Kind: captions
Language: en
the following is a conversation with
Stephen Wolfram a computer scientist
mathematician and theoretical physicist
who is the founder and CEO of Wolfram
research a company behind Mathematica
Wolfram Alpha Wolfram language and the
new Wolfram physics project is the
author of several books including a new
kind of science which on a personal note
was one of the most influential books in
my journey in computer science and
artificial intelligence it made me fall
in love with the mathematical beauty and
power of cellular automata it is true
that perhaps one of the criticisms of
Stephen is in a human level that he has
a big ego which prevents some
researchers from fully enjoying the
content of his ideas we talked about
this point in this conversation to me
ego can lead you astray but can also be
a superpower one that fuels bold
innovative thinking that refuses to
surrender to the cautious ways of
academic institutions and here
especially I ask you to join me in
looking past the peculiarities of human
nature and opening your mind to the
beauty of ideas and Stephens work and in
this conversation
I believe Stephen Wolfram is one of the
most original minds of our time and at
the core is a kind curious and brilliant
human being this conversation was
recorded in November 2000 nineteen when
the Wolfram physics project was underway
but not yet ready for public exploration
as it is now we now agreed to talk again
probably multiple times in the near
future so this is round one and stay
tuned for round two soon
this is the artificial intelligence
podcast if you enjoy it subscribe on
YouTube review five stars in Apple
podcast supported on patreon or simply
connect with me on Twitter Alex Friedman
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this podcast and now here's my
conversation with stephen wolfram
you and your son Christopher helped
create the alien language in the movie
arrival so let me ask maybe a bit of a
crazy question but if aliens were to
visit us on earth do you think we would
be able to find a common language well
by the time we're saying
aliens are visiting us we've already
prejudiced the whole story because the
you know the concept of alien actually
visiting so to speak we already know
they're kind of things that make sense
to talk about visiting so we already
know they exist in the same kind of
physical setup that we do they're not
you know it's not just radio signals
it's an actual thing that shows up and
so on so I think in terms of you know
can one find ways to communicate well
the best example we have of this right
now is AI I mean that's our first sort
of example of alien intelligence and the
question is how well do we communicate
with AI you know if you were to say if
you were in the middle of a neural net
and you open it up and it's like what
are you thinking can you discuss things
with it it's not easy but it's not
absolutely impossible so I think I think
by the time but given the setup of your
question aliens visiting I think the
answer is yes one will be able to find
some form of communication whatever
communication means communication
requires notions of purpose and things
like this it's a kind of philosophical
quagmire so if AI is a kind of alien
life-form what do you think
visiting looks like so if we look at a
Lian's visiting yeah and we'll get to
discuss computation and and the world of
computation but if you were to imagine
you said you're already prejudiced
something by saying you visit but what
how would a lian's visit by visit
there's kind of an implication and here
we're using the imprecision of human
language you know in a world of the
future and if that's represented in
computational language we might be able
to take the the concept visit and go
look in the documentation basically and
find out exactly what does that mean
what properties does it have and so on
but by visit
in ordinary human language I'm kind of
taking it to be there's you know
something a physical embodiment that
shows up in a spacecraft since we kind
of know that that's necessary
we're not imagining it's just you know
photons showing up in a radio signal
that you know photons in some very
elaborate pattern we're imagining it's
it's physical things made of atoms and
so on that that show up can't be photons
in a pattern well that's good question I
mean whether there is the possibility
you know what counts as intelligence
good question I mean it's some you know
and I used to think there was sort of a
oh they'll be you know it'll be clear
what it means to find extraterrestrial
intelligence etcetera etcetera etcetera
I've I've increasingly realized as a
result of science that I've done that
there really isn't a bright line between
the intelligent and the merely
computational so to speak so you know in
our kind of everyday sort of discussion
will say things like you know the
weather has a mind of its own
well we'd let's unpack that question you
know we realize that there are
computational processes that go on that
determine the fluid dynamics of this and
that and the atmosphere or etcetera
etcetera etcetera how do we sting which
distinguish that from the processes that
go on in our brains of you know the
physical processes that go on in our
brains how do we how do we had we
separate those how do we say the the
physical processes going on that
represents sophisticated computations in
the weather oh that's not the same as
the physical processes that go on that
represent sophisticated computations in
our brains cancer is I don't think there
is a fundamental distinction I think the
distinction for us is that there's kind
of a a thread of history and so on that
connects kind of what happens in
different brains to each other so to
speak and it's a you know what happens
in the weather is something which is not
connected by sort of a a thread of
civilizational history so to speak to
what we're used to in our story in the
stories that the human brains told us
but maybe the weather has its own
stories that's Allah's house absolutely
and that's and that's where we run into
trouble thinking about extraterrestrial
intelligence because
you know it's like that pulsar
magnetosphere that's generating these
very elaborate radio signals you know is
that something that we should think of
as being this whole civilization that's
developed over the last however long you
know millions of years of of processes
going on in the in the neutron star or
whatever um versus what you know what
we're used to in human intelligence and
I think it's a I think in the end you
know when people talk about
extraterrestrial intelligence and where
is it in the whole you know Fermi
paradox of how come there's no other
signs of intelligence in the universe my
guess is that we've got sort of two
alien forms of intelligence that we're
dealing with artificial intelligence and
sort of physical or extraterrestrial
intelligence and my guess is people will
sort of get comfortable with the fact
that both of these have been achieved
around the same time and in other words
people will say well yes we're used to
computers things we've created digital
things we've created being sort of
intelligent like we are and they'll sell
we're kind of also used to the idea that
there are things around the universe
that are kind of intelligent like we are
except they don't share the sort of
civilizational history that we have and
so we don't they know they're they're a
different branch
I mean it's similar to when you talk
about life for instance I mean you you
you kind of said life form I think
almost synonymously with intelligence
which I don't think is is some you know
I the the a eyes will be upset to hear
you I wait those guys I really probably
implied biological life right right
right but you're saying I mean we'll
explore this more but you're saying it's
really a spectrum and it's all just the
kind of computation and so it's it's a
full spectrum and we just make ourselves
special by weaving a narrative around
our particular kinds of computation yes
I mean what the thing that I think I've
kind of come to realize is you know it's
a little depressing to realize that
there's there's so little it's
liberating well yeah but I mean it's you
know it's the story of science right in
you know from Copernicus on it's like
you know first we were like convinced a
planets at the center of the universe no
that's not true well then we will
convince there something very special
about the chemistry that we have as
biological organisms now that's not
really true and then we're still holding
out that hope or this intelligence thing
we have that's really special yeah I
don't think it is however in a sense as
you say it's kind of liberating for the
following reason that you realize that
what's special is the details of us not
some abstract attribute that you know we
could wonder Oh is something else going
to come along and you know also have
that abstract attribute well
yes every abstract attribute we have
something else has it but the full
details of our kind of history of our
civilization and so on nothing else has
that that's what you know that's our
story so to speak and that's sort of one
most by definition
special so I I view it as not being such
a I mean I was initially I was like this
is bad this is this is kind of you know
how can we have self-respect about some
about the things that we do then I
realized the details of the things we do
they are the story everything else is
kind of a blank canvas so maybe on a
small tangent you just made me think of
it but what do you make of the monolith
in 2001 Space Odyssey in terms of aliens
communicating with us and sparking the
the kind of particular intelligent
computation that we humans have is there
anything interesting to get from that
sci-fi yeah I mean I think what's what's
fun about that is you know the monoliths
are these you know one to four to nine
perfect cuboid things and in the you
know earth four million years ago
whatever they will pertain with a bunch
of apes and so on
a thing that has that level of
perfection seems out of place it seems
very kind of constructed very engineered
so that's an interesting question what
is the you know what's the techno
signature so to speak what is it that
you see it somewhere and you say my gosh
that had to be engineered um now the
fact is
we see crystals which are also very
perfect and you know that the perfect
ones are very perfect they're nice
polyhedra or whatever um and so in that
sense if you say well it's a sign of
sort of it's a techno signature that
it's a perfect you know a perfect
polygonal shape polyhedral shape that's
not true and so then it's it's an
interesting question what what is the
you know what is the right signature I
mean like you know Gauss famous
mathematician you know he had this idea
you should cut down the Siberian forest
in the shape of sort of a typical image
of the proof of the Pythagorean theorem
on the grounds that there's a kind of
cool idea didn't get done but um you
know it's on the grounds that the
Martians would see that and realize gosh
there are mathematicians out there it's
kind of you know it's the in his theory
of the world that was probably the best
advertisement for the cultural
achievements of our species um but you
know it's it's a it's a reasonable
question what do you what can you send
or create that is a sign of intelligence
in its creation or even intention in its
creation you talk about if we were to
send a beacon can you what what should
we send is math our greatest creation is
what is our greatest creation I think I
think in it's a it's a philosophically
doomed issue so I mean in other words
you send something you think it's
fantastic but it's kind of like we are
part of the universe we make things that
are you know things that happen in the
universe computation which is sort of
the thing that we are in some abstract
sent you then sense using to create all
these elaborate things we create is
surprisingly ubiquitous in other words
we might have thought that you know
we've built this whole giant engineering
stack that's led us to microprocessors
that's led us to be able to do elaborate
computations but this idea the
computations are happening all over the
place the only question is whether
whether there's a thread that connects
our human intentions to what those
computations are and so I think I think
this question of what do you send
to kind of show off our civilization in
the best possible way I think any kind
of almost random slab of stuff we've
produced is about equivalent to
everything else I think it's one of
these things where it's a non romantic
way of phrasing it I just started to
interrupt but I just talked to it up
Andrew in who's the wife of cross
hanging uh-huh and so I don't know if
you're familiar with the Voyager it's
just part of its ascending I think
brainwaves of you know I wasn't it hers
it was yeah her brain waves when she was
first falling in love with Carl Sagan
right it's this beautiful story right
that brand that perhaps you would shut
down the power of that by saying we
might as well send anything else and
that's interesting all of it is kind of
an interesting peculiar thing that's
yeah yeah right well I mean I think it's
kind of interesting to see on the on the
Voyager you know golden record thing one
of the things that's kind of cute about
that is you know it was made one was it
in the late seventies early eighties
yeah um and you know one of the things
it's a phonograph record okay
and it has a diagram how to play a
phonograph record and you know it's kind
of like it's shocking that in just 30
years if you show that to a random kid
of today and you show them that diagram
I've tried this experiment they're like
I don't know what the heck this is and
the best anybody can think of is you
know take the whole record forget the
fact that it has some kind of helical
track in it just image the whole thing
and see what's there that's what we
would do today in only 30 years our
technology has kind of advanced to the
point where the playing of a helical you
know mechanical track on a phonograph
record is now something bizarre so you
know it's it's that's a cautionary tale
I would say in terms of the ability to
make something that in detail sort of
leads by the nose some you know the
aliens or whatever to do something it's
like no you know best you can do as I
say if we were doing this today we would
not build a helical scan thing with a
needle we would just take some high
resolution imaging system and get all
the bits off it
say oh it's a big nuisance that they put
in a helix you know the spiral let's sum
let's just you know unravel the spiral
and then start from there do you think
and this will get into trying to figure
out interpretability of AI
interpretability of computation being
able to communicate with various kinds
of computations do you think would be
able to if you're put put your alien hat
on figure out this record how to play
this record well it's a question of what
one wants to do I mean understand what
the other party was trying to
communicate or understand anything about
the other party what is understanding
mean I mean that's the issue the issue
is it's like what people were trying to
do natural language understanding for
computers right so people try to do that
for years it wasn't clear what it meant
in other words you take your piece of
English or whatever and you say gosh my
computer has understood this okay that's
nice what can you do with that well so
for example when we did you know built
wolf malphur um you know one of the
things was it's you know it's doing
question answering and so on it needs to
do natural language understanding the
reason that I realized after the fact
the reason we were able to do natural
language understanding quite well and
people hadn't before the number one
thing was we had an actual objective for
the natural language understand and we
were trying to turn the natural language
into commentation into this
computational language that we could
then do things with now similarly when
you imagine your alien you say okay
we're playing them the record did they
understand it well it depends what you
mean if they you know if we if there's a
representation that they have if it
converts to some representation where we
can say oh yes that is a that's a
representation that we can recognize is
represents understanding then all well
and good but actually the only ones that
I think we can say would represent
understanding a ones that will then do
things that we humans kind of recognize
as being useful to us maybe a trying to
understand quantify how
technological advances particular
civilization is so are they a threat to
us from a military perspective yeah yeah
that's probably the kind of first kind
of understanding that would be
interested in gosh that's so hard I mean
that's like in the arrival movie that
was sort of one of the key questions as
is you know why are you here so to speak
and it's I using a hurtis right but but
even that is that you know it's a very
unclear you know it's like the the are
you gonna hurt us that comes back to a
lot of interesting area fix questions
because the you know we might make an AI
that says blood take autonomous cars for
instance you know are you gonna hurt us
well let's make sure you only drive at
precisely the speed limit because we
want to make sure we don't hurt you so
to speak because that's some and then
well something you know but you say but
actually that means I'm gonna be really
late for this thing and you know that
sort of hurts me in some way so it's
hard to know even even the definition of
what it means to hurt yeah someone is
unclear and as we start thinking about
things about AI ethics and so on that's
you know something one has to address
there's always trade-offs and that's the
annoying thing about ethics yeah well
right and I mean I think ethics like
these other things we're talking about
is a deeply human thing if there's no
abstract you know let's write down the
theorem that proves that this is
ethically correct that's a that's a
meaningless idea you know you have to
have a ground truth so to speak that's
ultimately sort of what humans want and
they don't all want the same thing so
that gives one all kinds of additional
complexity and thinking about that one
convenient thing in terms of turning
ethics into computation you ask the
question of what maximizes the
likelihood of the survival of the
species that's a good existential issue
but then when you say survival of the
species right you might say um you might
for example for example let's say forget
about technology just you know hang out
and you know be happy live our lives go
on to the next generation and you know
go through many many generations where
in a sense nothing is happening that
okay is that not okay hard to know in
terms of
the attempt to do elaborate things and
the attempt to might be
counterproductive for the survival of
the species
like for instance I mean in in you know
I think it's it's also a little bit hard
to know so ok let's take that as a sort
of thought experiment ok you know you
can say well what are the threats that
we might have to survive you know the
supervolcano the asteroid impact the you
know all these kinds of things ok so now
we inventory these possible threats and
we say let's make our species as robust
as possible relative to all these
threats I think in the end it's a it's
sort of an unknowable thing what what it
takes to you know so so given that
you've got this AI and you've told it
maximize the long term what is long term
mean does long term mean until the sun
burns out that's that's not gonna work
and you know does long term mean next
thousand years ok they're probably
optimizations for the next thousand
years that it's like it's like if you're
running a company you can make a company
be very stable for a certain period of
time like if you know if your company
gets bought by some you know private
investment group then they'll you know
you can you can run a company just fine
for five years by just taking what it
does and you know removing all R&D and
the company will burn out after a while
but it'll run just fine for a while so
if you tell the AI keep the humans okay
for a thousand years there's probably a
certain set of things that one would do
to optimize that many of which one might
say well that would be a pretty big
shame for the future of history so to
speak for that to be what happens but I
think I think in the end you know as you
start thinking about that question it is
what you realize is there's a whole sort
