00:00:00
[Applause] solo no besties yeah everybody else was scared away I'm afraid yeah or you
00:00:13
scared them away yeah I mean it was uh um challenging prompt interview Steven Wolfram on stage in 40 minutes so here we go let your winners
00:00:25
ride rman [Music] David and instead we open sourced it to the fans and they've just gone crazy with it queen
00:00:37
[Music] of um it's a huge honor to talk to Steven Wolfram creator of Mathematica wolf from Alpha and the Wolf from language the
00:00:48
author of A New Kind of Science the originator of wolf physics project and head of wol
00:00:54
research Steven first used a computer in 1973 and quickly became a leader in the
00:00:59
emerging field of scientific Computing in 1979 he began the construction of SNP the first modern computer algebra system he published his first scientific paper
00:01:11
at age 15 and had received his PhD in theoretical physics from Caltech by 20 wolfram's early scientific work was mainly in high energy physics Quantum field Theory cosmology and complexity
00:01:24
discovering a number of fundamental connections between computation and nature and inventing such Concepts as
00:01:30
computational irreducibility which we'll talk about today wol firm's work led to a wide range of applications and
00:01:37
provided the main scientific foundation for such initiatives as complexity Theory and artificial life wolm used his
00:01:45
ideas to develop a new Randomness generation system and a new approach to computational fluid dynamics both of
00:01:50
which are now in widespread use the release of wolf from alpha in May of 2009 was a historic step that has
00:01:57
defined a new dimension for computation and AI now relied on by millions of people to compute answers both directly
00:02:04
and through intelligent assistants such as Siri and Alexa and so on among others um so thank you for being here thanks
00:02:11
for having me I worked on um the at the Lawrence Berkeley National Lab at the center for beam physics for two and a half years and was when I was undergrad and I worked exclusively in Mathematica
00:02:22
as I shared with you the other night so um that's when I first got to know about you and and your work um
00:02:30
who here has seen an interview that Steven's done before just to get a sense okay I want to try and guide the
00:02:36
conversation a little bit so maybe we could start with computers okay um you talk about this concept of
00:02:47
um computational irreducibility yes maybe you can just and I want to try and connect with a broad audience yeah yeah in what is computation what is computation okay so at it's at the base
00:03:00
computation is about you specify rules and then you let those rules you figure out what the consequences of those rules are computers are really good at that you give a little program the computer
00:03:10
will run your program it will generate output I would say that the the kind of the bigger picture of this is how do you
00:03:17
formalize anything you know we can just use words we can talk vaguely about things how do you actually put something
00:03:23
down that is a kind of precise formalism that lets you work out what will happen that's been done in logic it's been done
00:03:29
in mathematic itics it's done in its most general form in computation where the rules can be kind of anything you
00:03:34
can specify to a computer then the question is given that you have the rules is that the end of the story once
00:03:40
you have the rules do you then know everything about what will happen well that's kind of what one would assume kind kind of the the traditional view of
00:03:48
science is once you work out the equations and so on then you're done then you can predict everything you
00:03:54
you're kind of you you you've done all the hard work turns out this is not true turns out that even with with very
00:04:00
simple rules the consequences of those rules can be arbitrarily hard to work out it's kind of like if you just run
00:04:06
the rule step by step step by step it's making some pattern on screen or whatever else you can just run all those
00:04:13
steps see what happens that's all good then you can ask yourself can you jump ahead can you say I know what's going to
00:04:19
happen I know the answer is going to be 42 or something at the end well the point is that that isn't in general
00:04:25
possible that in general you have to go through all the steps to work out what will happen and that's kind of a
00:04:30
fundamental limitation of kind of the sort of prediction in science and something people people have gotten very
00:04:36
used to the idea that with science we can predict everything but from within science we see this whole phenomenon of computational irreducibility it's related to things like girdles theorem
00:04:47
undecidability halting problem all kinds of other other kinds of ideas but from within science we see this fundamental limitation this fundamental inability for us to be able to say what will
00:04:58
happen so so we can't just skip ahead in a lot of cases we can't just create simple heuristics or simple solves that
00:05:07
avoid all of the hard work to simulate something to calculate something to come up with the computational output of something we're trying to figure out in a sense this is a good thing for us
00:05:18
because it's kind of we lead Our Lives time progresses things happen if we could just say we don't need to go
00:05:25
through all of those steps of time we could just say and the end it will be 37 or something that would be a bad feature
00:05:32
for us feeling that it was worthwhile to to sort of lead our lives and see time progress it would be a kind of you don't
00:05:39
really need time to progress you can always just say what the answer will be I mean this kind of idea has has many
00:05:45
consequence I mean for example when it comes to AIS you say well you've got this AI system and it's doing all kinds of things can you figure out what it will do you might want to figure out
