Latest / The Innovation Show / The Half-Life of Facts with Samuel Arbesman: Why Knowledge Keeps Changing

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The Half-Life of Facts with Samuel Arbesman: Why Knowledge Keeps Changing · The Innovation Show

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  Thank you as always to Kyndryl and the Kyndryl Institute with a unique
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  blend of AI powered consulting, built on unmatched service capability.
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  Kyndryl helps leaders harness the power of technology for smarter
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  Also, check out the Kyndryl Institute the Kyndryl Institute has a plethora of
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  brilliant writing, brilliant thinking and thought leadership from many of
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  the guests who have been on the show and will be on the show in the future.
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  You can find that at Kyndryl.com/institute.
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  Facts change all the time.
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  Smoking has gone from doctor recommended to deadly.
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  Meat used to be good for you, then bad to eat, then good again,
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  and now it's a matter of opinion.
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  The average internet connection is far faster now than it was 10 years ago.
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  The language of science has gone from Latin to German to English
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  and is certain to change again.
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  Humanity has progressed from a population of less than 2 billion
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  to more than 7 billion people in the past hundred years alone.
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  We have gone from being earthbound to having had humans walk on the moon,
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  and we have sent our artifacts beyond the boundaries of the solar system.
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  Chess checkers and even jeopardy have gone from being the domains of human
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  experts to one of computerized mastery.
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  Our world seems to be in constant flux with our knowledge changing all the time.
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  Even the most informed people can barely keep up.
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  All this change may seem random and overwhelming.
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  Dinosaurs have feathers.
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  When did that happen?
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  But it turns out there is actually order within the shifting noise.
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  This order is regular and systematic and is one that can be described
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  by science and mathematics.
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  Today's guest offers a guide to the startling notion that our knowledge, even
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  what each of us has in our head, changes in understandable and systematic ways.
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  Knowing how knowledge works can help us make sense of our world, it can
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  allow us to anticipate the shortcomings in what each of us might know, and
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  help us to plan for these flaws in our knowledge, including the flaw.
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  If you didn't notice that we've gone from 7 billion to 8 billion in the time
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  that our guest wrote the book in 2012.
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  It is a pleasure to welcome the author of the Half Life of Facts, Samuel Aman,
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  welcome to the show.
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  Thank you so
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  much.
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  Great to be chatting
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  with you.
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  This is exciting.
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  Oh man, I have learned so much from this book.
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  We talked about o Off air that so much has changed since you wrote it, including
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  a lot of the tools that you mentioned in the book, but I found it a magnificent
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  read, , very well researched, et cetera.
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  I'd love you to tell us about your thoughts since you first wrote it.
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  What's changed, what hasn't changed?
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  And that'll set us up for some context for the book.
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  on the one hand, I think the general rules about how, what we know shifts over
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  time, those things are pretty, evergreen.
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  Kind of perennial truths.
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  In terms of what's changed?
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  Actually one, one fun thing that actually I discovered, I think between the time
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  book went to press and when it actually published in hardcover and then we
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  were actually able to change it for paperback, was one of the stories I tell
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  how errors propagate and how they're eventually rooted out is this well-known
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  story about the iron content in spinach.
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  So there's this well-known story that there was like a typo and then of course
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  then people thought that spinach had a lot more iron content than it did.
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  And as a result, that's why Popeye consumed spinach.
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  And it's this whole story.
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  And people have been writing about this story I think since like the early
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  1980s when someone wrote about this.
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  include it in this, I include it in the book.
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  And then of course once the book went to press, I discovered that it
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  turns out this story of how error.
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  Propagates and then gets rooted out, is itself erroneous?
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  It turns out someone has actually done research and shown that, no,
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  it was actually some sort of like, I think it was, it turns out that
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  in spinach it wasn't necessarily that much more iron filled.
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  There might've been some sort of mistake in how the science was done or whatever,
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  but it was this weird thing of , the story of error itself was erroneous and then
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  it took time to actually root that out.
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  And then I was able to modify it for the paperback and then actually mentioned
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  this in the in the afterward of the book.
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  But that was just a one, like one small example of.
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  Even when you're trying to understand how knowledge changes, how what we
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  know changes, there's always going to be things that are modified.
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  And so there was a professor of mine from graduate school, he once told
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  me this story that he was teaching.
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  He was teaching a course.
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  I think he like went in on a Tuesday, lectured on something.
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  The very next day he read an article that actually invalidated everything
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  he had taught the day before.
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  So he went in on Thursday or whenever the next day was when he was teaching.
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  And he said, remember what I taught you?
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  wrong.
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  And if that bothers you, you need to get out of science.
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  And so there's this idea that science, rather than it being just a body of
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  knowledge, which I think is often how we think about science, it's really a
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  rigorous means of querying the world.
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  And so that rigorous means is not going to change.
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  The body of knowledge might change though.
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  And so there's this understanding that we have to recognize these
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  things are always in draft form.
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  And so whether or not it's the facts in the book itself which.
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  Whi, which I'm sure have changed even more than I realized.
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  Or even just kind of other things that we think about in our textbooks we
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  have to recognize that kind of thing.
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  I would say.
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  One other thing that I will mention in the book is when I was talking about
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  people reproducing scientific experiments and how we don't often get a lot of
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  credit for just recapitulating what other things that other scientists have
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  done and saying that, oh yeah, probably a lot of science is kind of messy
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  and we're not necessarily gonna know whether or not those things are correct.
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  since I wrote the book, I'm actually gratified to see that a lot of people
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  have actually started doing a lot of things around reproducibility and actually
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  repeating experiments to see to what degree certain well-known scientific
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  results are actually true or don't really hold up on under scrutiny and
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  realize, oh yeah, these things were kind of just some fluke or whatever it was.
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  And so people have been doing that and so I'm actually really gratified
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  to see that my expectation that no one would actually be doing
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  those, those experiments is wrong.
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  I was literally just reading about Fermat's last theorem in that
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  respect where people thought it wasn't solvable and he said he had
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  the solution, but he didn't write it in the margins and the fact that
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  people assumed it was unsolvable.
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  I think that was one of the things I really appreciated about the book
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  was it makes you question what you're looking at or even what you reference.
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  And I do a lot of referencing in my writing and I was
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  actually thinking about that.
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  I was like, don't ever take for granted the reference that somebody else gives.
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  Go and search it for yourself, especially in this world.
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  Yeah, Fermat's last theorem.
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  He said he's a mathematician.
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  And then in the margin of one of, of, of a text, he basically said, okay,
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  here's this mathematical equation.
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  And he basically posits something , about that equation and saying like there's no
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  solutions beyond the cubes, or I forget exactly what it was, or the squares,
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  and, and he is like, oh, but like the margin is too small to contain the proof.
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  And of course this set off this like, I guess century quest to find it.
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  And and then eventually it actually was solved . But that proof, it
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  was not, oh, just a little too large to fit in that margin.
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  It was much too large.
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  And so there could be a situation where he probably was wrong about the proof,
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  but at least it goaded mathematicians to actually make a lot of discoveries
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  and do a lot of different bits of work.
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  But more broadly what you're talking about of like checking references and
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  things like that, it's very common where knowledge gets propagated from
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  like one generation to the next, or one article to the next, and it's
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  not always traced back to its source.
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  And you discover that, oh yeah, things have been I guess copied
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  incorrectly or written down.
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  Wrong.
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  Or certainly in the case of like references themselves, not even like
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  the knowledge in the references, you can actually see the way in which incorrect
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  references and like the page numbers are wrong or whatever has actually propagated.
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  And so people have actually looked at this kind of thing as sometimes a,
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  rough metric for whether or not people are actually reading the articles.
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  I'm not really sure.
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  That's entirely correct.
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  'cause especially if you're using some bibliographic tool, maybe
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  it just, it has an error in that.
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  And so that being said though, I think it just kind of is a, a broader
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  sort of proxy for recognizing that.
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  Sometimes it's useful to take things based on experts.
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  Sometimes you actually wanna chase things down, but recognizing the way in which we
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  should question certain things sometimes, I mean, and I'll say this always with
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  caveats and of course everything is full of gods, but but I think a healthy
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  dose of questioning is is very important to understand certainly as a scientist
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  are you kind of taking 'em as a given?
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  Are they worth reexamining?
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  Should you check the references?
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  Should you check other things, checking out underlying sources?
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  These kinds of things are actually very valuable to really understand
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  and root out error as well.
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  particularly I think, you know, we talk about AI hallucinations,
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  but we do it ourselves.
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  I was thinking recently about an episode that I love the way the dots connect.
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  When I read a book like this, I was thinking about an episode we did
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  with Elise Loehenen and who wrote a book called On Your Best Behavior,
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  and , it's about how women were kind of framed in society, but mostly true
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  literature and going right back to ancient books like the Bible for example.
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  But I was, when I was reading about like say the transcription error of
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  the spinach story, you told us that I was thinking about how much translation
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  is based on the interpreter who was, the scribe, who was writing it , from
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  maybe generation to generation or writing it through their own biases.
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  So it always suited the person who was writing the story, whatever
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  they believed in, et cetera.
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  No, I love that And actually it reminds me, I mean, before, before
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  we started recording, we were talking about publication editing process of
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  books and things like that, and that's actually one of the really hard.
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  hard aspects of writing is when you are trying to actually edit your own
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  writing or read it over you, there is an assumption of what I, of what you thought
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  you wrote versus what you actually wrote.