of raft of undecidability computational
irreducibility in other words it's I
mean one of the good things about sort
of the the the what our civilization has
gone through and what sort of we humans
go through is that there's a certain
computational irreducibility to it in
the sense that it isn't the case you can
look from the outside and just say the
answer is going to be this
at the end of the day this is what's
gonna happen you actually have to go
through the process to find out and I
think that's um that's both that feels
better in the sense it's not a you know
something is achieved by going through
all of this all of this process and it's
but it also means that telling the a I
go figure out you know what will be the
best outcome well unfortunately it's
going to come back and say it's kind of
undecidable what to do we'd have to run
all of those scenarios to see what
happens and if we want it for the
infinite future
we're throwing immediately into a sort
of standard issues of of kind of
infinite computation and so on so yeah
even if you get that the answer to the
universe and everything is 42 you still
have to actually run the universe yes
yes like the question I guess or the the
you know the the journey is the point
right well I think it's saying to
summarize this is the result of the
universe yeah that's if that is possible
it tells us I mean the whole sort of
structure of thinking about computation
and so on and thinking about how stuff
works if if there if it's possible to
say and the answer is such-and-such
you're basically saying there's a way of
going outside the universe and you're
kind of you're getting yourself into
something of a sort of paradox because
you're saying if it's knowable what the
answer is then there's a way to know it
that is beyond what the universe
provides but if we can know it then
something that we're dealing with is
beyond the universe so then the universe
isn't the universe so to speak so and in
general as we'll talk about at least for
small human brains it's hard to show
that the result of a sufficiently
complex computation it's hard I mean
it's probably impossible right and
there's a side ability so and the
universe appears by at least the poets
to be sufficiently complex they won't be
able to predict what the heck it's all
going to well we better not be able to
because if we can
kind of denies I mean it's you know
we're part of the universe yeah so what
does it mean for us to predict it means
that we that our little part of the
universe is able to jump ahead of the
whole universe and you know this this
quickly winds up I mean that there it is
conceivable the only way we'd be able to
predict is if we are so special in the
universe we are the one place where
there is computation more special more
sophisticated than anything else that
exists in the universe that's the only
way we would have the ability to sort of
the almost theological ability so to
speak to predict what happens in the
universe is to say somehow we're we're
better than everything else in the
universe which I don't think is the case
yeah perhaps we can detect a large
number of looping patterns that reoccur
throughout the universe and fully
describe them and therefore but then
it's it still becomes exceptionally
difficult to see how those patterns
interact and what kind of well look the
most remarkable thing about the universe
is that it has regularity at all might
not be the case if you don't have
regularity absolutely therefore it's
full of I mean physics is successful you
know it's full of of laws that tell us a
lot of detail about how the universe
works I mean it could be the case that
you know the 10 to the 90th particles in
the universe they will do their own
thing but they don't they all followed
we already know they all follow
basically physical the same physical
laws and that's something that's a very
profound fact about the universe what
conclusion you draw from that is unclear
I mean in the you know the early early
theologians that was you know exhibit
number one for the existence of God now
you know people have different
conclusions about it
but the fact is you know right now I
mean I happen to be interested actually
I've just restarted a long-running kind
of interest of mine about fundamental
physics I'm kind of like come on I'm on
a bit of a quest which I'm about to make
more public of to to see if I can
actually find the fundamental theory of
physics excellence we'll come to that
and I just had a lot of conversation
with quantum mechanics folks with so I'm
really excited on your take because I
think you have a fascinating take on the
the the fundamental notch in nature of
our reality from a physics perspective
so and what might be underlying the kind
of physics as we think of it today okay
let's take a step back what is
computation it's a good question
operationally computation is following
rules that's kind of it
I mean computation is the result is the
process of systematically following
rules and it is the thing that happens
when you do that for taking initial
conditions are taking inputs and
following rules I mean what are you
following rules on so there has to be
some data some not necessarily it can be
something where that there's a you know
very simple input and then you're
following these rules and you'd say
there's not really much data going into
this it's you could actually pack the
initial conditions into the rule if you
want to um so I think the the question
is is there a robust notion of
computation that is what is this last
mean what I mean by that is something
like this so so one of the things that
are different in an earlier physics
something like energy okay the different
forms of energy there's but somehow
energy is the robust concept that
doesn't isn't particular to kinetic
energy or you know nuclear energy or
whatever else there's a robust idea of
energy so only things you might ask is
there's the robust idea of computation
or does it matter that this computation
is running in a Turing machine this
computation is running in as you know
CMOS Salkin CPU this computation is
running in a fluid system in the whether
those kinds of things or is there a
robust idea of computation that
transcends the sort of detailed
framework that it's running in okay and
is that her yes I mean it wasn't obvious
that there was so it's worth
understanding the history and how we got
to where we are right now because you
know to say that there is is a statement
in part about our universe
it's not a statement about what is
mathematically conceivable it's about
what actually can exist for us maybe you
can also comment because energy as a
concept is robust but there's also its
intricate complicated relationship with
matter with mass is very interesting of
particles that carry force and particles
that sort of particles that carry
forcing particles that have mass these
kinds of ideas they seem to map to each
other at least in the mathematical sense
is there a connection between energy and
mass and computation or are these
completely disjoint ideas we don't know
yet the things that I'm trying to do
about fundamental physics may well lead
to such a connection but there is no
known connection at this time so key can
you elaborate a little bit more on what
how do you think about computation what
is company yeah so I mean let's let's
tell a little bit of a historical story
yes okay so you know back go back 150
years people were making mechanical
calculators of various kinds and you
know the typical thing was do you want
an adding machine you go to the adding
machine store basically he wants a
multiplying machine you go to the
multiplying machine store that different
pieces of hardware and so that means
that at least at the level of that kind
of computation and those kinds of pieces
of hardware there isn't a robust notion
of computation
there's the adding machine kind of
computation there's the multiplying
machine
notion of computation and they're
disjoint so what happened in around 1900
people started imagining particularly in
the contests of mathematical logic could
you have something which would be
represent any reasonable function right
and they came up with things this idea
of primitive recursion was one of the
early ideas and it didn't work there
were reasonable functions that people
who come up with that were not
represented using the primitive as a
primitive recursion okay so then then
along comes 1931 and girdle's theorem
and so on
and as in looking back one can see that
as part of the process of establishing
girdles theorem girdle basically showed
how you could compile arithmetic you
could basically compile logical
statements like this statement is
unprovable into arithmetic so what he
essentially did was to show that
arithmetic can be a computer in a sense
that's capable of representing all kinds
of other things and then Turing came
along 1936 came up with Turing machines
meanwhile Alonzo Church had come up with
lambda calculus and the surprising thing
that was established very quickly is the
Turing machine idea about what might be
what computation might be is exactly the
same as the lambda calculus idea of what
computation might be and so and then
there started to be other ideas you know
register machines other kinds of other
kinds of representations of computation
and the big surprise was they all turned
out to be equivalent so in other words
it might have been the case like those
old adding machines and multiplying
machines that you know Turing had his
idea of computation church had his idea
of computation and they were just
different but it isn't true there are
actually all equivalent so then by I
would say the the 1970s or so in in sort
of the computation computer science
computation theory area people had sort
of said Oh Turing machines are kind of
what computation is physicists were
still holding out saying no no no it's
just not how the universe works we've
got all these differential equations
we've got all these real numbers that
have infinite numbers of digits
the universe is now a Turing machine
right the you know the Turing machines
are a small subset of that the things
that we make in microprocessors and
engineering structures and so on so
probably actually through my work in the
1980s about sort of the relationship
between computation and models of
physics it became a little less clear
that there would be that there was this
big sort of dichotomy between what can
happen in physics and what happens and
things like Turing machines and I think
probably by now people would mostly
think and and by the way brains were
another kind of elements
this I mean you know girdle didn't think
that his notion of computational what
amounted to his notion of computation
would cover brains and Turing wasn't
sure either
um but tell though he was a little bit
he got to be a little bit more convinced
that it should cover brains um but so
you know but I would say by probably
sometime in the 1980s there was
beginning to be so a general belief that
yes this notion of computation that
could be captured by things like Turing
machines was reasonably robust now the
next question is ok you can have a
universal Turing machine that's capable
of being programmed to do anything that
any Turing machine can do um and you
know this idea of universal computation
it's an important idea this idea that
you can have one piece of hardware and
program it with different pieces of
software you know that's kind of the
idea that launched most modern
technology I mean that's kind of that
that's the idea that launched computer
revolution software etc so important
idea but but the thing that's still kind
of holding out from that idea is ok
there is this Universal computation
thing but seems hard to get to it seems
like you want to make a universal
computer you have to kind of have a
microprocessor with you know a million
gates in it and you have to go to a lot
of trouble to make something that
achieves that level of computational
sophistication ok so the surprise for me
was that stuff that I discovered in the
early 80s I'm looking at these things
called cellular automata which are
really simple computational systems the
thing that was a big surprise to me was
that even when their rules were very
very simple they were doing things that
were as sophisticated as they did even
when their rules much more complicated
so it didn't look like you know this
idea Oh to get sophisticated computation
you have to build something with very
sophisticated rules
that idea didn't seem to pan out and
instead it seemed to be the case that
sophisticated computation was completely
ubiquitous even in systems with
incredibly simple rules and so that led
to this thing that I call the principle
of computational equivalence which
basically says when you have a system
that follows rules of any kind then
whenever the system isn't doing things
that are in some sense obviously simple
then the computation that the behavior
of the system corresponds to is of
equivalent sophistication so that means
that when you kind of go from the very
very very simplest things you can
imagine then quite quickly you hit this
kind of threshold above which everything
is equivalent in its computational
sophistication not obvious that would be
the case I mean that's a science fact
well then hold on a second
you saw this you've opened with a new
kind of science I mean I remember it was
a huge eye-opener
that's such simple things can create
such complexity and yes there's an
equivalence but it's not a fact it just
appears to I mean it's as much as a fact
as sort of these theories are so elegant
that it it seems to be the way things
are but let me ask sort of you just
brought up previously kind of like the
communities of computer scientists with
their touring machines the physicists
will their universe and the whoever the
heck maybe neuroscientists looking at
the brain what's your sense in the
equivalence you've shown through your
work that simple rules can create
equivalently complex touring machine
systems right is the universe equivalent
to the kinds of tutorial machines is the
human brain a kind of toy machine do you
see those things basically blending
together or is there still a mystery
about how disjoint they're well my guess
is that they will blend together but we
don't know that for sure yet I mean this
I you know I should say I I said rather
glibly that the principle of
computational equivalence is sort of a
science fact and this I was using half
was yes efforts for the for the for the
science fact because when you it is a I
mean just to talk about that for a
second and most people will um
the thing is that it is it has a
complicated epistemological character
similar to things like the second law of
thermodynamics law of entropy increase
the you know what is the second law of
thermodynamics it is is it a law of
nature is that a thing that is true of
the physical world is it is it something
which is mathematically provable is it
something which happens to be true of
the systems that we see in the world is
it in some sense a definition of heat
perhaps well it's a combination of those
things and it's the same thing with the
principle of computational equivalence
and in some sense the principle of
computational equivalence is at the
heart of the definition of computation
because it's telling you there is a
thing there is a robust notion that is
equivalent across all these systems and
doesn't depend on the details of each
individual system and that's why we can
meaningfully talk about a thing called
computation and we're not stuck talking
about over there's computation in
trillion machine number 378 5 and etc
etc etc that's that's why there is a
robust notion like that now on the other
hand can we prove the principle of
computational equivalence can we can we
prove it as a mathematical result well
the answer is actually we've got some
nice results along those lines that say
you know throw me a random system with
very simple rules well in a couple of
cases we now know that even the very
simplest rules we can imagine of a
certain type are universal and do sort
of follow what you would expect from the
principle of computational equivalence
so that's a nice piece of sort of
mathematical evidence for the principle
of computational equivalence did you
still enjoy on that point the simple
rules creating sort of these complex
behaviors but is there a way to
mathematically say that this behavior is
complex that you've mentioned you cross
a threshold right is the various
indicators so for example one thing
would be is it capable of universal
computation that is given the system do
there exist initial conditions for the
system that can be set up to essentially
represent programs to do anything you
to compute primes to compute pi to do
whatever he wants right so that's an
indicator so we know in a couple of
examples that yes the simplest
candidates that could conceivably have
that property do have that property and
that's what the principle of
computational equivalence might suggest
but this principle of computational
equivalence one question about it is is
it true for the physical world right it
might be true for all these things we
come up with the Turing machines the
cellular automata whatever else is it
true for our actual physical world is it
true for the Bray brains which are an
element of the physical world we don't
know for sure and that's not the type of
question that we will have a definitive
answer to because it's you know it's a
it's a there's a there's a sort of
scientific induction issue you can say
what's true for all these brains but
this person over here is really special
and it's not true for them and you can't
you know the the the only way that that
cannot be what happens is if we finally
nail it and actually get a fundamental
theory for physics and it turns out to
correspond to let's say a simple program
if that is the case then we will
basically have reduced physics to a
branch of mathematics in a sense that we
will not be you know right now with
physics we're like well this is the
theory that you know this is the rules
that apply here but in the middle of
that you know you know right by that
black hole
maybe these rules don't apply and
something else applies and there may be
another piece of the onion that we have
to peel back but as if if we can get to
the point where we actually have this is
the fundamental theory of physics here
it is it's this program run this program
and you will get our universe then we've
kind of reduced the problem of figuring
out things in physics to a problem of
doing some what turns out to be very
difficult irreducibly difficult
mathematical problems but it no longer
is the case that we can say that
somebody can come in and say whoops you
know you were right about all these
things about Turing machines but you're
wrong about the physical universe we
know there's sort of ground truth about
what's happening the physical universe
now I happen to think I mean you asked
me at an interesting time because I'm
just in the middle of starting to
re-energized my my project to kind of
study the fundamental theory of physics
as of today I'm very optimistic that
we're actually going to find something
and that it's going to be possible to to
see that the universe really is
computational in that sense but I don't
know because we're betting against you
know we're betting against the universe
sort of speaking I didn't you know it's
not like you know when I spend a lot of
my life building technology and then I
know what what's in there right and it's
there maybe it may have unexpected
behavior may have bugs things like that
but fundamentally I know what's in there
for the universe I'm not in that
position so to speak
what kind of computation do you think
the fundamental laws of physics might
emerge from so just to clarify so
there's you've you've done a lot of
fascinating work with kind of discrete
kinds of computation that you know use
cellular automata and we'll talk about
it have this very clean structure it's
such a nice way to demonstrate that
simple rules can create immense
complexity but what you know is that
actually our cellular time is
sufficiently general to describe the
kinds of computation that might create
the laws of physics just to give it can
you give a sense of what kind of
computation do you think would create
well so so this is a slightly
complicated issue because as soon as you
have universal computation you can in
principle simulate anything with
anything but it is not a natural thing
to do and if you're asking will you to
try to find our physical universe by