00:05:56
what it will do because you might want to say I never want the AI system to do this very bad thing that you know I'm
00:06:01
trying to prevent so then you say well you know can I work out what it will do can I be sure that these rules that I'm
00:06:07
putting in for the AI system will never have it do this very bad thing well computational irreducibility says you
00:06:13
can't guarantee that you kind of have this trade-off with an AI system you can either say let's you know let's
00:06:19
constrain it a lot then we can know what it will do but then or let's let it sort
00:06:25
of have its way and do what it does if we don't let it have its way it's not really making use of the computational capabilities that it has right so we can't have this trade-off we either can
00:06:37
understand what's going to happen in which case we don't let our AIS really do what they can do or we uh or we say
00:06:44
okay we're going to run the risk of the AI doing something unexpected so can we just so
00:06:52
AI a lot of what people call AI are predictive models that are effectively built off of Statistics that make some prediction of what the right next step
00:07:05
in a sequence of things should be whether it's a pixel to generate an image or a series of words to generate a
00:07:12
chat uh response through an llm model like chat GPT or Bard or what have you um those are statistical models trained on past data are they is that different
00:07:25
than um the the problems in computation that you're talking about about better understanding the universe the nature of the universe solving bigger problems that AI has its limitation in how we
00:07:37
think and talk about it today and maybe you can connect computation and this idea of AI being this very simple
00:07:43
heuristical statistical thing that just predict stuff right well I mean the the computational universe of possible
00:07:49
programs possible rules is vast and there are I just want to I just want to make sure everyone understands that the
00:07:55
comp to say that the comput computational universe so so you know the set of things you can compute that
00:08:01
you want to try and compute I mean so so people are used to writing programs that are intended for particular human
00:08:06
purposes but let's say you just write a program at random you just put in the program it's a it's a random program
00:08:13
question is what does the random program do so a big thing that I discovered in the 1980s is the thing that greatly
00:08:20
surprised me was even a very simple program can do very complicated things I had assumed that if you want to do
00:08:26
complicated things you would have to set up complicated program turns out that's not true turns out that in nature nature
00:08:34
kind of discovered this trick in a sense you know we see all this complexity in nature it seems like the big origin of
00:08:41
that complexity is just this phenomenon that even a very simple program can do very complicated things so I mean that
00:08:48
that's the so this this sort of universe just give a quick example if you wouldn't mind like yeah yeah so I mean
00:08:53
like three three genes in a genome in in DNA okay so my my favorite example yes are things called cellular autometer yes and they work like this they have a a line of cells each one is either black
00:09:05
or white it's an infinite line of cells and you have a rule that says you go down the page making successive uh lines of cells you go down the page and you say the color of a cell on the next line
00:09:17
will be determined by a very simple kind of lookup from the color of the cell right above it into its left and right
00:09:23
okay so very simple setup there are 256 possible rules with just two colors and nearest neighbors you can just look at
00:09:29
what all of them do many of them do very simple things they'll just make some some triangle of black it looks like a
00:09:34
pyramid a triangle when it's done or right right right and then my my all-time favorite it's kind of you turn
00:09:40
the telescope into this computational universe and see what's out there my all-time favorite Discovery is Rule 30
00:09:46
and the numbering of these these rules you you you can specify that rule 30 you started off from one black cell and it
00:09:53
makes this really complicated pattern it makes a pattern where if you just saw it you would say somebody must have gone to
00:09:58
a huge amount it looks designed it looks like there was an architect yes that came in and designed that thing and
00:10:04
that's the only way that thing could have been created cuz it's so beautiful and intricate and right resonates but it
00:10:10
had two simple rules change be black if the left is black and the right is white and be white if right those those those
00:10:16
kinds of things so that's the kind of set up and and for example when you look at rule 30 you look at like the center
00:10:22
column of cells it looks for all practical purposes completely random even though you know that it was really
00:10:28
made from some simple rule When You See It produced it looks random it's kind of
00:10:34
like a a good analogy if you know you know digits of pie people memorize you know 3.14159 it's about as far as I can go
00:10:42
you got you were one ahead of me I that's why build software to do these things we can go to you know I don't
00:10:47
know about that PhD at 20 but uh but you know the the the point is that the the rule for generating those digits it's you know the ratio of the circumference to diameter of a
00:10:58
circle there's a very definite rule but once you've generated those digits they seem completely random yes it's the same
00:11:05
kind of phenomenon that that you know you can have a simple rule it produces things that look very complicated yes
00:11:10
now so so so that's a simple computer that's a simple computational exercise by the way it's not such a simple computer because turns out when you kind of try and rank you