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  And so there could be very obvious typos that you will just never notice
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  because you have an assumption.
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  And I, and I believe actually in
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  the hardcover version of my book I made some like very like simple error about
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  like describing something related to like, I don't know like metric prefixes.
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  And it was one of these things where multiple people read this.
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  And it was not until my grandfather read it over and it was like immediately
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  like, oh yeah, this is wrong.
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  And of course, and because I had, had the assumptions of what I thought.
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  And but yeah, but more broadly, right, there's always going to be a mental
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  framework to how you actually assimilate knowledge or assimilate information.
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  And that is going to color all these different kinds of things in terms of how
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  you perceive new facts, how you, how you choose to absorb knowledge or fail to, and
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  there's lots of things around like certain cognitive biases, like confirmation bias.
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  Like we tend to go towards things or are drawn to things that reinforce , the
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  framework that we already have in our minds, and which makes it that much
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  more difficult to root out errors.
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  And actually, and related to in, in your introduction you were talking about
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  with like since I wrote the book, it went from like having the population
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  of like 7 billion to like now we're well into the eight billions on Earth.
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  But there's a story, I believe I actually tell it in the book where.
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  Someone told me that he was listening, this is years ago, he
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  was listening to a, like, well-known like hedge fund manager or investor.
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  And he was like, he started his anecdote with as we all know, there
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  are 4 billion people on the planet.
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  And , yeah, it might have been a while ago, but there were not
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  4 billion people on the planet.
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  He was telling that, but it was because he had absorbed that bit of information
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  and then had just maintained it for years and years and years without
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  actually taking the effort to actually mentally update that bit of information.
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  And I think that's the thing, when you think about stories, you hear,
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  , for example, I'm into pushing the boundaries of my health, for example.
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  So I'll try new supplements, and people will go, oh, creatine for example,
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  is really bad for you, or whatever.
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  And they're basing it on studies that were like 30 years old.
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  And it's probably one of the most studied molecules at the moment.
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  But they don't update the research in their head because as you say,
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  it's kinda like this frozen moment in time that's sealed into the brain.
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  And that's the fact forever.
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  And that's what I love about the book, particularly when the speed of facts being
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  revealed seems to be increasing as well.
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  I thought what we'd do, Sam, was I'll pull out from the books gr some of
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  the brilliant examples that you give.
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  One of the ones at the moment that just came to mind, and I'll probably jump
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  around the book a little bit, was to the, your very point there is if I am in some
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  way incentivized, then my confirmation bias becomes even more enhanced.
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  And I thought about this great battle between these two paleontologists
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  to discover dinosaurs, which was one of your childhood passions as well.
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  But one of the things that led to was a false dinosaur that I'm sure many
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  of our audience think is still real.
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  I'd love you to take it from there.
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  right there, you know, I. Grew up loving dinosaurs when I was a little kid.
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  I feel like many little kids are obsessed with dinosaurs and and certainly in my
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  own like childhood, there were actually a number of dinosaur facts that were
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  changing quite a bit when I was young of like recognizing that in fact, like
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  many dinosaurs might have actually been warm-blooded instead of cold-blooded.
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  And, and now we have a much better understanding of, of that they actually
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  might have had colors and, and, and, and feathers and like, there was all this
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  information that's kind of changing.
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  And, of course I mean even though the picture of dinosaurs now is these like,
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  I dunno, like fearsome chickens, these kite colorful fearsome chickens versus
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  these like kind of slow plotting reptilian monsters that are kind of gray green.
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  We often, whatever we kind of learned when we were younger,
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  we often failed to update.
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  And one of the kind of well-known dinosaurs from my childhood was
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  the brontosaurus which was one of these like the giant four-legged
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  dinosaurs that kind of walked around like with a really long necks.
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  And and it turns out that, and that name just has not actually
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  been an acceptable name for a dinosaur for many, many decades.
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  I think at this point where it's actually the Apatosaurus.
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  And it had to do with, I think that yeah, there was these two
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  paleontologist Cope and Marsh.
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  If I'm remembering correctly, been a while since since I've remembered the
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  story so you can correct me on certain the details, but they kind of had this battle
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  of like trying to like discover dinosaur after dinosaur who could discover more.
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  And and it turns out, so the brontosaurus was actually, I think a version of
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  a previously discovered dinosaur.
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  In this case it was the Apatosaurus.
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  And I think it might not only was it described wrong or maybe it was actually
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  a juvenile of the original Pterosaurus but I believe they even put the wrong
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  head on it if I'm remembering correctly.
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  And so it was this thing that, like on the one hand, like there's this idea that
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  like it maybe was just classification , or priority of the name or whatever.
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  But it sounds like there actually might have been more in terms of
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  like actually what the understanding was of this kind of dinosaur.
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  And I'm sure, yeah, many people are like, oh yeah, Brontosaurus is sounds good.
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  We'll use that Apatosaurus maybe is not quite as alliterative.
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  I don't know.
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  But yeah, right that was an example of one of these dinosaurs that
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  really people might think is a valid dinosaur name, but not really.
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  And we have like example after example of these kinds of things.
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  One classic one is just Pluto, like is Pluto planet is not a planet.
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  And of course it was reclassified in I think 2006 as a dwarf planet.
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  And now it actually might even have different designation.
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  They might have actually even changed that title I vaguely remember something
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  reading something recently about that.
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  But it was not because in this case, Pluto stopped existing.
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  It wasn't like, oh, like it was a planet and then like it blew up
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  and then now it's not a planet.
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  It was more, it just, we learned more things about our own solar
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  system and realized that Pluto was simply one of this larger class of
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  things that were these very large objects beyond the orbit of une.
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  And so it really couldn't fit the designation of planet.
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  But of course, the first time that kind of thing has happened either.
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  When asteroids were first like being discovered, these like the asteroids
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  are these, the entire asteroid belt.
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  There are these large bodies between Mars and Jupiter.
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  The first few were actually classified as planets, like the,
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  I think series is the largest one, and there's some other ones as well.
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  And so in the same way that generations of children learned Pluto as a planet and
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  then had to grapple with that changing knowledge, I include myself in that.
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  That there were also generations, or at least a certain period of time where
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  children were taught, I think in the 18 hundreds, that there were these other
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  planets like series and other ones, which were eventually reclassified as asteroids.
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  And so there's just this constant like reclassification learning new
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  things and modifying how we actually have to think about the world.
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  And I think for some people that can be really overwhelming.
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  And I'm sympathetic to that.
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  And I think though.
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  At least from my perspective, it's actually really exciting.
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  It's not to say, oh, like therefore, because we thought this was true
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  and this now other thing is true, we have to throw everything out.
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  That's not really how science works.
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  That's this like, sort of asymptotic approach to the truth.
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  And we can talk more about that.
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  But for me, like at that frontier of like where we're learning all these new
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  things, yeah, there's gonna be a lot of shifting knowledge, like whether it's
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  like nutrition supplements or whatever kind of changing and people having
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  outdated knowledge.
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  But that means that's where the most interesting stuff is happening.
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  And so playing at that frontier means you're gonna have to
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  update your information.
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  But it's still worthwhile.
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  'cause then you get to play at that frontier of knowledge.
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  What it tells you as well is the importance of ongoing learning.
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  I think that was one of the things as well, that dawns on me
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  and why I love doing the show.
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  And I was complimenting Sam for those who were watching us on YouTube, on
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  his library behind him, and he says, oh, that's only one of my libraries.
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  And the one thing you learn by doing this is how little, you
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  know, that's the, the big thing.
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  And then, and then on top of that, that the facts keep getting updated.
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  And that means that that ongoing learning is, is a competitive advantage today.
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  I, I really felt that, but I thought we'd share some of your terms.
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  So you call you, you have this term meso facts or meso facts,
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  and I'd love you to explain that.
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  And then maybe a couple of places where they show up.
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  One of them, for example, and these are just fascinating examples, was that.
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  A cytologist is somebody who explores the interior of cells.
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  A famous one once said, the diploid chromosome number of 48 in man can now
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  be considered an established fact stamp.
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  That's done, never change again.
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  That's one.
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  And then the other one, and this is one you shared about your granddad,
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  was the fact that when he learned about the chemical elements in the periodic
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  table, there was a certain number.
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  Today there are far more.
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  idea behind, yeah, the meso fact is that we need to recognize that what
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  we know, like the different kinds of facts or different bits of knowledge
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  they change at different rates.
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  Now, it certainly feels like right now, like everything is
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  changing more and more rapidly.
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  And so maybe there is some, some truth to that.
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  But there are different kinds of knowledge that change.
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  And there are certain types that change very rapidly.
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  So like this is not necessarily like a scientific fact, but what
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  the weather's gonna be like tomorrow or what the stock market close at
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  yesterday, those kinds of things.
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  You know, that if I memorized what the weather's gonna be like tomorrow, a
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  month ago, that has little to no bearing on what that's gonna happen like now.
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  'cause I have to recognize, okay, seasons change information is updating.
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  And so those are things that change very rapidly.
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  And on the other extreme, you have facts that change very
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  solely or effectively never.
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  And those are the kind of things that you assume you should learn them once,
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  and then you don't have to worry about mentally updating those kinds of things.
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  And so the example would be, I don't know how many fingers are on the human hand.
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  You learn that fact and you're like, okay, that's like a, that's a set thing.
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  I don't have to worry about changing.
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  But in between are these meso facts.
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  These are the facts that change on the order of decades or on
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  the order of a human lifetime.
