on Fermat's library. And I sent him an email asking him if he could annotate a paper.

And his response was something like, I have very limited knowledge. I just know a couple of things

about certain fields. I'm not sure if I'm qualified to do that. That was his first response.

And this was someone that should have won a Nobel Prize and worked on a bunch of different fields,

did some really, really great work. And then just the interactions that I had with him,

every time I asked him a couple of questions about his papers, and he always responded saying,

I'm not here to answer your questions. I just want to open up more questions. And so that had a big

impact on me. It's like just an example of an extremely humble yet accomplished scientist.

And Feynman was also a big, big inspiration in the sense that he was able to be, you know,

again, extremely talented and scientist. But at the same time, socially, he was able to,

he was also really smart from a social perspective. And he was able to interact with people. It was

also a really good teacher. And it was also to did a awesome work in terms of explaining physics

to the masses and motivating and getting people interested in physics. And that for me was also

big inspiration. Yeah, I like the child like curiosity. Some of those folks like you mentioned

Feynman. I've, Daniel Kahneman, I got a chance to meet and interact with some of these truly

special scientists. What makes them special is that even in older age, they're still like,

there's still that fire of childlike curiosity that burns. And some of that is like, not taking

yourself so seriously that you think you've figured it all out, but almost like thinking that you

don't know much of it. And that's like step one in having a great conversation or collaboration

or exploring a scientific question. And it's cool how the very thing that probably earned people the

Nobel Prize or work that's seminal in some way is the very thing that still burns even after

they've won the prize. It's cool to see. And they're rare humans, it seems.

And to that point, I remember like the last email that I sent to Freeman Dyson was like in his

last birthday, he was really into number theory and primes. So what I did is I took like a photo

of him picture and then I turned that into like a giant prime number. So I converted the picture

into a bunch of one and eights. And then I moved some numbers around until it was a prime. And then

I sent him that. Oh, so the the visual like it still looked like the picture. It's made up of a

prime. That's tricky to do. It's hard to do. It looks harder than it actually is. So the way you

do it is like you convert the darker regions into eights and the lighter regions in once.

And then there's just keep flipping. Yeah. But there's like some primality tests that are cheaper

from a computational standpoint. But what he tells you is it excludes numbers that are not prime.

Then you end up with a set of numbers that you don't know if they are prime or not.

And then you run the full primality test on that. So you just have to keep iterating on that. And

it was it was it's funny because when he got the picture, he was like, how did you do that? It was

super curious too. And then we got into the details. And again, these was he was already 90,

I think 92 or something. And that curiosity was still there. So you can really see that in some

of these scientists. So could we talk about Fermat's library? Yeah, absolutely. What is it?

What's the main goal? What's the dream? It is a platform for annotating papers in its essence.

Right. And so academic papers can be one of the densest forms of content out there. And generally

pretty hard to understand at times. And the idea is that you can make them more accessible and

easier to understand by adding these rich annotations to the site, right? And so we can just imagine

a PDF view on your browser, and then you have annotations on each side. And then when you click

on them, a sidebar expands, and then you have annotations that support late tech and markdown.

And so the idea is that you can, say, explain a tougher part of a paper where there's a step

that is not completely obvious, or you can add more context to it. And then over time, papers can

become easier and easier to understand and can evolve in a way. But it really came from

myself, Luis and two other friends. We've had this long running habit of kind of running a journal

club amongst us. We come from different backgrounds, right? I studied CS, we studied physics. And so

we read papers and present them to each other. And then we try to bring some of that online.

And that's when we decided to build Fermat's library. Then over time, it kind of grew into

something with a broader goal. And really, what we're trying to do is trying to help

move science in the right direction. That's really the ultimate goal and where we want to take it

now. So there's a lot to be said. So first of all, for people who haven't seen it, the interface

is exceptionally well done. That's like execution is really important here. Absolutely. The other

thing is just to mention, for a large number of people, apparently, which is new to me,

don't know what latex is. So it's spelled like latex. So be careful Googling it if you haven't

before. It's a, sorry, I don't even know the correct terminology setting language. It's a

typesetting language where it's you're basically program writing a program that then generates

something that looks from a typography perspective beautiful. Absolutely. And so a lot of academics

use it to write papers. I think there's like a bunch of communities that use it to write papers.

I would say it's mathematics, physics, computer science. Yeah, that's yeah. That's because I'm

collaborating currently on a paper with two neuroscientists from Stanford. And they don't know

what. So I'm using Microsoft Word and Mendeley and like all of those kinds of things. And I'm

being very zen like about the whole process. But it's fascinating. It's a little heartbreaking,

actually, because it actually, it's funny to say, but we'll talk about open science, actually,

the bigger mission behind Fermat's library is like really opening up the world of science to

everybody. Is these silly two facts of like one community uses latex and another uses word

is actually a barrier between them. That's like, it's like boring and practical in a sense, but

this makes it very difficult to collaborate. Just on that, like I think that if there are some people

that should have received like a Nobel Prize that but will never get it. And I think one of those

is like Donald Knuth because of tech and latex and then because it had a huge impact in terms of

like just making it easier for researchers to put their content out there, like making it uniform

as much as possible. Oh, you mean like a Nobel Peace Prize? Maybe a Nobel Peace Prize.

Maybe a Nobel Peace Prize. I think so. I mean, he at a very young age got the Turing Award for

his work in algorithms and so on. So like an incredibly, I think it's in, it might be even

the 60s, but I think it's the 70s. So when he was really young, and then he went on to do like

incredible work with his book and yeah, with tech that people don't know. And going back just on

the reason why we ended up, because I think this is interesting, the reason why we ended up using

the name Fermat's Library, this was because of Fermat's last theorem. And Fermat's last theorem

is actually a funny story. So Pierre de Fermat, he was like a lawyer and he wrote like on a book

that he had a solution to Fermat's last theorem, which but that didn't fit the margin of that book.

And so Fermat's last theorem basically states that there's no solution. If you have integers a,

b and c, there's no solution to a to the power of n plus b to the power of n equals to c to the

power of n if n is bigger than 2. So there's no solutions. And he said that and that problem

remained open for almost 300 years, I believe. And a lot of the most famous mathematicians

tried to tackle that problem. No one was able to figure that out until Andrew Wiles,

I think was in the 90s, was able to publish the solution, which was I believe almost 300 pages

long. And so it's kind of an anecdote that there's a lot of knowledge and insights that can be

trapped in the margins, that there's a lot of potential energy that you can release if you

can release, if you actually spend some time trying to digest that. And that was the origin

story for the name. Yeah, you can share the contents of the margins with the world that could

inspire a solution or communication that then leads to a solution. And if you think about

papers, like papers are as Jean was saying, probably one of the densest pieces of text that

any human can read. And you have these researchers, like some of the brightest minds in this field

working on like new discoveries and publishing these work on journals that are imposing them

restrictions in terms of the number of pages that they can have to explain a new scientific

breakthrough. So at the end of the day, papers are not optimized for clarity and for a proper

explanation of that content, because there are so many restrictions. So there's, as I mentioned,

there's a lot of potential energy that can be freed if you actually try to digest a lot of the

contents of papers. Can you explain some of the other things, so margins, librarian, journal club?

So journal club is what a lot of people know us for, where every week we release an annotated

paper in all sorts of different fields, but physics, CS, math. Margins is kind of the same

software that we use to run the journal club and to host the annotations. But we've made that available

for free to anybody that wants to use it. And so folks use it at universities and for running

journal clubs. And so we've just made that freely available. And then librarian is a browser extension

that we developed that is sort of an overlay on top of archive. So it's about bringing some of the

same functionality around comments, plus adding some extra niceties to archive, like being able

to very easily extract the references of a paper that you're looking at or being able to extract

the BibDec in order to cite that paper yourself. So it's an overlay on top of archive.

Yeah, the idea is that you can have that commenting interface without having to leave archive.

It's kind of incredible. I didn't know about it. And once I've learned of it, it's like, holy shit.

Why isn't it more popular given how popular archive is? Like everybody should be using it.

Archive sucks in terms of its interface. Or let me rephrase that. It's limited in terms of its

interface. Archive is a pretty incredible project. And in a way, the growth has been completely

linear over time. If you look at like number of papers published on archive, it's pretty much

a straight line for the past 20 years. Especially if you're coming from a startup background,

and then you were trying to do archive, you'd probably try all sorts of growth acts and try to

then maybe have paid features and things like that. And that would kind of maybe ruin it.

And so there's a subtle balance there. And I don't know what aspects you can change about it.

Yeah. For some tools in science, it just takes time for them to grow. Archive is just turned 30,

I believe. And for people that don't know, archive is these kind of online repository where people

put preprints, which are versions of the papers before they actually make it to journals.

ARXIV, for people who don't know. And it's actually a really vibrant place to publish your

papers in the aforementioned communities of mathematics, physics, and computer science.

It started with mathematics and physics, and then over the last 30 years, it evolved. And now,

actually computer science now, it's a more popular category than physics and math on archive.

And there's also, which I don't know very much about, like biology, medical version of that as

bioarchive. Yeah, bioarchive. It's interesting because if you look at these platforms for

preprints, they actually play a super important role. Because if you look at a category like math,

for some papers in math, it might take close to three years after you click upload paper

on the journal website, and the paper gets published on the website of the journal.

So this is literally the longest upload period on the internet. And during those three years,

that content is just locked. And so that's why it's so important for people to have websites like

archive so that you can share that before it goes to the journal with the rest of the world.

That was actually on archive that Perlman published the three papers that led to the proof of the

Poincaré conjecture. And then you have other fields like machine learning, for instance,

where the field is evolving at such a high rate that people don't even wait before the papers

go to journals before they start working on top of those papers. So they publish them on archive,

then other people see them, they start working on that. And archive did a really good job at

like building that core platform to host papers. But I think there's a really, really big opportunity

in building more features on top of that platform apart from just hosting papers. So

collaboration, annotations, and like having other things apart from papers like code

and other things. Because for instance, in the field like machine learning,

there's a really big, you know, as I mentioned, people start working on on top of preprints,

and they are assuming that that that preprint is correct. But you really need a way, for instance,

to maybe it's not peer review, but distinguish what is good work from bad work on archive. How

do you do that? So like a commenting interface like librarian, it's useful for that so that

you can distinguish that in a field that is growing so fast as machine learning.

And then you have platforms that focus on just biology, bio archive is a good example.

Bio archive is also super interesting because there's actually an interesting experiment that

was run in the 60s. So in the 60s, the NIH supported this experiment called information

exchange group, which at the time was a way for researchers to share biology preprints via mail

or using libraries. And that project in the 1960s got canceled six years after it started.

And it was due to intense pressure from the journals to kill that project because they

were fearing competition from the preprints for the journal industry.

Creek was also one of the famous scientists that opposed to the information exchange group.

And it's interesting because right now, if you analyze the number of biology papers

that appear first as preprints, it's only 2% of the papers. And this was almost 50 years after

that first experiment. So you can see like that pressure from the journals to cancel that initial

version of a preprint repo added tremendous impact on the number of papers that are showing up in

biology as preprints. So it delayed a lot that revolution. But now platforms like bio archive

are doing that work. But there's still a lot of room for growth there. And I think it's super

important because those are the papers that are open that everyone can read.

Okay. So if we just look at the entire process of science as a big system, can we just talk about

how it can be revolutionized? So you have an idea, depending on the field,

you want to make that idea concrete, you want to run a few experiments in computer size,

there might be some code, there'd be a data set for, you know, some of the more sort of biology,

psychology, you might be collecting the data set that's called, you know, a study, right?

So that's part of that. That's part of the methodology. And so you are putting all that

into a paper form. And then you have some results. And then you submit that to a place for review

through the peer review process. And there's a process where how would you summarize the peer

review process? But it's really just like a handful of people look over your paper and comment and

based on that decide whether your paper is good or not. So there's a whole broken nature to it.

At the same time, I love the peer review process. When I buy stuff on Amazon,

for like the commenting system, whatever that is. So okay, so there's a bunch of possibilities for

revolutions there. And then there's the other side, which is the collaborative aspect of the

science, which is people annotating, people commenting, sort of the low effort collaboration,

which is a comment. Sometimes, as you've talked about, a comment can change everything.

But, you know, or a higher effort collaboration, like more like maybe annotations or even like

contributing to the paper, you can think of like collaborative updating of the paper over time.

So there's all these possibilities for doing things better than they've been done.

