The following is a conversation with Luiz and Joao Batala, brothers and co-founders 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.
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, the 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 in some of these most important
papers. I think it's super interesting to have that context of the backstory.
The Ian Goodfellow story is 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 sunset for a lot of the stuff you've looked
at, just looking back through history? It wasn't necessarily the grind of 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,
at that time, especially 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 a single night,
a single person could do it. 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. It was almost like the
early days. 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. You have an insight
in a bar. Why is it always a bar? You have an insight in 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, 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 the number of
new papers on archive with the keyword machine learning and 50% of those papers have been
published in the last 12 months. You can see just the sense 50, 50%. You can see the magnitude of
growth in that field. As fields mature, 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 back
story is that it can really make it more memorable in a way. By making it more memorable, it kind
of sediments the knowledge more. In your mind, I remember also reading the back story to
Dykstra's shortest path algorithm, where he came up with it essentially while he was
sitting down at a coffee shop in Amsterdam. He came up with that algorithm over 20 minutes.
One interesting aspect is that he didn't have any pen or paper at the time. He had to do it
all in his mind. There's only so much complexity that he can handle if you're just thinking about
it in your mind. When you think about the simplicity of Dykstra's shortest path finding
algorithm, it's knowing that back story 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 Dykstra.
He's trying to solve it and he comes up with some kind of random path. It's like my parents
aren't home and then he does. He figures out the algorithm for the shortest path.
He's trying 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.
It doesn't have to be completely on paper. 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. 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 backstory,
because there's a certain backstory 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 backstory then. And of course, you need to look at it in the context
and the time 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. Like 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, you know, some of the most incredible
breakthroughs would seem impossible. And I wonder the value of believing, almost like focusing and
dreaming of the impossible, such that it 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, it's 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 were trying, 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 more questions.
Yeah. And so that had a big impact on me. It was 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. He was also a really good teacher and was also to do the 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 childlike curiosity, some of those folks,
like you mentioned, Feynman. I have 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 a photo
of him, picture, and then I turned that into 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 visual, it still looked like the picture 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, I just keep flipping. Yeah. But there's like some primality tests that are
cheaper from a computational standpoint. Yes. 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 you got the picture, it was like, how did you do that?
It was super curious to, and then we got into the details. And again, this was, it 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. 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.
Sorry, I don't even know the correct terminology. Typesetting language? It's a typesetting language
where you're basically 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 a bunch of communities that use it to write papers. I would say it's mathematics,
physics, computer science. Yeah, that's the main thing. Because I'm collaborating currently on a
paper with two neuroscientists from Stanford. And they don't know that. So I'm using Microsoft Word
and Mendeley and all of those kinds of things. And I'm being very zen about the whole process.
But it's fascinating. It's a little heartbreaking, actually, because it's funny to say. And we'll
talk about open science, actually, the bigger mission behind Fermat's library is really opening
up the world of science to everybody. Is these silly two facts of one community uses Latex and
another uses Word is actually a barrier between them? It's boring and practical, in a sense,
but it makes it very difficult to collaborate. Just on that, I think there are some people
that should have received a Nobel Prize, but will never get it. And I think one of those
is like Donald Knuth because of Tech and Latex. Because it had a huge impact in terms of just
making it easier for researchers to put their content out there, 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.
Yeah, I think so. I mean, he at a very young age got the Turing Award for his work in algorithms
and so on. 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 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 on a book that he had a solution to Fermat's last theorem,
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. And there's a lot of potential energy that you
can release if you actually spend some time trying to digest that. And that was the origin story for
the name. Yes, you can share the contents of the margins with the world that could inspire
a solution or a 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 these fields,
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 we 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.
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. For some tools in science,
it just takes time for them to grow. Archive has just turned 30, I believe. And for people that
don't know, archive is this kind of online repository where people put the preprints,
which are versions of the papers before they actually make it to journals.