00:11:23
know you set up a computer you make it with uh electronics so you might make it you know some some mechanical computer
00:11:29
comp from the past or something like this you ask the question you build a computer of a certain kind how
00:11:34
sophisticated are the computations it can do is it just an adding machine is it just a multiplying machine yes how
00:11:39
far does it get big Discovery from the 1930s is you can make a fixed piece of Hardware that's capable of running any program that's capable of doing any computation that's the that's the
00:11:52
discovery that launched the possibility of software launched most of modern technology so one might have thought you
00:11:59
have to go to a lot of effort to make a universal computer turns out that's not true it's a thing I call the principle
00:12:05
of computational equivalence which kind of tells one something about how far one has to go and the answer is pretty much
00:12:11
as soon as you see complicated Behavior the chances are you can kind of use that complicated Behavior to do any
00:12:17
computation you want and that's actually that's the reason for this computational irreducibility phenomenon because here's
00:12:24
here's how it works so so let's say you've got some system and it's doing what it does and you're trying to
00:12:29
predict what it's going to do so both the system itself and you as the predictor are computational systems so
00:12:37
then the question is can you the predictor be so much smarter than the system you're predicting you can just
00:12:43
jump ahead and say I know what you're going to do I've got the answer or are you stuck being kind of equivalent in
00:12:49
computational sophistication to the system you're trying to predict right so this principle of computational equivalence says whether you have a
00:12:55
brain or a computer or mathematics or statistics or or whatever else you are really just equivalent in your
00:13:01
computational sophistication to the system that you're trying to predict and that's why you can't make that
00:13:07
prediction that's why computational irreducibility happens but you know you were asking
00:13:12
about AI I mean in the the the thing that we have only just started mining is this computational universe of all possible programs most programs that we use today were engineered by people
00:13:26
people said I'm going to put this piece in and that piece in and that piece in so now we have a program that can make programs yes well we have we have there
00:13:33
sort of a vast Universe of possible programs we can say if we know what we want the program to do we can just
00:13:39
search this computational universe and find a program that does it often I've done this for many years for many many
00:13:45
purposes just give me an example there so well so so very simple example actually from rule 30 is you want to
00:13:51
make a random number generator you say how do I make something that makes good Randomness well you can just search the
00:13:58
set of possible simple programs and pretty soon you find one that makes good Randomness you ask me why does it make
00:14:04
good Randomness I don't know it's it's not something there's no narrative explanation so now with AI we're
00:14:11
generating a ton of programs and we now have a bigger space of programs or bigger library to go select from to solve problems or figure stuff out for us is that actually I think AI is sort
00:14:23
of AI is very limited in the computational universe yes I mean the computation this the connection I wanted to make because yeah I mean it's it's
00:14:29
you know the computational universe is all these possible rules we can talk later about whether the I just want to
00:14:34
be clear because you use the term Universe in the sense of all the things and I want to disconnect that from everyone's concept of universe okay just
00:14:41
yes we're going to talk about whether the I hope we're going to talk about whether we talk about the universe and the nature of the universe which we're
00:14:47
going to talk about Consciousness and we're going to smoke weed and then we're going to go to lunch [Laughter] but we're going to talk about you know
00:14:54
whether our physical universe is part of this computational universe but what I when I say computational universe I just
00:15:00
mean this very abstract idea of all these possible programs all the program the library of possible programs yeah
00:15:06
and so there are many many things that those programs can do most of those things are things that we humans just
00:15:12
look at them and say well it's kind of interesting I don't know what the significance of that is they're very nonhuman kinds of things yeah so what
00:15:19
have we done in AI what we've done is we've given for example a large language model we've given it you know 4 billion
00:15:25
web pages for example we've given it kind of the spefic specific parts of essentially the computational universe
00:15:31
that we humans have selected that we care about yes we've shown what what we care about and then what it's doing is
00:15:38
to say Okay I I know what you humans care about so I'm going to make things that are like what you said youve care
00:15:44
about yes and that's a tiny part of the computational universe right just like we saw in Caleb's video his AI said this
00:15:53
video is me telling you that all the stuff you've talked about AI is Terminators and blowing stuff up
00:15:59
and you know that's the limit of what we've we've done right and it can only construct stuff from the limit of what
00:16:05
we've done recorded scen right our data sets so so I mean the thing is so in terms of so give us an example of
00:16:11
something that needs to be computed a a a computational exercise something we got to figure out something
00:16:17