Can we talk about some ideas in this space? Some ideas that you're working on,

some ideas that you're not yet working on, but should be revolutionized. Because it does seem

that archive and like open review, for example, are like the craigslist of science. Like,

yeah, okay, I'm very grateful that we have it. But it just feels like it's like 10 to 20 years.

Like it doesn't feel like that's a feature. The simplicity of it is a feature. It feels like

it's a bug. But then again, the pushback there is Wikipedia has the same kind of simplicity to it.

And it seems to work exceptionally well in the crowdsourcing aspect of it. So, sorry,

there's a bunch of stuff going on the table. Let's just pick random things that we can talk about.

Wikipedia, you know, for me, it's the cosmological constant of the internet.

It's like, I think we are lucky to live in the parallel universe where Wikipedia exists.

Yes. Because if someone had pitched me Wikipedia, like a publicly edited

encyclopedia, like a couple of years ago, it would be, I don't know how many people would have said

that that would have survived. I mean, it makes almost no sense. It's like having a Google

doc that everybody on the internet can edit. And like, that will be like the most reliable

source for knowledge. And I don't know how many, but hundreds of thousands of topics.

Yeah. It's insane. It's insane. And like you have, and then you have users, like there's one,

a single user that edited one third of the articles on Wikipedia. So we have these really,

really big power users. There are a substantial part of like what makes Wikipedia successful.

And so like, no one would have ever imagined that that could happen. And so that, that's,

that's one thing I completely agree with what you just said. I also started to interrupt briefly.

Maybe let's inject that into the discussion of everything else. I also believe I've seen

that with Stack Overflow, that one individual or a small collection of individuals contribute or

revolutionize most of the community. Like if you create a really powerful system for archive or

like open review and made it really easy and compelling and exciting for one person who is in

like a 10x contributor to do their thing, that's going to change everything. It seems like that

was the mechanism that changed everything for Wikipedia. And that's the mechanism that changed

everything for Stack Overflow is gamifying or making it exciting or just making it fun or

pleasant or fulfilling in some way for those people who are insane enough to like answer

thousands of questions or write thousands of factoids and like research them and check them,

all those kinds of things, or read thousands of papers. Yeah. No, Stack Overflow is another

great example of that. And those are both two incredibly productive communities that generate

a ton of value and capture almost none of it. In a way, it's almost like counter,

it's very counterintuitive that these communities would exist and thrive.

And it's really hard to, there aren't that many communities like that.

So how do we do that for science? Do you have ideas there? Like what are the

biggest problems that you see? You're working on some of them?

Like just on that, there are a couple of really interesting experiments that people are running.

An example would be like the Polymath Projects. So this is kind of a social experiment that was

created by Tim Gowers, Fields Medalist. And his idea was to try to prove that is it possible to do

mathematics in a massively collaborative way on the internet? So he decided to pick a couple of

problems and test that. And they found out that it actually is possible for specific types of

problems, namely problems that you're able to break down in little pieces and go step by step.

You might need, as with open source, you might need people that are just kind of reorganizing

the house every once in a while. And then people throw a bunch of ideas and then you make some

progress, then you reorganize, you reframe the problem, you go step by step. But they were actually

able to prove that it is possible to collaborate online and do progress in terms of mathematics.

And so I'm confident that there are other avenues that could be explored here.

Can we talk about peer review, for example? Absolutely. I think in terms of the peer review,

I think it's important to look at the bigger picture here of what the scientific publishing

ecosystem looks like. Because for me, there are a lot of things that are wrong about that

entire process. So if you look at what publishing means in a traditional journal, you have journals

that pay authors for their articles. And then they might pay reviewers to review those articles.

And finally, they pay people or distributors to distribute the content. In the scientific

publishing world, you have scientists that are usually backed by government grants.

They are giving away their work for free in the form of papers. And then you have other

scientists that are reviewing their work. This process is known as the peer review process,

again for free. And then finally, we have government backed universities and libraries

that are buying back all that work so that other scientists can read. So this is, for me,

it's bizarre. You have the government that is funding the research, is paying the salaries

of the scientists, is paying the salaries of the reviewers, and is buying back all that

product of their work, again. And I think the problem with this system, and it's why it's so

difficult to break this suboptimal equilibrium, is because of the way academia works right now,

and the way you can progress in your academic life. And so in a lot of fields, the competition in

academia is really insane. So you have hundreds of PhD students, they are trying to get to a

professor position, and it's hyper competitive. And the only way for you to get there is if you

publish papers, ideally in journals, with a high impact factor. In computer science, it's often

conferences are also very prestigious, or actually more prestigious than journals now.

Interesting. So that's the one discipline where, I mean, that has to do with the thing we've discussed

in terms of how quickly the field turns around. But like, NeurIPS, CVPR, those conferences are

more prestigious, or at the very least, as prestigious as the journals. But yeah, but doesn't

matter. The process is what it is. And so for people that don't know, the impact factor of a

journal is basically the average number of citations that a paper would get if it gets

published on that journal. But so you can really think that the problem with the impact factor

is that it's a way to turn papers into accounting units. And let me unpack this, because it's the

impact factor is almost like a nobility title. So because papers are born with impact, even before

anyone reads them. So the researchers, they don't have the incentive to care about if this paper

is going to ever a long term impact on the world, what they care their goal, their end goal is the

paper to get published. So that they get that value upfront. So for me, that is one of the

problems of that. And that really creates a tyranny of metrics. Because at the end of the day, if

you are a dean, what you want to hire is people, researchers that publish papers on journals with

high impact factors, because that will increase the ranking of your university and will allow you

to charge more for tuition, so on and so forth. And especially when you are in super competitive

areas, that people will try to gamify that system and misconduct starts showing up. There's a really

interesting book on this topic called Gaming the Metrics. It's a book by a researcher called

Mario Biajoli. It goes a lot into the impact factor and metrics affect science negatively.

And it's interesting to think, especially in terms of citations, if you look at the early

work of looking at citations, there was a lot of work that was done by a guy called Eugene Garfield.

And this guy, the early work in terms of citation, they wanted to use citations as

a descriptive point of view. So what they wanted to create was a map. And that map would create a

visual representation of influence. So citations would be links between papers. And ideally,

what they would show, they would represent is that you read someone else's paper and it had an

impact on your research. They weren't supposed to be counted. I think this inspired Larry and

Sergei's work for Google. Exactly. I think they even mentioned that. But what happens is, as you

start counting citations, you create a market. And the same way, and the work of Eugene Garfield

was a big inspiration for Larry and Sergei and for the page rank algorithm that led to the

creation of Google. And they even recognized that. And if you think about it, it's the same way

there's a gigantic market for search engine optimization, SEO, where people try to optimize

the page rank and how a web page will rank on Google, the same will happen for papers.

People will try to optimize the impact factors and the citations that they get. And that creates

a really big problem. And it's super interesting to actually analyze the, if you look at the

distribution of the impact factors of journals, you have like nature with,

nature, I believe it's like in the low 40s. And then you have, I believe science is high 30s.

And then you have a really good set of good journals that will fall between 10 and 30.

And then you have a gigantic tell of journals that have impact factor below two. And you can

really see two economies here. You see the universities that are maybe less prestigious,

less known, that where the faculty are pressured to just publish papers, regardless of the journal,

what I want to do is increase the ranking of my university. And so they end up publishing

as many papers as they can in like journals with low impact factor. And unfortunately,

this represents a lot of the global south. And then you have the luxury good economy.

So for instance, for, and there are also problems here in the luxury good economy. So if you look

at the journal like nature, so with impact factor of like in the low 40s, there's no way

that you're going to be able to sustain that level of impact factor by just grabbing the

attention of scientists. What I mean by that is like, for the journals, the articles that

get published in nature, they need to be New York Times great. So they need to make it to the,

you know, to the, to the big media, they need to be captured by the big media. And because

that's the only way for you to capture enough attention to sustain that level of citations.

Yes. And that of course creates problems because people then will try to again gamify the system

and have like titles or abstracts or that are bigger claim, make claims that are bigger than

what is actually can be sustained by the data or the content of the paper. And you will have

clickbait titles or clickbait abstracts. And again, this is all a consequence of metrics and

science or metrics. And, and this is a very dangerous cycle that I think it's very hard to

break, but it's happening in academia in a lot of fields right now. Is it fundamentally the

existence of metrics or the metrics just need to be significantly improved? Because like I said,

the metrics used for Amazon for purchasing, I don't know, computer parts is pretty damn good

in terms of selecting which are the good ones, which are not. In that same way, if we had Amazon

type of review system in the space of ideas, in the space of science, it feels like that those

metrics would be a little bit better. Sort of when it's significantly more open to the crowdsourced

nature of the internet, of the scientific internet, meaning as opposed to like my biggest problem

with peer review has always been that it's like five, six, seven people, usually even less. And

it's often nobody's incentivized to do a good job in the whole process, meaning it's anonymous

in a way that doesn't incentivize, like doesn't gamify or incentivize great work. And also,

it doesn't necessarily have to be anonymous. Like there has to be, the entire system doesn't

encourage actual sort of rigorous review. For example, like open review does kind of incentivize

that kind of process of collaborative review, but it's also imperfect. But it just feels like

the thing that Amazon has, which is like thousands of people contributing their reviews to a product.

It feels like that could be applied to science, where the same kind of thing you're doing with

from Oz Library, but doing at a scale that's much larger. It feels like that should be possible,

given the number of grad students, given the number of general public that's, for example,

I personally, as a person who got an education in mathematics and computer science, I can

be a quote unquote reviewer on a lot bigger set of things than is my exact expertise. If I'm one

of thousands of reviewers, if I'm the only reviewer, one of five, then I better be like an expert

in the thing. And I've learned this with COVID, which is like, you can just use your basic skills

as a data analyst to contribute to the review process on a particular little aspect of a paper

and be able to comment, be able to sort of draw in some references that challenge the ideas presented

or to enrich the ideas that are presented. It just feels like crowdsourcing the review process

would be able to allow you to have metrics in terms of how good a paper is that are much

better representative of its actual impact in the world, of its actual value to the world,

as opposed to some kind of arbitrary gamified version of its impact.

I agree with that. I think there's definitely the possibility, at least for a more resilient

system than what we have today. And I think that's kind of what you're describing. To an extent,

we kind of have a little bit of a Heisenberg uncertainty principle. When you pick a metric,

as soon as you do it, then maybe it works as a good heuristic for a short amount of time,

but soon enough, people start gamifying. But then you can definitely have metrics that are more

resilient to gamification, and they'll work as a better heuristic to try to push you in the best

direction. But I guess the underlying problem you're saying is there's just shortage of positions

in academia. That's a big problem for me. Yeah. And so they're going to be constantly gamifying

the metrics. It's a very competitive field. And that's what usually happens in very competitive

fields. But I think some of the peer review problems, scale helps, I think. And it's

interesting to look at what you're mentioning, breaking it down, maybe in smaller parts and

having more people jumping in. But this is definitely a problem. And the peer review

problem, as I mentioned, is correlated with the problem of academic career progression. And it's

all intertwined. And that's why I think it's so hard to break it. There are a couple of

really interesting things that are being done right now. There are a couple of, for instance,

journals that are overlay journals on top of platforms like archive and bio archive that

want to remove like the more traditional journals from the equation. So essentially, a journal

is just a collection of links to papers. And what they're trying to do is like removing that middle

man and trying to make the review process a little bit more transparent. And not charging

universities like there's there's a couple of there are a couple of more famous ones. There's

one discrete analysis in mathematics. There's one called the quantum journal, which we're actually

working with them. We have a partnership with them for the purpose that cat published in quantum

journal, they also get the annotations on formats. And they're doing pretty well, they've been able

to grow substantially, the problem there is getting to critical mass. So it's again, convincing the

researchers and especially the young researchers that need need that impact factor need those

publications to have citations to not publish on the traditional journal and go on an open journal

and publish their work there. There I think there are a couple of really high profiled

scientists of people like team Gowers that are trying to incentivize like famous scientists

that already have tenure and that don't need that to publish that to increase the reputation

of those journals. So that other maybe younger scientists can start publishing on on those as

well. And so they can try to break that vicious cycle of of the more traditional journals.