A-R-X-I-V, 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 a biology, medical version of that as
a bio-archive. Yeah, bio-archive. More recent. 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,
like, that content is just locked. And so that's why it's so important for people to have websites
like archives 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 top of preprints, and they are assuming 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. Bioarchive is a good example. Bioarchive 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 the 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 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 Bioarchive 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 some of the more sort of biology,
psychology, you might be collecting the data set that's called a study. So that's part of that,
that's part of the methodology. And so you are putting all of 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 the commenting system, whatever that is. 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,
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 crowd sourcing aspect of it. So,
sorry, there's a bunch of stuff going on the table. Let's just pick random things 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.
Because if someone had pitched me Wikipedia, like a publicly edited
encyclopedia, like a couple of years ago, like 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.
Yeah.
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 it's just,
and those are both two incredibly productive communities that generate a ton of value
and capture almost none of it. And in a way, it's almost like counter,
it's very counterintuitive that people, 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.
Look, 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, the field's 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,
it 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,
like 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 that 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 that are also very prestigious, or actually more prestigious than journals now.
Okay, 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 it 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 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 have 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 up front. 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, 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 Biagioli. It goes a lot into how 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 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 the 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 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. 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, make claims that are bigger than what is actually can be
sustained by the data or the content of the paper. And you'll have clickbait titles or clickbait
abstracts. And again, this is all a consequence of metrics and science or metrics. 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
when nobody is 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
is 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 Fermat's 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, like I can be a quote unquote like 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.
But if I, and I've learned this with COVID, which is like, you can just use your basic skills as a
data analyst and to contribute to the review process and a particular little aspect of a paper
and be able to comment, be able to 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 that's, I think, that's kind of what you're describing,
Alex. And I mean, to an extent, we kind of have like 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 will 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 constant 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 BioArchive that want to remove 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 removing that middleman and trying to
make the review process a little bit more transparent and not charging universities.
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 papers that Kat 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 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.
I think there are a couple of really high-profile scientists or people like Tim Gowers that are
trying to incentivize 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 those as well. And so they can try to break that vicious cycle
of the more traditional journals. I mean, another possible way to break this cycle is to
to raise public awareness and just by force ban paid journals. What exactly are they contributing
to the world? Basically making it illegal to forget the fact that it's mostly federally funded.
So that's a super ugly picture too. But why should knowledge be so expensive?
Like where everyone is working for the public good and then there's these gatekeepers
that most people can't read most papers without having to pay money. And that doesn't make any
sense. That should be illegal. I mean, that's what you're saying is exactly right. I went to
school here in the US. We studied in Europe and you would 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. But he's a student. Yeah, he's a grad student. He was a grad student. But that,
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 that just gave me an indication that this should be done for everything. It's absurd.
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 Fermat's 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. And they've,
the scanning of books, but they haven't really tried to make science accessible in the following
way. Like besides doing Google Scholar, they haven't like delved into the papers, right?
Especially curious given what we was saying, right? That it's kind of in their genesis. There's this
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 got canceled was used by a lot of researchers.
Yes, it was. Which I think was just a side, kind of a side effect. And 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 and why. 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 to 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. There 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 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 the what the Gates Foundation funds,
like the demand that it that it's accessible to everybody. So I think it's it's the question
of time before that that wall kind of falls. And that is going to help in a lot of possibilities.
Because, you know, imagine if 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 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 that 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 in 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
like couple the paper with a set of YouTube videos, like explaining it, like.
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 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, 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 even code like right now, people put
like code like on a PDF. Like, and there are so many things that are related to papers today,
you know, you use you have data, you have code, you might need videos to better explain the concepts.
So it's it, 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
and 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 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 he mentioned that I also think
like, yeah, it's why, why, why is that the only format? Why can't a blog post or we were even
experimenting experimenting with these a few months ago? Or 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,
approve 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. Like a concrete thing that you can
reference as 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, make concrete. And that's why, I mean, it's not broken. It just could be
better. 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 than 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, I would bring back
kind of what Louise was talking about it, 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 doesn't, that might not mean that the underlying
material is actually that hard. And so, so that's definitely something that, that, especially for
us, that we, we, we read papers and most of the times we'll be papers that are completely outside
of our, of our comfort zone, I guess. And so it'd be completely new areas to us. So I always try to,
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, this kind of hack when he was reading papers where you would
basically would, 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, 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, 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 you're a level of
understanding of 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 these quantum jump where all of
a sudden you, you, you understand the big picture of the paper. And I, I, 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, and it might
feel super chaotic, especially if you're jumping from reference to reference, you know, you might end
up with like 20 tabs open, 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 you
looks like you're lost. But, but that's the process to actually understand what they're talking about
in a paper. Yeah, I think that process, I enjoy I've found a lot of value in the process, especially
for things outside my field, a 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 site 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 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 on 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 is 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 where the field came from or where the field
stands, it's really valuable. And also, again, just to agree with fine in 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. 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,
Grigori 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 it's 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. I thought you're 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,
and it's often, I mean, every once in a while, I'll read back a paper that I annotated on Format,
and it's, and I'll read through my own annotations. And it's, I have completely forgotten what I had
written. And but it also, 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.