we want to solve outside of what AI is possibly able to to solve for today well I mean any any of these computationally irreducible problems anything where we're asking yeah so just just just to
00:16:28
connect it yeah yeah yeah right I mean oh gosh to pick an area I mean without without esoteric topology and algebra or
00:16:35
something like yeah yeah right I mean you know okay here's an example so you've got a biological system you've
00:16:40
got a good model great example biology okay so we we've got something we're trying to figure out this collection of
00:16:45
cells it behaves in this way is it going to grow forever and be really bad and make a tumor or is it going to
00:16:51
eventually Halt and stop growing okay that's a classic kind of computational irreducible type of problem where you
00:16:58
know you know we could imp if we knew enough detail we could simulate what every cell is going to do every molecule
00:17:03
every atom every cell every interaction if we knew enough we could simulate each of those steps and there's no easy way
00:17:09
to solve that answer that question you can't jump ahead and say so I know this thing is never going to turn into a tumor for example right and so
00:17:17
simulating the physical Universe whether you're simulating atoms in a cell or SpaceTime and discrete SpaceTime or non disc great space time itself becomes
00:17:30
this thing that where we don't have a simple heris a simple equation that says based on this condition this is how
00:17:37
things are going to end up but you have to actually go through a lot of calculate calculated steps we haven't known that I mean people have hoped that
00:17:43
you can just write down a formula for how physics works and then work out the answer directly from that that's that
00:17:49
was the big advance I mean in in you know if you go back to Antiquity people were just trying to sort of reason about
00:17:54
how the universe works yes sort of philosophically and then late 1600 unds sort of big Advance we can write down a
00:18:01
mathematical formula we can use calculus we can kind of just write essentially jump ahead and say what's going to happen in the universe we can make a prediction we can say you know the comet is going to be in this place at this
00:18:12
time and so on so there's all these hard problems that we can't solve with AI we have
00:18:17
today or can we and can you just help me and help everyone understand what are you excited about with respect to AI what is it that has happened in the last couple of months and years that you were
00:18:29
excited about what does that allow us to do that we couldn't do before using just raw approaches to okay so so I mean
00:18:38
several different things here I mean in in the first thing to say is you know we humans have been interested in a small
00:18:44
part of what's computationally possible AI is reflecting the part that we have been interested in that's sort of AI is
00:18:51
is is doing those kinds of things in terms of what's happened with AI I mean the big thing that happened you know a year ago was the arrival of successful
00:18:58
large language models and uh you know what does that tell us I think that um you know it was a surprise to everybody
00:19:05
including the people who were working on large language models that we kind of got past you know got to this point
00:19:10
where they seemed reasonable to us humans where they were producing text that was reasonable to us humans and not
00:19:16
just completely boring and and irrelevant and so on and I think the you know there's this you know this jump
00:19:22
that happened now in 2012 there was a sort of previous jump in machine learning that happened with images and
00:19:28
things like that image recognition and so on so what's the significance of large language models but one question
00:19:34
is why do they work you know why is it possible to make this neural net that uh can successfully kind of complete an essay or something and I think the answer is that it's kind of telling us a
00:19:46
piece of science that in a sense we should be embarrassed we hadn't figured out before it's a question of sort of
00:19:51
how do you construct language and we've known forever that there's kind of a syntactic grammar of language you know noun noun verb noun etc etc etc um but
00:20:03
what you know the llms are showing us is that there is a kind of semantic grammar of language there's a way of putting
00:20:09
together sentences that could make sense yeah and you know that for example people are always impressed that the LMS
00:20:14
have figured out how to quotes reason and I think the what's happening is you know logic is this thing that's kind of
00:20:21
this formalization of everyday language and it's a formalization that was discovered you know by Aristotle and
00:20:27
people in Antiquity and in a sense probably one can think about the way they discovered it they looked at lots
00:20:33
of speeches people had given and they said which ones make sense okay there's a certain pattern of how things are said
00:20:39
that make sense let's formalize that that's logic that's exactly what the LM has done as well it's noticed that there
00:20:45
are these patterns of language that you can kind of use again and that we call logic or reasoning or something like that so you know I think as a practical matter the you know llms provide this
00:20:57
kind of linguistic user interface we've had kind of graphical user interfaces and so on now we have this linguistic
00:21:03
user interface you say you know you've got some very small set of points you want to make you say I'm going to feed
00:21:10
it to an LM it's going to Puff it up into a big report I'm going to send that report to somebody else they're probably going to feed it to their own llm it's
00:21:16
going to grind it down to some small set of uh set of results it's kind of you know it's it's allowing one to use language as a transport layer I I think that's a you know there there are a lot
00:21:26