I mean, another possible way to break this cycle is to like raise public awareness and just by force

like ban paid journals. Like what exactly are they contributing to the world? Like basically

making it illegal to forget the fact that it's mostly federally funded. So that's that's a super

ugly picture too. But like, why should knowledge be so expensive? Like where everyone is working

for the public good. And then there's these gatekeepers that you know, most people can't read

most papers without having to pay money. And that's that doesn't make any sense. That's like

that that should be illegal. I mean, that's what you're saying is exactly right. I mean, for instance,

right, I went to school here in the US, we studied in Europe. And you would sit like you'd

ask me all the time to download papers and send it to him because he just couldn't get it and like

papers that he needed for his research. And so but he's a student like he's a grad student.

He was a grad student. But that you know, I'm even referring to just regular people. Oh yeah,

okay, that too. And I think during 2020, because of COVID, a lot of journals put down the

walls for certain kind of coronavirus related papers. But like that just gave me an indication

that like, this should be done for everything. It's absurd. Like people should be outraged

that there is these gates. Because so the moment you dissolve the journals,

then there will be an opportunity for startups to build stuff on top of archive. It'll be an

opportunity for like, for my library to step up to scale up to something much even larger. I mean,

that was the original dream of Google, which I've always admired, which is make the world's

information accessible. Actually, it's interesting that Google hasn't, maybe you guys can correct me,

but they have put together Google Scholar, which is incredible. But they, and they've the scanning

of books, but they haven't really tried to make science accessible in the in the following way.

Like, besides doing Google Scholar, they haven't like, delved into the papers, right?

Which is especially curious, given what we was saying, right? That it's kind of in their genesis.

There's this, you know, research that was very connected with our papers reference each other

and like building a network out of that. Interesting enough, like Google, I think there was a,

there was not intent Google plus was like the Google social network that caught counsel was

used by a lot of researchers. Yes, it was. Whether I think was just a, you know, side,

kind of the side effect, but then a lot of people ended up migrating to Twitter,

but it was not on purpose. But yeah, I agree with you, like they haven't gone past Google Scholar.

Well, that said, Google Scholar is incredible. For people who are not familiar, it's one of the

best aggregation of all the scientific work that's out there, and especially the network

that connects all of them, what sites, what, and also trying to aggregate all of the versions of

the papers that are available there and trying to merge them in a way that one particular work,

even though it's available in a bunch of places, counts as, you know, like a central hub of what

that work is across the multiple versions. But that almost seems like a fun head project of a

couple of engineers within Google as opposed to a serious effort to make the world science

accessible. But going back to just the journals when you're talking about that Lex, I believe that

in that front, I think we might be past the event horizon. So I think the model, the business model

for the journals doesn't make sense. They are a middle layer that is not adding a lot of value.

And you see a lot of motions, whereas like in Europe, a lot of the papers that are get

are funded by the European Union, they will have to be open to the public. And I think there's a

lot of the gates to like what the Gates Foundation funds, like the demand that it's accessible to

everybody. So I think it's the question of time before that wall kind of falls. And that is going

to open a lot of possibilities. Because, you know, imagine if you had like the layer of that

gigantic layer of papers all available online, you know, that unlocks a lot of potential as a

platform for people to build things on top of that. But to what you're saying, it is weird,

like you can literally go and listen to any song that was ever made on your phone, right? You open

Spotify and you might not even pay for it. You might be on the free version and you can listen to

any song that was ever made pretty much. But there's like you don't have access to a huge

percentage of academic papers, which is just like this fundamental knowledge that we're all funding,

but you as an individual don't have access to it. And somehow, you know, like the problem for music

got solved. But for papers, it's still like... It's just not yet. It could be ad supported,

all those kinds of things. And then hopefully that would change the way we do science. That's the

most exciting thing for me is especially once I started like making videos and this silly podcast

thing, I started to realize like that if you want to do science, one of the most effective ways is

to do a like couple the paper with a set of YouTube videos, like explaining it.

That also seems like there's a lot of room for disruption there. What is the paper 2.0

going to look like? I think like LaTeX and the PDF seems like if you look at the first

paper that got published in Nature and if you look at the paper that got published in Nature

today, if you look at the two side by side, they are fundamentally the same. And even though like

the paper that gets published today, you know, you get even code like right now, people put like

code on a PDF. And there are so many things that are related to papers today. You have data,

you have code, you might need videos to better explain the concepts. So I think for me it's

natural that there's going to be also an evolution there, that papers are not going to be just the

static PDFs or LaTeX. There's going to be a next interface.

So in academia, a lot of things that are judged, your judge by is often quantity, not quality.

I wonder if there's an opportunity to have like, I tend to judge people by the best work they've

ever done as opposed to, I wonder if there's a possibility for that to encourage sort of

focusing on the quality and not necessarily in paper form, but maybe a subset of a paper,

a subset of idea, almost even a blog post or an experiment. Like why does it have to be

published in a journal to be legitimate? And it's interesting that you mentioned that I also think

like, yeah, it's why is that the only format? Why can't a blog post or we were even experimenting

with these a few months ago, can you actually like publish something or like a new scientific

breakthrough or something that you've discovered in the form of like a set of tweets,

a Twitter thread? Why can't that be possible? And we were experimenting with that idea.

We even, yeah, we ran a couple of, like some people submitted a couple of those,

like I think the limit was three or four tweets. Maybe it's a new way to look at a,

you know, a proof or something. But I think it just serves to show that there should be other

ways to publish like scientific discoveries that don't fit the paper format. Well, but so even

with the Twitter thread, it would be, it would be nice to have some mechanism of formalizing it

and making it into an NFT. Maybe. Like a concrete thing that you can reference with a link that's

unique. Because I mean, everything we've been saying, all of that while being true,

it's also true that the constraints and the formalism of a paper works well. It like forces you,

constraints forces you to narrow down your thing and literally put it on paper. But, you know,

I agree. Make concrete. And that's why, I mean, it's not broken. It just could be better.

I agree. And that's the main idea. I think there's something about writing,

whether it's a blog post or Twitter thread or a paper, that's really nice to, to concretize

a particular little idea that they can then be referenced by other ideas, then it can be built

on top of with other ideas. So let me ask, you've read quite a few papers. You've annotated quite

a few papers. Let's talk about the process itself. How do you advise people read papers?

Or maybe you want to broaden it beyond just papers, but just read concrete pieces of information to

understand the insights that lay within. I would say for papers specifically, I would bring back

kind of what Louise was talking about, is that it's important to keep in mind that papers are not

optimized for ease of understanding. And so, right, there's all sorts of restrictions and size

and format and language that they can use. And so it's important to keep that in mind. And so

that if you're struggling to read a paper, that might not mean that the underlying material is

actually that hard. And so that's definitely something that, especially for us, that we

read papers and most of the times, the papers are completely outside of our comfort zone,

I guess, and so it'd be completely new areas to us. So I always try to keep that in mind.

So there's usually a certain kind of structure, like abstract introductions, methodology,

depending on the community and so on. Is there something about the process of like

how to read it, whether you want to skim it to try to find the parts that are easy to understand

and not reading it multiple times? Is there any kind of hacks that you can comment on?

I remember like Feynman had this kind of hack when he was reading papers where he would basically,

I think I believe he would read the conclusion of the paper, and we would try to just see if he

would be able to figure out how to get to the conclusion in like a couple of minutes by himself.

And he would read a lot of papers that way. And I think Fermi also did that, almost. And Fermi

was known for doing a lot of back of the envelope calculations. So he was a master at doing that.

In terms of like, especially when reading a paper, I think a lot of times people might

feel discouraged about the first time you read it, you know, it's very hard to grasp or you don't

understand a huge fraction of the paper. And I think it's having read a lot of papers in my life.

I think I've in peace with like the fact that you might spend hours where you're just reading a

paper and jumping from paper to paper, reading citations, and like your level of understanding

sometimes of the paper is very close to 0%. And all of a sudden, you know, everything kind of

makes sense and in your mind. And then, you know, you have this quantum jump where all of a sudden

you understand the big picture of the paper. And this is an exercise that I have to when

reading papers and especially like more complex papers, like, okay, you don't understand because

you're just going through the process and just keep going. And like, it might feel super chaotic,

especially if you're jumping from reference to reference, you know, you might end up with like

20 tabs opened and you're reading a ton of other papers, but it's just trusting that process

that at the end, like you'll find light. And I think for me, that's a good framework

when reading a paper. It's hard because, you know, you might end up spending a lot of time and it

looks like you're lost. But that's the process to actually understand what they're talking about

in the paper. Yeah, I think that process, I enjoy, I've found a lot of value in the process,

especially for things outside my field, reading a lot of related work sections and kind of

going down that path of getting a big context of the field because what's, especially when

they're well written, there's opinions injected into the really related work, like what work is

important, what is not. And if you read multiple related work sections that cite or don't cite

each other, like the papers, you get a sense of where the field, where the tensions of the field

are, where the field is striving. And that helps you put into context, like whether the work is

radical, whether it's overselling itself, whether it's underselling itself, all those things.

And added on top of that, I find that often the related work section is the most kind of

accessible and readable part of a paper because it's kind of, it's brief to the point, it's trying

to, like summarizing, it's almost like a Wikipedia style article. The introduction is supposed to

be a compelling story or whatever, but it's often like overselling, there's like an agenda

introduction. The related work usually has the least amount of agenda, except for the few

like elements where you're trying to talk shit about previous work where you're trying to sell

that you're doing much better. But other than that, when you're just painting where the field

came from or where the field stands, that's really valuable. And also, again, just to agree

with fine on the conclusion. But I get a lot of value from the breadth first search, kind of

read the conclusion, then read the related work, and then go through the references in the related

work, read the conclusion, read the related work, and just go down the tree until you like hit dead

ends or run out of coffee. And then through that process, you go back up the tree, and now you can

see the results in their proper, in their proper context. Unless, of course, the paper is truly

revolutionary, which even that process will help you understand that is, in fact, truly revolutionary.

Right. You've also, you talked about just following your Twitter thread in a depth first search.

You talked about that you read the book on Grisha Perlman, Grigore Perlman, and then you had a

really nice Twitter thread on it, and you were taking notes throughout. So, at a high level,

is there suggestions you can give on how to take good notes, whether we're talking about annotations

or just for yourself to try to put on paper ideas as you progress through the work in order to then

like understand the work better? For me, I always try not to underestimate how much you can forget

within six months after you've read something. Oh, I thought you were going to say five minutes,

but yeah, six months is good. Yeah, or even shorter. And so, that's something that I always try to

keep in mind, and it's often, I mean, every once in a while, I'll read back a paper that I annotated

on Fermat, and I'll read through my own annotations, and I have completely forgotten what I had written.

It also, it's interesting because in a way, after you just understood something, you're kind of the

best possible teacher that can teach your future self after you've forgotten it, you're kind of

your own best possible teacher at that moment. And so, it can be great to try to capture that.

It's brilliant. It just made me kind of realize it's really nice to put yourself in the position

of teaching an older version of yourself that returns to this paper, almost like thinking of

literally, that's underexplored. But it's super powerful because you were the person that you

can, like, if you look at the scale from like one, not knowing anything about the topic in 10,

like you are the one that progressed from one to 10, and you know which steps you struggled with.

So, you're really the best person to help yourself make that transition from one to 10.

And a lot of the times, like, and I really believe that the framework that we have to,

like, expose ourselves to, like, be talking to us when we were an expert, when we were taking

that class, and we knew everything about quantum mechanics. And then six months later, you don't

remember half of the things. How could we make it easier for, like, to have those conversations

between you and your past self, past expert self? I think there might be, it's an underexplored

idea. I think notes on paper are probably not the best way. I'm not sure if it's a combination of,

like, video, audio, where it's like you have a guided framework that you follow to extract

information from yourself so that you can later kind of revisit to make it easier to remember.

But that's, I think, it's an interesting idea worth exploring. I haven't seen a lot of people kind

of trying to distill that problem. Yeah, I'm creating the kind of tools. I find if I record,

it sounds weird, but I'll take notes, but if I record audio, like little clips of thoughts,

like rants, that's really effective at capturing something that notes can't. Because when I replay

them, for some reason, it loads my brain back into where I was when I was reading that in a way

that notes don't. When I read notes, I'll often be like, what? What was I thinking there? But

when I listen to the audio, it brings you right back to that place. And maybe with video, with

visual, that might be even more powerful. I think so. Yeah. And I think just the process of verbalizing

it, that alone kind of makes you have to structure your thought and put it in a way that somebody

else could come and understand it. And just the process of that is useful to organize your thoughts.