• 20:02
  And they can include things about the population of the earth, but they can
• 20:06
  also include facts about the state of our knowledge, like the number of elements in
• 20:10
  the periodic table and this thing, this fact, like how many there are has been
• 20:15
  changing slowly and subtly and surely.
• 20:17
  The problem is that we often learn meso facts, these slow changing
• 20:20
  facts, the same way we learn facts that are ones that never change.
• 20:25
  And we are not really incentivized to actually update our knowledge.
• 20:29
  And so we learn about.
• 20:32
  state of knowledge of dinosaurs, the current number of elements in
• 20:35
  the periodic table recognizing that these things actually are solely but
• 20:41
  surely subtly shifting over time.
• 20:43
  And so I think now the periodic table is, I think we're up to 118 elements.
• 20:47
  Certainly was far fewer.
• 20:49
  Yeah.
• 20:49
  My, grandfather, he lived to the age of 99.
• 20:51
  He learned lots of different things.
• 20:53
  He was trained as a dentist and actually related to the cytology thing.
• 20:55
  And like the number of chromosomes in a human cell, And so you
• 21:00
  quoted that cytologist, and of course that fact is wrong.
• 21:02
  There are not 48 chromosomes in a human cell.
• 21:04
  There are 46.
• 21:05
  it turns out there was a period of several decades where we were able to actually
• 21:10
  magnify the cell and actually count the number of chromosomes, but apparently not
• 21:14
  counted correctly, but it was miscounted and then it got propagated in textbooks.
• 21:18
  And my grandfather, he.
• 21:20
  Learned medicine during that period.
• 21:22
  And so he actually learned the wrong number.
• 21:24
  In addition to learning an outdated number of elements in the peric
• 21:27
  table, he actually learned the wrong number of human chromosomes.
• 21:31
  And I think yeah, and it wasn't, I think until the 1950s that that bit of it was
• 21:37
  actually like remeasured and or recounted, and they realize, oh, it's actually only,
• 21:40
  there's only 46 in, in instead of 48.
• 21:43
  But too often, we just don't realize that we need to constantly
• 21:47
  revisit a lot of these things.
• 21:49
  Sometimes that is until I dunno, our kid comes home and says, guess what?
• 21:53
  Dinosaurs look totally different.
• 21:55
  And then you're like, you're stunned by it.
• 21:56
  Even though of course knowledge has been changing slowly and subtly.
• 21:58
  But we often perceive it in these kind of like staccato jumps.
• 22:02
  There's kinda almost a generational overturn of knowledge.
• 22:05
  And, going back to what you were saying with reading and actually constantly
• 22:09
  having the need for revisiting and learning things constantly.
• 22:14
  And we talk, right?
• 22:16
  Certainly around the time and we, maybe we don't talk about it as much anymore,
• 22:20
  but certainly around the time I was writing the book, there was a lot of
• 22:22
  discussion around computer technology and the internet and whether or not
• 22:27
  these things are ruining our memory.
• 22:29
  And maybe that's true, but I actually think good part, like the upside of
• 22:33
  that, means that we're constantly to revisit knowledge and actually look
• 22:38
  things up and discover whether or not the things that are half remembered in
• 22:42
  our minds are actually still true or not.
• 22:44
  And so I actually think there is something to be said for, it's good to
• 22:47
  know a lot of facts and use that as a framework for understanding the world,
• 22:50
  but also you also need to have that like kind of mede fact, which is these
• 22:54
  things are all subject to revision.
• 22:55
  Most of the things, are actually these meso facts and they should be revisited.
• 23:00
  And yeah, I think having that sense of constantly needing to learn is
• 23:06
  very important and actually returning to medicine, I think medicine is one
• 23:10
  of the fields that has actually done a reasonably good job of inculcating
• 23:13
  that in medical students and physicians because, and they recognize that a huge
• 23:19
  number of things that they learn are going to become obsolete or outdated
• 23:23
  within a few years of medical school.
• 23:25
  Sometimes they're actually told this kind of thing explicitly within medical school.
• 23:29
  My father who's a retired dermatologist he mentioned a, he actually told me
• 23:32
  the story, which I believe I think I mentioned it in the beginning of the
• 23:34
  book, where a professor of his gave a multiple choice exam and the exam was.
• 23:41
  Identical from one year to the next, the actual choices were the same,
• 23:44
  but one year, one of the choices was correct, and the next year
• 23:47
  a different choice was correct.
• 23:48
  And you could, and medicine recognizes that.
• 23:49
  And there's, and there are tools to remain up to date, like there's online
• 23:52
  tools for these kinds of things.
• 23:54
  But I think we need to recognize that kind of thing in whatever field we're in.
• 23:58
  And I think the downside, going back to the kinda this
• 23:59
  generational approach to knowledge.
• 24:01
  When we are young, we are trained as generalists.
• 24:05
  We learn a lot of different things about lots of different fields and like
• 24:08
  geography and the sciences and history and English language and literature.
• 24:13
  And then as we get older, we specialize and we learn more
• 24:15
  and more about less and less.
• 24:16
  And we fail to mentally update any of the things that we
• 24:20
  learned when we were younger.
• 24:21
  And so that's why we often then are not confronted with the changing
• 24:25
  facts until our children maybe learn those kinds of things.
• 24:28
  And, yeah, maybe this is just sort of a pan or a plea or a
• 24:31
  charge to action of like, yeah, we actually should periodically try
• 24:34
  to actually revisit these things.
• 24:35
  'cause otherwise we're gonna be making decisions based on
• 24:38
  faulty and outdated information.
• 24:39
  We did a series, with a guy called Manu Kapur and his book
• 24:42
  called Productive Failure.
• 24:44
  And it was for that very reason.
• 24:45
  It was like the need to update these mental models.
• 24:48
  But to your point there about the idea of using some technology to be able to
• 24:54
  upgrade yourself by that, this idea of cognitive offloading where maybe you
• 24:59
  might use a calculator or chat, GPT is the whole idea there is that actually that
• 25:04
  allows you to actually connect more dots.
• 25:07
  He was saying that that's not, that's seen as a negative thing, but actually can be
• 25:10
  a good thing, especially if it's something that's basic and you have mastered
• 25:13
  already, then you can go up a level.
• 25:15
  I thought that was a really interesting connection that I I
• 25:18
  made as a result of doing the show.
• 25:20
  But there was one thing that, that you said there.
• 25:22
  I thought was really interesting was the idea that if you, if, if you're,
• 25:29
  say your, your, your, your dad, your granddad was a dentist, your dad was a
• 25:33
  dermatologist, but he used his skills to connect dots in a very unique way,
• 25:40
  and I'm bringing us here to the idea of using the, the, the power of the crowd.
• 25:44
  And here he actually solved one of the solutions on incent.
• 25:48
  Yeah,
• 25:48
  I'd love you to share that story.
• 25:50
  is this I actually don't even know if it's still around.
• 25:52
  it is actually, , they sponsored the show recently.
• 25:55
  So in INNOCENTIVE, it's this
• 25:56
  Crowd sourced solution platform where the idea behind it is that if you
• 26:02
  have a problem, some sort of like scientific question or some sort of
• 26:04
  other technical kind of question.
• 26:07
  It might feel insurmountable.
• 26:09
  There is a decent chance that the crowd who has access to different
• 26:13
  kinds of information and different bits of knowledge could potentially
• 26:16
  solve , that kind of problem.
• 26:17
  And so yes, , my father, he's a retired dermatologist.
• 26:21
  He saw a challenge related to finding new ways of diagnosing
• 26:27
  ALS the neurodegenerative disease.
• 26:29
  And one of the things he did was actually look to a book from 1880, , I
• 26:36
  think it might have actually been the first time ALS was actually described.
• 26:39
  And he noticed this curious fact that it, I think it mentioned just
• 26:43
  kind of in passing that patients with a LS did not develop bedsores.
• 26:47
  And so he realized, oh, wait, as someone.
• 26:50
  Who's a dermatologist and kind of understands things
• 26:51
  about skin, that's curious.
• 26:53
  Maybe it has to do with skin elasticity.
• 26:56
  And so he developed this hypothesis around being able to diagnose like very early
• 27:01
  on ALS by measuring skin elasticity.
• 27:05
  And eventually, yeah, and one, I think one of like the hypothesis tests, I
• 27:07
  don't remember exactly what it won, but it was one of these things of
• 27:10
  like being able to kind of combine different bits of knowledge , it's a
• 27:13
  neurodegenerative disease, but in this case, was drawing on information from
• 27:18
  dermatology, from understanding skin.
• 27:20
  And the truth is this is the kind of thing that we need more and more
• 27:25
  certainly as scientific knowledge and just kind of knowledge more
• 27:28
  generally becomes, larger and larger.
• 27:30
  There is no way that, even decades ago, anyone could hold
• 27:34
  all of that in their heads.
• 27:36
  And certainly now it's completely overwhelming.
• 27:38
  And so we need.
• 27:40
  Either individuals from different kind of areas to kind to draw on bits of
• 27:44
  information they that maybe subject matter experts would not otherwise know.
• 27:48
  Or even going back to your cognitive offloading, we need computational
• 27:51
  tools to actually allow us to help stitch information together.
• 27:54
  And so one of the first examples and kind of approaches to do this
• 27:57
  kind of thing was this information scientist by the name of Don Swanson.
• 28:02
  He wrote this paper called undiscovered Public Knowledge.
• 28:05
  And this is in the mid 1980s.
