Video summary
In this discussion, Stephen Wolfram reflects on his professional relationship with Richard Feynman during their time at Caltech and later while both served as consultants for Thinking Machines Corporation. Although they collaborated frequently, a fundamental philosophical divide existed regarding business strategy; Wolfram viewed running the company as an effective mechanism to build tools that empower scientific exploration, whereas Feynman considered such administrative interests a distraction from pure physics. Despite this difference in perspective on corporate management, Feynman remained deeply engaged with the technical aspects of their work, offering specific advice on mathematical computation and integrals used in particle physics. Wolfram recalls receiving an extensive pile of calculation methods from Feynman in the 1950s that he eventually returned years later, noting how Feynman's approach to systematizing complex calculations often clashed with the industrialized methods that had evolved by then. A central theme of their interaction was Feynman's unique ability to possess strong intuitions about difficult concepts while dismissing his own rigorous derivations as trivial exercises. Wolfram explains that Feynman would perform complicated quantum field theory calculations, arrive at correct results, and present only the simple final intuition without revealing the intricate steps he had taken by hand on a blackboard. This approach often mystified others who could not see why their methods worked until they realized it was because Feynman had already verified them through exhaustive calculation. Wolfram suggests that while Feynman understood this dynamic to some extent, his brilliance lay in compressing vast computational knowledge into simple intuitive frameworks, leaving observers unable to replicate the process without performing the underlying work themselves. The conversation shifts to a specific incident involving cellular automaton Rule 30 and the Connection Machine at Thinking Machines Corporation. Wolfram describes generating a massive printout of Rule 30's complex behavior using high-resolution printers typically used for microprocessor layouts, which led him and Feynman to physically measure features on the floor. When Wolfram explained that he had discovered the rule's complexity by enumerating all possible rules rather than through intuition, Feynman expressed relief at realizing there was no hidden intuitive shortcut. This exchange highlighted a significant dichotomy in scientific discovery: humans cannot possess clean intuitions about irreducible computational systems without first running experiments or simulations to understand them, challenging the notion that deep insight can always be derived purely from abstract reasoning. Wolfram attributes his openness to discovering such complexities partly to his experience building computer languages and artificial universes rather than solely engaging in reductionistic physics. He contrasts traditional science, where one must accept the universe as given, with programming, which allows scientists to construct their own systems by defining primitives and exploring what is possible within those constraints. By creating these "artificial brains," Wolfram argues that he developed a different attitude toward discovery, becoming more open to structures that might otherwise be missed if relying only on human cognition or standard physical intuition. He emphasizes the fundamental difference between tools like telescopes, which extend vision of existing reality, and computers, which generate new levels of abstraction and allow for the exploration of entirely novel domains. Ultimately, Wolfram concludes that building computational languages forces one to create higher-level abstractions, a process essential for avoiding blind spots where obvious phenomena are overlooked due to preconceived notions about how things should work. He acknowledges his own history of missing simple cases because he held an intuition that they shouldn't exist, illustrating the inherent difficulty in maintaining an open mind even for brilliant physicists like Feynman. The dialogue underscores the historical repetition in technology and science, where concepts take decades to be fully understood or "nailed down," suggesting that while tools can accelerate discovery, the human capacity to remain receptive to unexpected results remains a critical component of scientific progress.