Yeah, just that alone. Does the Fermat Library Journal Club have a video component or no?

Not natively. Sometimes we'll include videos, but it's always embedded. Do people build videos

on top of it to explain the paper? Because you're doing all the hard work of understanding deeply

the paper. Not, we haven't seen that happening too much. But we were actually playing around with

the idea of creating some sort of podcast version where we try to distill the paper on an audio

format that not maybe you could have access to. It might be tricky. Might be trickier, but there

are definitely people that could be interested in the paper and their topic, but are not willing to

read it. But they might listen to a 30 minute episode on that paper. Yes. You could reach more

people and you might even bring the authors to the conversation. But it's tricky and especially

for more technical papers. We've thought about doing that, but we haven't converged. I'm sure if

you have any tips. Well, I'm going to take that as a small project to take one a year. One of the

Fermat is almost like half advertisement and half as a challenge for myself to take one of the

annotated papers and use it as a basis for creating a quick video. I've seen, hopefully I'm saying

the name correctly, but machine learning street talk. I think that's the name of the show that I

recommend highly. That's the right name. But they do exactly that, which is multiple hour breakdown

of a paper with video component. Sometimes with authors, people love it. It's very effective.

There's also, I haven't seen the entire, in its entirety, but I've seen the founder of

comma.ai George. I've seen him just taking a paper and then distilling the paper and coding it,

coding it sometimes during 10 hours. He was able to get a lot of people interested in that and

viewing him. I'm a huge fan of that. George is a personality. I think a lot of people,

like, listen to this podcast for the same reason. It's not necessarily the contents. They like to

listen to a silly Russian who has a childlike brain and mumbles and all those, like, struggle with

ideas. George is a madman. People just enjoy, like, how is he going to struggle in implementing

this particular paper? How is he going to struggle with this idea? It's fun to watch and that actually

pulls you in. The personality is important there. True, but I agree with you, but

they're also, it's visible. There's an extraordinary ability that is there. It's

talented and you need to have, there's a craft. This guy definitely has talent and he's doing

something that is not easy. I think that also draws the attention of people. Oh yeah. The other

day we were actually, we ran into this YouTube channel of this guy that was restoring art.

Right. It was basically just a video of him. The production is really well done.

It's just him taking really old pieces of art and then paintings and then restoring them,

but he's really good at that and he describes that process. That draws the attention of people,

regardless of your craft. Be it, like, annotating a paper or restoring it.

Craftsmanship, excellence, yeah. George is incredibly good at programming.

You know those competitive programmers like Top Boat and all those kinds of stuff? He has the

same kind of element where the brain just jumps around really quickly and that's, yeah, just like

with art and motivation. It's motivating, but in your right, in watching people who are good

at what they do, it's motivating even if the thing you're trying to do is not what they're doing.

It's just contagious when they're really good at it. And the same kind of analysis with the paper,

I think, not just like the final result, but the process of struggling with it. That's really

interesting. Yeah. I think Twitch proved that there's really a market for that, for watching

people do things that they're really good at and you'll just watch it. You will enjoy that,

that might even spike your interest in that specific topic and yeah, and people will enjoy

watching sometimes hours on end of great craftsmen. Do you mind if we talk about some of the papers?

Do any papers come to mind that have been annotated on Formaz Library? The papers that we annotated

can be about completely random topics, but that's part of what we enjoy as well. It forces you to

explore these topics that otherwise maybe you'd never run into. And so the ones that come to mind

to me are fairly random, but one that I really enjoyed learning more about is a paper written

by a mathematician actually, Tom Apostol, and about a tunnel in a Greek island off the coast

of Turkey. It's very random. Okay, so what's interesting about this tunnel? So this tunnel

was built in the 6th century BC and it was built in the island of Samos, which is, as I said,

off the coast of Turkey. They had the city on one side, then the other mountain, and then they had

a bunch of springs on the other side, and they wanted to bring water into the city.

Building an aqueduct would be pretty hard because of the way the mountain was shaped,

and it would also, if they were under a siege, they could just easily destroy that aqueduct,

and then the city wouldn't have any water supply. And so they decided to build a tunnel,

and they decided to try to do it quickly. And so they started digging from both ends at the same

time through the mountain. And so when you start thinking about this, it's a fairly difficult

problem, and this is like 6th century BC, so you had very limited access to the mathematical tools

that you had at the time were very limited. And so what this paper is about is about the story

of how they built it, and about the fact that for about 2000 years, kind of the accepted explanation

of how they built it was actually wrong. And so this tunnel has been famous for a while. There

are a number of historians that talked about it since ancient Egypt, and the method that they

described for building it was just wrong. And so these researchers went there and were able to

figure that out. And so basically, kind of the way that they thought they had built it was,

basically, if you can imagine looking at the mountain from the top, and you have the mountain,

then you have both entrances. And so what they thought, and this is what the ancient historians

described is that they effectively tried to draw a right angle triangle with the two entrances

at each end of the hypotenuse. And the way they did it is like they would go around the mountain

and kind of walking in a grid fashion, and then you can figure out the two sides of the triangle.

And then after you have that triangle, you can effectively draw two smaller triangles at each

entrance that are proportional to that big triangle. And then you kind of have arrows pointing in each

way. And then you know at least that you have a line going through the mountain that connects

both entrances. The issue with that is once you go to this mountain and you start thinking of

doing this, you realize that especially given that the tools that they had at the time,

that your error margin would be too small, you wouldn't be able to do it. Just the fact of

trying to build this triangle in that fashion, the error would accumulate and you would end

up missing. You'd start building these tunnels and they would miss each other.

So the task ultimately is to figure out really perfectly as close as possible the direction

you should be digging. First of all, that it's possible they have a straight line through

and then what that direction would be. And then you are trying to infer that by constructing a

right triangle. I'm not exactly sure about how to do that rigorously by tracing the mountain,

by walking along the mountain. You said grids. Yeah, you kind of walk as if you were in a grid.

And so you just walk in right angles. But then you have to walk really precisely then.

You have to use tools to measure this. And then the terrain is probably a mess. So this

makes more sense than 2D and 3D gets even weirder. So okay, gotcha.

But so this method was described by like an ancient Egyptian historian, I think, hero of

Alexandria. And then for about, yeah, for about 2000 years, that's how we thought that they had

built this tunnel. And then these researchers went there and found out that actually they

must have had to use other methods. And then in this paper, they describe these other methods.

And of course, they can't know for sure, but they present a bunch of plausible alternatives.

The one that for me is the most plausible is that what they probably must have done is to

use something that is similar to an iron sight on a rifle, the way you can line up

your rifle with a target off in the distance by having an iron sight.

And they must have done something similar to that, effectively with three sticks.

And that way they were able to line up sticks along the side of the mountain that were all

on the same height. And so that then you could get to the other side. And you could and then you

could draw that line. So this for me is the most plausible way that they might have done that.

And they but then they they they describe this in detail and other possible approaches in this

paper. So this is a mathematician doing this? Yeah, this is a mathematician that did this.

Which I suppose is the right mindset instead of skills required to solve an ancient problem,

right? Yeah. Mathematicians and engineers, a lot of things. Because they didn't have computers or

drones or LiDAR back then or whatever technology you would use modern day for civil engineering.

Yeah. And another fascinating thing is that like, you know, after effectively after the

downfall of the Roman civilization, people didn't build tunnels for about a thousand years. We go

a thousand years without tunnels. And then like only in late middle ages that we start doing them

again. But but here is the tunnel like 6th century BC, like incredibly limited mathematics.

And they and they build it in this way. And for and it was a mystery for a long time exactly how

they did it. And then these mathematicians went there. And basically with no archaeology kind

of background, we're able to figure it out. How do annotations for this paper look like?

What is it? What's a successful annotation for paper like this?

Yes. So sometimes you're for this paper, sometimes adding some more context on a specific

part like sometimes they mentioned, for instance, these instruments that were common in ancient Greece

and ancient Rome for for building things. And and so in some of those annotations,

I described these instruments in more detail and how they worked. Because sometimes it can be

hard to visualize these. Then this paper, I forget exactly when this was published,

I believe maybe maybe the 70s. But then there was further research into this tunnel and more

interesting other interesting aspects about it. I add those to that paper as well. There's historical

context that I also go into there. For instance, the fact that as I said that effectively after

the downfall of the Roman Empire, no tunnels were built. That's something that I added to the paper

as well. So when other people look at the paper, how do they usually consume the annotations?

Is there a commenting feature? This is a really enriching experience the way you read a paper.

What aspects do people usually talk about that they value from this?

So yeah, so anybody can just go on there and and either add a new annotation or

other comments to an existing annotation. And so you can start kind of a thread within an

existing annotation. And that's something that happens relative frequency. And then

because I was the original author of the initial annotation, I get pinged. And so oftentimes I'll

go back and add on to that thread. How'd you pick the paper? I mean, first of all, this whole process

is really exciting. I'm gonna, especially after this conversation, I'm gonna make sure I participate

much more actively on papers that I know a lot about. And on paper, I know nothing about. I should

get the annotated paper. I would love to. I also, I mean, I realized that there's like it's an

opportunity for people like me to publicly annotate a paper, like, or do an AMA around the

paper. Yeah, exactly. But yeah, but like be, be in the conversation about a paper. It's like a

place to have a conversation about an idea. You get the other way to do it that's much more at

hawk is on Twitter, right? But this is more like formal. And you could actually probably integrate

the two have a conversation about the conversation. So the Twitter is the conversation about a

conversation and the main conversation is in the space of annotations. There's an interesting

effect that we see sometimes with the annotations on our papers is that a lot of people, especially if

we the annotations are really well done, people sometimes are afraid of adding more annotations

because they see that as a kind of a finished work. Yes. And so they don't want to pollute that or

and especially if it's like a silly question, this is, I don't think that's good. I think, you know,

we should as much as possible try to lower the barrier for someone to jump in and ask questions.

I think it only like most of the times it adds value, but it's some feedback that we got from

users and readers. Not exactly sure how to, to kind of fight that. But well, I think I,

I think if I serve as an inspiration in any way is by asking a lot of dumb questions and saying a

bunch of dumb shit all the time. And hopefully that inspires the rest of the other folks to

do the same because that's the only way to knowledge, I think, is to be willing to ask the

dumb questions. And there are papers that are like, you know, we have a lot of papers on Formaz

where it's just one page or really short papers. And we have like the shortest paper ever published

in a math journal. Like we'd like just a couple of words. One of my favorite papers on the platform

is actually a paper written by Enrico Fermi. And the title of the paper is My Observation,

I think is My Observations at Trinity. So basically Fermi was part of the Manhattan Project. So

he was in New Mexico when they exploded the first atomic bomb. And so he was a couple of miles away

from the explosion. And he was probably one of the first persons to calculate the energy of the

explosion. And so the way he did that was he took a piece of paper and he tore down a piece of

paper in little pieces. And when the bomb exploded, the Trinity bomb was the name of the bomb,

like he waited for the blast to arrive at where he was. And then he threw those pieces of paper in

the air. And he calculated the energy based on the displacement of the paper, the pieces of paper.

And then he wrote a report which was classified until like a couple of years ago, one page report,

like calculating the energy of the explosion. So badass. And we actually went there and kind of

unpacked and I think he just mentions basically the energy and we actually went and one of the

annotations is like explaining how he did that. I wonder how accurate he was. It was maybe I

think like 20 or 25% off. Then there was another person that actually calculated the energy based

on images after the explosion at the rate and the rate at which the like the mushroom of the

explosion expanded. And it's more accurate to calculate the energy based on that. And I think

it was like 20% off. But it's really interesting because you know, Fermi was known for all these

being a master at these back of the envelope calculations. Like the Fermi problems are well

known for that. And it's super interesting to see like that just one page report that was also

actually classified. And it's interesting because a couple of months ago, when the Beirut explosion

happened, there was a video circulating of these a bride that was doing a photo shoot

when the explosion in Beirut happened. And so you can see a video of her with the wedding dress

and then the explosion happens and the blast arrives at where she was. She was a couple of

miles away from the blast. And you can see like the displacement of the dress as well. And I

actually looked and that video went viral on Twitter. And I actually looked at that video and

based I use the same techniques that Fermi used to calculate the energy of the explosion

based on the displacement of the dress. And you could actually see where she was at the

the distance from the explosion because there was a store behind her and you could look the name of

the store. And so I calculated that it was the distance and then you can based on the distance

where she was from the explosion and also on the displacement of the dress. Like because you can

when the blast happens, like you can see the dress going back and then going back to the

original position and like by just looking at like how much the dress moved, you can

estimate the explosion, the energy of the explosion. I assume you published this

on Twitter. It was just a Twitter thread. But it actually like a lot of people share that and

it was picked up by a couple of news outlets. But I was hoping it would be like a formal title

and it would be an archive. No, no, no, it may be submitted just to Twitter, Twitter thread.

