I'm going to tell you.

Something that's going to sound so
obvious that

you're going to wonder why even bother
with the idea is basically this.

If something is watered it takes
less information to describe it

OK So for example.

Groups.

Hope to do this.

That should better it.

OK so it's obvious to everybody here OK

that the power is more
disordered than the monthly yeah

right no question now I can't quantify for

you which one you like better but
I can quantify it for

you which one is more ordered and
the way you do this naturally

like you do everything nowadays
is to get the information or

to get the answer from
your cell phone OK but

it's not the usual place that you
look to solve this problem or

your cell phone OK instead of calling
a Google or something like that

you simply take pictures of the images
with the camera on your cell phone.

And then you save them and depending on
the cell phone you have you can save them

without compression which is what
this bitmap is over here that just is

the number of bits in the picture OK and
here is mold.

And white because the some overhead
in these programs Here's a lossy

compression program which is not good you
want something which is lossless which

means that when you recover the image
you get the entire image back it has and

smooth things out it hasn't lost
information and you just look at the size

of the files and P.M.G.
is a loss this compression algorithm OK.

I subtract from the compression
Politan the month of

the white OK And what I find is the.

Is three killer bytes.

The Pollock is a megabyte.

I can quantify for

you now that this is more ordered
than that simply from the in from.

That's needed in order to
store it was the slate.

OK So the basic idea them to
tell use the following The more

organized ordered something is the less
information you need to describe it

people have looked at this problem before
how much information need in particular

to encode a sequence to send
a transmission Shannon worked on that and

you guys know about the shot and chirpy
and all remind you that there's a much

more to me a much more clever thing that
was done although that's pretty clever

what Shannon did which is by coma
gorup this is the same commodore of

the DID turbulence He's also the guy
that wrote mathematics textbooks which

make the Russians much better than
the rest of us in the West in math OK and

he said the real measure of
entropy of information should be

the size of the smallest
computer code you can write

to regenerate at that information
exactly Now the problem for

this with with this is the fact that
it's the smallest and it's hard to get

the smallest On the other hand this lease
the idea that you can write a computer

program which includes your information
and it's always going to be slightly

larger then this or
maybe much larger than this that

is the length of a lossless the compressed
data set is an approximation if you like

to the Commodore of complexity OK And
the question is if.

This is compressed data
set is like the coma or

of complexity is like the Shannon
entropy then you have a measure of

the entropy in your system simply by
the length of the compressed file and

that's easy to get I should
remind you I want to

actually is there a clock around so I can
keep track of time there isn't is there.

OK so I probably won't run way over but
you got to stop me anyway.

So I go out of the program to show you

every single file in your computer you
click on it you right click on that and

they give you a list of options what you
do with it one of the options is compress

what I'm telling you is if you have a data
file like from an experiment you can

take that data file you can push compress
and you compare compare two files and

find out which is more
ordered simply by doing that.

That won't give you something
completely quantitative will help so

now the question is how Ward How good
are these compressor How good are these

lawsuits compressions and why might you
expect something like this would work in

the air Surya's our entire society
is built on compression and

that the compression it's used for
communication computing Sokrates cell

phone streaming data storage everything
depends upon that and the people

that write these compression programs
these lossless compression programs you

have to have at last the city doing the
stock trades right you don't want to lose

information and money in the stock trade
there's lots of money resting on this and

they know what's a limit is they know the
limit of the compression is the Shannon

and shipping OK So they want to
get as close as they can here is

what the information industry is
worth at least in two thousand and

twelve it's worth much more now but
it's like three by now maybe five trillion

dollars OK and a lot of that is
compression OK so what I'm going

to tell you is work that they have
of being the ones that Martin Jani.

Are Doing this is the collaboration
with them looking at this problem and

now you're probably saying to yourself
OK OK this is going to give me

something roughly I could compare and
I can say this.

Is bigger than this or

something that the answer is No you
can get quantitative information and

you can look at systems and really find
out what's going on with him here for

example is a model system a dynamical
system is a dynamical models a sandpile

model man a model is a model Were
you do is you have a grid we throw

particles their own on it if there's more
than one per site that site is active

you take the particles and you empty
that site in neighboring sites and

you keep on doing this until all of your
sites are singly occupied like that.

And now this shows you some of the
dynamics of that system what you do is you

throw the particles down randomly
OK with different densities over

here you compress the file
you plot out here the length

of the file per particle
normalized per particle OK And

then you let it evolve with
time until it's looking for

the state where I have low double
occupancy right and as a function of time

it gives you this curve after maybe ten to
the six iterations or something it finds

absorbing States over here that is states
that have solved this problem OK And

over here it finds only active states it
can never find the state of high enough

density where it's only singly occupied
even though such states exists and

this cusp over here tells you this is
second order phase transition dynamical

face transition in this case but this is
show you that it's not just going to be

something general you can get quantitative
information out of this and plot it

OK I should mention it also working on
this problem but in a different aspect but

using compression to get
affectively entropy of their system

is a group a tele be one of the guys
as a post op in my group and

he's somewhere over there.