of these practical use cases for for this it's always seemed to me like the rate limiting step in humans is
00:21:33
communication like the rate at which you and I are speaking to one another is pretty low bandwidth like a couple words
00:21:40
a minute or something and yeah the question is what really is communication I mean you know in our brains there are
00:21:45
all these neurons that are firing and you know there's 100 billion of them in each of our brains and and there's a lot of sensory input besides the words that
00:21:51
you're saying that are traveling through vibrations in the air to my ear and that's some information from you but
00:21:56
there's so much more information that the human brain can gather and is building models around all the time making predictions around whether this
00:22:02
light's going to be on or off or that person's going to go to the bathroom or sit back down or what have you um but
00:22:09
you know I think one of the things that's sort of interesting is this you know we've got stuff in our brains we are trying to package up those thoughts
00:22:16
you know the structure of each of our brains is different so the the particular nerve firings are different
00:22:22
but we're trying to package up those thoughts in a kind of transportable way that's what language tends to do it kind
00:22:28
of packages Concepts and that's what these llms have done yes I mean because they're outputting a packet of
00:22:33
communicate to me and yes yes I mean so but is there anything else that's exciting to you from a
00:22:40
computational perspective what else can the AI do and what else okay we learn a lot from the AIS you know that they're telling us there is a science of llms which is completely not worked out yet
00:22:53
there's kind of a a bulk science of knowledge and things that that the llms are kind of of showing us is there
00:22:59
there's a kind of a science of the semantics of language which llms are showing us is there we haven't found it
00:23:04
yet it's kind of like like we just saw some new piece of Nature and we now get to make science about that kind of
00:23:11
nature now it's not obvious that we can make sort of science where we can tell a narrative story about what's going on
00:23:17
right it could be that we're just we're just sort of dumped into computational irreducibility and we just say it does this because there's this black box that
00:23:24
the that the the training model created that black box we don't know what it does I put a bunch of words in a bunch
00:23:30
of words come out it's amazing now you're saying we're going to try and understand the the nature the graphs the
00:23:36
nature of that box and that'll tell us a little bit something about I think we'll discover that for example we'll discover
00:23:43
that that human language is much less it's it's it's much simpler to describe human language than we had thought yeah in other words it's showing us rules of
00:23:54
human language that we didn't know yes and that's that's that's an interesting thing now if you ask what what else do
00:23:59
we learn from the AI I'll give you another example of something I was was playing with recently so you know use
00:24:05
image generation uh generative AI for making images as we can now see also making videos and so on there's this question of of you you go inside the AI and you say you know in inside the AI
00:24:17
you know the concept of a cat is represented by some Vector of a thousand numbers let's say the concept of a dog
00:24:22
and another thousand numbers you just say let's take these vectors of numbers and let's just take arbitrary numbers
00:24:29
what what does the AI think what what is the thing that corresponds to the sort of arbitrary Vector of numbers okay so
00:24:35
you can have these definite Concepts like cat and dog they particular numbers in this sort of space of all possible
00:24:40
Concepts and there's this idea I've been calling it interc concept space what's between kind of the concept of a cat and the concept of a dog and the answer is there's a huge amount of stuff from even
00:24:52
from a generative AI That's leared from us yeah it is finding these these kind of interc concept things right that are in between the things for which we have words sort of embarrassing to us that
00:25:05
you know simple estimate simple case if you say what fraction of the space of all possible concept so to speak is now filled with actual words that we have you of the 50,000 words that are common
00:25:17
in English for example what what um uh you know what yeah that's a that's a didn't know that
00:25:23
number that's interesting yeah right if you're an llm person that's the uh you know when it produces when it says what's the next word going
00:25:30
to be cuz that's what llms are always doing they're just trying to predict the next word what it's doing is it says here's this list of 50,000 numbers which
00:25:36
are the probabilities for each of the possible 50,000 words in English and then it'll pick the most likely one or
00:25:42
or the next most likely one or whatever else but in any case the the um you know this when you ask the question in the
00:25:49
space of sort of all possible Concepts how many do we have words for the answer is it's 1 in 10 600 wow it's like we
00:25:57
have a we have have explored a tiny tiny fraction of even the kind of Concepts that are revealed by the things that
00:26:03
sort of we put out there on the web and so on that's more than there are atoms in the universe yeah they 10 to the 8th
00:26:09
atoms in the universe right 10 to the 8th is very small compared to a lot of the numbers one deals with but um uh you
00:26:15
know I think the yeah it's a we should talk about the universe that I want to talk about the universe yeah right it's pretty cool so
00:26:26
um people want to talk about AI That's why I wanted to just make sure we we got your point of view so I it's appreciated
00:26:33