But it was interesting because it was exactly the same math that Fermi used.

Is there something else that jumps to mind? Like what is there something I know like in terms of

papers, like I know the Bitcoin paper is super popular. Is there something interesting to be

said about any of the white papers in the cryptocurrency space?

Yeah, the Bitcoin paper was the first paper that we put on Fermat's. Why that choice as the first

paper? This was a while ago. And it was one of the papers that I read and then kind of explained

it to Luiz and there are two other friends that do this journal club with us. And I did some research

in cryptography as an undergrad. And so it was a topic that I was interested in. But even for me,

that I had that background, but reading the Bitcoin paper, it took me a few reads to really kind of

wrap my head around it. It uses very Spartan, precise language in a way. You feel like you

can't take any word out of it without something falling apart. And it's all there. I think it's

a beautiful paper and it's very well written, of course. But we wanted to try to make it accessible

so that anybody that maybe is an undergrad and computer science could go on there and know that

you have all the information in that page that you're going to need to understand the mechanics

of Bitcoin. And so I explain the basic public key cryptography that you need to know in order to

understand it. You can explain, okay, what are the properties of a hash function and how they

are useful in this context? Explain what a Merkle tree is. So a bunch of those basic concepts that

maybe if you're reading it for a first time and you're an undergrad and you don't know those

terms, you're going to be discouraged because maybe, okay, now I have to go and Google around

until I understand these before I can make progress in the paper. And this way, it's all there.

So there's a magic to also to the fact that over time, more people went on there and added

further annotations. So the idea that the paper gets easier and more accessible over time, but

that's still you're still looking at the original content, the way the author intended it to be.

But there's just more context and the toughest bits have more in depth explanations.

Okay, I think like there's just so many interesting papers there. I remember reading the paper that

was written by Freeman Dyson on the like the first time that he explained or he came up with a concept

of the Dyson sphere. And he put that out like it's again, it's one page paper. And what he explained

was that eventually, if civilization develops and grows, there's going to be a point where

when the resources on the planet are not enough for the energy requirements of that civilization.

So if you want to go, the next step is you need to go to the next star and extract energy from

that star. And the way to do it is you need to build some sort of cap around the star that

extracts the energy. So he theorized this idea of the Dyson sphere. And he went on to kind of

analyze how he would build that, the stability of that sphere, like if something happens, like

if there's like a small oscillation with that fear collapsing to the star or no, what would happen.

And he even went on to kind of say that a good way for us to look for signs of intelligent life out

there is to look for signals of these Dyson spheres. And because, you know, according to the law of

second law of thermodynamics, like there's going to be some a lot of infrared radiation,

there is going to be emitted as a consequence of extracting energy from the star. And we should

be able to see those signals of like infrared if we look at the sky. But all these like from

the introduction of the concept, like how to build a Dyson sphere, the problems of like having a

Dyson sphere, how to detect how that could be used as a signal for intelligence life.

Wait, really? That's all in the paper?

All in one, like one page paper. And it's like, it's, for me, it's beautiful.

It's like...

What was this published?

Don't remember.

It's fascinating that papers like that could be, I mean, the guts it takes to put that all

together in a paper form. You know, that kind of challenges our previous discussion of paper.

I mean, papers can be beautiful. You can play with the format, right?

But there's a lot to unpack there. That's like the starting point. But it's beautiful that

you're able to put that in one page. And then people can build on top of that.

But the key ideas are there.

Yeah, exactly.

What about, have you looked at any of the big seminal papers throughout the history of science?

Like you look at simple like Einstein papers.

Have any of those been annotated?

Yeah, we have some more seminal papers that people have heard about.

We have the DNA double elix paper on there. We have the Higgs boson paper.

Yeah, there's papers that we know that they're not going to be finding out about them because

of us. But it's papers that we think should be more widely read and that folks would benefit

from having some annotations there. And so we also have a number of those.

A lot of discovery papers for fundamental party calls and all that. We have a lot of those on

from us library. We haven't annotated that one, but I'd like to on the Riemann hypotheses.

That's a really interesting paper as well. But we haven't annotated that one.

But there's a lot of like more historical landmark papers on the platform.

Have you done Poincaré conjecture with Perlman?

That's too much for me. But it's interesting that in going back to our discussion,

like the Poincaré paper was like published on archive and it was not on a journal,

like the three papers.

Yeah, what do you make of that? I mean, he's such a fascinating human being.

I mentioned to you offline that I'm going to Russia. He's somebody I'm really...

She tried to interview. Yeah. Well, so I definitely will interview him.

And I believe I will. I believe I can. I just don't know how to... I know where you live.

Okay. My hope is, my conjecture is that if I just show up to the house and look desperate enough

that or threatening enough for some combination of both, that like the only way to get rid of me

is to just get the thing done. That's the hope.

It's sexually interesting that you mentioned that because after...

So a couple of weeks ago, I was searching for stuff about Perlman online and I ended up on

this Twitter account of this guy that claims to be Perlman's assistant.

And he has been posting a bunch of pictures next to Perlman.

You can see Perlman in a library and he's next to him taking a selfie or Perlman walking

on the street and like, maybe you could reach out to his assistant and I'll send you...

I'll send you this Twitter account. Maybe you're onto something.

No, but going back to Perlman is super interesting because the fact that he published the proofs

on archive was also like a way for him to... Because he really didn't like the scientific

publishing industry and the fact that you had to pay to get access to articles.

And that was a form of protest and that's why he published those papers there.

I mean, I think Perlman is just a fascinating character and for me, it's this kind of ideal of...

Platonic ideal of what a mathematician should be. It's someone that is...

It just cares about... Deeply cares about mathematics.

It cares about fair attribution of disregard's money.

And the fact that he published on archive is a good example of that.

What about the Fields Medal that he turned down the Fields Medal? What do you make of that?

If you look at the reasons why he rejected the Fields Medal, so Perlman did a post talk in the

US and when he came back to Russia... Do you know how good his English is?

I think it's fairly good.

I think it's pretty good, right?

I think it's really good.

Especially in these given lectures in the American Union.

But I haven't been able to...

Listen to anything.

Well, certainly not listen, but I haven't been able to get anybody,

because I know a lot of people have been to those lectures.

I'm not able to get a sense of like, yeah, but how strong is the accent?

What are we talking about here? Is this going to have to be in Russian?

Is it going to have to be in English? It's fascinating.

But he writes the papers in English, so...

True. But there's so many... It's such a fascinating character and

there are a couple of examples like him, like at I think 28 or 29, he proved like a really

famous conjecture called the Soul Conjecture.

I believe it was like in a very short four page proof of that.

It was a really big breakthrough.

Then he went to Princeton to give a lecture on that.

And after the lecture, the chair of the math department at Princeton, a guy called Peter

Sarnak, went up to Perlman was trying to recruit him.

Trying to offer him a position at Princeton.

And at some point, he asked for Perlman's resume.

And Perlman responded saying, just give a lecture on like this really tough problem.

Why do you need my resume? I'm not going to send you.

Like I just proved my value.

But going back to the Fields Medal, like when Perlman went back to Russia,

he arrived at a time where the salary of postdocs was so much off in regards to

inflation that they were not making any money.

Like people didn't even bother to pick up the checks at the end of the month

because it was like ridiculous.

But thankfully he had some money that he had gained while he was doing his postdocs.

So he just concentrated on like the Pankara conjecture problem.

Which he, when he took that, it took it after it was reframed by this

mathematician called Richard Hamilton, which posed the problem in a way that it turned into

this super math Olympiad problem with perfect boundaries well defined.

And that was perfect for Perlman to attack.

And so he spent like seven years working on that.

And then in 2002, he started publishing those papers on archive.

And people started jumping on that, reading those papers.

And there was like a lot of excitement around that.

A couple of years later, there were two researchers, I believe it was,

they were from Harvard that took Perlman's work.

They sanded some of the edges.

And they republished that saying that based on Perlman's work,

they were able to figure out the Pankara conjecture.

And then there was at the time at the International Conference of Mathematics in 2006,

I believe that's when they were going to give out the Fields Medal.

There was a lot of debate of who should get the credit for solving this big problem.

And for Perlman, it felt really sad that people were even considering that

he was not the person that solved that.

And the claims that those researchers, when they published after Perlman,

they were false claims that they were the ones, they just sanded a couple of edges.

Like Perlman did all the really hard work.

And so just the fact that they doubted that Perlman had done that was enough for him to say,

I'm not interested in this prize.

And that was one of the reasons why he rejected the Fields Medal.

Then he also rejected the Clay Prize.

So the Pankara conjecture was one of the Millennium Prizes.

There was a million dollar prize associated with that problem.

And that has to do with the fact that for them to attribute that prize,

I think it had to be published on a journal, the proof.

And again, Perlman's principles have interfered here.

And he also just didn't care about the money.

He was like, Clay, I think was a businessman.

And he's like, doesn't have to do anything with mathematics.

I don't care about this.

That's one of the reasons why he rejected the Prize.

Yeah, it's hard to convert into words, but at MIT,

I'm distinctly aware of the distinction between when I enter a room,

there's a certain kind of music to the way people talk when we're talking about ideas

versus what that music sounds like when we're talking, when it's like bickering

in the space of like, whether it's politics or funding or egos.

It's a different sound to it.

And I'm distinctly aware of the two.

And I kind of sort of, to me personally, happiness was just like swimming around

the one that like is the political stuff or the money stuff and all that or egos.

And I think that's probably what Perlman is as well.

Like the moment he senses there's any, as with the Fields Medal,

like the moment you start to have any kind of drama around credit assignment,

all those kinds of things, it's almost not that it's important who gets the credit.

It's like the drama in itself gets in the way of the exploration of the ideas

or the fundamental thing that makes science so damn beautiful.

And you can really see that there's also a product of that Russian school of like doing science.

And you can see that, that people were, during the Cold War, a lot of mathematicians,

they were not making any money.

They were doing math for the sake of math, like for the intellectual pleasure

of like solving a difficult problem.

And even if it was a flawed system and there were a lot of problems with that,

they were able to actually achieve these and Perlman for me is the perfect product of that.

He just cared about like working on tough problems.

He didn't care about anything else.

It was just math, pure math.

Yeah, there's like for the broader audience, I think another example of that is like professional sports versus Olympics.

Especially in Russia, I've seen that clear distinction where because the state manages

so much of the Olympic process in Russia, as people know with the steroids, yes, yes, yes.

But outside of the steroids thing is like the athlete can focus on the pure artistry of the sport.

Like not worry about the money, not just in the way they talk about it, the way they think about it,

the way they define excellence versus like in the perhaps a bit of a capitalist system

in the United States with American football, with baseball, with basketball.

So much of the discussion is about money.

Now, of course, at the end of the day, it's about excellence and artistry and all that.

But when the culture is so richly grounded in discussions of money and sort of this capitalistic like

merch and businesses and all those kinds of things, it changes the nature of the activity.

In a way that's hard again to describe in words, but when it's purely about the activity itself,

it's almost like you quiet down all the noise enough to hear the signal, enough to hear the beauty.

Like whenever you're talking about the money, that's when the marketing people come and the

business people, the non creators come and they fill the room and they create drama and they know

how to create the drama and the noise as opposed to the people who are truly excellent at what they

do, the person in their arena, right? Like when you remove all the money and you just let that

thing shine, that's when true excellence can come out. And that was of the few things that

worked with the communist system in the Soviet Union to me at least as somebody who loves sport

and loves mathematics and science, that worked well. Removing the money from the picture.

Not that I'm saying poverty is good for science. There's some level in which not worrying about

money is good for science. It's a weird, I'm not exactly sure what to make of that because

capitalism works really damn well. But it's tricky how to find that balance.

One Fields Medalist that is interesting to look at, and I think you mentioned it earlier,

but is Cedric Villani, which might be the only Fields Medalist that is also a politician now.