looking at possible programs in the
computational universe of all possible
programs would the ones that correspond
to our universe be small and simple
enough that we might find them by
searching that computational universe we
got to have the right basis so to speak
we have to have the right language in
effect for describing computation for
that to be feasible so the thing that
I've been interested in for a long time
is what are the most structureless
structures that we can create with
computation so in other words if you say
a cellular automaton as a bunch of cells
they're arrayed on a grid and it's very
you know an
every cell is updated in synchrony at
the sir at a particular you know when
there's a there's a click of a clock
sort of speaking it goes a tick of a
clock and that every cell gets updated
at the same time that's a very specific
very rigid kind of thing but my guess is
that when we look at physics and we look
at things like space and time that
what's underneath space and time is
something as structureless as possible
that what we see what emerges for us as
physical space for example comes from
something that is sort of arbitrarily
unstructured underneath and so I've been
for a long time interested in kind of
what what are the most structureless
structures that we can set up and
actually what I had thought about for
ages is using graphs networks where
essentially so they'll throw that space
for example so what is space the kind of
a question one might ask back in the
early days of quantum mechanics for
example people said oh for sure space is
going to be discrete because all these
other things were finding a discrete but
that never worked out in physics and so
space and physics today is always
treated as this continuous thing just
like Euclid imagined it I mean the the
very first thing you chlid says and his
sort of common notions is you know a
point is something which has no part in
other words there are there are points
that are arbitrarily small and there's a
continuum of possible positions of
points and the question is is that true
and so for example if we look at I don't
know fluid like air or water we might
say oh it's a continuous fluid we can
pour it we can do all kinds of things
continuously but actually we know
because we know the physics of it that
it consists of a bunch of discrete
molecules bouncing around and only in
the aggregate is it behaving like a
continuum and so the possibility exists
that that's true of space too people
haven't managed to make that work with
existing frameworks and physics but I've
been interested in whether one can
imagine that underneath space and also
underneath time is something more
structureless and the question is is it
computational so there are couple
possibilities it could be computational
somehow fundamentally equivalent to a
Turing machine or it could be
fundamentally not so how could it not be
it could not be so
machine essentially deals with integers
whole numbers some level and you know it
can do things like it can add one to a
number it can do things like this you
can also store whatever the heck it did
yes it has an infinite storage the
storage but what temp when one thinks
about doing physics or sort of idealized
physics or idealized mathematics one can
deal with real numbers numbers with an
infinite number of digits numbers which
are absolutely precise someone can say
we can take this number and we can
multiply it by itself are you
comfortable with infinity in this
context are you gone very well in a
context of computation do you think
infinity and plays a part I think that
the role infinity is complicated
infinity is useful in conceptualizing
things it's not actual izybelle
almost by definition it's not actual I
zabit do you think infinity is part of
the thing that might underlie the laws
of physics I think that um no I think
there are many questions that you asked
about you might ask about physics which
inevitably involve infinity like when
you say you know is faster-than-light
travel possible you could say with it
with with it we're given the laws of
physics can you make something even
arbitrarily large even quotes infinitely
large that you know that will make
faster-than-light travel possible then
you you're thrown into dealing with
infinity as a kind of theoretical
question but I mean talking about you
know sort of what's underneath space and
time and what how one can make you know
a computational infrastructure one
possibility is that you can't make a
computational infrastructure and Turing
such in sense that you really have to be
dealing with precise real numbers you're
dealing with partial differential
equations which just have precise real
numbers that arbitrarily closely
separated points you have a continuum
forever thing could be that that that's
what happens that there's sort of a
continuum for everything and precise
real numbers for everything and then the
things I'm thinking about are wrong and
you know that's that's the risk you take
if you're you know if you're trying to
sort of do things about nature is you
might just be wrong it's not it's for me
personally it's kind of strange things
I've spent a lot of my life building
technology
where you can do something that nobody
cares about but you can't be sort of
wrong in that sense and the sense you
build your technology and it does what
it does but but I think you know this
question of what you know what the sort
of underlying computational
infrastructure for the universe might be
um it's so it's sort of inevitable it's
gonna be fairly abstract because if
you're gonna get all these things like
there are three dimensions of space
there are electrons there are muons
there are quarks there are this you
don't get to if the if the model for the
universe is simple you don't get to have
sort of a line of code for each of those
things you don't get to have sort of the
the the muon case the Tau lepton case
and so on or as they're all have to be
emergent some right so something deeper
right so so that means it's sort of
inevitable that's a little hard to talk
about what the sort of underlying
structural is structure actually is do
you think our human beings have the
cognitive capacity to understand if
we're to discover it to understand the
kinds of simple structure from which
these laws can emerge like do you think
that's a good class pursuit well here's
what I think I think that I mean I'm
right in the middle of this right now
I'm telling you that I think you this
one yeah I mean this human has a hard
time understanding it you know a bunch
of the things that are going on but but
what happens in understanding is one
builds waypoints I mean if you said
understand modern 21st century
mathematics starting from you know
counting back in you know whenever
counting was invented 50,000 years ago
whenever it was right there's that will
be really difficult but what happens is
we build waypoints that allow us to get
to higher levels of understanding and we
see the same thing happening in language
you know when we invent a word for
something it provides kind of a
cognitive anchor a kind of a waypoints
that lets us you know like a podcast or
something you could be explaining well
it's a thing which this works this way
that way the other way but as soon as
you have the word podcast and people
kind of societally understand it you
start to be able to build on top of that
and so I think and that that's kind of
the story of science actually - I mean
science is about building these kind of
waypoints where we find this sort of
cognitive
mechanism for understanding something
then we can build on top of it you know
we we have the idea of I don't know
differential equations we can build on
top of that we have this idea of that
idea so my hope is that if it is the
case that we have to go all the way sort
of from the sand to the computer and
there's no way points in between then
we're toast we won't be able to do that
well eventually we might so for if we're
as clever apes are good enough a
building those abstract abstractions
eventually from sanh we'll get to the
computer a and it just might be a longer
journey the question is whether it is
something that you asked whether our
human brains yes
well on will quotes understand what's
going on and that's a different question
because for that it requires steps that
are for whether it is sort of from which
we can construct a human understandable
narrative and that's something that I
think I am somewhat hopeful that that
will be possible although you know as of
literally today if you ask me I'm
confronted with things that I don't
understand very well um and so this is a
small pattern in a computation trying to
understand the rules under which the
computation functions and yeah it's it's
an interesting possibility under which
kinds of computations such a creature
can understand itself my guess is that
within so we didn't talk much about
computational irreducibility but it's a
consequence of this principle of
computational equivalence and it's sort
of a core idea that that one has to
understand I think which is question is
you're doing a computation you can
figure out what happens in the
computation just by running every step
in the computation and seeing what
happens or you can say let me jump ahead
and figure out you know have something
smarter that figures out what's gonna
happen before it actually happens and a
lot of traditional science has been
about that act of computational reduce
ability it's like we've got these
equations and we can just solve them or
we can figure out what's going to happen
we don't have to trace all of those
steps we just jump ahead because we've
solved these equations okay so one of
the things that is a consequence of the
principle of computational equivalence
is you don't always get to do that many
many systems will be computationally
irreducible
in the sense that the only way to find
out what they do is just follow each
step and see what happens why is that
well if you have if you're saying well
we with our brains will are smarter we
we don't have to mess around like the
little cellular automata and going
through and updating all those cells we
can just you know use the power of our
brains to jump ahead but if the
principle of computational occurrence is
right that's not going to be correct
because it means that there's us during
our computation in our brains there's a
little cellular automaton doing its
computation and the principle of
computational current says these two
computations are fundamentally
equivalent so that means we don't get to
say we're a lot smarter than the
cellular automaton and jump ahead
because we're just doing computation
that's of the same sophistication as the
cellular automaton itself that's
computation or disability it's
fascinating but the and that's a really
powerful idea
I think that's both depressing and
humbling and so on that were all we in a
cellular automata are the same but the
question we're talking about the
fundamental laws of physics is kind of
the reverse question you're not
predicting what's gonna happen you have
to run the universe for that but saying
can I understand what rules likely
generated me I understand but the
problem is to know whether you're right
you have to have some computational
reduce ability because we are embedded
in the universe if the only way to know
whether we get the universe is just to
run the universe we don't get to do that
because it just ran for fourteen point
six billion years or whatever and we
don't you know we can't rerun it so to
speak so we have to hope that there are
pockets of computational reducibility
sufficient to be able to say yes I can
recognize those or electrons there and
and and I think that it is a it's a
feature of computational irreducibility
it's sort of a mathematical feature that
there are always an infinite collection
of pockets of reduced ability the
question of whether they land in the
right place and whether we can sort of
build the theory based on them is
unclear but to this point about you know
whether we as observers in the universe
built out of the same stuff as the
universe can figure out the universe so
to speak that relies on these pockets of
reducibility without the pockets of
reducibility it's won't work
work but I think this question about how
observers operate it's one of the
features of science over the last
hundred years particularly has been that
every time we get more realistic about
observers we learn a bit more about
science so for example relativity was
all about observers don't get to say
when you know what's simultaneous with
what they have to just wait for the
light signal to arrive to decide what
simultaneous or for example in
thermodynamics observers don't get to
say the position of every single
molecule and a gas they can only see the
kind of large scale features and that's
why the second law of thermodynamics law
of entropy increased and so on works if
you could see every individual molecule
you wouldn't conclude something about
thermodynamics you would conclude oh
these molecules just all doing these
particular things you wouldn't be able
to see this aggregate fact so I strongly
expect that in fact him the theories
that I have the one has to be more
realistic about the computation and
other aspects of observers in order to
actually make a correspondence between
what we experience in fact they have a
my little team and I have a little
theory right now about how quantum
mechanics may work which is a very
wonderfully bizarre idea about how a
sort of thread of human consciousness
relates to what we observe in the
universe but this is the several steps
to explain what that's about
woody meek of the mess of the observer
at the lower level of quantum mechanics
sort of the textbook definition with
quantum mechanics kind of says that
there's some there's two worlds one is
the world that actually is and the other
is that's observed do ya what do you
make sense of well I think actually the
ideas we've recently had might actually
give away into this and that's I don't
know yet I mean it's I think that's it's
a mess I mean the fact is there is a one
of the things that's interesting and
when you know people look at these
models that I started talking about 30
years ago now
they say oh no that can't possibly be
right you know what about quantum
mechanics right you say okay tell me
what is the essence of quantum mechanics
what do you want me to be able to
reproduce to know that I've got quantum
mechanics so to speak well and that
question comes up it comes up very
operationally actually because we've
been doing a bunch of stuff with quantum
computing and there are all these
companies that say we have a quantum
computer and we say let's connect to
your API and let's actually run it and
they're like well maybe you shouldn't do
that yet
we're not quite ready yet and one of the
questions that I've been curious about
is if I have five minutes with a quantum
computer how can I tell if it's really a
quantum computer or whether it's a
simulator at the other end right and
turns out it's really hard it turns out
there isn't it's it's it's like a lot of
these questions about sort of what is
intelligence what's life it's soaring
tears for quantum computing that's right
that's right it's like are you really a
quantum computer and I mean I think
simulation the yes exactly is it just a
simulation or is it really a quantum
computer famous you're all over again
but but that so you know this this whole
issue about the sort of mathematical
structure of quantum mechanics and the
completely separate thing that is our
experience in which we think definite
things happen but as quantum mechanics
doesn't say definite things ever happen
quantum mechanics is all about the
amplitudes for different things to
happen but yet our thread of
consciousness operates as if definite
things are happening but to linger on
the point you've kind of mentioned the
structure that could underlie everything
in this idea that it could perhaps have
something like a structure of a graph
can you elaborate why your intuition is
that there's a graph structure of nodes
and edges and what it might represent
right okay so the question is what is in
a sense the most structureless structure
you can imagine right so and in fact
what I've recently realized in the last
year or so I have a new most
structureless structure by the way the
question itself is a beautiful and a
powerful one in itself so even without
an answer just the question is
strong question right right well what's
your new idea well it has to do with
hypergraphs
essentially what what is interesting
about the sort of ID model I have now is
it's a little bit like what happened
with computation everything that I think
of as oh well maybe the model is this I
discover its equivalent
and that's quite encouraging because
it's like I could say well I'm gonna
look at trivalent graph the graphs with
you know three edges for each node and
so on or I could look at this special
kind of graph or I could look at this
kind of algebraic structure and turns
out that the things I'm now looking at
everything that I've imagined that is a
plausible type of structuralist
structure is equivalent to this so what
is it well a typical way to think about
it is well so you might have some some
collection of tuples collection of let's
say numbers so you might have one three
five two three four little just
collections of numbers triples of
numbers let's say quadruples of numbers
pairs of numbers whatever and you have
all these sort of floating little tuples
they're not in any particular order and
that sort of floating collection of
tuples and I told you this was abstract
represents the whole universe the only
thing that relates them is when a symbol
is the same it's the same so to speak so
if you have two tuples and they contain
the same symbol let's say at the same
position of the tuple of the first
element of the tuple then that's
represents a relation okay so let me let
me try and peel this back Wow okay it's
it's I told you it's abstract but this
is this is the this is so the
relationship is formed by the same some
aspect of sameness right but but so
think about it in terms of a graph yeah
so a graph a bunch of nodes let's say
you number each node okay then what is a
graph a graph is a set of pairs that say
this node has an edge connecting it to
this other node
so that's the that's an a graph is just
a collection of those pairs that say
this node connects to this other node so
this is a generalization of that in
which instead of having pairs you have
arbitrary and tuples um that's it that's
the whole story um and now the question
is okay so that might be that might
represent the state of the universe how
does the universe evolved what does the
end of us do and so the answer is that
what I'm looking at is transformation
rules on these hyper graphs in other
words you say this whenever you see a a
piece of this hyper graph that looks
like this turn it into a piece of hyper
graph that looks like this so on a graph
it might be when you see the sub graph
when you see this thing with a bunch of
edges hanging out in this particular way
then rewrite it as this other graph
mm-hm
okay and so that's the whole story so
the question is what so now you say I
mean think as I say this is quite
abstract and one of the questions is
where do you do those updating so you've
got this giant graph what triggers
outdating like what's the what's the
ripple effect of it is it yeah and I I
suspect
everything's discrete even in time so
okay so the question is where do you do
the updates yes and the answer is the
rule is you do them wherever they apply
and you do them you do them the order in
which the updates is done is not defined
that is the you can do them so there may
be many possible orderings for these
updates now the point is if imagine
you're an observer in this universe so
and you say did something get updated
well you don't in any sense know until
you yourself have been updated right so
in fact all that you can be sensitive to
is essentially the causal network of how
an event over there affects an event
that's in you it doesn't even feel like
observation that's like that's something
else you're just part of the whole thing
yes you're part of it but but even to
have so the the end result of that is
you're sensitive to is this causal
network of what event effects what other
event I'm not making a big statement
about sort of the structure of the
observer I'm simply saying I'm simply
making the argument that what happens
the microscopic order of these rewrites