um what is the nature of the universe and what do we have wrong what's that and what do we have wrong yeah what do what does consensus have wrong yeah well so I mean this this is
00:26:46
where this is like where you like let the Reigns go and then you you go yes like we had CH at dinner the other night
00:26:53
so I'm excited yeah it's it's um you know physics as we know it right now was kind of 100 years ago big advances made in physics three big theories in physics
00:27:05
um general relativity the theory of gravity quantum mechanics the theory of small kinds of things and statistical
00:27:11
mechanics the theory of heat and uh sort of how and the second law of Thermodynamics law of entropy increase
00:27:16
things like this okay so those are three big theories that were invented about 100 years ago physics has been sort of
00:27:22
on a gradual incremental trajectory since then I've been interested in trying to understand kind of what what
00:27:28
could be underneath everything that we see in in the world and I think we figured it out which is kind of exciting
00:27:35
not something I expected to see in my lifetime not something I kind of expected to see for 50 100 more than
00:27:41
that years and kind of the the the number one thing that you say what do people get wrong one question is what is
00:27:48
space well you know people just sort of say space is the continuous thing you put things in any different place in
00:27:54
space people have been arguing about whether space is continuous or discreet since Antiquity discret means that it's
00:28:00
broken up into little pieces yes yes so so you know this argument also happened for matter is matter continuous or
00:28:07
discrete you know you have water is it just this continuous fluid or is it made of discrete kinds of things that got
00:28:13
answered about 120 years ago the answer is water is made of discrete molecules same thing happened for light light is
00:28:20
made of discrete photons space got kind of left out space people still think it's a continuous kind of thing so the kind of the starting point of things I've tried to figure out is actually
00:28:31
know that's not true space is discret there are atoms of space so to speak not like physical atoms like hydrogen helium and things they're just uh these sort of
00:28:41
points there're slots where things can fit is that a way you think about it they're they're really just things
00:28:48
Nothing fits in them I mean they're just all that one knows about them they're abstract things all one knows is that
00:28:55
one is different from another so there are the two discret things next to each other there's no next there's no next
00:29:02
there's no space we're about to build space so there is a graph is that a way to think about it a relationship between
00:29:08
two things right well relationship between several things it's kind the giant friend network of the atoms of
00:29:14
space right and that's that's all there is that's what our universe is made of and you know all the things that we
00:29:20
observe you know electrons black holes all these kinds of things they're all just features of this network and that's
00:29:26
it's it's kind of like you might say how could that possibly be the way things are you know if you think about something like water you think about a
00:29:32
little Eddie in the water that Eddie is a thing where you can say there's a definite Eddie that's moving through the
00:29:37
water yet it's made of lots of discrete molecules of water yet we can identify it as a definite thing and so it is with electrons etc etc in the universe it's
00:29:49
all features of this giant Network and it's it's so that's the way it seems to be help so does each particle as we know
00:29:57
it an electron a proton have a feature that defines its relative connectedness to other particles and that definition that
00:30:10
little number is what we look at as space as a whole is that a way to think about it no an electron is a pretty big
00:30:17
thing relative to the atoms of space we don't know exactly how big but it's a big floppy thing um the the the the
00:30:24
feature I mean right now in in in it has been assume that electrons are actually infinitesimally small but that doesn't
00:30:30
seem to be true but the thing that that kind of defines something like an electron it's kind of like it's sort of
00:30:36
a topological kind of thing it's like you can have a you can have a piece of string and you can either knot it or you can not knot it right and you know there
00:30:42
are lots of different ways you can make the knot but it's still it's either knotted or it's not knotted and that's there's either an electron or there
00:30:48
isn't an electron so you know kind of the the structure of space it's kind of much like what happens between molecules
00:30:54
and a fluid like water there are all these discret atoms of space and they have these relations to each other and
00:31:01
then if you look kind of at a large scale what are all these I should say by the way that these atoms of space the
00:31:07
main thing that's happening is pieces of this network are getting Rewritten so a little piece of network this is really
00:31:13
important yes I think this is really important because it I'm going to ask the follow on question all right so so so what is time time is this kind of
00:31:20
computational process where this network that represents the change through this
00:31:26
physical Network is time yes yes so so time is a computational phenomenon time is the is the progressive change of the network one particle touches another one
00:31:37
touches another this thing changes this one this changes this one and sounds to me like you're describing your cellular
00:31:43
automata Yes except it's it's very much like that it's a computational process and it happens to be operating on these
00:31:50
Piper graphs rather than just lines of black the universe itself is a computer yes running a
00:31:58