• 28:06
  And he basically developed a thought experiment and he said, okay, imagine
• 28:10
  there's somewhere in the literature that a. A paper that says A implies
• 28:14
  B, there's somewhere else in the literature, maybe in a different subfield,
• 28:17
  maybe an entirely different field.
• 28:19
  There's another paper that says B implies C. And so if you combine
• 28:22
  them together, you would say, oh, actually maybe A implies C 'cause
• 28:25
  I can combine them together.
• 28:26
  But no one human has ever read these things.
• 28:28
  And so therefore that knowledge, that underlying information is actually
• 28:32
  undiscovered, but it's public.
• 28:33
  And so it's there, but no one actually knows this kind of thing.
• 28:36
  And so at the time he actually, he was not contend leaving
• 28:38
  it as a thought experiment.
• 28:39
  And so he used the kind of then cutting edge computer technology, which in
• 28:43
  this case was, I think looking for keywords and like a medical database.
• 28:47
  But he actually found a relation, like he found, I think he made a number
• 28:50
  of different medical discoveries, including one where he found the, a
• 28:53
  relationship between like consuming fish oil and helping to reduce like
• 28:59
  certain issues around like some sort of a circulatory disorder.
• 29:02
  And then he actually, I think he ended up publishing in a medical journal
• 29:04
  even though he was not a physician.
• 29:06
  And of course since then.
• 29:09
  made huge strides in the kinds of computational tools that can be
• 29:13
  used to actually surface this kind of undiscovered public knowledge.
• 29:16
  I mentioned several of them in the book, I believe.
• 29:19
  And of course since then, now with generative AI and tools that can
• 29:22
  ingest huge amounts of scientific literature, we can just do more and more.
• 29:26
  And I think that kind of collaboration between humans who can notice interesting
• 29:31
  things or even, or, but also use computational tools to help say,
• 29:36
  okay, how I, how can I find unexpected collisions between these different
• 29:41
  ideas or take these computational tools and say, okay, now I can aim
• 29:44
  them towards these kinds of things.
• 29:45
  I think that kind of, that partner, that human machine partnership is
• 29:48
  going to become increasingly important allow us to actually do this sort
• 29:52
  of combinatorial discovery process.
• 29:55
  I think it's really interesting when you read in different fields, if you
• 29:57
  read it eclectically, you start to notice patterns with things and you go,
• 30:02
  that thing that happens over here is the exact same that happens over here.
• 30:04
  I write a weekly article and for that reason I've quoted you many
• 30:07
  times in recent articles including the spinach paradox I call it as
• 30:12
  well, but the, it's because you spot these either metaphors or analogies.
• 30:17
  You also spot that if you read right back to the early literature, for example,
• 30:22
  in disruption in companies, you find that the Mark Twain quote is very true.
• 30:26
  That history doesn't repeat what it rhymes.
• 30:28
  So you see these templates all the time and you can see them very clearly.
• 30:32
  And I think that's one of the things I look forward to, technology now
• 30:36
  being able to mass provide this way of thinking for the world.
• 30:41
  Which leads me to this point, and I want to just say to our audience,
• 30:44
  stay tuned for exponential growth.
• 30:46
  So I've said at the start, Sam gives us lots of tools to understand
• 30:50
  exponential growth is one.
• 30:51
  The Moore's law of everything is another one.
• 30:53
  Many people will be familiar with Moore's Law, but I, I thought about
• 30:56
  something there, Sam, which is.
• 30:59
  The speed of discovery of everything.
• 31:02
  And you mentioned, for example, with medicine is also enabled
• 31:05
  by the changes in technology.
• 31:08
  And I thought recently about how, say for example, you see these true crime
• 31:12
  documentaries or maybe some type of series on Netflix or whatever platform you watch
• 31:19
  on and you see that these, they have these resurrected cold cases and it's now
• 31:24
  because now the technology is available to be able to look at the DNA and then
• 31:30
  they're able to solve the crimes, et cetera, even identify bodies, for example.
• 31:34
  And I thought, well, that's the same thing, same template is
• 31:38
  what happens with knowledge.
• 31:39
  Because as the technology enhances our access to the knowledge or our
• 31:43
  understanding of the knowledge changes.
• 31:45
  Right as we learn more, it's going to affect, how we place everything that
• 31:50
  has come before us kind of in context.
• 31:51
  And certainly as we develop new technologies that in turn allows
• 31:56
  us to understand new things.
• 31:57
  And I think right there is this deep interrelationship between
• 32:01
  technological advancement and certainly tool development and the
• 32:05
  growth of our scientific knowledge.
• 32:06
  And so certainly one simple thing will just be around like measurement.
• 32:11
  as we have gotten better and better tools to measure our surroundings
• 32:15
  and quantify it visualize at all these different kinds of things,
• 32:18
  we have in turn learned new things.
• 32:20
  I mean, like certainly the, I mean the interior of the cell for example, like
• 32:26
  that, I mean, as we've developed better microscopy techniques, better staining
• 32:30
  techniques to actually kind of highlight certain things, we've learned a lot about.
• 32:35
  The organelles, like the kind of the, these, these things
• 32:37
  within, with, within the cell.
• 32:39
  But also we've learned, I mean that's kind of at a very small level, but
• 32:42
  we've also learned things at the level of the globe as we have gained better
• 32:46
  and better techniques to visualize our planet with satellite imagery to
• 32:50
  measure certain things down to very, very tight precision with like global
• 32:54
  positioning system, things like that.
• 32:56
  We have in turn created a better and better model of our own planet.
• 33:00
  And of course, and you can see this with, and there's like weather prediction,
• 33:03
  as we have more and more weather tools, we've not only been able to predict
• 33:07
  weather better, but we might actually understand how, how weather works better.
• 33:11
  But there's just example after example of like, I dunno, increasingly
• 33:16
  powerful particle accelerators that has in turn led to new knowledge about
• 33:21
  fundamental ideas around physics.
• 33:23
  The idea is that, right?
• 33:25
  Like we think of kind of technological advancement as kind of, oh, this
• 33:28
  thing of like, oh, we're getting a new gadgets or I dunno, better, better
• 33:31
  battery power for our phones, whatever.
• 33:33
  And yeah, there is a lot of that, but a lot of those things are also
• 33:37
  deeply connected to our ability to actually understand the world itself.
• 33:41
  And so see that as these technological advancement hap happen, they move hand
• 33:48
  in hand with better scientific knowledge.
• 33:50
  And certainly even with like, as we now have these new AI tools that
• 33:54
  can in turn allow us to maybe stitch together different domains that
• 33:57
  we might not otherwise have been able to actually connect together.
• 33:59
  And so there's, yeah, there's a lot of these tightly intertwined marches of
• 34:05
  technological advancement going hand in hand with scientific advancement.
• 34:08
  Which tees us up beautifully for exponential growth.
• 34:11
  And I, I have this little just excerpt to tee you up here, Sam, to take us
• 34:14
  away on this because this is such an important aspect of technological change.
• 34:20
  So imagine you were given a penny and you're, I, by the way, Sam,
• 34:25
  I have to admit this to you.
• 34:26
  This is a terrible thing I did.
• 34:27
  I remember saying to my son this one day, I said, Hey, do you
• 34:31
  want me to give you a penny?
• 34:33
  And I gave him this.
• 34:34
  I said.
• 34:37
  So I'll set this up because it was kind of cruel, but he, he just gave me this
• 34:42
  terrible look and I could tell what the thought bubble was saying about me.
• 34:47
  So imagine you're given a penny and begin doubling it each day.
• 34:50
  After a week, you would be receiving less than $1 50 a
• 34:54
  day, but give it one more week.
• 34:56
  Now you're getting more than $80 a day within a month.
• 35:01
  Your allowance is more than $100 million a day.
• 35:04
  That's the power of exponential growth.
• 35:07
  exponential growth, right?
• 35:08
  It's not and it's something that our.
• 35:11
  are we did not evolve to kind of think about these like highly
• 35:14
  nonlinear systems where things double and there's kinda this feedback.
• 35:18
  But right.
• 35:18
  Exponential growth this process of doubling and cumulative where you're
• 35:23
  growing not by a constant amount.
• 35:26
  You're going by a constant rate.
• 35:28
  It could be a doubling, it could be some smaller rate.
• 35:30
  And turns out that a lot of our scientific knowledge and even our technological
• 35:36
  knowledge, and certainly we can discuss Moore's Law as well, in the context,
• 35:39
  they obey these kinds of doublings and these kinds of like rapid growth.
• 35:43
  And so it can be, so in terms of like scientific knowledge, even like one rough
• 35:47
  proxy is just the number of scientific papers that are being published and that
• 35:51
  obeys these kinds of growth properties.
• 35:53
  And you can look at, abilities of our, I think particle accelerators might adhere
• 35:56
  to one of these kinds of things in terms of the kinds of things can be done.
• 35:59
  But also yeah, just like how what we know right , is growing.
• 36:04
  More and more rapidly.
• 36:06
  And it also goes hand in hand, going back to what I was saying
• 36:09
  with like technological growth.
• 36:10
  Technological advancement also obeys this exponential kind of
• 36:14
  curve and exponential property.
• 36:16
  And the yeah, so like the kind of, the classic example is Moore's Law.
• 36:23
  So Moore's Law was developed by
• 36:25
  gordon Moore I think this might have been before, he was one
• 36:29
  of the co-founders of Intel.