But so it's this brilliant French mathematician that won the Fields Medal. And after that,

he decided that one of the ways that he could have the biggest leverage in pushing science in

the direction that he thinks science should go would be to try to go into politics. And so that's

what he did. And he has ran, I'm not sure if he has won any election, but I think he's running for

a mayor of Paris or something like that. But it's this brilliant mathematician that before

winning the Fields Medal had only been just a brilliant mathematician. But after that,

he decided to go into politics to try to have an impact and try to change some of the things that

he would complain about before. So there's that component as well.

Yeah. And I've always thought mathematics and science should be like, James Bond

would, in my eyes, I think be sexier if he did math. We should, as a society, put

excellence in mathematics at the same level as being able to kill a man with your bare hands.

Those are both useful features. That's admirable. It's like, oh, that makes the person interesting.

Being extremely well read about history or philosophy, being good at mathematics, being

able to kill a man with bare hands. Those are all the same in my book. So I think all are useful

for action stars. And I think a society will benefit for giving more value to that. One of

the things that bothers me about American culture is the, I don't know the right words to use,

but the nerdiness associated with science. I don't think nerd is a good word in American

culture because it's seen as weakness. There's images that come with that. And it's fine. You

could be all kinds of shapes and colors and personalities, but to me, having sophisticated

knowledge of science, being good at math, doesn't mean you're weak. In fact, it could be the very

opposite. And so it's an interesting thing because it was very much differently viewed in the Soviet

Union. So I know for sure as an existence proof that it doesn't have to be that way.

I also feel like we lack a lot of role models. If you ask people to mention one mathematician

that they know that he's alive today, I think a lot of people would struggle to answer that question.

And I also think, I love Neil deGrasse Tyson. But there is, having more role models is good,

like different kinds of personalities. He has kind of fun and it's like Bill Nye the

science guy. I don't know if you guys know him. But Feynman is no longer there.

Those kinds of personalities. Carl Sagan, man. Even Carl Sagan, yeah. A seriousness that's not

playful. Not apologetical. Yeah, exactly. Not apologetic about being knowledgeable.

In fact, the kind of energy where you feel self conscious about not having thought about

some of these questions. Just like when I see James Bond, I feel bad about that I don't

have never killed a man. I need to make sure I fix that. That's the way I feel. So the same

way I want to feel like that way. Well, Carl Sagan talks, I feel like I need to have that

same kind of seriousness about science. Like if I don't know something, I want to know it well.

What about Terence Tao? He's kind of a superstar. What are your thoughts about him?

True. It's probably one of the most famous mathematicians alive today. And probably one of

I mean, regardless of like, is of course, he won a Fields Medal, is really smart and talented

mathematician. It's also like a big inspiration for us, at least for some of the work that we

do with Formaz Library. So Terence Tao is known for having, you know, a big blog and is pretty open

about like his research and he also he tries to make his work as public as possible through his

blog posts. In fact, there's a really interesting problem that got solved a couple of years ago.

So Tao was working with on a problem on an Erdos problem, actually. So Paul Erdos was this

mathematician from Hungary. And he was known for like the Erdos for a lot of things. But

one of the things that he was also known was for the Erdos problem. So he was always like

creating these problems and usually associating prizes with those problems.

And a lot of those problems are still open. And some of them will be open for like maybe

a couple of hundred years. And I think that's actually an interesting hack for him to collaborate

with future mathematicians. You know, his name will keep coming up for future generations. But so

Tao was working on one of these problems called the Erdos discrepancy. And he published a blog

post on like about that problem about that problem. And he reached it like a dead end. And then all of

a sudden there was this guy from Germany that wrote like a comment on his blog post saying,

okay, like some of the so this problem is like a Sudoku like flavor and some of the machinery

that we're using to solve Sudoku could be used here. And that was actually the key to solve the

Erdos discrepancy problem. So there was a comment on his blog. And I think that that that for me is

an example of like how to do again, going back to collaborative science online, and the power that

it has. But Tao is also like pretty public about like some of the struggles and of being a mathematician,

like and even he wrote about some of the unintended consequences of having extraordinary

ability in a field. And he used himself as an example when he was growing up, he was extremely

talented in mathematics from a young age like Tao was a person he won an medal in like one of the

IMOs at the age I think was a gold medal at the age of 10 or something like that. And so he mentioned

that when he was growing up, like and especially in college, when he was in a class that he enjoyed,

it didn't it just came very natural for him and it didn't have to work hard to just ace the class.

And when he found that the class was boring, like it didn't work and he barely passed, barely passed

I think in college he almost failed two classes. And he was talking about that and how he brought

those studying habits or like in existence of studying habits when he went to Princeton for

his PhD. And in Princeton, when he started kind of delving into more complex problems

in classes, he struggled a lot because he didn't have that those those habits like it wasn't taking

notes and it was it wasn't studying hard when he when he faced problems. And he almost failed out of

his PhD. He almost failed his PhD exam. And it talks about like having this conversation with

this advisor and the advisor pointing out like you're not this is not working, you might have to get

out of the program. And like how that was a kind of a turning point for him. And like it was super

important in his career. So I think Tao is also like this figure that apart from being just an

exceptional mathematician is also pretty open about what what it takes to to to be a mathematician

and some of the struggles of these type of careers. And I think it's that's super important.

In many ways, he's a contributor to open science and open humanity. He's being an open human.

True. By communicating Scott Aronson is another in computer science world who's a very different

style, very different style. But there's something about a blog that is authentic and real and just

gives us a window into the into the mind and soul of all of these brilliant folks. So it's

definitely a gift. Let me ask you about Fermat's library on Twitter, which I mean, I don't know

how to describe it, people should definitely just follow from my library on Twitter. I keep

following and unfollowing from my library because because it's so it gives when I follow it leads

me on down rabbit holes often that that are very fruitful. But anyway, so the the posts you do

with the on Twitter are just these beautiful are things that reveal some beautiful aspect of

mathematics. Is there is there something you could say about the approach there? Yeah. And maybe

maybe broadly what you find beautiful about mathematics and then more specifically how

you convert that into a rigorous process of revealing that in tweet form.

That's a good point. I think there's something about math that, you know, a lot of the mathematical

content and you know, paid papers or like little proofs, you know, has in a way sort of an infinite

off life. What I mean by that is that if you look at like Euclid's elements, it's as valid today as

it was when he was created like 2000 years ago. And that's not true for a lot of other scientific

fields. And so in regards to Twitter, I think there's also a very it's a very undexed underexplored

platform from a learning perspective. I think if you look at content on Twitter, it's very easy to

consume. It's very easy to read. And especially when you're trying to explain something, you know,

we humans get a dopamine hit if we learn something new. And that's a very, very powerful feeling.

And that's why, you know, people go to classes when you have a really good professor, you know,

it's looking for those dopamine hits. And that's something that we try to explore when

we're producing content on Twitter. Imagine if we could, if you would on a line to a restaurant,

you could go to your phone to learn something new instead of going to a, you know, social network.

And so, and I think it's very hard to sometimes to kind of provide that feeling because you need to

sometimes digest content and put it in a way, you know, that it feeds 280 characters. And it

requires a lot of sometimes time to do that, even though it's easy to consume, it's hard to make.

But once you are able to provide that eureka moment to people, like that's very powerful,

they get that dopamine hit and like you create this feedback cycle and people come back for more.

And in Twitter, compared to like, you know, an online course for a book, you have a 0%

dropout. So people will read the content. So that it's like, it's part of the creators,

like the person that is creating the content, if you're able to actually get that feedback cycle,

it's super, super powerful. Yeah. But some of this stuff is like, like, how the heck do you find

that? And I don't know why it's so appealing. Like, this is from a, what is it? A couple days ago,

I'll just read out the number 23456789 is the largest prime number with consecutive

increasing digits. I mean, that is so cool. That's like some weird like glimpse into some

deep universal truth, even though it's just a number. I mean, that's like so arbitrary. Like,

why, why is it so pleasant that that's a thing, but it is in some way, it's almost like it is

a little glimpse at some much bigger, like. And I think like, especially if we're talking about

science, there's something unique about you go and with a lot of the tweets, you go sometimes from

a state of not knowing something to knowing something. And that is very particular to science,

science, math, physics. And that, again, is extra extremely addictive. And that's that's how I feel

about that. And that's why I think people engage so much with our tweets and go into rabbit holes.

And then they, you know, we start with prime numbers. And all of a sudden, you are spending hours

reading number theory things, and you go into Wikipedia. And it was a lot of time there.

Well, the variety is really interesting, too. There's human things. There's, there's physics

things. There's like numeric things, like I just mentioned, but there's also more rigorous

mathematical things. There's stuff that's tied to the history of math and the proofs. And

there's visual, there's animations. There are looping animations that are incredible,

that reveal something. There's Andrew Wiles on being smart. This is just me now, like,

ignoring you guys and just going through. No, yeah, we're a bit like math drug dealers. We're

just trying to get you hooked. We're trying to give you that hit and trying to get you hooked.

Yes, some people are brighter than others. But I really believe that most people can really get

to to quite a good level of mathematics if they're prepared to deal with these

psychological issues of how to handle the situation of being stuck.

Yeah, there's some truth to that. That's truth. I feel that's like really,

that's some truth in terms of research and also about startups. You're stuck a lot of the time

before you get to a breakthrough. And it's difficult to endure that process of like being stuck

because you're not trying to to be in that position. I feel, yeah, that's

Yeah, most people are broken by the stuckness or like their district. Like,

uh, I've been very cognizant of the fact that more and more social media becomes a thing.

Like distractions become a thing that that moment of being stuck is your mind wants to

to go do stuff that's unrelated to being stuck and you should be stuck. I'm referring to small

stucknesses. Like you're like trying to design something and it's a dead end, basically little

dead ends, dead ends and programming dead ends and trying to think through something. And then

your mind wants to like, like, like, uh, this is the problem with this like work life balance

culture is like take a break. Like as if taking a break will solve everything. Sometimes it solves

quite a bit, but like sometimes you need to sit in the stuckness and suffer a little bit and then

take a break. But you definitely need to be and like most people quit from that psychological

battle of being stuck. So success is people who, who, who, uh, persevere through that.

Yeah. Yeah. And in the creative process, that's also true. I was the other day I was, I think

was reading about this. What is his name? Ed Sheeran, like the musician was talking a little

bit about the creative process and using was using this analogy of a faucet, like

where you, when you turn on a faucet is as like the dirty water coming out in the beginning.

And you just have to, you know, keep trusting that at some point you're clean, clean, clear water

will come out, but you have to endure that process. Like in the beginning it's going to be dirty water

and, and, and just, you know, embrace that. Yeah. Actually this, uh, the entirety of my

YouTube channel and this podcast have been following that philosophy of dirty water.

Like I've been, you know, I do believe that like you have to get all the crap out of your system

first. And uh, sometimes it's all, it's all, sometimes it's all crappy work. I mean, I tend

to be very self critical, but I do think that quantity leads the quality for some people.

It does for my, the way my mind works is like just keep putting stuff out there, keep creating

and, uh, the quality will come as opposed to sitting there waiting, not doing anything until

the thing seems perfect because the perfect may never come.

But just, just on like, on, on, on our Twitter, uh, like profile, I really, and sometimes when

you look on, on some of those tweets, they might seem like pretty kind of, um, you know,

why is this interesting? It's like so raw, uh, like it's just a number. But I really believe

that especially with math or physics, it is possible to get everyone to love math or physics,

even if you think you hate it. It's, it's not a function of the student or the person that is

on the other side. I think it's just purely a function of like how you explain, uh, hidden

beauty that they hadn't realized before. It's not easy, but I think it's like a lot of the times

is on, like on the creator side to, to be able to like show that beauty to the other person.

I think some of that is native to, to humans. We just have that curiosity and you look at

small toddlers and babies and like them trying to figure things out. And there's just something

that is born with us that we, we, we want for that understanding. We want to figure out the

world around us. And, and so yeah, it shouldn't be like, uh, whether or not people are going,

are going to, to enjoy it. Like I also really believe that everybody has that capacity to

fall in love with, with math and physics. You mentioned startup. What do you think it takes

to build a successful startup? Yeah, that it's what, what Louise was saying that, um, you need to

end, to be able to endure being stuck. And, and I think the best way to put it is that startups

don't have a linear reward function, right? You oftentimes don't get rewarded for effort.