computational exercise yes well I don't know whether you call it an exercise but yes it's a it is Computing yes it is
00:32:04
Computing and that's that's what the progress of time is time is the is the progress of that computation yes and
00:32:10
that is what people mean when they say are we living in a simulation no no I think that's a philosophically rather confused thing I mean there's there's there's a little bit deeper in
00:32:21
the rabbit hole that you have to go to understand why that doesn't really make sense
00:32:27
but but um no so so I mean you know then then the question is just like you have
00:32:32
all these molecules bouncing around they make sort of continuous fluids like water you can ask what do all these
00:32:38
atoms of space make turns out they make Einstein's equations that describe gravity and describe the structure of SpaceTime so that's kind of a kind of a neat thing yes that that's because it's
00:32:49
not been imagined that one could derive the properties of of something like that from something lower level I mean I should say okay this is this is a big complicated
00:32:59
subject but but um the the um the thing that you guys doing okay okay good the the the thing that's pretty interesting in all of this is the way
00:33:14
that the nature of us as observers is critical to the kind of thing that we obser this is the most important thing I've heard in the last year when I watched your interview a few weeks ago so I I just want you to walk everyone
00:33:26
through this statement again cuz this is so important well all right so that the
00:33:32
okay this this um let's let's the the question is what for for example if we're looking at a bunch of molecules bouncing around in a gas and we say what do we see in those
00:33:47
molecules well we see things like the gas laws pressure volume things like this we see things like the gas tends to
00:33:54
get more random things like this that's what we see because we're observers with certain characteristics if we were
00:34:01
observers who could trace every molecule do all the computations to figure out what all the molecules would do we would
00:34:06
make quite different conclusions about what happens in gases it's because we are observers who are bounded in our
00:34:12
computational capabilities we're not capable of untangling all those all those kind to see all those atoms we
00:34:18
have to look at the whole can of gas we can't look at each atom individually right we can't we can't trace the motion
00:34:25
of each atom individually so ouris is the PV equals nrt the the gas laws right we look at that gas and we say this is the ratio of temperature which is the total energy of the system
00:34:39
as opposed to the energy of each individual atom and there's a lot of different energy states of all these different atoms so we kind of sum sum
00:34:44
everything up we take an average of a lot of stuff to understand it right and and the point is that people haven't
00:34:50
understood the fact that we sort of have to take that average because what's underneath is computationally ired
00:34:56
usable but we comp are computationally bounded and it turns out that exact same phenomenon is the origin of SpaceTime and gravity that these all these atoms of space operating in this network it's
00:35:10
all this computationally irreducible process but observers like us observers who are computationally bounded necessarily observe this kind of uh sort of aggregate Behavior which turns out to
00:35:23
correspond to the structure of SpaceTime same happens in quantum mechanics quantum mechanics is a little bit harder
00:35:28
to understand one of the big features of quantum mechanics is in in classical physics you say you know I throw a ball
00:35:35
it follows a definite trajectory in quantum mechanics you say I do something there are many possible Paths of history
00:35:41
that could be followed we only get to work out things about averages of those paths so it's sort of there are many
00:35:47
different Paths of history that being being pursued in these networks and things what's happening is that there are many different rewrites to the network that can occur we get these many branches of history and so then the
00:35:58
question is and like a quantum computer is trying to make use of the fact that there are many branches of history that
00:36:04
it can kind of follow in parallel but then the question is well well how do we observe what's actually happening well
00:36:10
we are embedded in this whole system that has all these branching parts of history and so and we're limited well we
00:36:17
we we our brains are full of sort of branching behavior and so on but we are we're sort of computationally bounded in what we can do we are effectively we we're sort of the question you have
00:36:29
to ask is how does sort of the branching brain perceive the branching universe and as soon as the brain has this sort
00:36:35
of computational boundedness in its characteristics and it also has one other assumption which is it assumes
00:36:41
that it is persistent in time right so it's like we are at every moment we are made of different atoms of space yet we
00:36:49
believe that it's the same us right at every successive moment right as soon as so our concept of consciousness
00:36:56
precludes us from being able to see perhaps a different nature of the universe a different okay yes told you
00:37:02
guys we were going to go like really right so so you know it's kind of like observers like us who are
00:37:08
computationally bounded believe they're persistent in time the big result is that that observers like us inevitably
00:37:15
observe laws of physics like the ones we know yeah and so you know imagine sort of the alien that isn't like us that
00:37:21
isn't computationally bounded doesn't believe it's persistent in time it will observe different law of the universe
00:37:27
and so the laws of the universe are only based on our nature yes but but very that's what I thought was so interesting right it's a very coarse feature of our nature it's not like we have to know