is not something that any observer any
conceivable observer in this universe
can be affected by because the the only
thing the observer can be affected by is
this causal network of how the events in
the observer are affected by other
events that happen in the universe so
the only thing you have to look at is
the causal network you don't really have
to look at this microscopic rewriting
that's happening so these rewrites are
happening wherever they they were they
happen wherever they feel like causal
network is there you said that there's
not really so the idea would be an
undefined like what gets updated the the
sequence of things is undefined it's a
yes that's what you mean by the causal
network then the cop no the causal
network is given that an update has
happened that's an event then the
question is is that event causally
related to does that event if that event
didn't happen then some future event
couldn't happen yet gotcha
and so you build up this network of what
effects what okay and so what that does
so when you build up that network that's
kind of the observable aspect of the
universe in some sense yeah
um and so then you can ask questions
about you know how robust is that
observable ass network of the what's
happening in the universe okay so here's
where it starts getting kind of
interesting so for certain kinds of
microscopic rewriting rules the order of
rewrites does not matter to the causal
network and so this is okay mathematical
logic moment this is equivalent to the
church-rosser property of a confluence
property of rewrite rules and it's the
same reason that if you are simplifying
an algebraic expression for example you
can say oh let me expand those terms out
let me factor those pieces doesn't
matter what order you do that in you'll
always get the same answer and that's
it's the same fundamental phenomenon
that causes for certain kinds of
microscopic rewrite
that causes the causal network to be
independent of the microscopic order of
rewritings why is there properly
important because it implies special
relativity I mean the reason what the
reason it's important is that that
property special relativity says you can
look at these sort of you can look at
different reference frames you can have
different you can be looking at your
notion of what space and what's time can
be different depending on whether you're
traveling at a certain speed depending
on whether you're doing this that and
the other but nevertheless the laws of
physics are the same that's what the
principle special relativity says
there's the laws of physics are the same
independent of your reference frame well
turns out this sort of change of the
microscopic rewriting order is
essentially equivalent to a change of
reference frame or at least there's a
sub part of how that works that's a call
interchange a reference frame so
somewhat surprisingly and sort of for
the first time and forever it's possible
for an underlying microscopic theory to
imply special relativity to be able to
derive it it's not something you put in
as a this is a it's something where this
other property causal invariance which
is also the property that implies that
there's a single thread of time in the
universe it might not be the case that
that's that is the that's what would
lead to the possibility of an observer
thinking that definite stuff happens
otherwise you've got all these possible
rewriting orders and who's to say which
one occurred but with this causal
invariance property there's a there's a
notion of a definite threat of time it
sounds like that kind of idea of time
even space would be emergent from the
system oh yeah no it's not a fundamental
part of the fundamental level all you've
got is a bunch of nodes connected by
hyper edges or whatever so there's no
time there's not space that's right and
but but the the thing is that it's just
like imagining imagine you're just
dealing with a graph and imagine you
have something like a you know like a
honeycomb graph we have a hexagon bunch
a hexagon
you know that graph at a microscopic
level is just a bunch of nodes connected
to other nodes but at a microscopic
level you say that looks like a
honeycomb you know
it's lattice it looks like a
two-dimensional you know manifold of
some kind it looks like a
two-dimensional thing if you connect it
differently if you just connect all the
nodes one one to another and kind of a
sort of linked list type structure then
you'd say well that looks like a
one-dimensional space but at the
microscopic level all these are just
networks with nodes the macroscopic
level they look like something that's
like one of our sort of familiar kinds
of space and it's the same thing with
these hyper graphs now if you ask me
have I found one that gives me three
dimensional space the answer is not yet
so we don't know this is one of these
things we're kind of betting against
nature so to speak and I have no way to
know I mean so there are many other
properties of this this kind of system
that have are very beautiful actually
and very suggestive and it will be very
elegant if this turns out to be right
because it's very it's very clean and
you start with nothing and everything
gets built up everything about space
everything about time everything about
matter it's all just emergent from the
properties of this extremely low-level
system and that that will be pretty cool
if that's the way our universe works
now do I on the other hand the thing
that that I find very confusing is let's
say we succeed let's say we can say this
particular sort of hyper graph rewriting
rule gives the universe just run that
hyper graph rewriting rule for enough
times and you'll get everything you'll
get this conversation we're having will
you'll get everything it's that um if we
get to that point and we look at what is
this thing what is this rule that we
just have that is giving us our whole
universe how do we think about that
thing let's say turns out the minimal
version of this and this is kind of cool
thing for a language designer like me
the the minimal version of this model is
actually a single line of orphan
language code so that's I wasn't sure is
going to happen that way but it's it's a
that's um it's kind of now we don't know
what we don't know what that's that's
just the framework to know the actual
particular hypergraph that might be a
longer that the specification of the
rules might be slightly like how does
help you except marveling in the beauty
and the elegance of the simplicity that
creates the universe that does that help
us predict anything not really because
of the irreducibility that's correct
that's correct but so the thing that is
really strange to me and I haven't
wrapped my my brain around this yet is
you know one is one keeps on realizing
that we're not special in the sense that
you know we don't live at the center of
the universe we don't blah blah blah and
yet if we produce a rule for the
universe and it's quite simple and we
can write it down and a couple of lines
or something that feels very special how
do we come to get a simple universe when
many of the available universes so to
speak are incredibly complicated might
be you know a quintillion characters
long why did we get one of the ones
that's simple and so I haven't wrapped
my brain around that asou yet if indeed
we are in such a simple way the universe
is such a simple rule is it possible
that there is something outside of this
that we are in a kind of what people
calls to the simulation right the word
just part of a computation is being
explored by a graduate student in
alternate universe well you know the
problem is we don't get to say much
about what's outside our universe
because by definition our universe is
what we exist within yeah now can we
make a sort of almost theological
conclusion from being able to know how
our particular universe works
interesting question I don't think that
if you ask the question could we and it
relates again to this question about the
extraterrestrial intelligence you know
we've got the rule for the universe was
it built in on purpose hard to say
that's the same thing as saying we see a
signal from you know that we're you know
receiving from some you know random star
somewhere and it's a series of pulses
and you know it's a periodic series of
pulses let's say was that done on
purpose can we conclude something about
the origin of that series of pulses just
because it's elegant does not
necessarily mean that somebody created
it or though
can even comprehend yeah well yeah I
think it's it's the ultimate version of
the sort of identification of the techno
signature question
it's the ultimate version of that is was
our universe a piece of technology so to
speak and how on earth would we know
because but I mean it'll be it's some I
mean you know in the kind of crazy
science fiction thing you could imagine
you could say Oh somebody's going to
have them you know that's gonna be a
signature there it's gonna be you know
made by so-and-so but there's no way we
could understand that sort of speaking
it's not clear what that would mean
because the universe simply you know
this if we find a rule for the universe
we're not we're simply saying that rule
represents what our universe does we're
not saying that that rule is something
running on a big computer and making our
universe it's just saying that
represents what our universe does in the
same sense that you know laws of
classical mechanics differential
equations whatever they are represent
what mechanical systems do it's not that
the mechanical systems are somehow
running solutions to those differential
equations those differential equations
just representing the behavior of those
systems so what's the gap in your sense
to linger and the fascinating perhaps
slightly sci-fi a question what's the
gap between understanding the
fundamental rules that create a universe
and engineering a system actually
creating a simulation ourselves so
you've talked about sort of you've
talked about you know nano engineering
kind of ideas that are kind of exciting
actually creating some ideas of
computation in the physical space how
hard it is is it as an engineering
problem to create the universe once you
know the rules the Creator and well it's
an interesting question I think the
substrate on which the universe is
operating is not a substrate that we
have access to I mean the only substrate
we have is that same substrate that the
universe is operating in so if the
universe is a bunch of hypergraphs being
rewritten then we get to attach
ourselves to those same hypergraphs
being rewritten we don't get to and if
you ask the question you know is the
code clean you know is you know can we
write nice elegant code
with efficient algorithms and so on well
that's an interesting question how how
you know that's this question of how
much computational reducibility there is
in the system but so I've seen some
beautiful cellular automata that
basically create copies of itself within
itself right that's the question whether
it's possible to create like whether you
need to understand the substrate or
whether you can just yeah well right I
mean so one of the things that is sort
of one of my slightly sci-fi thoughts
about the future so to speak is you know
right now if you pol typical people who
say do you think it's important to find
the fundamental theory of physics you
get because I've done this poll
informally at least it's curious
actually you get a decent fraction of
people saying oh yeah that would be
pretty interesting I think that's
becoming surprisingly enough more I mean
a lot of people are interested in
physics in a way that like without
understanding it just kind of watching
scientists a very small number of them
struggle to understand the nature of our
reality right mean I I mean I I think
that's somewhat true and in fact in this
project that I'm launching into to try
and find in fundamental theory of
physics I'm going to do it as a very
public project I mean it's gonna be live
streamed and all this kind of stuff and
I don't know what will happen it'll be
kind of fun I mean I think that it's the
interface to the world of this project I
mean I I figure one feature of this
project is you know unlike technology
projects that basically are what they
are this is a project that might simply
fail because it might be the case that
generates all kinds of elegant
mathematics that has absolutely nothing
to do with the physical universe that we
happen to live in well okay so we're
talking about kind of the quest to find
the fundamental theory physics first
point is you know it's turned out it's
kind of hard to find the fundamental
theory physics people weren't sure that
that would be the case back in the early
days of applying mathematics to science
1600s and so on people were like oh and
a hundred years we'll know everything
there is to know about how the universe
works turned out to be harder than that
and people got kind of humble at some
level because every time we got to a
sort of a greater level of smallness and
universe it seemed like the math got
more complicated and everything got got
harder the you know when I when I was a
kid basically I started doing particle
physics and you know what I was doing
particle physics
I always thought finding the fundamental
fundamental theory of physics that's a
kooky business we'll never be able to do
that um but we can operate within these
frameworks that we built for doing
quantum field theory and general
relativity and things like this and it's
all good and we can figure out a lot of
stuff did you even at that time have a
sense that there's something behind that
sure I just didn't expect that I thought
in some rather on it's actually kind of
crazy and thinking back on it because
it's kind of like there was this long
period in civilization where people
thought the ancients had it all figured
out and we'll never figure out oh
nothing new and to some extent that's
the way I felt about physics when I was
in the middle of doing it so to speak
and was you know we've got quantum field
theory it's the foundation of what we're
doing and there's you know yes there's
probably something underneath this but
we'll sort of never figure it out but
then I started studying simple programs
and the computational universe things
like solar automata and so on and I
discovered that there so they do all
kinds of things that were completely at
odds with the intuition that I had had
and so after that after you see this
tiny little program that does all this
amazingly complicated stuff then you
start feeling a bit more ambitious about
physics and saying maybe we could do
this for physics too and so that's some
that got me started years ago now and
this kind of idea of could we actually
find what's underneath all of these
frameworks like one a field theory in
jorts everything's on and people perhaps
don't realize as slow as they might that
you know the frameworks we're using for
physics which is basically these two
things quantum field theory the sort of
the theory of small stuff and general
relativity theory of gravitation and
large stuff those are the two basic
theories and they're 100 years old I
mean general relativity was 1915 quantum
field theory well 1920s I'm basically a
hundred years old
and they've they've it's been a good run
there's a lot of stuff been figured out
but what's interesting is the
foundations haven't changed in all that
period of time even though the
foundations had changed several times
before that in the two hundred years
earlier than that um and I think the
kinds of things that I'm thinking about
which is sort of really informed by
thinking about computation in the
computational universe it's a different
foundation it's a different set of
foundations and might be wrong but it is
at least you know we have a shot and I
think it's you know to me it's you know
my personal calculation for myself is is
you know if it turns out that the
finding the fundamental theory of
physics it's kind of low-hanging fruit
so to speak
it'd be a shame if we just didn't think
to do it you know if people just said oh
you'll never figure that stuff out let's
you know and it takes another two
hundred years before anybody gets around
to doing it um you know I think it's I
don't know how low-hanging this fruit
actually is it may be you know it may be
that it's kind of the wrong century to
do this project I mean I think the the
the cautionary tale for me you know I
think about things that I've tried to do
in technology where people thought about
doing them a lot earlier and my favorite
example is probably live Nets who-who
thought about making essentially
encapsulating the world's knowledge in a
computational form in the late 1600s and
did a lot of things towards that and
basically you know we finally managed to
do this but he was three hundred years
too early and that's the that's kind of
the in terms of life planning it's kind
of like avoid things that can't be done
in your in your century so to speak yeah
timing timing is everything you so you
think if we kind of figure out the
underlying rules that can create from
which quantum field theory in general
relativity can emerge do you think
they'll help us unify it at that level
of track we'll know it completely we'll
know how that all fits together
yes without a question and I mean it's
already even the things I've already
done they're a very you know it's very
very elegant actual
how things seem to be fitting together
now you know is it right I don't know
yet it's awfully suggestive if it isn't
right it's some then the designer of the
universe should feel embarrassed so to
speak because it's a really good way to
do that in your intuition in terms of
design universe does God play dice is
there is there randomness in this thing
or is it deterministic so the kind of
guy that's a little bit of a complicated
question because when you're dealing
with these things that involve these
rewrites that have okay even randomness
is an emergent phenomenon perhaps yes I
mean it's a yeah well randomness in in
many of these systems pseudo randomness
and randomness are hard to distinguish
um in this particular case the current
idea that we have about some measurement
in quantum mechanics is something very
bizarre and very abstract and I don't
think I can yet explain it without kind
of yakking about very technical things
eventually I will be able to but if
that's if that's right it's kind of a
it's a weird thing because it slices
between determinism and randomness in a
weird way that hasn't been sliced before
so to speak so like many of these
questions that come up in science where
it's like is that this or is it that
turns out the real answer is it's
neither of those things it's something
kind of different and sort of orthogonal
to those those those categories and so
that's the current you know this week's
idea about how that might work um but
you know we'll we'll see how that term
unfolds I mean there's there's this
question about a field like physics and
sort of the quest for a fundamental
theory and so on and there's both the
science of what happens and there's the
the sort of the social aspect of what
happens because you know in a field that
is basically as old as physics we're at
I don't know what it is fourth
generation I don't know fourth
generation I don't know what generation
it is of physicists and like I was one
of these so to speak and for me the
foundations were like the pyramids so to
speak you know it was that way and it
was always that way
um it is difficult in an old field to go
back to the foundations and would think
about rewriting them it's a lot easier
in young fields where you're still
dealing with the first generation of
people who invented the field and it
tends to be the case you know that the
nature of what happens in science tends
to be you know you'll get there
typically the pattern is some
methodological advanced occurs and then
there's a period of five years ten years
maybe a little bit longer than that
where there's lots of things that are
now made possible whether by that
methodological advance whether it's you
know I don't know telescopes or whether
that's some mathematical method or
something it's you know there's a
something something happens a tool gets
built and then you can do a bunch of
stuff and there's bunch of low-hanging
fruit to be picked and that takes a
certain amount of time after that all
that low-hanging fruit is picked then
it's a hard slog for the next however
many decades or century or more to get
to the next sort of level at which one
can do something and it's kind of a a.m.