And, and, and in most of our lives, we go through these processes that do give you those small

rewards for effort, right? In school, you study hard, generally you'll get a good grade. And

then you get, you get like good grades ever, or you get grades every semester. And so you're,

you're slowly getting rewarded and pushed in the right direction for, for startups. And startups

are not the only thing that is like this, but for startups, it's, you know, you can put in a ton of

effort into something that, and then get no reward for it, right? It's, it's like, like Sisyphus

Boulder, where you were pushing that Boulder up the mountain and, and, and you get to the top

and then it just rolls all the way back down. And, and so that's something that I think a lot of

people are not equipped to deal with and can be incredibly demoralizing, especially if that happens

more than, than a few times. And so, but I think it's absolutely essential to, to power through it,

because by the nature of startups, it's often times, you know, you're dealing with, with,

with non obvious ideas and things that, that might be contrarian. And so you're gonna, you're

gonna run into, into that a lot. You're gonna do things that are not going to work out. And you

need to be prepared to deal with that. But, but if we're not coming out of college, you're, you're

just not equipped. I'm not sure if there's a way to train people to deal with those non linear

reward functions, but it's definitely, I think, one of the most difficult things to, you know,

about doing a startup. And also happens in research. Sometimes, you know, we're talking

about the default state is being stuck. You just, you know, you don't know, like you try things,

you get zero results, you close doors, you constantly closing doors, until you, you know,

find something. And yeah, that is a big thing. What about sort of this point when you're stuck,

there's a kind of decision, whether you, if you have a vision to persist through, through with

this direction that you've been going along, or what a lot of startups do or businesses pivot.

How do you decide whether like, to give up on a particular flavor of the way you've imagined

the design, and to like, adjust it or completely, like, alter it.

I think that's a core question for startups that I've asked myself exactly. And like, I've never

been able to come up with a great framework to make those decisions. I think that's really at the

core of, yeah, out of a lot of the toughest questions that, that people that's that started a

company have to deal with. I think maybe the best framework that I was able to figure out is like,

I was able to figure out is like, when you run out of ideas, you just, you know, you're exploring

something is not working, you try it in a different angle, you know, we try different business model.

When you run out of ideas, like, you don't have any more cards, just switch and yeah, it's not

perfect. Because you also, it's, you have a lot of stories of startups was like,

people kept pushing, and then, you know, that paid off. And then you have philosophies is like,

fell fast, and pivot fast. So it's, you know, it's hard to, you know, balance these two worlds

and understand what is the best framework. And I mean, if you look at Fermat's library, you're,

maybe you can correct me, but it feels like you're an operating in a space

where there's a lot of things that are broken, and or could be significantly improved. So it

feels like there's a lot of possibilities for pivoting. Or like, how do you revolutionize

science? How do you revolutionize the aggregation, the, the annotation, the commenting the community

around information of knowledge, structured knowledge? I mean, that's kind of what like

Stack Overflow and Stack Exchange has struggled with to come up with a solution. And they've come

up, I think with an interesting set of solutions, that are also, I think flawed in some ways,

but they're much, much better than the alternatives. But there's a lot of other possibilities. If we

just look at papers, as we talked about, there's so many possible revolutions, and there are a lot

of money to be potentially made as revolutions, plus coupled with that, the benefit to humanity.

And so like, you're sitting there, like, I don't know how many people are legitimately,

from a business perspective, playing with these ideas. It feels like there's a lot of ideas here.

True. There is.

Are you right now grinding in a particular direction? Like, is there a like a five year

vision that you're thinking in your mind? For us, it's more like a 20 year vision,

in the sense that we've consciously tried to make the decision of, so we run Formats as

it's a side project. And it's a separate in the sense like it's not what we're working on full

time. And, but our thesis there is that we actually think that it's that's a good thing,

at least for this stage of Formats library. And also because some of these projects,

you just, if you're coming from a start, from a startup framework, you probably try to try to fit

every single idea into something that can change the world within three to five years. And there's

just some problems that take longer than that. Right. And so, you know, we're talking about

archive, and I'm very doubtful that you could grow like archive into what it is today, like within

two or three years, no matter how much money you throw at it, there's just some things that can

take longer. But you need to be able to power through the time that it takes. But if you look

at it as it's okay, this is a company, this is a startup, we have to grow fast, we have to raise

money, then then sometimes you might forgo those ideas because of that, because they don't very

well fit into the typical startup framework. And so, for us, Formats, it's something that we're

okay with growing, with having it grow slowly, and maybe taking many years. And that's why we

think it's not a bad thing that it is a side project, because it makes it much more acceptable

in a way to be able to be okay with that. That said, I think what happens is if you keep pushing

new little features, new little ideas, I'd feel like there's like certain ideas will just become

viral. And then you just won't be able to help yourself, but it'll revolutionize things. It

feels like there needs to be, not needs to be, but there's opportunity for viral ideas to change

science. Absolutely. And maybe we don't know what those are yet. It might be a very small kind of

thing. Maybe you don't even know if should this be a for profit company doing these? It's the

Wikipedia question. Yeah. There are a lot of questions, like really fundamental questions

about this space that we've talked about. I mean, you take Wikipedia and you try to run it as a

startup, and by now, we'd have a paywall, you'd be paying $9.99 a month to read more than $20.

I mean, that's one view. The other, the ad driven model, so they rejected the ad driven model.

I don't know if we could, I mean, this is a difficult question, you know,

if archive was supported by ads, I don't know if that's bad for archive. If Fermat's library was

supported by ads, I don't know. I don't, I'm not, it's not trivial to me. I'm, unlike I think a lot

of people, I'm not against advertisements. I think ads when done well are really good. I think the

problem with Facebook and all the social networks are the way, the lack of transparency around the

way they use data, and the lack of control the users have over their data, not the fact that

data is being collected and used to sell advertisements. It's a lack of transparency,

lack of control. If you, if you do a good job of that, I feel like it's really nice way to make

stuff free. Yeah, it's like Stack Overflow, right? I think they've done a good job with that,

even though as we said, like they're capturing very little of the value that they're putting out

there, right? But, but it makes it a sustainable company, and they're providing a lot of, it's a

fantastic and very productive community. Let me ask a ridiculous tangent of a question.

Louise, you wrote a paper on Game of Thrones, Battle of Winterfell, just as a side, I'm sorry,

I noticed, I'm sure you've done a lot of ridiculous stuff like this. I just noticed that particular

one by ridiculous, I mean, ridiculous, the awesome. Can you describe the approach in this work,

which I believe is a legitimate publication? So going back to the original, like when we were

talking about the backstory of papers and the importance of that, so this is actually, you

know, there was a, when the last season of the show was airing, this was a during a company

lunch, there was, in the last season, there's a really big battle against the forces of evil

and the forces of good, and it's called the Battle of Winterfell. And in this battle,

there are like these two armies, and there's a very particular thing that they have to take

into account is that in the army of death, like if someone dies in the army of the living,

like that person is going to, you know, be reborn as a soldier in the army of the dead.

And so that was an important thing to take into account. And the initial conditions,

as you specified, it's about 100,000 on each side. Exactly. So I was able to, like, based on

some images, like on previous episodes, to figure out what was the size of the armies.

And so what I want, what we wanted to, what we were theorizing was like, how many soldiers does,

like, a soldier on the army of the living has to kill in order for them to be able to destroy

the army of the dead without, like, losing, because every time one of the good soldiers dies,

going to turn into, like, the other side. And so it's, so we were theorizing that, and I wrote

a couple of differential equations, and I was able to figure out that based on the size of the armies,

I think, I think was the ratio had to be, like, 1.7. So it had to kill, like, 1.7 soldiers of,

like, the army of the dead in order for them to win the battle. Well, yeah, that's science,

it's the most powerful. And this is also somehow a pitch for, like, a hiring pitch, in a sense,

like, this is the kind of important science you do at lunch. Exactly. Well, it turned out to be,

you know, as for people that have watched these shows, they know that every time you try to predict

something that is going to happen, you're going to fail miserably, and that's what happens. So it

was not at all important for the show, but we ended up, like, putting that out, and there

was a lot of people that shared that. And I think it was some, like, elements of the show,

the cast of the show, that actually retweeted that and shared that. That person was fun.

I would love if this kind of calculation happened, like, during the making of the show, or, you know,

I love it, like, in, for example, I now know Alex Garland, the director of Ex Machina,

and I love it, and he doesn't seem to be some, not many people seem to do this, but I love it

when directors and people who wrote the story really think through the technical details,

like, whether it's knowing, like, how things, even if it's science fiction,

if you were to try to do this, how would you do this? Like, Stephen Wolfram and his son were,

were collaborating with the movie Arrival in designing the alien language of how you

communicate with aliens. Like, how would you really have a math based language that could

span the alien and being and the human being? So, I love it when they have that kind of rigor.

The Martian was also big on that, like, the book in the movie was all about, like, can we actually,

is this plausible? Can this happen? It was all about that.

And that can really bring you in, like, sometimes those small details. I mean,

the guy that wrote the Martian book is another book that is also filled with those, like,

things that when you realize that, okay, these are grounded in science can just really bring you

in. Yeah. Like, he has a book about a colony on the moon.

A colony on the moon. And he goes about, like, all the details that would, you know,

be required about setting up a colony in the moon and, like, things that he wouldn't think about,

like, the fact that they would, you know, it's hard to bring, like, air to the moon so they wouldn't,

like, how do you make that breathable, that environment breathable? You need to bring oxygen.

But, like, you probably wouldn't bring nitrogen. So, what you do is, like, instead of having an

atmosphere that is 100% oxygen, you, like, decrease the pressure so that you have the

same ratio of oxygen on Earth, but, like, lowering the pressure here. And so, like,

things like water boils at the lower temperature. So, people would have coffee and the coffee would

be colder. Like, there was a problem in this environment in the moon. So, like, and these

are, like, small things in the book. But I studied physics. So, like, when I read these,

that throws me into, like, tangents. And I start researching that. And it's like,

I really like to read books and watch movies when they go to that level of detail about science.

Yeah. I think Interstellar was one where they also consulted heavily with the number of

these. And I think even resulted in a couple of papers about, like, the black hole visualizations.

Yeah. And there's even more examples of interesting science around, like, these

fantasy. We were reading at some point, like, these guys that were trying to figure out if

the Tolkien's Middle Earth, if it was round, if it was, like, a sphere, if it was, like, a flat

flat. Based on the map. Based on the map and some of the references in the books. And so,

yeah, we actually, I think we tweeted about that. Yeah, we did. Based on the distance

between the cities, you can actually prove that that could be, like, a map of a sphere,

or, like, a spheroid. And you can actually calculate the radius of that planet.

That's fascinating. I mean, yeah, that's fascinating. But there's something about,

like, calculating the number, like, exactly the calculation you did for the Battle of Winterfell

is something fascinating about that. Because it's not, like, being, that's very mathematical,

versus, like, grounded in physics. And that's really interesting. I mean,

that's like injecting mathematics into fantasy. There's something magical about that.

I see what you're saying. And that, for me, that's why I think it's also when you look at things like

Fermat's last theorem, like, problems that are very kind of self contained and simple to state.

I think, like, that's the same with that paper. It's very easy to understand the boundaries

of the problem. And that, for me, that's why math is so appealing. And those, like,

problems are also so appealing to the general public. It's not that they look simple, or that

people think that they're easy to, like, solve. But I feel that a lot of the times they are

almost intellectually democratic, because everyone understands the starting point.

You know, you look at Fermat's last theorem, everyone understands, like, this is the universe

of the problem. And the same maybe with that paper, everyone understands, okay, these are the

starting conditions. And, yeah, the fact that it becomes intellectually democratic, and I think

that's a huge motivation for people. And that's why so many people gravitate towards these, like,

Riemann hypotheses, or Fermat's last theorem, or that simple paper, which is, like, just one page.

It was very simple. And I just talked to somebody, I don't know if you know who he is,

Jaco Willink, who was this person who, among many things, loves military tactics. So he would

probably either publish a follow on paper, maybe you guys should collaborate. But he would see the

fundamental, the basic assumptions that he started that paper with is flawed. Because, you know,

there's like dragons, too, right? There's like, like, you have to integrate tactics. Because

it's not, it's not, it's not a homogeneous system. It's not, I don't take into account the dragons

and like, and he would say tactics fundamentally change the dynamics of the system. And so like,

that's what happened. So, yeah, so at least from a scientific perspective, he was right,

but he never published. So there you go. Let me ask the most important question. You guys are from

Portugal, both in Portugal. So who is the greatest soccer player, footballer of all time?