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when you were a Caltech did you get to
interact with richard fineman analogy of
a nice Richard we we work together quite
a bit actually
in fact on and in fact both when I was
at Caltech and after I left Caltech we
were both consultants at this company
called Thinking Machines Corporation
which was just down the street from here
actually um ultimately ill-fated company
but um I used to say this company is not
going to work with the strategy they
have and dick Feynman always used to say
what do we know about running companies
just let them run their company anyway I
was he was not into into that kind of
thing and he always thought he always
thought that my interest in doing things
like running companies was a was a
distraction so to speak um and I for me
it's a it's a mechanism to have a more
effective machine for actually getting
things figuring things out and getting
things to happen did he think of it
because essentially what you used you
did with the company I don't know if you
were thinking of it that way but you're
creating tools to empower your to
empower the exploration of the
University do you think did he did he
understand that point that the point of
tools of I think not as well as he might
have done I mean I think that but you
know he was actually my first company
which was also involved with well was
involved with more mathematical
computation kinds of things um you know
he was quite he had lots of advice about
the technical side of what we should do
and so on um giving examples memories of
thoughts oh yeah yeah he had all kinds
of lucky in in the business of doing
sort of you know one of the hard things
in math is doing integrals and so on
right and so he had his own elaborate
ways to do integrals and so on he had
his own ways of thinking about sort of
getting intuition about how math works
and so his sort of meta idea was take
those intuitional methods and make a
computer follow those in traditional
methods now it turns out for the most
part like when we do integrals and
things what we do is is we build this
kind of bizarre industrial machine that
turns every integral
enter you know products of Meiji
functions and generates this very
elaborate thing and actually the big
problem is turning the results into
something a human will understand it's
not quotes doing the integral and
actually Fineman did understand that to
some extent and I I am embarrassed to
say he once gave me this big pile of you
know calculational methods for particle
physics that he worked out in the 50s
and he said you know it's more used to
you than to me type thing and I I was
like I were intended to look at it and
give it back and I start my files now
but that's what happens when when fits
finiteness of human lives it's um I hate
you know maybe if he'd live another 20
years I would have I would remember to
give it back but I think it's you know
that that was his attempt to systematize
um the ways that one does integrals that
show up in particle physics and so on
turns out the way we've actually done it
is very different from that way what do
you make of that difference between so
fireman was actually quite remarkable at
creating sort of intuitive like diving
in you know creating intuitive
frameworks for understanding difficult
concepts is I'm smiling because you know
the funny thing about him was that the
thing he was really really really good
at his calculating stuff and but he
thought that was easy because he was
really good at it
and so he would do these things where he
would calculate some do some complicated
calculation in quantum field theory for
example come out with a result wouldn't
tell anybody about the complicated
calculation because thought that was
easy
he thought the really impressive thing
was to have this simple intuition about
how everything worse so he invented that
at the end and you know because he done
this calculation and knew what how it
worked it was a lot easier it's a lot
easier to have good intuition when you
know what the answer is and then and
then he would just not tell anybody
about this calculation he wasn't meaning
that maliciously so to speak is just he
thought that was easy
yeah um and and that's you know that led
to areas where people were just
completely mystified and they kind of
followed his intuition but nobody could
tell why it worked because actually the
reason it worked was because he done all
these calculations and he knew that it
was would work and you know when I he
and I worked a bit on quantum computers
actually back-end
1980-81 but before anybody had heard of
those things and you know the typical
mode of um I mean he was used to say and
I now think about this because I'm about
the age that he was when I worked with
him and you know I see that people have
one-third my age so to speak and he was
always complaining that I was one-third
his age and so for various things but
but you know he would do some
calculation by by hand you know
blackboard and things come up with some
answer I'd say I don't understand this
you know I do something with a computer
and he'd say you know I don't understand
this so that'd be some big argument
about what was you know what was going
on but that it was always some and I
think actually we many of the things
that we sort of realized about quantum
computing that were sort of issues that
have to do particularly with the
measurement process are kind of still
issues today
and I kind of find it interesting it's a
funny thing in science that these you
know that there's there's a remarkable
it happens in technology - there's a
remarkable sort of repetition of history
that ends up occurring eventually things
really get nailed down but it often
takes a while and it often things come
back decades later well for example I
could tell a story it actually happened
right down the street from here um I