• 36:30
  I can't remember exactly the timing, but it was this empirical observation
• 36:34
  he noticed, which was, he just noticed that the number of transistors.
• 36:39
  On circuits on chips was doubling.
• 36:43
  I think initially he said maybe about every 18 months and eventually it was
• 36:45
  modified to be about every two years.
• 36:48
  And he did this bit with , three or four data points.
• 36:50
  It was some very little, very small kind of thing.
• 36:52
  And remarkably, this relationship of the number of transistors doubling
• 36:58
  has held up for a very, very long period of time for many, many decades.
• 37:02
  And I think people have always talked about like whether or not it's gonna break
• 37:04
  down, but it has held up remarkably well.
• 37:09
  Exponential growth holds across different technological techniques being used to
• 37:14
  allow this exponential curve to happen.
• 37:17
  And the interesting thing is, 'cause each tech, each individual
• 37:20
  technology, underlying technology for memory storage or whatever
• 37:22
  it is might eventually saturate.
• 37:24
  Rather than getting this like exponential growth, you get something called an
• 37:26
  S-curve where it starts exponentially and then eventually saturates as we I dunno.
• 37:31
  Lose the ability to continue to advance that technology.
• 37:35
  But as one s-curve saturates, another S-curve kind of takes its place as a
• 37:41
  new emerging technology takes its place.
• 37:43
  And people have looked at this kind of thing of like how to understand
• 37:45
  innovation is these series of s-curves that when they're stacked
• 37:48
  together, something approximating this sort of ex exponential growth.
• 37:53
  But it's this remarkable yeah, this remarkable process where
• 37:58
  even though the underlying technologies might be changing, the
• 38:06
  like advancement of processing power, information, storage, what have you
• 38:12
  these kinds of things are actually remarkably regular and there's a lot of.
• 38:15
  Discussion around why is that so how do we get these things?
• 38:17
  And some people talk, maybe it's a self-fulfilling prophecy of like,
• 38:20
  oh, could, because we want Moore's law to hold, we're gonna just keep on
• 38:23
  trying to do these kinds of things and maybe there's something to be that.
• 38:26
  I think also there's, to a certain degree, just the fact that as our population
• 38:31
  is growing rapidly, that often goes hand in hand with more scientists and
• 38:35
  more technologists and more engineers being able to make discoveries.
• 38:38
  And of course that's part of this larger conversation of whether or
• 38:41
  not there is an increasing burden of knowledge and how we make discoveries.
• 38:44
  Are discoveries slowing?
• 38:46
  This is also kind of part of these just larger body of knowledge known as
• 38:50
  like science toomes , or meta science of trying to actually quantitatively
• 38:54
  measure how science, the process of science and how it's done.
• 38:57
  What are the things that actually make science easier?
• 39:01
  What makes it more likely?
• 39:02
  How do we actually get these scientific discoveries?
• 39:04
  What, what is, what are the sort of the underlying properties
• 39:08
  of scientific creativity?
• 39:09
  How do we think about multiple independent discovery?
• 39:11
  There's, this is a massive field.
• 39:13
  It's unbelievably fascinating, which is one of the reasons I wrote the book.
• 39:17
  But it's wild to see that we actually do have the capacity to quantitatively
• 39:21
  understand this thing that you would think, oh, it's it's so difficult to
• 39:24
  describe like discovery and creativity are these very special processes.
• 39:27
  No, it turns out in the aggregate there are regularities and whether it
• 39:31
  takes the form of kind of a Moore's law of everything or this kind of
• 39:34
  exponential growth of scientific papers there are these, yeah, these regular
• 39:38
  patterns to how what we know grows.
• 39:41
  if you're working in future orientated aspects like strategy, you're able
• 39:46
  to actually use Moore's Law as a map.
• 39:48
  You're able to understand exponential growth.
• 39:50
  You're able to see that yes, this product or service that we're building may not
• 39:56
  work today, but actually will work in the future because these laws continue.
• 40:00
  They will continue the whole way.
• 40:02
  The iPod came around.
• 40:03
  That way, they understood that Moore's law would actually solve the storage
• 40:07
  issue, and they actually built the whole.
• 40:08
  Frame the advertising behind the iPod before it was even
• 40:11
  developed, and then they saw that Toshiba had solved the problem.
• 40:15
  They bought the patent for the Toshiba storage device.
• 40:17
  So I thought that's why it's so valuable to understand all these aspects and
• 40:23
  all these different mathematical models and scientific models because it just
• 40:26
  helps you deal with the uncertainty or the seeming uncertainty of everything.
• 40:31
  But one thing, Sam, I'd love you to lean into a bit was you
• 40:34
  were talking about the S curve.
• 40:36
  And how the S-curve goes from a rapid growth stage to a plateau stage.
• 40:42
  And one of the things that you pointed out was, if you think of, for example, a Petri
• 40:46
  dish and there's bacteria grown in that Petri dish, it has a certain capacity.
• 40:51
  And I thought about the S-curve as you only has a, have a certain amount
• 40:55
  of customers for your product, or so many people will buy your product and
• 40:59
  then they turn into repeat customers.
• 41:01
  So you can't keep growing.
• 41:03
  And one of the terms that you introduced me to here was carrying capacity.
• 41:07
  the
• 41:08
  simple way to understand it right, is bacteria within a Petri dish,
• 41:10
  when you have one bacterium that's doubling and like, so it creates two
• 41:14
  bacteria and then kind of four and then eight and so on and so forth.
• 41:17
  At the very early stages, , the doubling is the process that is
• 41:22
  kind of the most important feature.
• 41:24
  But eventually as these bacteria double more and more, it is going to saturate
• 41:30
  that Petri dish and it's going to slow down because it turns out there is not
• 41:34
  an infinite amount of nutrients, or there are, or you're going to bump into
• 41:38
  other bacteria or you're going to bump into just the borders of the Petri dish.
• 41:41
  And so.
• 41:43
  On the one hand, you have that very early exponential growth, but then
• 41:46
  eventually you're the, you reach caring capacity kind of slows down.
• 41:50
  You you saturate and you reach these other kinds of features of the system and right.
• 41:55
  And then, and you can analogize this to many different things.
• 41:58
  And so in technol, in technological advancements, it can be things around
• 42:01
  the the maturity of a technology.
• 42:03
  And so like very early on, maybe when something is first being developed,
• 42:08
  there's a lot of low hanging fruit.
• 42:09
  And so it's , very easy to make this thing faster and faster
• 42:12
  and or better and better.
• 42:12
  And eventually you pick all these low hanging fruit and the
• 42:15
  development gets slower and slower.
• 42:16
  And you can also look at the same kind of thing of like right as
• 42:19
  a new product hits the market.
• 42:22
  Every customer is going to be new, and, but eventually, right you're going to
• 42:26
  slow down 'cause they're, and you're going to run out of new customers and
• 42:30
  like this is, I know, and it reminds me of was it the CEO of Netflix.
• 42:34
  Reed Hastings, he has the like famous quote of , like our
• 42:36
  biggest competitor is sleep.
• 42:38
  Like, when you saturate the market, that's the kind of thing you're working on.
• 42:41
  Like you're not trying to find new customers, you are trying to compete with
• 42:44
  a fundamental like physiological thing.
• 42:48
  And so yeah, like being aware of the ways in which an exponential can
• 42:55
  potentially break down I think can be really valuable for understanding whether
• 43:00
  or not you're going to be bumping up, up against these carrying capacities or
• 43:03
  kind of the slower part of the S-curve.
• 43:05
  I always think about rabbits.
• 43:06
  So you have a population rabbits and they're going, oh, this is great.
• 43:09
  We've got a nice piece of land here.
• 43:11
  We're gonna populate the land.
• 43:12
  And then it's kind of like you break a marketplace and then people
• 43:17
  kind of want to steal your lunch.
• 43:18
  But in this case, the rabbits are the lunch and a lamb comes a competitor
• 43:23
  like a, an apex predator for example.
• 43:25
  And they go, actually, this is where we're gonna settle down.
• 43:27
  There's lots of pastures here for us to feed on.
• 43:31
  And it's the same thing as the carrying capacity.
• 43:33
  They'll get to a certain size competitor comes in, or predator in this case.
• 43:37
  And then the ecosystem changes.
• 43:40
  And I think that's a thing that we have to accept about the, the pace
• 43:45
  of change is that this just happens.
• 43:47
  It's natural, it happens in every aspect of evolution.
• 43:50
  And this brings us to the pace of discovery.
• 43:53
  And I'm jumping right back here to the exponential chapter where you
• 43:57
  talked about Derek, the Hoya Price.
• 43:59
  I probably butchering his name there,
• 44:01
  And the science of Trics because again, it was a term and I'd love to
• 44:05
  share with our audience some of these terms just to make sense of things.
• 44:08
  Derek deSolla Price, he was a I think by trade initially, like a historian
• 44:13
  of science he was actually involved in kind of the antikythera mechanism
• 44:17
  of like trying to understand this like ancient Greek gear structure that
• 44:22
  actually was involved, I think, in trying to understand certain things
• 44:25
  around like celestial behavior.
• 44:28
  But he also was, I would say probably, I would say widely considered like
• 44:33
  the father of psychometrics, like this quantitative study of science.
• 44:36
  And the story goes of like, one of the, kind of his first discoveries
• 44:39
  was, he was studying somewhere and there was like, I think it might've
• 44:43
  been like somewhere in Singapore, the university library was under renovation.
• 44:47
  And so they gave him like a complete series of like some
• 44:49
  like scientific journal.