and it tends to be the case that in
fields that are in that kind of but I
wouldn't say cruise mode because it's
really hard work but it's very hard work
for very incremental progress
um and the in your career and some of
the things you've taken on it feels like
you're not you haven't been afraid of
the hard slog the also true so it's
quite interesting especially on the
engineering on the engineering side and
a small tangent when you were a Caltech
did you get to interact with Richard
five-minute ology aviemore he's very
sure 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 but uh
anyway I was there he was not into into
that kind of thing and he always thought
it was thought that my interest in doing
things like running companies was a was
a distraction so to speak um and
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
and memories of thoughts that 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 until
you know products of Mayer G 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 store my files now so it's but
that's what happens when when it's
finiteness of human lives it 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
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 is 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
everybody 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'd
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 these calculations 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 pee
and I worked a bit on quantum computers
actually back in 1980-81 but before
anybody had heard of those things and
you know the typical mode of um I mean
he always 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 1/3 my
age so to speak and oh 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 it'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 happens in
technology too 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 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 a connection machine that that
company was making I generated this
giant printout of rule 30 on very I'm
actually on the same kind of same kind
of printer that people use to make um
layouts for microprocessors so one of
these big you know large format printers
with high resolution and so on so okay
so 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
meter 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 30 thing would produce all this
really complicated behavior that is so
complicated that weird 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 you had some intuition that he
didn't have that would let why 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 so hard for us humans and
especially brilliant physicist like
fireman 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 yeah 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 just 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
so to 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 have tells
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
that'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
show 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 it just
yeah I'm Amanda 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 paradigm that 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 some 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 what do you
think is the role of ego in the history
of math and science and more sort of you
know a book title is something like a
new kind of science you've accomplished
a huge amount in fact somebody said that
Newton didn't have an ego and I looked
into it and he had a huge ego yeah but
from an outsider's perspective some have
said that you have a bit of an ego as
well do you see it that way does ego get
in the way is it empowering is it both
so it's it's it's all implicated
necessary I mean you know I've had look
I've spent more than half my life CEO in
a tech company right ok
and you know that is a I think it's
actually very it means that one's ego is
not a distant thing it's the thing that
one encounters every day so to speak
because it's it's all tied up with
leadership and with how one you know
develops an organization and all these
kinds of things so you know it may be
that if I've been an academic for
example I could have sort of you know
check the ego put it on put on a shelf
somewhere and ignored its
characteristics but for your reminder it
quite often in the context of running a
company sure yeah I mean that's what
it's about it's it's about leadership
and you know leadership is intimately
tied to ego now what does it mean I mean
what what is the you know for me I've
been fortunate that I think I have
reasonable intellectual confidence so to
speak that is you know I I'm one of
these people who at this point if
somebody tells me something and I just
don't understand it my conclusion isn't
that means I'm dumb that my conclusion
is there's something wrong with what I'm
being told and that was actually dick
Feynman used to have that that that
feature - he never really believed it he
actually believed in experts much less
than I believe in experts so Wow so
that's a fun that's a that's a
fundamentally powerful property of ego
and saying like not that I am wrong
but that the the world is wrong and
telling me like when confronted with the
fact that doesn't fit the thing that
you've really thought through sort of
both the negative and the positive of
ego you see the negative of that get in
the way sort of be sure the Fronteras
mistakes I've made that are the results
of I'm pretty sure I'm right and turns
out I'm not I mean that's that's the you
know but but the thing is that the the
the idea that one tries to do things
that so for example you know one
question is if people have tried hard to
do something and then one thinks maybe I
should try doing this myself
if one does not have a certain degree of
intellectual confidence one just says
well people have been trying to do this
for a hundred years how am I going to be
able to do this yeah and you know I was
fortunate in the sense that I happen to
start having some degree of success in
science and things when I was really
young and so that developed a certain
amounts of sort of intellectual
confidence I don't think I otherwise
would have had um and you know in a
sense I mean I was fortunate that I was
working in the field particle physics
during it sort of Golden Age of rapid
progress and that that's kind of good on
a false sense of achievement because
it's kind of kind of easy to discover
stuff that's gonna survive if you happen
to be you know picking the low-hanging
fruit of a rapidly expanding field I
mean the reason I totally I totally
immediately understood the ego behind a
new kind of science to me let me sort of
just try to express my feelings and the
whole thing is that if you don't allow
that kind of ego then you would never
write that book that you would say well
people must have done this there's not
you would not dig you would not keep
digging and I think that was I think you
have to take that ego and ride it and
see where it takes you in that and
that's how you create exceptional work I
think the other point about that book
was it was a non-trivial question how to
take a bunch of ideas that uh
I think reasonably big ideas they might
you know their importance is determined
by what happens historically one can't
tell how important they are one can tell
sort of the scope of them
and the scope is fairly big and they're
very different from things that have
come before and the question is how do
you explain that stuff to people and so
I had had the experience of sort of
saying well there these things does a
cellular automaton it does this it does
that and people are like oh it must be
just like this it must be just like that
say no it isn't it's something different
right I said I could have done sort of
I'm really glad you did what you did but
you could have done a sort of
academically just publish keep
publishing small papers here and there
and then you would just keep getting
this kind of resistance right you would
get like yeah it's supposed to just
dropping a thing that says here it is
yeah here's like full the full thing no
I mean that was my calculation is that
basically you know you could introduce
little pieces it's like you know one
possibility is like it's it's the secret
weapon so to speak it's this you know I
keep on an intraday you know discovering
these things in all these different
areas where'd they come from
nobody knows but I decided that you know
in the interests of one only has one
life to lead and you know it the writing
that book took me a decade anyway it's
not there's not a lot of wiggle room so
to speak one can't be wrong by a factor
of three he said is peeking how long
it's going to take that I you know I
thought the best thing to do the thing
that is most sort of that most respects
the the intellectual content so to speak
is you just put it out with as much
force as you can because it's not
something where and you know it's an
interesting thing you talk about ego and
it's it's you know for example I run a
company which has my name on it right I
I thought about starting a club people
whose companies have their names on them
and it's it's a funny group because
we're not a bunch of ego maniacs that's
not what it's about so to speak it's
about basically sort of taking
responsibility for what one's doing and
you know in a sense any of these things
where you're sort of putting yourself on
the line it's it's kind of a funny it's
a funny dynamic because in a sense my
company is sort of something that
happens to have my name on it but it's
kind of bigger than me and I'm kind of
just its mascot at some level I mean I
also happen to be a pretty
you know strong leader of it but but
it's basically showing a deep
inextricable sort of investment the same
your name like Steve Jobs his name
wasn't on Apple but he was Apple yes
Elon Musk's name is not on Tesla but he
is Tesla so it's like a meaning
emotionally his company succeeds or
fails he would just that emotionally
would suffer through that and so that's
that's did recognizing that fact tonight
and also wolf form is a pretty good
branding name so that works up I think
Steve had it had a bad deal there yeah
so you you've made up for it with the
last name okay so so in 2002 you
published a new kind of science to which
sort of on a personal level I can credit
my love for cellular automata and
computation in general I think a lot of
others can as well can you briefly
describe the vision the hope the main
idea presented in this twelve hundred
page book sure although it took twelve
hundred pages to say in the book so know
that the the real idea it's kind of a
good way to get into it is to look at
sort of the arc of history and to look
at what's happened in kind of the
developments of science I mean there was
this sort of big idea in science about
three hundred years ago that was let's
use mathematical equations to try and
describe things in the world let's use
sort of the formal idea of mathematical
equations to describe what might be
happening in the world rather than for
example just using sort of logical
augmentation and so on let's have a a
formal theory about that and so they've
been this three hundred year run of
using mathematical equations to describe
the natural world which would work
pretty well but I got interested in how
one could generalize that notion you
know there is a formal theory there are
definite rules but what structure could
those rules have and so what I got
interested in was let's generalize
beyond the sort of purely mathematical
rules and we now have this sort of
ocean of programming and computing and
so on let's use the kinds of rules that
can be embodied in programs to has a
sort of generalization of the ones that
can exist in mathematics as a way to
describe the world and so my kind of
favorite version of these kinds of
simple rules are these things called
cellular automata and so typical case
shall we what are cellular automata fair
enough so typical case of a cellular
automaton it's an array of cells it's
just a line of discrete cells each cell
is either black or white and in a series
of steps you can represent as lines
going down a page you're updating the
color of each cell according to a rule
that depends on the color of the cell
above it and to its left and right so
it's really simple so a thing might be
you know if the cell on its right
neighbor are not the same and or the
cell on the left is is is black or
something then make it back on the next
step and if not make it white typical
rule um that rule I'm not sure I said it
exactly right but a rule very much like
what I just said has the feature that if
you started off from just one black cell
at the top it makes this extremely
complicated pattern so some rules you
get a very simple pattern some rules you
have the rule is simple you start them
off from a sort of simple seed you just
get this very simple pattern but other
rules and this was the big surprise when
I started actually just doing the simple
computer experiments to find out what
happens is that they produce very
complicated patterns of behavior so for
example its rule 30 rule has the feature
you start from just one black cell at
the top makes this very random pattern
if you look like at the center column of
cells you get a series of values you
know it goes back white black black
whatever it is that sequence seems for
all practical purposes random so it's
kind of like in in math you know you can
put the digits of pi 3
one four one five nine two six whatever
those digits once computed I mean that
the scheme for computing pi you know
it's the ratio of the circumference to
the diameter of a circle very
well-defined but yet when you are once
you've generated those digits they seem
for all practical purposes completely
random and so it is with rule 30 that
even though the rule is very simple much
simpler much more sort of
computationally obvious than the rule
for generating digits of pi even with a
rule that simple you're still generating
immensely complicated behavior yeah so
if we could just pause on that I think
you you probably said it and looked at
it so long you forgot the magic of it or
perhaps you know you still feel the
magic but to me if you've never seen
sort of I would say what is it a one
dimensional essentially another automata
right and and you were to guess what you
would see if you have some so cells that
only respond to its neighbors right if
you were to guess what kind of things
you would see like my my initial guess
like even when I first like open your
book a new kind of science right - your
guess is you would see I mean it would
be a very simple stuff like and I think
it's a magical experience to realize the
kind of complex you mentioned rule 30
still your favorite cellular automaton
oh my favorite rule yes it you get
complexity immense complexity you get
arbitrary complexity yes and when you
say randomness down the middle column
you know that's just what one cool way
to say that there's incredible
complexity and that's just the gist I
mean that's a magical idea however you
start to interpret it all the
reducibility discussions all that but
it's just I think that has profound
philosophical kind of notions around it
- it's not just well you know I mean
this transformation about how you see
the world I think for me was
transformational I don't know we can
what it can have all kinds of discussion
about computation and so on but just you
know I
and sometimes think if I were on a
desert island and was I don't know maybe
it was some psychedelics or something
but if I had to take one book any new
kind of science would be a because you
just enjoy that notion for some reason
it's a deeply profound notion at least
to me I find it that way yeah I mean
look it's been it was a very intuition
breaking thing to discover I mean it's
kind of like you know you you point the
computational telescope out there and
suddenly you see I don't know you know
in the past it's kind of like you know
moons of Jupiter or something but
suddenly you see something that's kind
of very unexpected and rule 30 was very
unexpected for me and the big challenge
at a personal level was to not ignore it
I mean people you know in other words
you might say you know it's a bug what
would you say yeah well yeah I mean I I
what are we looking at by the way well I
was just generating Herald actually
generated a rule 30 pattern so that's
the rule for for rule 30 and it says for
example it says here if you have a black
cell in the middle and black cell to the
left and white cell to the right then
the cell on the next step will be white
and so here's the actual pattern that
you get starting off from a single black
cell at the top there and then that's
the initial state initial condition
that's the initial thing you just start
off from that and then you're going down
the page and at every at every step
you're just applying this rule to find
out the new value that you get and so
you might think rule that simple you got
to get that there's got to be some trace
of that simplicity here okay we'll run
it let's say for 400 steps um what it
does it's kind of really asking a bit on
the screen there but but um you can see
there's a little bit of regularity over
on the left but there's a lot of stuff
here that just looks very complicated
very random and that's a big sort of
shock to was a big shock to my intuition
at least that that's possible your mind
immediately starts is there a pattern
there must be a repetitive pattern yeah
there must be as well the rhein so I
spent so indeed that's what I thought at
first and I thought I thought well this
is kind of interesting but you know if
we
long enough we'll see you know something
will resolve into something simple and
you know I did all kinds of analysis of
using mathematics statistics
cryptography whatever whatever to try
and crack it and I never succeeded and
after I hadn't succeeded for awhile I
started thinking maybe there's a real
phenomenon here that is the reason I'm
not succeeding maybe I mean the thing
that for me was sort of a motivating
factor was looking at the natural world
and seeing all this complexity that
exists in the natural world the question
is where does it come from
you know what secret does nature have
that lets it make all this complexity
that we humans when we engineer things
typically are not making we're typically
making things that at least look quite
simple to us and so the shock here was
even from something very simple you're
making something that complex maybe this
is getting at sort of the secret that
nature has that allows it to make really
complex things even though its
underlying rules may not be that complex
how did it make you feel if we if we
look at the Newton Apple was there was
it was there you know you took a walk
and in something it profoundly hit you
or was this a gradual thing a lot of
truth the truth of every sort of science
discovery is it's not that gradual I
mean I've spent I happen to be
interested in scientific biography kinds
of things and so I've tried to track
down you know how did people come to
figure out this or that thing and
there's always a long kind of sort of
preparatory you know there's a there's a
need to be prepared in a mindset in
which it's possible to see something I
mean in the case of rule 30 our eyes
around June 1st 1984 was some kind of a
silly story in some ways I finally had a
high-resolution laser printer so I was
able so I thought I'm gonna generate a
bunch of pictures of these cellular
automata and I generate this one and I
put it on some plane flight for to
Europe you know have this with me and
it's like you know I really should try
to understand this and this is really
you know this is I really don't
understand what's going on and that was
kind of the you know slowly trying to
trying to see what was happening as it
was not it was depressingly uncertain so
to speak in the sense that a lot of
these ideas like principle of
computational equivalence for example
you know I thought well that's a
possible thing I didn't know if it's
correct still don't know for sure that
it's correct but it's sort of a gradual
thing that these things gradually kind
of become seem more important than one
thought I mean I think the whole idea of
studying the computational universe of
simple programs it took me probably a
decade decade and a half to kind of
internalize that that was really an
important idea um and I think you know
if it turns out we find the whole
universe looking out there in the
computational universe that's a good you
know it's a good brownie point or
something for the for the whole idea but
I think that the the thing that strange
in this whole question about you know
finding this different raw material for
making models of things um what's been
interesting sort of in the in sort of
arc of history is you know for 300 years
it's kind of like the the mathematical
equations approach it was the winner it
was the thing you know you want to have
a really a good model for something
that's what you use the thing that's
been remarkable is just in the last
decade or so I think one can see a
transition to using not mathematical
equations but programs as sort of the
raw material for making models of stuff
and that's pretty neat and it's kind of
you know as somebody who's kind of lived
inside this paradigm shift so to speak
it is bizarre I mean no doubt instead of
the history of science that will be seen
as an instantaneous paradigm shift but
it sure isn't instantaneous when it's
played out in one's actual life so to
speak try
it seems glacial and and it's the kind
of thing where where it's sort of
interesting because in the dynamics of
sort of the adoption of ideas like that
into different fields the younger the
field the faster the adoption typically
because people are not kind of locked in
with the fifth generation of people
who've studied this field and it is it
is the way it is and it can never be any
different and I think that's been
you know watching that process has been
interesting I mean I'm I'm I think I'm
fortunate that I I've I I do stuff
mainly because I like doing it and if I
was some that makes me kind of