You know, after you've forgotten it, you can, you're kind of your own best possible teacher
at that moment. And so it's, 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 old version of yourself that returns to this paper, almost like thinking
it literally. That's underexplored. But it's, it's super powerful because you were the person that
you can like, if you, if you look at the scale from like one, not knowing anything about the
topic and 10, like you are the one that progressed from one to 10, and you know which steps you
struggled with. So you're the really the best person to help yourself make that transition from
one to 10. And a lot of the times like, and we don't, I really believe that the framework
there we have to like, expose ourselves to like, be talking to like 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. I, how could we make it easier for like
to have those conversations between you and your past self, past expert self. I think there, 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
it to make it easier to remember. But that's, I think it's an interesting idea worth, worth
exploring that not, 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. Like 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. And yeah,
just that alone. Does the Fermat Library Journal Club have a video component or no?
Not natively. We sometimes will 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. And he was able to get a lot of people interested in that
and viewing him. So I'm a huge fan of that. George is a personality. I think a lot of
people 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 child-like brain and mumbles and
all those struggle with ideas. And George is a madman who 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 also it's visible. There's an extraordinary
ability that is there. It's talented and you need to have, there's a craft. And this guy
definitely has talent and he's doing something that is not easy. And I think that also draws the
attention of people. Oh yeah. And the other day, we were actually, we ran into this YouTube channel
of this guy that was restoring art. And it was basically just a video of him. The production
is really well done. And he'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. And that draws the
attention of people regardless of your craft, be it like annotating a paper or like restoring it.
Excellency, 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 restoration. 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 try to do is not what they're doing. It's just like 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, I mean, I think
Twitch proved that like, you know, 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, people will enjoy
watching sometimes hours on end of great craftsmen's. Do you mind if we talk about some of the papers?
Do any papers come to mind that have been annotated on from our 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 mathematician, actually, Tom Apostol, and about a tunnel in a Greek island off the coast
of Turkey. So it's very random. So this, okay, so what's interesting about this tunnel? So this
tunnel was built in the sixth century BC. And it was built in this, in the island of Samos,
which is, as I said, off the coast of Turkey. And 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 sixth century BC. So you had very limited access to,
you know, 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 what
this is what the ancient historians described is that they effectively tried to draw a right angle,
a right angle triangle with the two entrances at each end of the hypotenuse. And the way they did
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 these
that you have a line going through the mountain that connects both entrances.
The issue with that is like, once you 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 like 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, like 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.
Exactly. You have to use tools to measure this. And 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 like, yeah, for about 2000 years, that's how we thought
that they had built this tunnel. And then, yeah, 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 cut 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. 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 like 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 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 the 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 building things. 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, like 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?
Yeah, so anybody can just go on there and either add a new annotation or a comment
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 going to, especially after this conversation, I'm going to make sure I participate much more
actively on papers that I know a lot about and on paper that I know nothing about. I should both
annotate the paper. I would love to. I realized that it's an opportunity for people like me
to publicly annotate a paper. Or do an AMA around the paper.
Yeah, exactly. But be in the conversation about a paper. It's like a place to have a
conversation about an idea. The other way to do it that's much more ad hoc is on Twitter,
right? But this is more like formal. And you could actually probably integrate the two.