00:37:38
every detail of us yes the laws of physics only require these and actually I suspect that as we there are other
00:37:45
things that we probably take for granted about the nature of us as observers and as we start putting these in we'll
00:37:51
probably actually find more things that are inevitable about the way the physic that physics works so the the belief as
00:37:57
I use that term I kind of feel um like I'm deluding myself because I am not the same Adams as I was a second ago a second ago second ago second ago this belief that we have a self talk
00:38:11
about your understanding I know this is a bit farfetched from maybe your specialty I'm sure you have a point of view on then what is this concept of
00:38:18
Consciousness that we have where we think we're persistent in time where we have this concept of self-identity you
00:38:25
know what does this all and how do you think about this notion of Consciousness and the observer in the
00:38:30
context of the universe I'm observing stuff in the universe and I'm think I'm a human body and I'm really I'm a bunch
00:38:36
of atoms floating around with a bunch of other atoms like yeah you know I used to think kind of there was a sort of hierarchy where Consciousness was at the
00:38:42
top but I don't think that anymore I think Consciousness is a very it's just just like the AIS are not doing all
00:38:48
possible computation you know we are actually rather limited in our kind of uh observation of the universe we have you know we're localized in space we have this belief that we're persistent
00:38:59
in time and so on imagine what you would feel like so to speak if you were much more extended in the universe if you were in fact one one of the things that we see in our in our models is this
00:39:10
thing we call the ruad which is this kind of this entangled limit of all possible computations and we are every
00:39:17
mind in a sense is just at some small Point some small region in this ruad and so it it's and you you imagine what happens if if you uh and and by the way
00:39:29
as we as we learn more in science we're effectively expanding in this kind of Ral space where just like we can send
00:39:35
spacecraft out our aggregate Consciousness yes as a species yes yes so you know just like we can send spacecraft out that Explore More of the physical universe so as we expand our
00:39:47
science as we expand kind of the ideas that we use to describe the universe we're kind of expanding in this Ral
00:39:52
space and so you might say well what happens if we kind of expand you know that that should be the future of
00:39:57
civilization to expand in Ral space to expand kind of our domain of understanding of things it's a do is is
00:40:05
is a shifting Consciousness like hippie type question but like there's this um there's this uh a guy in the UK named Darren Brown he's a mentalist he puts
00:40:17
these two advertising execs in a room and he tells them hey come up with an ad the name of the company come up with a
00:40:23
logo come up with a a catchphrase he goes out for a few hours comes back pulls off a thing he copied exactly what
00:40:29
they he had written down exactly what they were going to do the way he did it is as they drove over he subliminally put a little image in the cab he had
00:40:35
some kids walk across the street with a logo and they just basically were programmed to Output what he asked them
00:40:43
to do and they they they thought that they were creative Geniuses they're like well these high paid adex look at our genius look at what we did and it always
00:40:49
struck me as like the human is just the unconscious computer we just the you know the the node in the the neural net
00:40:57
that you know takes the input gets sensory programmed output and we're part of the computational exercise is that a
00:41:05
um like a a way to think about this idea that we are part of this broader computation and as we do that this
00:41:11
Consciousness you're the person you mentioned you know they're cheating computational irreducibility so to speak
00:41:16
yes they're they're they're saying I'm going to I'm going to put this thing which is going to be the answer and
00:41:21
that's you know that that the you know the more interesting way to sort of lead life in a sense is just by this process
00:41:28
of letting time progress and sort of this irreducible computation occur and is that what gives you
00:41:35
Joy yeah I think so I mean I think it's it's a it's a funny thing because when you when you kind of think you know sort
00:41:41
of what's underneath the universe and and how kind of all this all these ideas fit together and you kind of realize
00:41:48
that uh it's it's you know I I'm I'm a person who likes people and so it seems very bizarre that I should be interested in these in these things that kind of deconstruct everything about about
00:42:00
humans and I I realized at some point you know one of the things about doing science that's one of the more difficult
00:42:06
human things about doing science is you have to kind of you know get rid of your prejudices about what might be true and
00:42:12
just you know follow what the science actually says and so I've done that for years and then I realized actually it
00:42:19
turns out the thing that I've done puts humans right back in the middle of the picture you know with these things about
00:42:25
the fact that matters what the Observer is like realizing that you know in this sort of space of possibilities that we
00:42:32
are just you know what we care about is this part that is the result of human history and so on um you guys asked for
00:42:39
more science corner so I hope this fit the bill guys please join me thanking Steven
00:42:45
[Applause] wolf let your winners ride Rainman [Music] David and said open source it to the
00:42:57
fans and they've just gone crazy with [Music] it