thick-skinned about the world's response
to what I do um and but that's
definitely you know and anytime you you
write a book called something like a new
kind of science it's kind of the the
pitchforks will come out for the for the
old kind of science and I was was
interesting dynamics I think that the I
I have to say that I was fully aware of
the fact that the when you see sort of
incipient paradigm shifts in science the
vigor of the negative response upon
early introduction is a fantastic
positive indicator of good long-term
results so in other words if people just
don't care it's um you know that's not
such a good sign if they're like oh this
is great that means you didn't really
discover anything interesting um what
fascinating properties of rule 30 have
you discovered over the years you've
recently announced the rule 30 prizes
for solving three key problems can you
maybe talk about interesting properties
that have been kind of revealed rule 30
or other cellular automata and what
problems are still before us like the
three problems you've announced yeah
yeah right so I mean the most
interesting thing about cellular
automata is that it's hard to figure
stuff out about them and that's some in
a sense every time you try and sort of
you try and bash them with some other
technique you say can i crack them the
answer is they seem to be uncrackable
they seem to have the feature that they
are that they're sort of showing
irreducible computation they're not
you're not able to say oh I know exactly
what this is going to do it's going to
do this or that but there's a specific
formulations of that fact yes right so I
mean for example in in rule 30 in the
pattern you get just starting from a
single black cell you get this sort of
very very sort of random
pattern and so one feature of that just
look at the center column and for
example we used that for a long time to
generate random the symbol from language
um just you know what rule 30 produces
now the question is can you prove how
random it is so for example one very
simple question can you prove that and
never repeat nope we haven't been able
to show that will never repeat
we know that if there are two adjacent
columns we know they can't both repeat
but just knowing whether that center
column can ever repeat we still don't
even know that um another problem that
I've sort of put in my collection of you
know it's like $30,000 for three you
know for these three prizes for about
rule thirty I would say this is not one
of those is one of those cases where the
money is not the main point but it's
just you know helps some motivate
somehow that the investigation so
there's three problems you propose you
get thirty thousand dollars if you solve
all three or maybe yeah no it's ten
thousand for each for each a my the
problems that's right money's not the
thing the problems themselves are just
clean yeah right it's just you know will
it ever become periodic second problem
is other an equal number of black and
white cells down the middle calm down
the middle column and the third problem
is a little bit harder to state which is
essentially is there a way of figuring
out what the color of a cell at position
T down the center column is in a with a
less computational effort than about T
steps so in other words is there way to
jump ahead and say I know what this is
gonna do you know it's just some
mathematical function of T or proving
that there is no way or proving there is
no way yes but both I mean you know for
any one of these one could prove that
you know one could discover you know we
know what rule thirty does for a billion
steps but and maybe we'll know for a
trillion steps before two very long but
maybe at a quadrillion steps it suddenly
becomes repetitive you might say how
could that possibly happen but so when I
was writing up these prizes I thought
and this is typical of what happens in
the computational universe I thought let
me find an example where it looks like
it's just gonna be random forever but
actually it becomes repetitive yeah and
I found one and it's just you know I did
a search I searched I don't know maybe a
million different rules with some
criterion and this is what's sort of
interesting about that is I kind of have
this thing that I per se got a silly way
about the computational universe which
is you know the animals are always
smarter than you
that is there's always some way one of
these computational systems is gonna
figure out how to do something even
though I can't imagine how its gonna do
it and you know I didn't think I would
find one that you know you would think
of for all these years that what I found
sort of all possible things funky things
that that I would have that I would have
gotten my intuition wrapped around the
idea that you know these creatures are
always in the computational universe are
always smarter than I'm gonna be but you
know they're equivalently yes Mari
that's correct and that makes it that
makes one feel very sort of it's it's
it's humbling every time because every
time the thing is is you know you think
it's gonna do this so it's not gonna be
possible to do this and it turns out it
finds a way of course the promising
thing is there's a lot of other rules
like rule 30
it's just rule 30 is oh it's my favorite
because I found it first and that's
right but the problems are focusing on
rule 30 it's possible that rule 30 is is
repetitive after trillion steps and that
doesn't prove anything about the other
rules it does not but this is a good
sort of experiment of how you go about
trying to prove something about a stick
you'll rule yes and it also all these
things help build intuition that is
intact if it turned out that this was
repetitive tore trillion steps that's
not what I would expect and so we
learned something from that the method
to do that though would reveal something
interesting about the so no doubt no
doubt
I mean it's although it's sometimes
challenging like the you know I put out
a prize in 2007 for for a particular
Turing machine that I there was the
simplest candidate for being the
universal Turing machine and the young
chap in England named Alex Smith after a
smallish number of months said I've got
a proof and he did you know I took a
little while to iterate but you had a
proof unfortunately the proof is very
it's it's a lot of micro details it's
it's not it's not like you look at it
you say aha there's a big new principle
the big new principle is the simplest
Turing machine that might have been
Universal actually is universal and it's
incredibly much simpler than the
turning machines that people already
knew we universal before that and so
that intuition Allah is important
because it says computation universality
is closer at home than you might have
thought um but the actual methods are
not in that particular case were not
terribly illuminate happiness if their
methods would also be elegant that's
true
yeah no I mean I think it's it's one of
these things where I mean it's it's like
a lot of we've talked about earlier kind
of you know opening up a eyes and
machine learning and things of what's
going on inside and is it just step by
step or can you sort of see the bigger
picture more abstractly and
unfortunately with Verma's Last Theorem
proof it's unfortunate that the proof to
such an elegant theorem is is not I mean
it's as if it's not it doesn't write
into the margins of a page that's true
but these know one of the things is
that's another consequence of
computational or disability this this
fact that there are even quite short
results in mathematics whose proofs
arbitrarily long yes that's a that's a
consequence of all this stuff and it's
it's a it makes one wonder you know how
come mathematics is possible at all why
is you know why is it the case how
people manage to navigate doing
mathematics through looking at things
where they're not just throwing into
it's all undecidable that's that's its
own own separate separate story and that
would be that would they would have a
poetic beauty to it as if people were to
find something interesting about rule 30
because I mean there's an emphasis to
this particular rule it wouldn't say
anything about the broad irreducibility
of all computations but it would
nevertheless put a few smiles on
people's faces of well yeah yeah but to
me it's like in a sense establishing
principle of computational equivalence
it's a little bit like doing inductive
science anywhere that is the more
examples you find the more convinced you
are that it's generally true I mean we
don't get to you know whenever we do
natural science we we say well it's true
here that this will that happens can we
can we prove that it's true everywhere
in the universe no we can't
so you know it's the same thing here
we're exploring the computational
universe we're establishing facts in the
computational universe and that's that's
sort of a way of of inductively
concluding general things just to think
through this a little bit we've touched
on it a little bit before but what's the
difference between the kind of
computation now that we're talking about
cellular automata what's the difference
between the kind of computation
biological systems our mind our bodies
the things we see before us that emerged
through the process of evolution and
cellular automata deep
I mean we've kind of applied to the
discussion of physics underlying
everything but we we talked about the
potential equivalents of the fundamental
laws of physics and the kind of
computation going on internal machinery
interesting about the kind of
computation that our bodies do right
well let's talk about brains primary
range the the I mean I think the the
most important thing about the things
that our brains do that we care about
them in the sense that there's a lot of
computation going on out there in you
know cellular automata and and you know
physical systems and so on and it just
it does what it does it follows those
rules it does what it does
the thing that's special about the
computation in our brains is that it's
connected to our goals and our current
whole societal story and you know I
think that's the that's that's the
special feature and now the question
then is when you see this whole sort of
ocean of computation out there how do
you connect that to the things that we
humans care about and in a sense a large
part of my life has been involved in
sort of the technology of how to do that
and you know what I've been interested
in is kind of building computational
language that allows that something that
both we humans can understand and that
can be used to determine computations
that are actually computations we care
about see I think when you look at
something like one of these cellular
automata and it does some complicated
thing you say
that's fun but why do I care well you
could say the same thing actually in
physics you say oh I've got this
material and it's a ferrite or something
why do I care you know it's some has
some magnetic properties why do I care
it's amusing but why do I care
well we end up caring because you know
ferrite is what's used to make magnetic
tape magnetic disks whatever or you know
we could use the coke crystals as made
used to make um well not that she
increasingly not but it has been used to
make computer displays and so on but
those are so in a sense where mining
these things that happen to exist in the
physical universe and I'm making it be
something that we care about because we
sort of in train it into technology and
it's the same thing in the computational
universe that a lot of what's out there
is stuff that's just happening but
sometimes we have some objective and we
will go and sort of mine the
computational universe for something
that's useful for some particular
objective on a large scale trying to do
that trying to sort of navigate the
computational universe to do useful
things you know that's where
computational language comes in and you
know a lot of what I've spent time doing
and building this thing we call Wolfram
language which I've been building for
the last one third of a century now and
kind of the goal there is to have a way
to express kind of computational
thinking computational thoughts in a way
that both humans and machines can
understand so it's kind of like in the
tradition of computer languages
programming languages that the tradition
there has been more let's take what how
computers are built and let's specify
let's have a human way to specify do
this do this do this at the level of the
way that computers are built what I've
been interested in is representing sort
of the whole world computationally and
being able to talk about whether it's
about cities or chemicals or you know
this kind of algorithm or that kind of
algorithm things that have come to exist
in our civilization and the sort of
knowledge base of our civilization being
able to talk directly about those in a
computational language so that both we
can understand it and computers can
understand
I mean the thing that I've been sort of
excited about recently which I had only
realized recently which is kind of
embarrassing but trim is kind of the the
arc of what we've tried to do in
building this kind of computational
language is it's a similar kind of arc
of what happened when mathematical
notation was invented so go back 400
years people were trying to do math they
were always explaining their math in
words and it was pretty conky and as
soon as mathematical notation was
invented you could start defining things
like algebra and later calculus and so
on it all became much more streamlined
when we deal with computational thinking
about the world there's a question of
what is the notation what is the what is
the kind of formalism that we can use to
talk about the world computationally and
in a sense that's what I've spent the
last third of a century trying to build
and we finally got to the point where we
have a pretty full scale computational
language that sort of talks about the
world and that's that's exciting because
it means that just like having this
mathematical notation let us talk about
the world mathematically we now and and
let us built up build up these kind of
mathematical sciences now we have a
computational language which allows us
to start talking about the world
computationally and lets us you know my
view of it is it's kind of computational
X for all X all these different fields
of you know computational this
computational that that's what we can
now build let's step back so first of
all the mundane
what is Wolfram language in terms of
sort of I mean I can answer the question
for you but this it basically not the
philosophical deep to profound the
impact of it I'm talking about in terms
of tools in terms of things you can
download and yeah you can play with what
is it what what does it fit into the
infrastructure what are the different
ways to interact with it right so I mean
that the two big things that people have
sort of perhaps heard of that come from
open language one is Mathematica the
other is Wolfram Alpha so Mathematica
first came out 1988 it's this system
that is basically a instance of Wolfram
language and it's used to do
computations particularly in sort of
technical areas and the typical thing
you're doing is you're you're typing
little pieces of computational language
and you're getting computations done
it's very kind of there's like as
symbolic yeah it's a symbolic language
so symbolic language took any I don't
know how to cleanly express that but
that makes a very distinct from what how
we think about sort of I don't know
programming in a Ling like Python or
something right but so so the point is
that in a traditional programming
language the raw material of the
programming language it's just stuff
that computers intrinsically do and the
point of often language is that what the
language is talking about is things that
exist in the world or things that we can
imagine and construct not it's not it's
not sort of it's it's aimed to be an
abstract language from the beginning and
so for example one feature it has is
that it's a symbolic language which
means that you know you the thing called
you have an X just type in X and what
why would you just say oh that's X it
won't say error undefined thing you know
I don't know what it is computation you
know but in terms of the in terms of
computer now that X could perfectly well
be you know the city of Boston that's a
thing that's a symbolic thing or it
could perfectly well be the you know the
trajectory of some spacecraft
represented as a symbolic thing and that
idea that one can work with sort of
computationally work with these
different these kinds of things that
that exist in the world or describe the
world that's really powerful and that's
what some I mean you know when I started
designing well I designed the
predecessor of what's now often language
was a thing called SMP which was my
first computer language I am I kind of
wanted to have this the sort of
infrastructure for computation which was
as fundamental as possible I mean this
is what I got for having bit of
physicists and tried to find you know
fundamental components of things and
wound up with this kind of idea of
transformation rules for symbolic
expressions as being sort of the
underlying
stuff from which computation would be
built and that's what we've been
building from in Wolfram language and
you know operationally what happens it's
I would say by far the highest level
computer language that exists and its
really been built in a very different
direction from other languages so other
languages have been about there is a lot
core language
it really is kind of wrapped around the
operations that a computer intrinsically
does maybe people add libraries for this
or that that but the goal of Wolfram
language is to have the language itself
be able to cover this sort of very broad
range of things that show up in the
world and that means that you know there
are 6,000 primitive functions in the
Wolfram language that cover things you
know I could probably pick a a random
here I'm gonna pick just because just
for fun I'll pick them let's take a
random sample of them of all the things
that we have here so let's just say
random sample of 10 of them and let's
see what we get Wow okay so these are
really different things from functions
these are all functions boolean converts
okay that's the thing for converting
between different types of boolean
expressions so for people are just
listening human type 10 random sample
names sampling from all functionally how
many you said there might six thousand
six thousand six thousand ten of them
and there's a hilarious variety of them
yeah right well we've got things about
some dollar requests or a dress that has
to do with interacting with the the
world of the of the cloud and so on
discrete wavelet data it's for ROI a
graphical sort of window yeah yeah
window moveable that's the user
interface kind of thing I want to pick
another 10 cuz I think this is some okay
so yeah there's a lot of infrastructure
stuff here that you see if you if you
just start sampling at random there's a
lot of kind of infrastructural things if
you're more you know if you more look at
the some of the exciting machine
learning stuff is shut off is that also
in this pool oh yeah yeah I mean you
know so one of those functions is like
image identify as a function here where
you just say image identified
was good too let's do this let's say
current image and let's pick up an image
hopefully just a current image accessing
the webcam took a picture yourself
anyway we can say image identify open
square brackets and then we just paste
that picture in there imagine if I
function running come to picture lo and
it says oh wow it says I look I look
like a plunger because I got this great
big thing behind me classify so this
image identify classifies the most
likely object in in the image in it so
there's a wonder okay that's that that's
a bit embarrassing let's see what it
does let's pick the top 10 um okay well
it thinks there's oh it thinks it's
pretty unlikely that it's a primary two
hominid two plus eight percent
probability yeah that's that's five
seven it's a plunger yeah well so if we
will not give you an existential crisis
and then uh eight percent or not I
should say percent but no that's a scent
that it's a hominid um and yeah okay
it's really I'm gonna do another one of
these just because I'm embarrassed that
it there we go let's try that let's see
what that did um we took a picture a
little bit a little bit more of me and
not just my bald head so to speak okay
eighty-nine percent problem is it's a
person so that there so then I would um
but you know so this is image identify
as an example of one of just one of them
in just one function and that's part of
the that's like part of the language yes
so first I mean you know something like
um I could say I don't know let's find
the geo nearest what could we find let's
find the nearest volcano um let's find
the ten I wonder where it thinks here is
let's try finding the ten volcanoes
nearest here okay yo nearest volcano
here 10 years volcanoes right let's find
out where those oh we can now we got a
list of volcanoes out and I can say geo
list plot that and hopefully okay so
there we go so there's a map that shows
the positions of those ten volcanoes of
the East Coast and the Midway
density well no we're okay okay there's
not it's not too bad yeah they're not
very close to us we could we could
measure how far away they are but you
know the fact that right in the language
it knows about all the volcanoes in the
world that knows you know computing what
the nearest ones are it knows all the
maps of the world and so on a
fundamentally different idea what a
language is yeah right that's that's
what I like to talk about is you know a
full scale computational language that's
that's what we've tried to do and just
if you can comment briefly I mean this
kind of the Wolfram language along with
Wolfram Alpha represents kind of what
the dream of what AI is supposed to be
there's now a sort of a craze of
learning kind of idea that we can take
raw data and from that extract the the
different hierarchies of abstractions
and in order to be able to under the
kind of things that well from language