They 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 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. And so they don't want to pollute that. And especially if it's like a
silly question. This is, I don't think that's good. I think we should, as much as possible,
try to lower the barrier for someone to jump in and ask questions. I think most of the times
it adds value, but it's some feedback that we got from users and readers. I'm not exactly sure how
to kind of fight that, but... Well, 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... We have a lot of papers on Fermat, where it's just one page,
or really short papers. And we have the shortest paper ever published in a math journal.
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, 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. Oh, that's so badass. And we actually went there and kind of unpacked and
like 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 Fermi was known for all these being a master at
these back of the envelope calculations. The Fermi problems are well known for that.
And it's super interesting to see that just one page report that was 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 I used 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 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. Because when the blast happens, you can see the dress going back and then going back
to the original position. And by just looking at how much the dress moved, you can estimate
the energy of the explosion. I assume you published this on Twitter. It was just a
Twitter thread. But actually, a lot of people share that and it was picked up by a couple of
news outlets. I was hoping it would be a formal title and it would be an archive.
No, no, no. It may be submitted. It's just a Twitter thread. But it was interesting
because it was exactly the same method that Fermi used.
Is there something else that jumps to mind? I know in terms of papers, 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 for months. 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, 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 explained the basic public key cryptography that you need to know in order
to understand it. You can explain what are the properties of a hash function and how they are
useful in this context. 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 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 there's so many interesting papers there. I remember
reading the paper that was written by Freeman Dyson on the first time that he came up with
the concept of the Dyson sphere. And he put that out. 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, according to the law of
second law of thermodynamics, there's going to be 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
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?
Yeah, I mean, if you look at the reasons why he rejected the Fields Medal.
So Perlman did a post-doc 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 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 gave 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 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 like 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 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 like, oh, who's like, we 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 add to 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 of like, 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.
And 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 it kind of, to me personally, happiness was just like swimming around the one that 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 a 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 doing science.
And you can see 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 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. And it's 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, 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 is 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 you like,
that makes the person interesting. Like 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 the society will benefit for
giving more value to that. Like one of the things that bothers me about American culture
is the, I don't know the right words to use, but like the nerdiness associated with science.
Like, I don't think nerd is a good word in American culture because it's seen as like
weakness. There's like images that come with that. And it's fine. You could be all kinds of
shapes and colors and personalities. But like 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. But it... 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 very... It's like
Bill Nye, the science guy. I don't know if you guys know him. So like that...
That spectrum.
That, yeah. But there's not... Feynman is no longer there. Those kinds of personalities.
Carl Sagan, man.
Even Carl Sagan, yeah. Like 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.
Like 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.
Well, what about Terence Tau? 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, one of Fields Metal 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 Fermat's library. So Terence Tau is known for having a big blog and is pretty open about
his research. And he also 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 Tau was
working on an Erdos problem, actually. So Paul Erdos was these mathematicians from Hungary.
And he was known for a lot of things, but one of the things that he was also known
was for the Erdos problem. So he was always 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 maybe a couple of hundred years. And I think that's actually an interesting hack
for him to collaborate with future mathematicians. His name will keep coming up for future generations.
But so Tau was working on one of these problems called the Erdos discrepancy. And he published a
blog post about that problem and he reached a dead end. And then all of a sudden, there was
this guy from Germany that wrote a comment on his blog post saying, okay, so this problem is
like a Sudoku 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 for me is an example of how to do, again, going back to
collaborative science online and the power that it has. But Tau is also pretty public about
some of the struggles of being a mathematician. 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 Tau was
a person, he won a 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 it 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, you know,
started kind of delving into more complex problems in classes, he struggled a lot because he didn't
have those habits. Like, he wasn't taking notes, he wasn't studying hard 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, 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, you know, what what it takes 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. So he's being an open human through 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
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 Fermat's library on Twitter. I keep following and unfollowing Fermat's
library because because it's so it gives when I follow it leads me on down rabbit holes often that
that that are very fruitful. But anyway, so the the posts you do with on Twitter are just these
beautiful are things that reveal some beautiful aspects of mathematics. Is there is there something
you could say about the approach there? 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 into it form. That's a good point. I think there's something about math that a lot of the
mathematical content and you know, beat papers or like little proofs has in a way sort of an
infinite of life. What I mean by that is that if you look at 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 undex
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. 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 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 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. But. 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 quite a good level in 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 be in that position. I feel, yeah, that's.