• 44:51
  And so he just like took the whole thing, took it home and
• 44:53
  like stacked it against the wall.
• 44:55
  And he started looking at the shape of like the height of these journals.
• 44:59
  And he found that it wasn't like this linear relationship and it wasn't flat.
• 45:04
  It actually had this very clear exponential curve.
• 45:07
  And he realized, oh wait science has this very clear shape.
• 45:11
  And , he began then to just collect bits of information and facts about scientific
• 45:17
  discovery and scientific knowledge growth.
• 45:19
  And he realized there were these clear quantitative relationships.
• 45:24
  And it's one of these kinds of things where yes, every single
• 45:28
  discovery has its own special story.
• 45:31
  And they're all every disco discovery is special in its own way.
• 45:34
  But when you look at all of science in the aggregate, then suddenly everything
• 45:39
  goes from being fairly unpredictable to actually realizing though there are
• 45:43
  these clear quantitative relationships, like how what we know grows over time.
• 45:47
  Is it having these exponential growths?
• 45:50
  we reaching an S-curve in certain developments are, can we actually
• 45:53
  try to quantify these things?
• 45:54
  And the truth is, this is also behind in the underlying title of my book,
• 45:58
  the Half-Life of Facts, which is a kind of by analogy to radioactive material.
• 46:04
  I had a single atom of some radioactive.
• 46:07
  Material.
• 46:08
  I can't tell you if it's going to decay in the next fraction of a second or in
• 46:11
  the next million years or whatever it is.
• 46:13
  But things change when I get a whole chunk of that radioactive material.
• 46:18
  Suddenly it goes from being entirely unpredictable to
• 46:20
  actually being quite regular.
• 46:21
  And you can actually trace out the actual half life of how long it's gonna
• 46:23
  take for half of the atoms to decay.
• 46:25
  I'm, I can't really tell you which of those fa, which of
• 46:27
  those atoms are going to decay.
• 46:29
  But I know that half of them are going to decay.
• 46:31
  And the same kind of thing is true with scientific knowledge
• 46:34
  that there is this regularity.
• 46:36
  When we look at science, when we take a step back and look at the larger picture,
• 46:42
  we realize that there is this shape to how knowledge grows, how scientific
• 46:47
  discovery is being made, how the tools we use and the technologies we have
• 46:51
  can impact scientific discoveries and kinda the shape of technologicals and
• 46:55
  even how knowledge gets overturned there are actually regularities
• 46:57
  to that kind of thing as well.
• 46:59
  So that's scientometrics, but there was another term also,
• 47:02
  you introduced Eureka metrics.
• 47:03
  Yeah, this is a term I developed along with some collaborators.
• 47:06
  I think when I was doing my postdoc, and this was actually around
• 47:10
  the quantification of scientific discovery itself and trying to see
• 47:14
  whether or not discoveries adhere to some very clear pattern in time
• 47:19
  and how that kind of relates to the difficulty of discovery over time.
• 47:22
  And and I believe actually DeSolla Price, he was also invi.
• 47:25
  He looked at a little bit of I think it was going back to actually the
• 47:29
  periodic table, like looking at like the discovery of new chemical elements.
• 47:32
  And so we looked at, yeah, chemical elements and I think certain things around
• 47:38
  like minor planets, like asteroids as and, and, and it turns out there are these
• 47:44
  kind of like quantitative relationships.
• 47:47
  And you can kind of create interesting models around, okay, based on certain
• 47:50
  assumptions of like, are we plucking the low hanging fruit or is all the
• 47:54
  easy stuff being discovered first?
• 47:55
  Then do we have to learn more things?
• 47:57
  And it requires more effort to actually make further discoveries.
• 48:00
  And I think there's actually a great deal of truth to that kind of thing
• 48:02
  where it's, and oftentimes when a field is new, the discoveries
• 48:06
  come fast and relatively easily.
• 48:09
  And then of course as we learn more and more the discoveries might continue
• 48:13
  to come quite regularly, but it's not because they're of equal, ease
• 48:19
  for discovery, but it's because we're exerting exponentially more effort or more
• 48:24
  scientists are involved in that field.
• 48:27
  And this is actually one of the things people have looked at like
• 48:29
  the increased burden of knowledge.
• 48:30
  And whether it's like harder and harder to make discoveries at
• 48:33
  the frontier of knowledge because you have to learn more and more.
• 48:35
  And similarly it might require more and more scientists working together to
• 48:39
  actually continue to make discoveries at this kind of constant rate.
• 48:42
  And thus far we've actually, our human population, which I assume is
• 48:45
  correlated with scientific population, has been growing quite rapidly.
• 48:48
  And so therefore science is proceeding a pace.
• 48:50
  But all these different kind of aspects are features of that, overall
• 48:56
  shape of discoveries themselves,
• 48:58
  Since we've started a show, we've gone to 9 billion people on the planet.
• 49:01
  talking quite that long.
• 49:03
  but actually I wanted to talk about that.
• 49:04
  'cause we had on the show, brilliant guy, Geoffrey West,
• 49:07
  he wrote a book called Scale.
• 49:09
  I see a nodding brilliant book.
• 49:11
  So we did four parts actually with Geoffrey on that book.
• 49:13
  It was a magnificent book, but you talked about the Santa Fe Institute, where
• 49:17
  he was the leader of that institute.
• 49:20
  But.
• 49:21
  One of the reasons I bring it up is you talked about the fact that if the
• 49:25
  earth is getting bigger, and cities in particular are getting bigger,
• 49:29
  there's more collaboration, and as a result of more collaboration,
• 49:32
  there's more science or there's more breakthroughs as a result of that.
• 49:34
  I'd love you to share your thoughts on that, because I thought that
• 49:37
  was, this was really important, Sam.
• 49:39
  I wrote an article a few years ago, and it was after COVID where people
• 49:44
  started to work from home and it was WFH work from home what did I say?
• 49:49
  DFI, disastrous for innovation.
• 49:52
  And what I would get was getting at was that the lack of collaboration,
• 49:56
  bashing out ideas, disagreeing when it needs to happen respectfully, all this
• 50:01
  kind of stuff, that it's very good, difficult to do that on a call like this.
• 50:06
  Even depending on if people are working on that independently and
• 50:10
  then sharing their knowledge together.
• 50:12
  But it's much easier to do as a team where you're bringing all these
• 50:14
  different people with their different backgrounds, different neurodiversity
• 50:18
  together in order to collaborate.
• 50:21
  And I thought it was really interesting you said a group of researchers at
• 50:25
  Harvard Medical School looked at tens of thousands of articles published
• 50:29
  by its scientists and mapped out buildings on campus where they worked.
• 50:33
  And through that work, they were able to look at the effect
• 50:36
  of distance on collaboration.
• 50:39
  And they had found exactly what they had assumed, but no one had ever measured,
• 50:43
  which was the closer two people are the higher the impact of the research
• 50:47
  that results from their collaboration.
• 50:50
  And they found that just by being in the same building as your collaborator,
• 50:54
  it actually makes your work better.
• 50:55
  . Right, like distance, even down to the level of the building, can have that
• 51:00
  kind of impact on , the combinatorial aspects of collaboration or the
• 51:03
  serendipitous kind of kind connections or just the ability to share and exchange
• 51:08
  ideas in some sort of productive way.
• 51:10
  I think that's, yeah, super interesting.
• 51:12
  And anyway, and it mirrors many of the kinds of things, that Geoffrey West
• 51:15
  and his collaborators have studied, which is this idea, that there are
• 51:18
  these increasing returns to scale.
• 51:19
  That, that when cities get bigger.
• 51:22
  They're not just generating more innovation and more ideas,
• 51:25
  they're actually generating more innovation and ideas per person.
• 51:28
  There's the kind of the super linear relationship and I think like we
• 51:33
  have to take into account kind of the geographic aspect of this kind of thing.
• 51:36
  I'm of a complicated mind about working from home and the ability
• 51:40
  to collaborate across distance.
• 51:41
  'cause I actually think when it comes to
• 51:46
  there are ways of overcoming distance.
• 51:48
  And certainly one of the other interesting things though is certainly
• 51:50
  when it comes to scientific innovation, kind of other kinds of innovation,
• 51:54
  there's also increasing returns to like intellectual diversity.
• 51:59
  And presumably the more distance you are, the might, you might be exposed
• 52:02
  to very wildly different ideas now, geography versus intellectual distance,
• 52:06
  that, that could be an open question.
• 52:07
  You, you could be very far apart and still have be exposed to
• 52:10
  the exact same kinds of ideas.
• 52:11
  But I do think.
• 52:13
  That maybe there's this interesting interplay on the one hand you want to be
• 52:17
  geographically proximate to have rapid exchange of ideas, but you also want to be
• 52:22
  intellectually distant because then you're going to be exposed to different domains.
• 52:27
  And if you can productively overcome jargon, barriers and things really allow
• 52:31
  you to do this kind of like combinatorial recombination kind of thing that we've
• 52:34
  been talking about of combining things from different domains together.
• 52:37
  I think it's a complicated dance there of how we can do those kinds of things.
• 52:41
  But I do think that, trying to understand what are the determinants
• 52:46
  of creativity and discovery, whether it's factors of geography, whether
• 52:50
  it's certain things around people coming together from different areas.
• 52:53
  And I think there, there was research done a number of years ago looking
• 52:57
  at patents that arose from like very different science, like dis like
• 53:00
  disciplines or technical domains.