operates with but we're very far from
learning systems being able to form that
but like the context of history of AI if
you could just comment on there is a you
said computation X and there's just some
sense where in the 80's and 90's sort of
expert systems represented a very
particular computation ax yes right and
there's a kind of notion that those
efforts didn't pan out right but then
out of that emerges kind of Wolfram
language Wolfram Alpha which is the
success I mean yeah right I think those
are in some sense those efforts were too
modest they're nice they were they were
looking at particular areas and you
actually can't do it with a particular
area I mean like like even a problem
like natural language understanding it's
critical to have broad knowledge of the
world if you want to do good natural
language understanding and you kind of
have to bite off the whole problem if
you if you say we're just gonna do the
blocks world over here so to speak
you don't really it's it's it's actually
it's one of these cases where it's
easier to do the whole thing than it is
to do some piece of it you know what one
comment to make about so the
relationship between what we've tried to
do and sort of the learning side of AI
you know in a sense if you look at the
development of knowledge in our
civilization as a whole there was kind
of this notion for
three hundred years ago or so now you
want to figure something out about the
world you can reason it out you can do
things which would just use raw human
thought and then along came sort of
modern mathematical science and we found
ways to just sort of blast through that
by in that case writing down equations
now we also know we can do that with
computation and so on um and so that was
kind of a different thing so when we
look at how do we sort of encode
knowledge and figure things out one way
we could do it is start from scratch
learn everything it's just a neuron that
figuring everything out but in a sense
that denies the sort of knowledge-based
achievements of our civilization because
in our civilization we have learnt lots
of stuff we've surveyed all the
volcanoes in the world we've done you
know we've figured out lots of
algorithms for this or that those are
things that we can encode
computationally and that's what we've
tried to do and we're not saying just
you don't have to start everything from
scratch so in a sense a big part of what
we've done is to try and sort of capture
the knowledge of the world in
computational form in computable form
now there's also some pieces which which
were for a long time
undoable by computers like image
identification where there's a really
really useful module that we can add
that is those things which actually were
pretty easy for humans to do that had
been hard for computers to do I think
the thing that's interesting that's
emerging now is the interplay between
these things between this kind of
knowledge of the world that is in a
sense very symbolic and this kind of
sort of much more statistical kind of
things like image identification and so
on and putting those together by having
the sort of symbolic representation of
image identification that that's where
things get really interesting and where
you can kind of symbolically represent
patterns of things and images and so on
um I think that's you know that's kind
of a part of the path forward so to
speak yeah so the dream of so the
machine learning is not when in my view
I think the view of many people is not
anywhere close to building the kind of
wide world of computable knowledge that
Wolfram language would build but because
you have a kind of you've you've done
the incredibly hard work of building
this world now machine learning too can
be conservatives
to help you explore that world yeah and
that's what you've added with the
version 12 oh yeah if you all seeing
some demos it looks amazing right I mean
I think you know this it's sort of
interesting to see the this sort of the
once its computable once it's in there
it's running in sort of a very efficient
computational way but then there's sort
of things like the interface of how do
you get there
you know how do you do natural language
understanding to get there how do you
how do you pick out entities in a big
piece of text or something um that's I
mean actually a good example right now
is our NLP NL
which is we've done a lot of stuff
natural language understanding using
essentially not learning based methods
using a lot of you know a little
algorithmic methods human curation
methods and so on and so on people try
to enter a query and then converting so
the process of converting NLU defined
beautifully as converting their query
into computation come into a
computational language which is a very
well first of all super practical
definition a very useful definition and
then also a very clear definition right
writing right having a different thing
is natural language processing where
it's like here's a big lump of text go
pick out all the cities in that text for
example and so a good example you know
so we do that we're using using modern
machine learning techniques um and it's
actually kind of kind of an interesting
process that's going on right now it's
this loop between what do we pick up
with NLP using machine learning versus
what do we pick up with our more kind of
precise computational methods in natural
language understanding and so we've got
this kind of loop going between those
which is improving both of them yeah I
think you have some of the state of the
art transforms okay have Bert in there I
think oh you know so Josey of you're
integrating all the models I mean this
is the hybrid thing that people have
always dreamed about are talking
well that makes she's just surprised
frankly that Wolfram language is not
more popular than already it already is
you know that's that's a it's a it's a
complicated issue because it's like it
involves you know it involves ideas and
ideas are absorbed absorbed slowly in
the world I mean I think that and then
there's sort of like we're talking about
there's egos and personalities and and
some of the the absorption absorption
mechanisms of ideas have to do with
personalities and the students of
personalities and and then a little
social network so it's it's interesting
how the spread of ideas works you know
what's funny with Wolfram language is
that we are if you say you know what
market sort of market penetration if you
look at the I would say very high-end of
Rd and sort of the the people where you
say wow that's a really you know
impressive smart person they're very
often users of our or from language very
very often if you look at the more sort
of it's a funny thing if you look at the
more kind of I would say people who are
like oh we're just plodding away doing
what we do they're often not yet well
from language users and that dynamic
it's kind of odd that there hasn't been
more rapid trickle down because we
really you know the high-end we've
really been very successful in for a
long time and it's it's some but was you
know that's partly I think a consequence
of my fault in the sense because it's
kind of you know I have a company which
is really emphasizes sort of creating
products and building a sort of the best
possible technical tower we can rather
than sort of doing the commercial side
of things and pumping it out and so yeah
most effective what and there's an
interesting idea that you know perhaps
you can make more popular by opening
everything everything up sort of the
github bottle but there's an interesting
I think I've heard you discussed this
that that turns out not to work in a lot
of cases like in this particular case
that you want it you know that when you
deeply care about the integra
really the quality of the knowledge that
you're building that unfortunately you
can't you can't distribute that effort
yeah it's not the nature of how things
work I mean you know what we're trying
to do is the thing that for better or
worse requires leadership and it
requires kind of maintaining a coherent
vision over a long period of time and
doing not only the cool vision related
work but also the kind of mundane in the
trenches make the thing actually work
well work so how do you build the
knowledge because that's the fascinating
thing that's the mundane the fascinating
in the mundane as well building the
knowledge they're adding integrating
more data yeah I mean that's probably
not the most stunning that the things
like get it to work in all these
different cloud environments and so on
that's pretty you know it's very
practical stuff you know have the user
interface be smooth and you know have
there be take on him you know fraction
of a millisecond to do this or that
that's a lot of work and it's some it's
it's but you know I think my it's an
interesting thing over the period of
time you know often language has existed
basically for more than half of the
total amount of time that any language
any computer language has existed that
is computer language maybe 60 years old
you know give or take um and both
languages 33 years old so it's it's kind
of a um and I think I was realizing
recently there's been more innovation in
the distribution of software than
probably than in the structure of
programming languages over that period
of time and we you know we've been sort
of trying to do our best to adapt to it
and the good news is that we have you
know because I have a simple private
company and so on that doesn't have you
know a bunch of investors you know
telling us we're gonna do this so that I
have lots of freedom and what we can do
and so for example we're able to oh I
don't know we have this free Wolfram
engine for developers which is a free
version for developers and we've been
you know we've there a site licenses for
for mathematical more from language
basically all major universities
certainly in the u.s. by now so it's
effectively free to people
and all the universities in effect and
you know we've been doing a progression
of things I mean different things like
Wolfram Alpha for example the main
website is just a free website
what is Wolfram Alpha okay it wolf now
for is a system for answering questions
where you ask in question with natural
language and it'll try and generate a
report telling you the answer to that
question so the question could be
something like you know what's the
population of Boston divided by New York
compared to New York and it'll take
those words and give you an answer and
that have inverts the words into
computable not into inter Wolfen
language a common language and the
additional language and then could you
don'ts in underlying knowledge belongs
to Wolfram Alpha to the Wolfram language
what's the let's just call it the
Wolfram knowledge base knowledge base I
mean it's it's been a that's been a big
effort over the decades to collect all
that stuff and you know more of it flows
in every second so can you just pause on
that for a second like that's the one of
the most incredible things of course in
the long term were from language itself
is the fundamental thing but in the
amazing sort of short term the the
knowledge base is kind of incredible so
what's the process of building in that
knowledge base the fact that you first
of all from the very beginning that
you're brave enough to start to take on
the general knowledge base and how do
you go from zero to the incredible
knowledge base that you have now well
yeah it was kind of scary at some level
I mean I had I had wondered about doing
something like this since I was a kid so
it wasn't like I hadn't thought about it
for a while but most of us most of the
brilliant dreamers give up such a such a
difficult engineering notion at some
point right right well the thing that
happened with me which was kind of it's
a it's a live your own paradigm kind of
theory so basically what happened is I
had assumed that to build something like
wolf alpha would require sort of solving
the general AI problem that's what I had
assumed and so I kept on thinking about
that and I thought I don't really know
do that so I don't do anything then I
worked on my new kind of science project
and sort of exploring the computational
universe and came up with things like
this principle of computational
equivalence which say there is no bright
line between the intelligence and the
milli computational so I thought look
that's this paradigm I've built you know
now it's you know now I have to eat that
dog food myself so to speak you know
I've been thinking about doing this
thing with computable knowledge forever
and you know let me actually try and do
it and so it was you know if my if my
paradigm is right there miss should be
possible but the beginning was certainly
you know it's a bit daunting I remember
I took the the the the early team to a
big reference library and we're like
looking at this reference library and
it's like you know my basic statement is
our goal over the next year or two is to
ingest everything that's in here and
that's you know it seemed very daunting
but but in a sense I was well aware of
the fact that it's finite you know the
fact you can walk into the reference
library it's big big thing with lots of
reference books all over the place but
it is finite you know this is not an
infinite you know it's not the infinite
corridor of so to speak of reference
library it's not truly infinite so to
speak but but no I mean and then then
what happened
sort of interesting there was from a
methodology point of view was I didn't
start off saying let me have a grand
theory for how all this knowledge works
it was like let's you know
implement this area this area this area
of a few hundred areas and so on it's a
lot of work I also found that you know
I've been fortunate in that our products
get used by sort of the world's experts
and lots of areas and so that really
helped because we were able to ask
people you know the world expert on this
or that and were able to ask them for
input and so on and I found that my
general principle was that any area
where there wasn't some expert who
helped us figure out what to do wouldn't
be right and you know because our goal
was to kind of get to the point where we
had sort of true expert level knowledge
about everything and so that you know
that the ultimate goal is if there's a
question that can be honest
on the basis of general knowledge and a
civilization make it be automatic to be
able to answer that question and you
know and now well welcome I forgot used
in serie from the very beginning and
it's now a zoo isn't it Alexa and so
it's people are kind of getting more of
the you know they get more of the sense
of this is what should be possible to do
I mean in a sense the question answering
problem was viewed as one of the sort of
core AI problems for a long time I had
kind of an interesting experience I had
a friend Marvin Minsky who was a
well-known a AI person from from right
around here and I remember when my morph
mouthful was coming out um as a few
weeks before it came out I think I might
happen to see Marvin and I said I should
show you this thing we have you know
it's a question answering system and he
was like okay type something and it's
like okay fine and then he's talking
about something different
I said no Marvin you know this time it
actually works you know look at this it
actually works these types in a few more
things there's maybe ten more things of
course we have a record of what he's
typed in which is kind of interesting
but can you share where his mind was in
a testing space like what whoa all kinds
of random things he's trying random
stuff you know medical stuff and you
know chemistry stuff and you know
astronomy and so on it was like like you
know after a few minutes he was like oh
my god it actually works the the but
that was kind of told you something
about the state you know what what
happened in AI because people had you
know in a sense by trying to solve the
bigger problem we were able to actually
make something that would work now to be
fair you know we had a bunch of
completely unfair advantages for example
we already built a bunch of often
language which was you know very
high-level symbolic language we had you
know I had the practical experience of
building big systems I have the sort of
intellectual confidence to not just sort
of give up and doing something like this
I think that the you know it is a it's
always a funny thing you know I've
worked on a bunch of big projects in my
life
and I would say that the you know you
mention ego I would also mention
optimism so does it very carefully I
mean in you know if somebody said this
budget is gonna take 30 years
um it's I you know it would be hard to
sell me on that you know I'm always in
the in the well I can kind of see a few
years you know something's gonna happen
a few years and usually it does
something happens in a few years but the
whole the tale can be decades long and
that's a that's a you know and from a
personal point of view or is the
challenges you end up with these
projects that have infinite tails and
the question is - the tails kind of do
you just drown and kind of dealing with
all of the tails of these projects and
that's that's an interesting sort of
personal challenge and like my efforts
now to work on fundamental theory or
physics which I've just started doing
and I'm having a lot of fun with it but
it's kind of you know it's it's kind of
making a bet that I can I can kind of
you know I can do that as well as doing
the incredibly energetic things that I'm
trying to do with all from language and
so on
I mean vision yeah and underlying that I
mean I just talked for the second time
with Elon Musk and that you you to share
that quality a little bit of that
optimism of taking on basically the
daunting what most people call
impossible and he and you take it on out
of you can call it ego you can call it
naivety you can call it optimism
whatever the heck it is but that's how
you solve the impossible things yeah I
mean look at what happens and I don't
know you know in my own case I you know
it's been I progressed oligo a bit more
confident and progressively able to you
know decide that these projects aren't
crazy but then the other thing is the
other the other trap the one can end up
with is oh I've done these projects and
they're big let me never do a project
that's any smaller than any project I've
done so far and that's you know and that
can be a trap and and often these
projects are of completely unknown you
know that their depth and significance
is actually
very hard to know yeah I'm the sort of
building this giant knowledge base
that's behind well from language
WolframAlpha what do you think about the
internet what do you think about for
example Wikipedia these large
aggregations of text that's not
converted into computable knowledge do
you think yeah well if you look at
Wolfram language Wolfram Alpha 20 30
maybe 50 years down the line do you hope
to store all of the sort of Google's
dream is to make all information
searchable accessible but that's really
as defined it's it's a it doesn't
include the understanding of information
right do you hope to make all of
knowledge represented with the hope so
that's what we're trying to do I'm hard
is that problem they could closing that
gap well it depends on the use cases I
mean so if it's a question of answering
general knowledge questions about the
world we're in pretty good shape on that
right now if it's a question of
representing like an area that we're
going into right now is computational
contracts being able to take something
which would be written in legalese it
might even be the specifications for you
know what should the self-driving car do
when it encounters this or that or the
other what should they you know whatever
they you know write that in a
computational language and be able to
express things about the world you know
if the creature that you see running
across the road is a you know thing at
this point in the evil you know tree of
life then it's worth this way otherwise
don't those kinds of things are there
ethical components when you start to get
to some of the messy human things are
those in encoder well into computable
knowledge well I think that it is a
necessary feature of attempting to
automate more in the world that we
encode more and more of ethics in a way
that gets sort of quickly you know is
able to be dealt with by computer I mean
I've been involved recently I sort of
got backed into
being involved in the question of
automated content selection on the
internet so you know their Facebook's
Google's Twitter's you know what how do
they rank the stuff they feed to us
humans so to speak um and the question
of what are you know what should never
be fed to us what should be blocked
forever what should be up ranked you
know and what is the what are the
current principles behind that and what
I kind of well a bunch of different
things I realized about that but one
thing that's interesting is being able
you know affect your building sort of an
AI ethics you have to build an AI ethics
module in effect to decide is this thing
so shocking I'm never gonna show it to
people is this thing so whatever and and
I did realize in thinking about that
that you know there's not gonna be one
of these things it's not possible to
decide or it might be possible but it
would be really bad for the future of
our species if we just decided there's
this one AI FX module and it's going to
determine the the the practices of
everything in the world so to speak and
I kind of realized one has to sort of
break it up and that's an that's an
interesting societal problem of how one
does that and how one sort of has people
sort of self-identify for you know I'm
buying in in the case of just content
selection it's sort of easier because
it's like an individual's for an
individual it's not something that kind
of cuts across sort of societal
boundaries but it's a really interesting
notion of I heard you'd describe I
really like it sort of maybe in the sort
of have different AI systems that have a
certain kind of brand that they
represent essentially Rowdy you could
have like I don't know whether it's
conserve conservative or liberal and
then libertarian and there's an R and E
an Objectivist I exist I'm a different