Yeah, I think we're a little bit biased on this topic. But I mean, I have a huge,

I have a tremendous respect for what. Here we go.

You can convince me. I mean, I have tremendous respect for what Ronaldo has achieved in his

career. And I think soccer is one of those sports where I think you can get to maybe be

one of the best players in the world. We, if you just have like natural talent, and even if you

don't put a lot of hard work and discipline into soccer, you can be one of the best players in the

world. And I think Ronaldo is kind of like, of course, he's naturally talented, but he also,

Ronaldo would say the football from exactly from Portugal and not the Brazilian in this case.

And so, and Ronaldo came from nothing, he's known from being probably one of the hardest working

athletes in the game. And I see that sometimes a lot of these discussions about the best player,

a lot of people tend to gravitate towards like, you know, this person is naturally talented,

and the other person has to work hard. And so, as if it was bad, if he had to work hard to be

good at something. And I think that's, you know, the, I think so many people fall into that trap.

And the reason why so many people fall into that trap is because if you're saying that someone

is good and achieved a lot of success by working hard, as opposed to achieving success because

he has some sort of God given natural talent, that you can't explain why the person was born with

that. What does it tell you about you? It tells you that maybe if you work hard on a lot of

fields, you could have, could accomplish a lot of great things. And I think that's hard to digest

for a lot of people. And in that way, Ronaldo's inspiring that. I think so. So you find hard

work inspiring, but he's way too good looking. That's the, I don't like him probably. No, I like

the part of the hard work and like, of him being like one of the hardest working athletes in Saga.

So he is to you the greatest of all time? Is he up there? Is he will be number one? Okay.

Okay. Do you agree with this? Hard or disagree? Well, I definitely disagree. I mean, I like him

very much. He works hard. I admire, I admire, you know, would like, he's incredible goal scorer,

right? But I, so first of all, Leo Messi, and there was some confusion because I've kept saying

Maradona is my favorite player, but I think, I think Leo has surpassed them. So it's messy

than Maradona than Pele for me. But the reason is, is there's certain aesthetic definitions of

beauty that I admire, whether it came by hard work or through God given talent or through

anything. It doesn't, it doesn't really matter to me. There's certain aesthetic,

like, like genius when I, when I see it to me, and especially it doesn't have to be consistent.

It isn't a case of Messi in case of the Ronaldo, but just even moments of genius,

which is where Maradona really shines. It's, I, even if that doesn't translate into like

results and goals being scored, right, right? And that's the challenge. I'm like, I did that

because that's where people that tell me that Leo Messi is never, even on strong teams have led

his, the national team, people as part of the World Cup, right? That's really important. And

to me, no, it's the moment, like winning to me was never important. What's more important is the

moments of genius. And, but you're, you're talking to the human story. And

yeah, Cristiano Ronaldo definitely has the beautiful human story.

Yeah. And I think you can't, for me, it's hard to decouple those two. I don't, I don't just look at,

you know, the, the list of achievements, but I like how he got there and how he keeps pushing

the boundaries at like almost 40. And how that sets up an example. Like maybe 10 years ago,

I wouldn't have ever imagined that like one of the top players in the world could be a top

player at like 37 or, but so, and there's an interesting tent. The human story is really

important. But like, if you look at Ronaldo, he's like, he's somebody like kids could aspire to be.

But at the same time, I also like Maradona, who like is a, is a tragic figure in many ways.

It's like the, you know, the drugs, the, the temper, all of those things, that's beautiful too.

Like I don't necessarily think to me, the flaws are beautiful too in athletes. I don't think

you need to be perfect from a personality perspective. Those flaws are also beautiful.

So, but yeah, there is something about hard work. And there's also something about the

being an underdog and being able to carry a team. That's, that's an argument from Maradona.

I don't know if you can make that argument for Messi and Ronaldo either, because they've all

played on superstar teams for most of their lives. So I don't know how,

you know, it's, it's difficult to know how they would do

when they had to work, like did what Maradona had to do to carry a team on the shoulders.

True. And Pele did as well. And so, depending on the, the context.

Maybe you could argue that with the Portuguese national team, but we have a good team.

Yeah. But maybe what Maradona did with, you know, Napples and, and a couple other teams,

it's, it seems incredible.

It speaks to the beauty of the game that, you know, we're talking about all these different

players that have, or especially, you know, if you're comparing Messi and Ronaldo,

they have such different, you know, styles of play and also even their bodies are so different.

And, and, and, but these two very different players can be at the top of the game. And

that's not, that's the, there are not a lot of other sports where you, where you have that,

you know, like you have kind of a mental image of a basketball player and like the top basketball

players kind of fit that mental image and they look a certain way. And, but for soccer, there's

some, there's the, it's, it's not so much like that. And, and that's, I think that's, that's

beautiful. But that's, that really adds something to the sport.

Well, do you play soccer yourself? Have you played that in your life? What do you find

beautiful about the game?

Yeah. I mean, it's one of the, I'd say it's the biggest sport in Portugal. And so growing up,

we played a lot.

Did you see the paper from DeepMind? I didn't look at it where they're like,

doing some analysis on soccer strategy.

Yeah. Interesting.

I saved that paper. I haven't read it yet. It's actually, I, when I was in college,

I actually did some research on, on applying machine learning and statistics in sports.

And in our case, in our case, we're doing it for basketball. But what they're effectively

trying to do was, have you ever watched Moneyball? So they're trying to do something

similar, right? Taking that, in this case, basketball, taking a statistical approach

to, to basketball. The interesting thing there is that baseball is much more about having these

discrete events that happen kind of in similar conditions. And so it's easier to take a statistical

approach to it. Whereas basketball, it's a much more dynamic game. It's harder to measure.

It's hard to replicate these conditions. And so you have to think about it in a slightly

different way. And so we were doing work on that and working like with the Celtics to analyze the,

the data that they had, like they had these cameras in the, in the arena, they were tracking

the players. And so you, so they have, they had a ton of data, but they didn't really know what to

do with it. And so we, we were doing work on that. And, and, and soccer is maybe an even a step

further. It's, it's right. It's a game where you don't have as many in basketball, you have a lot

of field goals. And so you can measure success. Soccer, it's, it's right. It's more of a Poisson

process almost where it's like, you have a goal like or two in a game. In terms of metrics,

I wonder if there's a way, and I've actually have thought about this in the past, never coming up

with any good solution. If there's a way to definitively say whether it's messy or not,

they're the greatest of all time. Like, like honestly, sort of measure, like convert the game

of soccer into metrics, like you said, baseball, but like those moments are genius. Like, like,

you know, if it's just about goals or passes that led to goals, that feels like it doesn't capture

the genius of the play. Yeah, they'll be like, you know, like, like you kind of do, you have

more metrics, for instance, in chess, right? And you can try to understand all heart of a move

that was, you know, there's like Bobby Fisher as this move that like, that it's, I think it's

called the move of the century, where you have to go so deep into the tree to understand that

that was the right move, and you can quantify how hard it was. So it'd be interesting to try to think

of those type of metrics, but say for soccer and computer vision, unlock some of that for us.

That's, that's one possibility. I have a cool idea, a computer vision product legs that you

could build for soccer. Like, if you could detect the ball and like imagine that it seems like

totally doable right now. But like, if you could detect when the ball enters one of the goals,

and like just had like, you know, a crowd cheering for you when you're playing soccer with your

friends, every time you score a goal, or you had like the, the Champions League song going on.

And like having that, like you go play soccer with your friends, just turn that on. And there's

like a computer vision, like program, analyzing the ball every time there's a goal. Like if you

miss, like there's a, you know, the fan reacting to that. And then it should be pretty simple by now.

It's like, I think there's an opportunity. Yeah, just throwing that. I would go all out. But by the

way, I did, I've never released, I was thinking of just putting on GitHub, but I did write exactly

that, which is the trackers for the players for the, for the bodies of the player is this is the

hard part, actually, the detection of player bodies and the ball is not hard. What's hard is

is very like robust tracking through time of each of those. So like, so I've wrote a track of this

pretty damn good. Is that open source? You open source? No, I've never released it because interesting

because I thought like, I need to, this is the perfection thing because I knew it was going

to be like, it's going to pull me in and it wasn't really that done. And so I've never actually been

part of a GitHub project where it's like really active development. And I didn't want to make it,

I knew there's a non zero probability that will become my life for like a half a year.

That's just how much I love soccer and all of those kinds of things. And, and ultimately,

it will be all for just the, the joy of analyzing the game, which I'm all for.

I remember you also like one of, in one of the episodes you mentioned that you did also a lot

of eye tracking analyses on like Joe Rogan's. That was the, that was the research side of my life.

Interesting. Yeah. And you have that library, right? You, you kind of downloaded all the episodes.

Yep. Allegedly. I, of course I didn't. If you're a lawyer, I'm listening to this.

No, it is, I was listening to the episode where you mentioned that. And I was actually,

there was something that I might ask you for, for access to that, to allegedly that library.

But I was doing some, not, not regarding like eye tracking, but I was playing around with

analyzing the distribution of silences on one of the Joe Rogan episodes. So like,

I did that for the Elon conversation where it's like, you just take all the silences

after Joe asks the question and Elon responded and you plot that distribution and like and see

how, how, how, how that looks like. Yeah. I think there's a huge opportunity,

especially long form podcast to do that kind of analysis bigger than Joe.

Exactly. But it has to be in fairly unedited podcasts so that you don't cut the silences.

So one of the benefits I have, like how I'm doing this podcast is like the, what we're

recording today is there's individual audio that being recorded. So like I have the raw

information. It's, when it's published, it's all combined together and individual video feeds. So

even when you're listening, which I usually don't, I only show one video stream, I, I'll know,

I can track your blinks and so on. But, but ultimately the hope is you don't need that raw

data because if you don't need the raw data for whatever analysis you're doing, you can then

do a huge number of podcasts because there's, it's quickly growing now, the number, especially

comedians. There's quite a few comedians with, with long form podcasts and they have a lot of

facial expressions. They have a lot of fun and all those kinds of things. And it's, it's prone

for analysis. And it's, there's so many interesting things. That, that, that idea actually sparked

because I was watching a Q and A by, by Steve Jobs and I think it was at MIT. And then like

people's like, it did a talk there. And then the Q and A started and people starting asking

questions that I was, I was working while listening to it. And like, someone asked the question

and he goes like on a 20 second silence before answering the question. I like, I had to check

if the, if the video hadn't paused or something. And, and I was thinking about like, if that is a

feature of a person, like how long on average you take to respond to a question and if it's like,

as to do with the, like how thoughtful you are, and if that changes over time.

But it also could be, this is really fascinating metric because it also could be,

it's certainly a feature of a person, but it's also a function of the question.

True. Like if you normalize to the person, you can probably infer a bunch of stuff about the

question. So it's a nice flag. Like it's a really strong signal, the length of that silence,

relative to the usual silence they have. So one, the silence is a measure of how thoughtful they are.

And two, the particular silence is a measure how thoughtful the question was.

Thoughtful the question was. It's really interesting. I mean, yeah.

Yeah. I just analyzed Elon's episode, but I think there's like room for exploration there.

I feel like the average for comedians would be like, I mean, the time would be so small because

you're trained to like, I would think you're reacting to Hickler, to reacting to all sorts

of things. You have to be like so quick. Maybe. Yeah. But some of the greatest comedians are very

good at sitting in the silence. I mean, there's Lucy Kay, they played with that because you have

a rhythm. Like Dave Chappelle, a comedian who did a Joe's show recently, he has a, especially when

he's just having a conversation, he does long pauses. It's kind of cool. It's one of the ways

to have people hang in your ward is to play with the pauses, to play with the silences

and the emphasis and like mid sentence. There's a bunch of different things that, it'd be interesting

to really, really analyze, but still soccer to me is that one. I just want a conclusive

definitive statement about, because like there are so many soccer highlights of both Messi and

Ronaldo. I just feel like the raw data is there. I'm just in the side. Because you don't have

that with Pelé and Maradona. Yeah, true. But here's a huge amount of high dev data,

then the annoying, the difficult thing. And this is really hard for tracking. And this is actually

where I kind of gave up. I didn't really give much effort, but I gave up to the way that highlights

or usually football match filmed is they switched the camera. So they'll do a different

switch perspective. So you have to, it's a really interesting computer vision problem. When the