• 53:03
  I'm not sure I'm gonna remember this entirely correctly, but it was something
• 53:05
  to the effect of that the more varied the domains or where the people were
• 53:09
  coming from who were involved in these kinds of things, the lower, the average
• 53:12
  impact of that kind of patent or that kind of work, but the higher the variance.
• 53:16
  And so oftentimes when you're coming from different areas, yeah, it's
• 53:19
  actually really hard sometimes to make useful collaborations because you may
• 53:23
  be talking past each other, you don't really know what you're talking about.
• 53:25
  But when it does work, it can have this high variance, it can be really
• 53:28
  valuable and really impactful.
• 53:30
  And so I think just trying to understand, what are the features of creativity and
• 53:36
  novel discovery and how those things can be balanced together is something
• 53:40
  that yeah, we need from the world of meta science and scientometrics
• 53:43
  and I just want there to be more research into this kind of thing.
• 53:46
  Serendipitously when I was researching your book, I was reading parts of
• 53:51
  Everett Rogers book, the Diffusion of Innovations, and he talked about people
• 53:54
  he called Cosmopolites or Cosmopolites.
• 53:58
  And these were people who traveled widely or read eclectic and read
• 54:03
  widely more broad than deep.
• 54:06
  And I thought about how, to your point there, it's that necessity
• 54:11
  , so I like working on my own.
• 54:12
  I like working, like going to my cave, going to my library and just studying.
• 54:17
  But what I think.
• 54:18
  Is that it's the collaboration or the conversations.
• 54:22
  I'm very lucky I have conversations like this, but very few people get to
• 54:26
  have those conversations because they are so busy or so focused on a target.
• 54:32
  And I think in doing that, you miss subtleties of a conversation,
• 54:37
  the throwaway comments, the water cooler moments that, that
• 54:41
  is difficult to reproduce unless you're a certain type of person.
• 54:46
  And what I mean is if you have more forum type conversations where people
• 54:51
  can discuss and beat up ideas, et cetera, after they come outta their cave, that,
• 54:57
  that you can just miss the collaboration.
• 54:59
  And I'm setting us up here for a very interesting story.
• 55:02
  You tell in the book about the collapse of essentially the bridge that connected
• 55:07
  Tasmania to Australia and what happened as a consequence of that collapse.
• 55:12
  I love this idea of needing these kinds of collaboration interactions among people
• 55:16
  and the story that you're mentioning is there used to be this, land bridge
• 55:20
  between Australia and then Tasmania.
• 55:24
  And when it vanished there was a much smaller population remaining in Tasmania.
• 55:29
  And as a result, not only did, so, it was a smaller
• 55:34
  population relative to Australia.
• 55:36
  Knowledge was actually lost where it was because the population was so small.
• 55:41
  Not only could there not be, I guess there, there couldn't be.
• 55:45
  Specialization, but there also couldn't be the collaborative
• 55:47
  power of not only not building new technologies, but actually certain
• 55:50
  technologies can no longer be preserved.
• 55:52
  'cause there wasn't in that population, the the critical mass required to
• 55:58
  preserve that kind of thing as opposed to, as compared to the technological
• 56:01
  complexity on the mainland of Australia.
• 56:05
  and I think the researcher who was involved in this also just kind
• 56:07
  of correlated it with the size of populations and the sizes of land
• 56:10
  mass in general, which showing that like the larger population you have
• 56:13
  that is interacting, the more in innovation that can be happening.
• 56:21
  But but I would say one thing, and actually I didn't mention this, don't
• 56:24
  believe I mentioned this research in my book, is that we often, certainly
• 56:30
  within science, we don't necessarily optimize or incentivize certain of
• 56:35
  those kinds of connect like kind of connective kind of tissue roles.
• 56:40
  And so there's some really interesting research that came out maybe 10, 15
• 56:44
  years ago or so, it was looking at the productivity I think it was like
• 56:51
  50 years worth of like immunologists.
• 56:55
  And it was looking to see which researchers were like the most
• 56:59
  highly cited, but they also looked at in terms of like their papers
• 57:01
  being referenced by other papers.
• 57:03
  But they also looked to see which researchers were more highly
• 57:08
  acknowledged at the end of the papers.
• 57:09
  And so it was like, like at the end of a paper, in addition, you just say
• 57:12
  like, oh, thanks to these following people who kind of like helped me with
• 57:14
  like conversations or whatever it was.
• 57:16
  They looked at this one category of people who had a, mediocre level of citation.
• 57:20
  So they wouldn't necessarily be considered a top-notch
• 57:21
  like, like superstar scientist.
• 57:23
  But they had a very high level of acknowledgement.
• 57:25
  And it turns out when the, when those people died, the productivity of all
• 57:30
  their collaborators actually dropped.
• 57:32
  And so it shows that these people were actually really valuable for
• 57:36
  that scientific process of like maybe sharing ideas or connecting different
• 57:40
  kinds of bits of information together.
• 57:42
  and we need them.
• 57:43
  And there's a whole larger question of, okay, how do we
• 57:45
  incentivize this in science?
• 57:46
  But I think this idea that have to recognize that there is this much less
• 57:54
  tangible or maybe more, less easily measured kind of thing there is this
• 57:58
  role of like, when you have a large population, if it's all siloed or
• 58:01
  people are siloed, that doesn't matter.
• 58:03
  It is the cross connections and the cross pollination of ideas
• 58:07
  that is really really valuable.
• 58:08
  And, and one of the stories actually I mentioned in my book
• 58:11
  related to, I think when I was.
• 58:13
  I was a postdoctoral fellow.
• 58:15
  Me and a friend of mine, we were working on a paper and we had to find
• 58:17
  some sort of statistical metric for clustering of data or whatever it was.
• 58:21
  And we couldn't figure it out.
• 58:22
  We were searching for things on like online, couldn't find any
• 58:25
  answers, and we're like, okay, we'll just make up some measure.
• 58:27
  It's not gonna be great, but whatever.
• 58:28
  And I was like, oh, why don't we just talk to the statistician down the hall?
• 58:31
  We went to him and I feel like within under a minute he gave
• 58:34
  us the exact answer we needed.
• 58:36
  And it was because he had, he, I mean he was very smart and really
• 58:40
  good at statistics, but he also was able to fulfill that role of being
• 58:44
  that kind of connective tissue.
• 58:45
  And he was also able to overcome jargon, barriers because we didn't even know the
• 58:49
  kind of terms we should even look for.
• 58:51
  And I think that's one of these things where we think, oh, all
• 58:54
  this knowledge is out there.
• 58:55
  We should be able to stitch together bits of information.
• 58:59
  This should be this should be this golden age of scientific
• 59:01
  discovery and innovation.
• 59:02
  And in some ways it is, but even though everything is out there.
• 59:07
  If you don't even know the terms to look for, it's going to remain going back
• 59:10
  to the undiscovered public knowledge.
• 59:11
  It's just gonna remain fallow.
• 59:12
  It's gonna be there and be hidden.
• 59:14
  And so there is still, and maybe with AI this will change like maybe
• 59:17
  the AI will fulfill this role.
• 59:19
  But there still very much is a role for , people, or tools that can
• 59:22
  act as this connective tissue to allow the population as a whole to
• 59:26
  allow innovation to move forward.
• 59:28
  This is what I was getting at with the, with the people working together.
• 59:32
  But it's, it's more than physical proximity.
• 59:34
  It's the willingness for that colleague to give you the time and
• 59:38
  not to see you as a competitor and why should I help you, kind of thing.
• 59:41
  Which can sneak into academic organizations, unfortunately and
• 59:46
  scientific and every organization where there's more competition within than there
• 59:50
  is to the outside world . And the reason I mentioned that is I loved the study
• 59:55
  that you shared a. About Nobel laureates where when they were more of a mensch,
• 1:00:00
  they actually were more successful.
• 1:00:02
  It was linked to scientific productivity.
• 1:00:04
  To set you up here, you give the example in the 1960s of a lady called Harriet
• 1:00:09
  Zuckerman, a sociologist of science, which is somebody who studies the interactions
• 1:00:13
  and the interactions and people underlying the scientific entire scientific ventures.
• 1:00:19
  And she decided to study the scientific output of Lobel of Nobel laureates to
• 1:00:25
  see if any patterns could be seen in how they work that might distinguish
• 1:00:30
  them from their less successful peers.
• 1:00:32
  I'd love you to share the output of this.
• 1:00:34
  It was a brilliant one.
• 1:00:35
  I believe what they did was they looked at the the position of
• 1:00:39
  authorship in papers 'cause oftentimes.
• 1:00:42
  I think within the li within the literature, and of course
• 1:00:44
  it varies from field to field.
• 1:00:45
  It's like the most central person involved was the like the first author.
• 1:00:51
  And it was one of these things where I think she found that these,
• 1:00:56
  I'm not, it might have actually been eventual Nobel alerts, like
• 1:00:58
  fairly , early on in the career.
• 1:01:00
  They gave kind of pride of place to their younger collaborators.
• 1:01:04
  They let them have, be the first author very early on.
• 1:01:06
  And it was kinda this like very sort of, yeah, like mensch like approach of like,
• 1:01:10
  oh yeah, I, I like generosity of spirit.
• 1:01:12
  I wanna kind of allow other people to be successful.
• 1:01:15
  it turns out that there was a correlation of like that to give up.
• 1:01:21
  The first, like first authorship was actually correlated
• 1:01:25
  to massive productivity.