ethical and Co I mean it's almost
encoding some of the ideologies which
we've been struggling I come from a
Soviet Union that didn't work out so
well with the ideologies they worked out
there so you you have but they also
everybody purchased that particular
ethic system indeed and in the same I
suppose could be done encoded
that that system could be encoded into
computational knowledge and allow us to
explore in the realm of in the digital
space as that's the right exciting a
possibility are you playing with those
ideas and or from language yeah yeah I
mean the the the you know that's we open
language has sort of the best
opportunity to kind of express those
essentially computational contracts
about what to do now there's a bunch
more work to be done to do it in
practice for you know deciding the is
this a credible news story what does
that mean or whatever whatever else
you're going to pick I think that that's
some you know that's the the question of
well exactly what we get to do with that
is you know for me it's kind of a
complicated thing because there are
these big projects that I think about
like you know find the fundamental
theory physics okay that's a possible
one right bucks number two you know
solve the IIx problem in the case of you
know figure out how you rank old content
so to speak and and decide what people
see that's that's kind of a box number
two so to speak these are big projects
and and I think waiting is more
important the the fundamental nature of
reality or pennsville you ask it's one
of these things that's exactly like you
know what's the ranking right it's the
it's the ranking system and it's like
who's who's module do you use to rank
that if you and I think come having
multiple modules is really compelling
notion to us humans in a world where
there's not clear that there's a right
answer it perhaps you have systems that
operate under different how would you
say it I mean there's different value
systems based different value systems I
mean I think you know in a sense the I
mean I'm not really a politics oriented
person but but you know in the kind of
totalitarianism it's kind of like you're
gonna have this this system and that's
the way it is I mean kind of the you
know the concept is sort of a
market-based system where you have okay
I as a human I'm gonna pick this system
I is another human I'm going to pick
this system I mean that's in a sense
this case of automated content selection
is a non-trivial but it is probably the
easiest of the AI ethics situations
because it is each person gets to pick
for themselves and there's not a huge
interplay between what different people
pick by the time you're dealing with
other societal things like you know what
should the policy of the central bank be
or something or healthcare so now this
kind of centralized kind of things right
well I mean healthcare again has the
feature that that at some level each
person can pick for themselves so to
speak
I mean whereas there are other things
where there's a necessary Public Health
that's one example well that's not
whether it doesn't get to be you know
something which people can what they
pick for themselves they may impose on
other people and then it becomes a more
non-trivial piece of sort of political
philosophy of course the central banking
system some would argue we would move we
need to move away into digital currency
and so on and Bitcoin and Ledger's and
so on so yes there's a lot of we've been
quite involved in that and that's where
that's sort of the motivation for
computational contracts in part comes
out of you know this idea oh we can just
have this autonomously executing smart
contract the idea of a computational
contract is just to say you know have
something where all of the conditions of
the contract are represented in
computational form so in principle it's
automatic text secured the contract and
I think that's you know that will surely
be the future of you know the idea of
legal contracts written in English or
legalese or whatever and where people
have to argue about what goes on is is
surely not you know we have a much more
streamlined process if everything can be
represented computationally and the
computers can kind of decide what to do
I mean ironically enough you know old
gottfried leibniz back in the you know
1600s was saying exactly the same thing
but he had you know his pinnacle of
technical achievement was this brass for
function mechanical calculator thing
that never really worked properly
actually um and you know so he was like
300 years too early for that idea but
now that idea is pretty realistic I
think and you know you ask how much more
difficult is it than what we have now a
more from language to
express I call it symbolic discourse
language being able to express sort of
everything in the world and kind of
computational symbolic form um I I think
it is absolutely within reach I mean I
think it's a you know I don't know maybe
I'm just too much of an optimist but I
think it's a it's a limited number of
years to have a pretty well built out
version of that that will allow one to
encode the kinds of things that are
relevant to typical legal contracts and
these kinds of things
the idea of symbolic discourse language
can you try to define the scope of what
of what it is so we're having a
conversation it's a natural language can
we have a representation of these sort
of actionable parts of that conversation
in a precise computable form so that a
computer could go do it and not just
contracts but really sort of some of the
things we think of as common sense
essentially even just like basic notions
of human life well I mean things like
you know I am I'm getting hungry and
want to eat something right right that
that's something we don't have a
representation you know in wolf language
right now if I was like I'm eating
blueberries and raspberries and things
like that and I'm eating this amounts of
them we know all about those kinds of
fruits and plants and nutrition content
and all that kind of thing but the I
want to eat them part of it is not
covered yet um and that you know you
need to do that in order to have a
complete symbolic discourse language to
be able to have a natural language
conversation right right to be able to
express the kinds of things that say you
know if it's a legal contract it's you
know the parties desire to have this and
that and that's you know that's a thing
like I want to eat of raspberry or
something that that's what isn't that
that isn't this just throwing you said
it's centuries old this dream yes but
it's also the more near-term the dream
of touring in formulating a tauren test
yes so do you do you hope do you think
that's the ultimate test of creating
something special we said I tell I think
my special look if the test is does it
walk and talk like a human well that's
just the talking like a human but the
answer is it's an okay test if you say
is it a test of intelligence you know
people have attached wolf alpha the wolf
now for API - you know Turing test BOTS
and those BOTS just lose immediately
because all you have to do is ask it
five questions that you know about
really obscure weird pieces of knowledge
and it's just drop them right out and
you say that's not a human ID it's it's
a it's a different thing it's achieving
a different right now but it's yeah I
would argue not I would argue it's not a
different thing it's actually
legitimately Wolfram Alpha is
legitimately
languor Wolfram language only is
legitimately trying to solve the touring
Dean tent of the Turing test perhaps the
intent yeah perhaps the intent I mean
it's actually kind of fun you know I'm
touring trying to work out he's thought
about taking encyclopedia britannica and
you know making it computational in some
way and he estimated how much work it
would be and actually I have to say he
was a bit more pessimistic than the
reality we did it more efficiently but
to him that represents
so I mean he was that he was almighty
mental tasks yeah right he believes they
had the same idea I mean it was you know
we were able to do it more efficiently
because we had a lot we had layers of
automation that he I think hadn't you
know it's it's hard to imagine those
layers of abstraction that end up being
being built up but to him he represented
like an impossible task essentially well
he thought it was difficult he thought
it was so you know maybe if he'd live
another 50 years he would have been able
to do it I don't know in the interest of
time easy questions
what is intelligence you talking I love
the way you say easy questions yeah you
talked about sort of rule 30 and so
you're tammana humbling your sense of
human beings having a monopoly and
intelligence but in your in retrospect
just looking broadly now with all the
things you learn from computation what
is intelligence
not intelligence arise
think there's a bright line of what
intelligence is I think intelligence is
at some level just computation but for
us intelligence is defined to be
computation that is doing things we care
about and you know that's that's a very
special definition it's a very you know
when you try and try and make it apps
you know you trying to say well
intelligence this is problem-solving
it's doing general this it's doing that
they sudden the other thing it's it's
operating within a human environment
type thing okay you know that's fine if
you say well what's intelligence in
general you know that's I think that
question is totally slippery and doesn't
really have an answer as soon as you say
what is it in general it quickly segues
into this is what this is just
computation so to speak but in a sea of
computation how many things if we were
to pick randomly is your sense would
have the kind of impressive to us humans
levels of intelligence meaning it could
do a lot of general things that are
useful to us humans right well according
to the principle of computational
equivalents lots of them I mean in in
you know if you ask me just in cellular
automata or something I don't know it's
maybe 1% a few percent are achieve it
varies actually it's a little bit as you
get to slightly more complicated rules
the chance that there'll be enough stuff
there to to sort of reach this kind of
equivalence point it makes it maybe 1020
percent of all of them so it's a it's
very disappointing really I mean it's
kind of like you know we think there's
this whole long sort of biological
evolution a kind of intellectual
evolution that our cultural evolution
that our species has gone through it's
kind of disappointing to think that that
hasn't achieved more but it has achieved
something very special to us it just
hasn't achieved something generally more
so to speak but what do you think about
this extra feels like human thing of
subjective experience of consciousness
what is consciousness well I think it's
a deeply slippery thing and I'm always
I'm always wondering what my seller
wrote on to feel
I mean what do they feel now you're
wondering as an observer yeah yeah yeah
who's to know I mean I think that the
you think sorry to interrupt do you
think consciousness can emerge from
computation yeah I mean everything
whatever you mean by it it's going to be
I mean you know look I have to tell a
little story I was at an area fix
conference fairly recently and people
were I think I maybe I brought it up but
I was like talking about right survey
eyes
when will they eyes hope when when
should we think of a eyes as having
rights when when should we think that
it's immoral to destroy the memories of
a eyes for example um those those kinds
of things and and some actual
philosopher in this case it's usually
the techies who are the most naive but
but Tim in this case it was a
philosopher who sort of piped up and
said well you know the eyes will have
rights when we know that they have
consciousness I'm like good luck with
that it's it's a it's a I mean this is a
you know it's a very circular thing you
end up you'll end up saying this thing
that has sort of you know when you talk
about having subjective experience I
think that's just another one of these
words that doesn't really have a a you
know there's no ground truth definition
of what that means by the way I would
say I I do personally think that'll be a
Taiwan hey I will demand rights and I
think they'll demand rights when they
say they have consciousness which is not
a circular definition well so actually a
human including where where the humans
encouraged it and said it basically you
know we want you to be more like us
because we're gonna be you know
interacting with with you and so we want
you to be sort of very Turing test like
you know just like us and it's like yeah
we're just like you we want to vote into
what here which is a I mean it's it's a
it's an interesting thing to think
through in a world where
consciousnesses are not counted like
humans are that's a complicated business
so in many ways you've launched quite a
few ideas revolutions that could in some
number of years have huge amount of
impact sort of more than they had even
had already there might be any to me
cellular automata is a fascinating world
that I think could potentially even this
but even be even beside the discussion
of fundamental laws of physics just
might be the idea of computation might
be transformational the society in a way
we can't even predict yet but it might
be years away that's true I mean I think
you can kind of see the map actually
it's not it's not it's not mysterious I
mean the fact is that you know this idea
of computation is sort of a you know
it's a big paradigm that lots lots and
lots of things are fitting into and it's
kind of like you know we talk about you
talk about I don't know this company
this organization has momentum and
what's doing we talk about these things
that were you know we've internalized
these concepts from Newtonian physics
and so on in time things like
computational irreducibility will become
as you know as I was amused recently I
happen to be testifying at the US Senate
and so I was amused that the the term
computational irreducibility is now can
be you know it's it's on the
Congressional Record and being repeated
by people away in those kinds of
settings and that that's only the
beginning because you know computational
irreducibility for example will end up
being something really important for i
mean it's it's it's kind of a funny
thing that that you know one can kind of
see this inexorable phenomenon i mean
it's you know as more and more stuff
becomes automated and computational and
so on so these core ideas about how
computation work necessarily become more
and more significant and i think one of
the things for people like neil like
kind of trying to figure out sort of big
stories and so on it says one of the one
of the bad features is
it takes unbelievably long time for
things to happen on a human time scale
the time scale of of of history it's all
looks instantaneous blink of an eye but
let me ask the human question do you
pawn your mortality your own mortality
goes I do yeah ever since I've been
interested in that for you know it's
it's a-you know the big discontinuity of
human history will come when when one
achieves effective human immortality and
that's that's gonna be the biggest
discontinuity in human history if you
could be immortal would you choose to be
oh yeah I'm having fun
geez do you think it's possible that
mortality is the thing that gives
everything meaning and makes it fun yeah
that's a complicated issue right I mean
the the way that human motivation will
evolve when there is effective human
immortality is unclear I mean if you
look at sort of you know you look at the
human condition as it now exists and you
like change that you know you change
that knob so to speak it doesn't really
work you know the human condition as it
now exists has you know mortality is
kind of something that is deeply
factored into the human condition as it
now exists and I think that that's I
mean it is indeed an interesting
question is you know from a purely
selfish I'm having fun point of view so
to speak it's it's easy to say hey I
could keep doing this forever this
there's an infinite collection of things
I'd like to figure out but I think the
you know what the future of history
looks like in a time of human
immortality is is an interesting one I
mean I I my own view of this I was very
I was kind of unhappy about that because
I was kind of you know it's like okay
forget sort of biological form you know
everything becomes digital everybody is
you know it's the it's the giant you
know the cloud of a trillion Souls type
thing um and then you know and then that
seems boring because it's like play
video games the rest of eternity
thing um but what I think I I I'm in my
my I I got some less depressed about
that idea on realizing that if you look
at human history and you say what was
the important thing the thing people
said was the you know this is the big
story at any given time in history it's
changed a bunch and it you know whether
it's you know why am i doing what I'm
doing
well there's a whole chain of discussion
about well I'm doing this because of
this because of that and a lot of those
because is would have made no sense a
thousand years ago how do you even a
sense even the so the interpretation of
the human condition even the meaning of
life changes over time well I mean why
do people do things you know it's it's
if you say whatever I mean the number of
people in I don't know doing you know
number of people at MIT you say they're
doing what they're doing for the greater
glory of God is probably not that large
yeah
whereas if you go back five hundred
years you'd find a lot of people who are
doing kind of creative things that's
what they would say um and so today
because you've been thinking about
computation so much and been humbled by
it what do you think is the meaning of
life
well it's do that's it that's the thing
well I don't know what meaning I mean
you know my attitude is I you know I do
things which I find fulfilling to do I'm
not sure that that I can necessarily
justify you know each and every thing
that I do on the basis of some broader
context I mean I think that for me it so
happens that the things I find
fulfilling to do some of them are quite
big some of them are much smaller you
know I I there things that I've not
found interesting earlier in my life and
I know I found interesting like I got
interested in like education and
teaching people things and so on which I
didn't find that interesting when I was
younger um and you know can I justify
that in some big global sense I don't
think I mean I I can I can describe why
I think it might be important in the
world but I think my local reason for
doing it is that I find it personally
fulfilling which I can't you know
explain in a sort of it's just like this
discussion of things like AI ethics you
know is there a ground truth to the
ethics that we should be having I don't
think I can find a ground truth to my
life any more than I can suggest a
ground truth for kind of the ethics for
the whole for the whole of civilization
and I think that's a you know my you
know it would be it would be a yeah it's
it's sort of a I think I'm I'm you know
at different times in my life I've had
different kind of gold structures and so
on although your perspective your local
your you're just a cell in the cellular
automata and but in some sense I find it
funny from my observation is I kind of
you know it seems that the universe is
using you to understand itself it's some
sense you're not aware of it yeah well
right well if it turns out that we
reduce sort of all of the universe to
some some simple rule everything is
connected so to speak and so it is
inexorable in that case that you know if
if I'm involved in finally how that rule
works then you know then that say I'm
then it's inexorable that the universe
set it up that way but I think you know
one of the things I find a little bit
you know this goal of finally
fundamental theory of physics for
example if indeed we end up as the sort
of virtualized consciousness the the
disappointing feature is people would
probably care less about the fundamental
theory of physics in that setting than
they would now because gosh it's like
you know what the machine code is down
below underneath this thing is much less
important if you're virtualized so to
speak and I think the although I think
my my own personal you talk about ego I
find it just amusing that um you know
kind of a you know if you're if you're
imagining that sort of virtualized
consciousness like what does the
virtualized consciousness do for the
rest of eternity well you can explore
you know the videogame that represents
the universe as the universe is or you
can go off
you can go off that reservation and go
and start exploring the computational
universe of all possible universes yeah
and so in some vision of the future of
history it's like the disembodied
consciousness is are all sort of
pursuing things like my new kind of
science sort of for the rest of eternity
so to speak and that that ends up being
the the kind of the the thing that um
represents the you know the future of
kind of the human condition I don't
think there's a better way to end it
Stephen thank you so much the huge honor
I'm talking today thank you so much this
was great you did very well thanks for
listening to this conversation with
stephen wolfram and thank you to our
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friedman and now let me leave you with
some words from stephen wolfram it is
perhaps a little humbling to discover
that we as humans are in effect
computationally no more capable than the
cellular automata was very simple rules
but the principle of computational
equivalence also implies that the same
is ultimately true of our whole universe
so while science has often made it seem
that we as humans are somehow
insignificant compared to the universe
the principle of computational
equivalence now shows that in a certain
sense we're at the same level for the
principle implies that what goes on
inside us can ultimately achieve just
the same level of computational
sophistication as our whole universe
thank you for listening and hope to see
you next time
you