Yeah, most people are broken by the stuckness or like their district. Like,
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
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 of programming dead ends and trying to think through something. And then
your mind wants to like, this is the problem with this 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 a 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 persevere through that.
Yeah, yeah. And in the creative process, that's also true. I was the other day, I think I 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 as like the dirty water coming out in the beginning. And you just have to, you know,
keep trusting that at some point, your 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, 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 sometimes it's all,
sometimes it's all crappy work. I tend to be very self-critical, but I do think that quantity
leads to quality for some people. It does for my, the way my mind works is like, just keep
putting stuff out there, keep creating. And 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, like profile, I really, and sometimes when you
look on, on some of those tweets, they might seem like pretty kind of, you know, why is this
interesting? It's like so raw. 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 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 so, yeah, it shouldn't be like, 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 you need to
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, you oftentimes don't get rewarded for
effort. And, and, and 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 startups, and startups are,
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 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 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 oftentimes, you know, you're dealing with,
with, with non obvious ideas and things that, that might be contrarian. And so you're going to,
you're going to run into, into that a lot, you're going to 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 is 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
people that started a company have to deal with. I think maybe the best framework that I
was able to figure out is like when you run out of ideas, you just, you know, you're exploring
something, it's not working, you try it in a different angle, you know, we try a 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, you have a lot of stories of startups with
like, people kept pushing, and then, you know, that paid off. And then you have philosophizers
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 Formos Library,
I mean, if you look at Formos 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, 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 in those 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?
And 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 side project,
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'll 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, okay, this is a company,
this is a startup, we have to grow fast, we have to raise money, 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, for 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,
it's not a bad thing that it is a site 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 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 articles. Or, 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. It's not trivial to me. Unlike, I think, a lot of people,
I'm not against advertisements. I think, as when done well or really good, I think the problem
with Facebook and all the social networks are 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 do a good job of that, I feel like it's a really nice way to make stuff free.
It's like Stack Overflow, right? I think they've done a good job with that,
even though, as we said, they're capturing very little of the value that they're putting out there,
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. You wrote a paper
on Game of Thrones, Battle of Winterfell, just as a side note. 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, you're ridiculous. The awesome. Can you describe the approach in this
work, which I believe is a legitimate publication? Going back to the original,
like when we were talking about the backstory of papers and the importance of that. When the last
season of the show was airing, during a company lunch, in the last season, there's a really big
battle against the forces of evil and the forces of good. This is called the Battle of Winterfell.
In this battle, there are these two armies and there's a very particular thing that they have
to take into account is that in the army of dead, if someone dies in the army of the living,
that person is going to be reborn as a soldier in the army of the dead. That was an important
thing to take into account. The initial conditions, as you specified, it's about 100,000 on each side.
Exactly. I was able to, based on some images on previous episodes, figure out what was the size
of the armies. What we were theorizing was how many soldiers, 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 losing,
because every time one of the good soldiers dies, it's going to turn into the other side.
And 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 was the ratio had to be 1.7.
So it had to kill 1.7 soldiers of the army of the dead in order for them to win the battle.
Yeah, that's science. It's the most powerful. And this is also somehow a pitch
for a hiring pitch. In a sense, this is the kind of important size you do at lunch.
Exactly. Well, it turned out to be, 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 happened. So it was not at all important for the show. But we ended up putting
that out and there was a lot of people that shared that. I think it was some elements of the show,
the cast of the show, that actually retweeted that and shared that. So it was fun.
I would love if this kind of calculation happened during the making of the show.
For example, I now know Alex Garland, the director of Ex Machina. And I love it. And he
doesn't seem to be somebody, 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
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 the sometimes those small details. I mean, the guy that wrote the Martian book
as 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. The 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 this, 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 with a number of, I think even resulted in a couple of papers about like
the black hole visualizations. And yeah, but 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
art. Based on the map. And 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 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. And that
for me, that's why I think it's also when you look at things like, 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 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 is 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 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'm Maradona. I have a huge, I have a tremendous respect for, for what
here we go. You can convince you. 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'd, 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 the football from exactly from Portugal and not not the Brazilian in this case.