• 1:01:26
  It was like these Nobel laureates were, yeah, some of these people
• 1:01:29
  who were actually very, very giving and willing to not just take all
• 1:01:34
  the glory for themselves, but allow right their students or other
• 1:01:38
  researchers they were collaborating with to also share in that success.
• 1:01:42
  And that actually was a really, really important feature.
• 1:01:45
  And I think that , going back to what we're saying of like certain, certain
• 1:01:48
  things we incentivize in science of like making all these discoveries
• 1:01:51
  and making discoveries by yourself.
• 1:01:52
  But the truth is science and all of innovation is this highly collaborative
• 1:01:56
  effort and it's very team oriented.
• 1:01:58
  And actually there's been a lot of research in recent years around the nature
• 1:02:02
  of team science and like what are the kinds of teams and the makeup of teams.
• 1:02:05
  But there also needs to be this understanding that like if it's a team
• 1:02:10
  sport, then you actually need to really.
• 1:02:13
  think of it as a team sport.
• 1:02:14
  Like you you all rise and fall together and how you think about
• 1:02:17
  that kind of thing is very important.
• 1:02:18
  And making sure that other people can be apprenticed and mentored and
• 1:02:24
  share in that success so that they can become even more successful.
• 1:02:26
  And then maybe then they will create their own lab and guess actually
• 1:02:30
  mimic that same kind of behavior.
• 1:02:31
  And so those kinds of things are really, really important,
• 1:02:33
  Yeah, having played in sports and teams.
• 1:02:36
  I have played in teams where some of the best players are playing
• 1:02:39
  for themselves and the team never does well as a result of that.
• 1:02:42
  They're just playing for their own personal glory, which
• 1:02:45
  came to mind when I read this.
• 1:02:46
  I thought we'd finish Samuel on the asymptote of truth, and maybe
• 1:02:50
  you'll explain that term asymptote as well for those who don't know it.
• 1:02:54
  But I have a little quote here, and I love this.
• 1:02:56
  This was one of my, probably one of my favorite stories
• 1:02:59
  along with the dinosaur story.
• 1:03:01
  You said that anyone who was alive during the late Cretaceous between
• 1:03:04
  about 100,000,060 5 million years ago, I don't think there's many of them
• 1:03:09
  listening to the show, would've seen many familiar creatures such as the
• 1:03:13
  fearsome, tyrannosaurus, the duck billed.
• 1:03:15
  hadrosaurs , numerous birds and even a selection of small mammals.
• 1:03:19
  But if you ventured into the sea, you would've seen a wide
• 1:03:22
  variety of marine creatures.
• 1:03:25
  Among these animals was what's called a coelacanths, a gray, thick lipped fish.
• 1:03:31
  They were one of the most hideous creatures of this period.
• 1:03:34
  Sam tells us.
• 1:03:36
  And the coelacanth is an example of what's known as a Lazarus taxa living
• 1:03:41
  things that are presumed long extinct until contrary evidence is discovered.
• 1:03:46
  Seems like there's not a lot of them.
• 1:03:48
  Turns out Sam's gonna tell us a different story.
• 1:03:50
  The coelacanth yeah, it's this weird, like ugly fish.
• 1:03:53
  I imagine it's attractive to other coelacanths But it was presumed extinct
• 1:03:58
  for, thought, yeah, it was extinct for of millions of years until I think
• 1:04:05
  about a hundred years ago or so someone.
• 1:04:09
  Caught a coelacanth and realized, oh wait, this thing that people thought
• 1:04:13
  was long extinct actually still alive.
• 1:04:16
  And it was one of the, and one of these fish or one of these
• 1:04:19
  creatures that presumably was very well adapted to its specific niche.
• 1:04:25
  And it did not need to change over time or e evolution.
• 1:04:28
  And and it there, but it was just not known to people that it was
• 1:04:33
  still actually alive and right.
• 1:04:34
  These Lazarus taxa, these taxa that people thought were extinct but actually have
• 1:04:38
  been effectively resurrected because we now discover living versions of them
• 1:04:43
  is actually not entirely a rare thing.
• 1:04:46
  And so there was some research where someone looked at a number
• 1:04:49
  of different animals that had been thought to be extinct at some point.
• 1:04:53
  And I think they found like a third of them had actually been and for were
• 1:04:59
  found actually to still be absent.
• 1:05:00
  They were still, actually still alive.
• 1:05:02
  And it shows this larger idea.
• 1:05:05
  That we are slowly but surely approaching a true understanding of
• 1:05:08
  the world sometimes by discovering things that we thought were extinct
• 1:05:11
  or now actually still alive.
• 1:05:12
  But this is this idea of the asymptote, which is like in a graph
• 1:05:16
  you might never approach a certain value, but you're slowly but surely
• 1:05:19
  getting ever closer and ever closer.
• 1:05:21
  And so it's, you're asymptotically getting closer and closer to this kind of thing.
• 1:05:25
  And, the way to think about this in terms of knowledge again, is we're
• 1:05:29
  not just constantly overturning every, everything we thought we knew.
• 1:05:33
  We are slowly but surely asymptotically getting closer and closer to
• 1:05:36
  a true version of the world.
• 1:05:38
  And the way to think about this is there's a great quote by the the
• 1:05:42
  science fiction writer, Isaac Asimov that I mentioned in the book, he, he
• 1:05:46
  received from a reader a letter saying like, oh, we thought the earth was
• 1:05:49
  flat at one point and we were wrong.
• 1:05:51
  We thought the earth was a perfect sphere.
• 1:05:54
  But then it turned out it was actually an ablate spheroid.
• 1:05:57
  Like we were also wrong.
• 1:05:58
  Like how can we know anything?
• 1:06:00
  And.
• 1:06:02
  Isaac Asimov's response is basically if you think that thinking of the earth is
• 1:06:06
  flat is just as wrong as thinking of the earth is a perfect sphere, then your view
• 1:06:10
  is wronger than both of them put together.
• 1:06:12
  And it's this idea like, and it turns out you can actually measure the amount
• 1:06:15
  of error of believing the earth is flat versus like to what we actually have
• 1:06:18
  versus believing it's like a perfectly sphere spherical versus an ablate steroid.
• 1:06:22
  And we are solely but surely removing a certain amount of error.
• 1:06:25
  And so even though these views seem wildly different overall, we are
• 1:06:30
  approaching a, ideally a more complete version of what the world is now.
• 1:06:35
  Of course.
• 1:06:36
  And I imagine every generation has thought that that're like we've known it all.
• 1:06:39
  And now the rest is just like rooting out a few little errors.
• 1:06:41
  And of course, we're going to discover things that we thought
• 1:06:44
  we, we were true are actually quite different from the way the world is.
• 1:06:48
  for me that's very exciting.
• 1:06:49
  But I do think slowly but steadily, we actually do have a really good
• 1:06:53
  understanding of the world and we are asymptotically approaching hopefully
• 1:06:57
  a true understanding of reality.
• 1:06:58
  Beautiful.
• 1:06:59
  And I loved what you said.
• 1:07:00
  This is my final little quote for today, and then I'm gonna come
• 1:07:03
  to you, Sam, and ask where people can find you and reach out more.
• 1:07:06
  And don't forget, we have more from Sam as well.
• 1:07:08
  I'll show the other books now in a moment.
• 1:07:10
  You said here, this sort of large scale analysis is not just for understanding
• 1:07:15
  the nature of a Lazarus taxa.
• 1:07:17
  It can be extended more generally to enable us to understand the entire
• 1:07:21
  edifice of science and all education we might argue, and how we overturn
• 1:07:26
  long held scientific beliefs.
• 1:07:28
  By looking at how science changes overall we can see the patterns in how scientific
• 1:07:33
  knowledge is revised and it can lead us to measuring the half life of facts.
• 1:07:38
  Beautiful.
• 1:07:40
  Nice cherry on top from me today.
• 1:07:42
  Sam, maybe we'll come to you to share where people can find out more about
• 1:07:45
  you, your work, your writing, et cetera.
• 1:07:47
  So my website is arbesman.net, so just my last name.net.
• 1:07:51
  I I have a newsletter you can subscribe to there.
• 1:07:54
  I think it's at Arbesman dot sub stack.com.
• 1:07:58
  And yeah, and then you can find a whole links to selections of
• 1:08:02
  my writing my books as well.
• 1:08:04
  And yeah, I've written three, and it sounds like we're gonna be discussing
• 1:08:07
  more of them, but yeah, so Arbesman.Net and my newsletter as well those
• 1:08:10
  are the places to find me online.
• 1:08:12
  Absolutely brilliant.
• 1:08:13
  So that was that half life of facts.
• 1:08:14
  Let's put that one on back on the shelf.
• 1:08:17
  I can feel like I've moved a little bit towards more knowledge today.
• 1:08:21
  And next up we're gonna have this book, Sam's second book Over Complicated.
• 1:08:24
  That's gonna be our next book next week.
• 1:08:26
  And then we're gonna move to his latest book just released, which
• 1:08:30
  is the Magic of Code as well.
• 1:08:31
  And Sam, I'll share all the links of where to find You, Substack, et
• 1:08:34
  cetera, for those people who are interested in finding out more.
• 1:08:38
  And just final word for me is to.
• 1:08:41
  Thank you, the author of Halflife of Facts, Samuel Arbeman,
• 1:08:45
  thank you for joining us.
• 1:08:46
  Thank you so much This is so much fun.
• 1:08:48
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