will move both that thinking machines I
had been working on this particular
cellular automaton will rule 30 that has
this feature that it from very simple
initial conditions it makes really
complicated behavior okay so and
actually of all silly physical things
using this big parallel computer called
the connection machine that that company
was making I generated this giant
printout of rule 30 on very on actually
on the same kind of same kind of printer
that people use to make layouts for
microprocessors so one of these big you
know large format printers with high
resolution and so on so okay so to print
this out lots of very tiny cells and so
there was sort of a question of how some
features of that pattern
and so it was very much a physical you
know on the floor with me two rules
trying to measure different things so so
Feynman kind of takes me aside we've
been doing that for a little while and
takes me aside he says I just want to
know this one thing he says I want to
know how did you know that this rule
thirty thing would produce all this
really complicated behavior that is so
complicated that we're you know going
around this big printout and so on and I
said well I didn't know I just
enumerated all the possible rules and
then observed that that's what happened
he said ah I feel a lot better you know
I thought she had some intuition that he
didn't have and that would let what I
said no no no intuition just
experimental science so that's such a
beautiful sort of dichotomy there of
that's exactly showed is you really
can't have an intuition about an
irreducible I mean you have to run us
yes that's right that's not hard for us
humans and especially brilliant
physicist like firemen to say that you
can't haven't compressed clean intuition
about how the whole thing yes works yes
no he was I mean I think he was sort of
on the edge of understanding that point
about computation and I think he found
that I think he always found computation
interesting and I think that was sort of
what he was a little bit poking at I
mean here that intuition you know the
difficulty of discovering things like
even you say oh you know you just didn't
write all the cases in just find one
that does something interesting right
sounds very easy turns out like I missed
it when I first saw it because I had
kind of an intuition that said it
shouldn't be there and so I had kind of
arguments oh I'm gonna ignore that case
because whatever um and so how did you
have an open mind enough because you're
essentially the same person is your
fight like for the same kind of physics
type of thinking how did you find
yourself having a sufficiently open mind
to be open to watching rules and them
revealing complexity yeah I think that's
an interesting question I've wondered
about that myself because it's kind of
like you know you live through these
things and then you say what was the
historical story and sometimes the
historical story that you realized after
the fact was not what you lived through
speak and so you know what I realized is
I think what happened is you know I did
physics kind of like reductionistic
physics where you're throw-in the
universe and you told go figure out
what's going on inside it and then I
started building computer tools and I
started building my first computer
language for example and computer
language is not like it's sort of like
physics in the sense that you have to
take all those computations people want
to do and kind of drill down and find
the primitives that they can all be made
of but then you do something it's really
different because you just you're just
saying okay these are the primitives now
you know hopefully they'll be useful to
people let's build up from there so
you're essentially building an
artificial universe in a sense where you
make this language you've got these
primitives you're just building whatever
you feel like building and that's and so
it was sort of interesting for me
because from doing science where you
just throw in the universe as the
universe is to then just being told you
know you can make up any universe you
want and so I think that experience of
of making a computer language which is
essentially building your own universe
so to speak is you know that's kind of
the that's that's what gave me a
somewhat different attitude towards what
might be possible it's like let's just
explore what can be done in these
artificial universes rather than
thinking the natural science way of
let's be constrained by how the universe
actually is yeah by being able to
program essentially you've as opposed to
being limited to just your mind and a
pen you you now have you've basically
built another brain that you can use to
explore the universe but yeah computer
program you know this is kind of a brain
right and it's well it's it's or
telescope or you know it's a tool and it
lets you see stuff but there's something
fundamentally different between a
computer and a telescope I mean he just
yeah I'm hoping amantha sighs the notion
but it's more general and it's it's I
think I mean this point about you know
people say oh such and such a thing was
almost discovered at such and such a
time the the distance between you know
the building the parrot
it allows you to actually understand
stuff or allows one to be open to seeing
what's going on
that's really hard and you know I think
in I've been fortunate in my life that I
spent a lot of my time building
computational language and that's an
activity that in a sense works by sort
of having to kind of create another
level of abstraction and kind of be open
to different kinds of structures but you
know it's it's always I mean I'm fully
aware of I suppose the fact that I have
seen it a bunch of times of how easy it
is to miss the obvious so to speak that
at least is factored into my attempt to
not miss the obvious although it may not
succeed
you