And so and Ronaldo put like came from nothing he is known from being probably one of the hardest
working athletes in the game. And, and I see that sometimes a lot of these discussions about the
best player, a lot of people trend tend to gravitate towards like, you know, this person is
naturally talented, and the other person has to work hard. And so, 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 he 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, and in that way, Ronaldo's inspiring that I think so. So you find hard work
inspiring, but he's, 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, in soccer. So he is to you the greatest of all time. Is he up there? Is he
will be number one? Okay. Do you, 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,
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 genius when I, when I see it to me, and especially it doesn't have to be
consistent. It isn't a case of messy in case in the Ronaldo, but just even moments of genius,
which is where Maradona really shines. It, 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 is 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 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 temper, all of those things, that's beautiful too. Like I
don't necessarily think to me, the flaws are beautiful too. And 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 it, 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. 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.
But 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 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, 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, 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? Like, 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 it.
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 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 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 of 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 how hard of a move there was, you know, there's like Bobby Fisher
as this move that like, that it's kind of, I think it's called the move of the century where
you have to go so deep into the tree to understand that there 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
yeah, for soccer. And computer vision unlocks some of that for us, that's one possibility.
I have a cool idea, a computer vision product legs that you could build for soccer.
Let's go. I'm taking notes. 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'm gonna 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 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 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 a 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 it 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 ultimately, it will
be all for just the joy of analyzing the game, which I'm all for. I remember you also, like 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 kind of downloaded all the episodes. Yeah. Allegedly. And of course I didn't,
if you're a lawyer, I'm listening to this. No, I was listening to the episode where you mentioned
that. And I was actually, there was something that I might ask you 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 that looks like. Yeah. I think there's a huge opportunity, especially long form podcasts,
to do that kind of analysis, bigger than Joe. Exactly. But it has to be a fairly unedited
podcast so that you don't cut the silences. So one of the benefits I have, like,
doing this podcast is like, what we're recording today is there's individual audio
that being recorded. Makes it easier. So like, I have the raw information.
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'll know, I can track your blinks and so on. Interesting. 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. It's quickly growing now, the number, especially
comedians. There's quite a few comedians 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 prone for analysis.
There's so many interesting things. That idea actually sparked because I was watching a Q&A
by Steve Jobs and I think it was at MIT. And then people did talk there and then the Q&A
started and people started asking questions. I was working while listening to it and someone
asked the question and he goes on a 20 second silence before answering the question. I had to
check if the video hadn't paused or something. 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
asked to do with 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.
If you normalize to the person, you can probably infer a bunch of stuff about the question.
So it's a nice flag. 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. It's really interesting.
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,
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.
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 because it's one of the ways to have people hang in your
word 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. 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 switch the camera. So they'll do a different switch
perspective. It's a really interesting computer vision problem. When the perspective is switched,
you still have a lot of overlap about the players, but the perspective is sufficiently
different that you have to like recompute everything. So there's two ways to solve this.
One is doing it the full way where you're constantly doing the slam problem. You're
doing a 3D reconstruction the whole time and projecting into that 3D world.
But you could also, there could be some hacks that I wonder like some trick where you can hop
like when the perspective shifts, do a high probability tracking hops from one object to
another. But I thought especially in exciting moments when you're passing players, like you're
doing a single ball dribble across players and you switch perspective, which is when they often
do when you're making a run on goal. If you switch a perspective, it feels like that's going to be
really tricky to get right automatically. But in that case, for instance, I feel like if somebody
released that data set where it's like you just have all like these this data set a massive data
set of all these games from from say Ronaldo and Messi like and just you just add that in like
whatever CSV format and some some publicly available data set like that. I feel like people
just there will be so many cool things that you could do with it. And you just set it free. And
then like the world would like do its thing. And then like interesting things would come out of it.
By the way, I have this data set. So the two the two things I did of this scale is soccer. So
his body pose and ball tracking for soccer. And then I try it's the pupil tracking and blink
tracking for it was Joe Rogan and a few other podcasts that I did. So those are the two data
sets I have. Do you analyze any of your podcasts? No, I think I really started doing this podcast
after after doing that work and it's difficult to maybe I'd be afraid of what I find. I'm already
annoyed with my own voice and video like editing it. But perhaps that's the honest thing to do
because one useful thing I bought doing computer vision about myself is like I know what I was
thinking at the time. So you can start to like connect the particular the behavioral peculiarities
of like the way you blink, the way you squint, the way you close your eyes like talking about
details. There's it's like for example, I just closed my eyes. Is that a blink or no? Like
figuring that out in terms of timing in terms of the blink dynamics is tricky. It's very doable.
I think there's universal laws about what is a blink and what is a closed eye and all those
things plus makeup and eyelashes actually have annoyingly long eyelashes. So I remember when
I was doing a lot of this work, I cut off my eyelashes, especially funny like female colleagues
were like, what the fuck are you doing? Like no, keep the eyelashes because it got in the way,
made the computer vision a lot more difficult. But super interesting topics. Yeah, but speaking
about the one still on the topic of data sets for sports, there's one paper that and I actually
annotated it on Format and it was published in the 90s. 90s, I believe. 90s or 80s, I forget.
But the researcher was effectively looking at the hot and phenomena in basketball. So whether like
the fact that you just made a field goal, if on your next attempt, if you're more likely to make
it or not. And it was super interesting because he pulled like I think 100 undergrads and I think
from Stanford and Cornell and asking people like, do you think that you have a higher likelihood
of making your free throw if you just made one? And I think it's like 68% said yes. They believe
that. And then he looked at the data and this was back in, as I said, like a few decades ago. And so
I think he had the data set of about, he looked at it specifically for free throws and he had a
data set of about 5000 free throws. And effectively what he found was that specifically in the case
of free throws, he didn't, for the aggregate data, he didn't find that he couldn't really spot that
correlation, that hot and correlation. So if you made the first one, you weren't more likely to
make the second one. What he did find was that they were just better at the second one. Because
you just got like maybe a tiny practice and you just attempted once and then you were going to
be better at the next one. And then I went and there's a data set on Kaggle that has like 600,000
free throws. And I re-ran the same computations and confirmed like you can see a very clear pattern
that they're just better at their second free throw. That's interesting. Because I think there's
similar, that kind of analysis is so awesome. Because I think with tennis, they have like a
fault, like when you serve, they have analysis of like, are you most likely to miss the second
serve if you missed the first, obviously. I think that's the case. So that integrates,
that's so cool when psychology is converted into metrics in that way. And in sports,
it's especially cool because it's such a constrained system that you can really study human psychology
because it's repeated. It's constrained. So many things are controlled, which is something you
rarely have in the wild psychological experiments. So it's cool. Plus, everyone loves it. Like sports
is really cool to analyze. People actually care about the results. Yeah. I still think, well,
like I know we'll definitely publish this work on Messi versus Ronaldo and objective. I'd love to peer review.
Yeah, this is very true. This is not past peer review. Let me ask sort of an advice question
to the young folks. You've explored a lot of fascinating ideas in your life. You built a startup,
worked on physics, worked on computer science. What advice would you give to young people
today in high school, maybe early college, about life, about career, about science and mathematics?
I remember reading that Poincaré was once asked by a French journal about his advice
for young people and what was his teaching philosophy. He said that one of the most
important things that parents should teach their kids is how to be enthusiastic in regards to the
mysteries of the world. He said striking that balance was actually one of the most important
things in education. You want to have your kids be enthusiastic about the mysteries of the world,
but you also don't want to traumatize them if you really force them into something.
I think especially if you're young, I think you should be curious and I think you should
explore that curiosity to the fullest, to the point where you even become almost as an expert
on that topic. You might start with something that it's small. You might start with
your interest in numbers and how to factor numbers into primes. Then all of a sudden,
you go and you're lost in number theory and you discover cryptography and then all of a sudden,
you're buying Bitcoin. I think you should do this. You should really try to fulfill this
curiosity and you should live in a society that allows you to fulfill this curiosity,
which is also important. I think you should do this not to get to some sort of status or fame
or money, but I think this is the way, this iterative process, I think this is the way to
find happiness and I think this also allows you to find the meaning for your life. I think it's
all about being curious and being able to fulfill that curiosity and that path to fulfilling your
curiosity. Yeah, the start small and let the fire build is an interesting way to think about it.
You never know where you're going to end up. For us, it's just a really good example. We
started by doing this as an internal thing that we did in the company and then we started putting
out there and now a lot of people follow it and know about it. And you still don't know where
from our library is going to end up, actually. True, exactly. I think that would be my piece
of advice with very limited experience, of course. I agree. I agree. Is there something from
particular as you're all from the computer science versus physics perspective? Do you regret not doing
physics? Do you regret not doing computer science? Which one is the wiser, the better human being?
This is messy versus Ronaldo. Those are very, I don't know if you would agree, but they're
kind of different disciplines. True, yeah, very much so. Actually, I had that question in my mind.
I took physics classes as an undergrad or besides what I had to take. It's definitely
something that I considered at some point. I do feel like later in life that might be something
that I'm not sure if regret is the right word, but it's kind of something that I can imagine
in an alternative universe. What would have happened if I had gone into physics?
I try to think that, well, it depends on what your path ends up being, but that it's not
super important. Exactly what you decide to major on. I think Tim Urban, the blogger, had a
good visualization of this where he has a picture where you have all sorts of paths that he could
pursue in your life and then maybe you're in the middle of it and so there's maybe some paths
that are not accessible to you, but the tree that is still in front of you gives you a lot of
optionality. There's still less to learn from that. We have a huge number of options now
and probably you're just one to try to derive wisdom from the one little path you've taken so far.
It may be flawed because there's all these other paths you could have taken. One, it's inspiring
that you can take any path now and two, the path you've taken so far is just one of many possible
ones, but it does seem that physics and computer science both open a lot of doors and a lot of
different doors. It's very interesting. In this case, and especially in our case,
because I could see the difference, I went to college in Europe and João went to college here
in the US, so I could see the difference. The European system is more rigid in the sense that
when you decide to study physics, especially in the early years, you can't choose to take a class
from a computer science course or something like that. You don't have a lot of freedom to explore
in that sense in university, as opposed to here in the US where you have more freedom.
I think that's important. I think that's what constitutes a good kind of educational system
is one that gravitates towards the interests of a student as you progress, but I think in order
for you to do that, you need to explore different areas. I felt like if I had a chance to take,
say, more computer science class when I was in college, I would have probably
have taken those classes, but I ended up focusing maybe too much in physics, and I think here at
least my perception is that you can explore more fields. There is a kind of, it's funny, but physics
can be difficult, so I don't see too many computer science people then exploring into physics.
Not the one, but one of the beneficial things of physics, it feels like it,
was it Rutherford that said, basically that physics is the hard thing and everything is easy.
There's a certain sense once you've figured out some basic physics that it's not that you
need the tools of physics to understand the other disciplines, it's that you're empowered by having
done difficult shit. I mean, the ultimate, I think, is probably mathematics there. Yeah, true.
So maybe just doing difficult things and proving to yourself that you can do difficult things,
whatever those are. That's not positive, I believe. Not positive. And I think,
before I started a company, I worked in the financial sector for a bit, and I think having
a physics background, I felt I was not afraid of learning finance things. And I think when you
come from those backgrounds, you are generally not afraid of stepping into other fields and learning
about those, because I feel we've learned a lot of difficult things and that's an added benefit,
I believe. This was an incredible conversation, Luis, Joao. We started with, who do we start with?
Feynman ended up with Messi and Ronaldo. So this is like the perfect conversation.
It's really an honor that you guys would waste all this time with me today. It was really fun.
Thanks for talking. Thank you so much for having us. Yeah, thank you so much.
Thanks for listening to this conversation with Luis and Joao Batala. And thank you to
Skiff, Simply Safe, Indeed, Netsuite, and ForSigmatic. Check them out in the description to support
this podcast. And now, let me leave you with some words from Richard Feynman. Nobody ever figures
out what life is all about. And it doesn't matter. Explore the world. Nearly everything
is really interesting if you go into it deeply enough. Thank you for listening. I hope to see you
next time.