OK luckily they're working on protein
folding and work in an active systems so

we won't get in each
other's way OK Shannon

to find entropy probabilistically you guys
know this he will seen this before for

one thing everybody tells
you when you do and

should pay OK you can write
the entropy as minus P.

lot that's essentially
what Sharon showed and

you also shown from the source code
in theory that you can't translate

information what's the sli at lower
than the Shannon entropy The only

problem with this is it's only meaningful
actually for an infinite ensemble and

first stationary process OK that is
that things don't vary with time here's

Kolmogorov Common Core of said instead
of a probabilistic definition let's use

an algorithmic definition length of the
shortest computer code on the universal

computer that yields the sequence OK
this specifically yields that sequence.

The problem with this is there's no way to
computers because it is the smallest and

there's no way of proving ever that
you have the smallest code for

writing this state OK so I should
also mention this this always tries

to be nuts it's Commodore of
Chaiten no relation to me

this guy over here also came up
with Kolmogorov complexity as it's

called with the idea of the smallest
code he was a Bronx High School

of Science when I was at Stuyvesant OK
crosstown we were rivals and

stuff I end there were knew this guy he
figured out of complexity was sixteen

years old and wrote the programmers in
high school is known as Kolmogorov cheated

complexity these guys
are the guys that essentially

wrote down the most you the most used now.

Compression algorithms and see and

the interesting thing about what they
did is there is computer ball for

any sequence just give with any sequence
any length you want it doesn't have

to be stationary it doesn't have to be
an infinite ensemble stuff like that and

it's computer bull in time the scales
with the length of the sequence

OK Let me give you some idea of
what compression is about the is

this compression nobody knows the same or
but I learned that as a Cub Scout or

something like that is Morse code you in
code your letters you in code whatever you

want to send so used to short a symbol for
the things are cursed most frequently

OK that's variable length coding so
an easy is just one a T.

is one day ash and
things that are used very much like a Z.

are def there stop right there
longer OK that now has become

what's known as Huffman coding
you make a table from you know

sequences you can take two
sequences as long as you want and

the ones that use most frequently see
code small in the smallest region.

This is kind of interesting this is a play
made of the year it turns out to show you

how massaging the computer computer
people were at the start of the field

of computer science what they did
is they said OK we're going to try

in coding this this is going
to be our reference for

how well we can code things this
is part of the picture the rest is

this is a playmate centerfold from Playboy
OK so I'm not showing you the rest but

this is what they use five twelve
pixels by type to use pixels

known as Lena one of the ways that you
do compression is used to predictive

in coding where you say is I'm in
a look at the last couple of pixels and

predict what the next pixel
will be I mean a picture.

Like this even though it looks
fairly complicated most of it is or

a lot of it is just saying the next
pixel is the previous pixel and

instead of having to used to fifty six.

Different numbers or when numbers in
the picture sensually all of them are in

a range of around twenty around
the value of the pixel before so

that's one kind of Equality Now
these guys came up with a level and

see if there are several
forms of one pool and see if.

They come up with several methods of doing
this I will show you one of them here I'll

skip most of that and to show you the way
it's done the way it's done is you look at

a a look up a buffer before
a dictionary that you make before and

a buffer zone ahead and what you do
is you say for instance this is my.

This is my look ahead buffer over
here there's nothing before it so

the first thing I write down as I'm
starting I'm going to write down zero

course that's where there's nothing
before and then I write that in a OK I

move the buffer up I write down
scene a before so I go back one and

I copy one and
the next literacy here I go back

my buffer is now over here I look
forward I see a C N A I look back and

I say go back three from
here go back three copy for

copy for you do cyclically So
you actually copy a A C.

A and that gives you that and
then you put the next letter which is B.

and now you are starting to see
that as things read reoccur and

particularly if they or
something like periodic For

instance if it was now a C A A C A E C
a million times you'd write go back three.

Copy three million right and that's
where your compression is going to coat

come from and then you continue right and
you go back and you find out where your

sequences occurred before he just tell
where how far back to go read it and

then put in how one how much you're
in a copy and the next letter and for

this there are fifteen letters here and
there fifteen symbols here so

this is not going to work well for
really short sequences but for

a long we sequences it can really
pay off OK this is used in

the Flavian ceilidh usually use it
in just zip or G.'s IP or in P.N.

she OK When you say photos so
these guys are for photos P.

and G.
and ship.

OK Now one of the things is there's
a lot of them work the spin done on this

people know how little approach
to Shannon and Sharpie but

I doing them pulls the compression OK And
it turns out to pens

with your sequence is going to go to
something with finite venture pay or

with zero interest zero and should pay for
instance would be a periodic sequence of.

Finite entropy is a range of.

These It converges not so slowly for
a periodic sequence is a la get over N.

but for a random sequence it converges
Vers slowly log log and over

again and it turns out the coefficient
in front of here this is all log log N.

which means you don't really know
what the coefficient in front is

on the other hand you can check
with a quasiperiodic sequence

this is the compression this is
the length of the compressed code per.

Limits of your code periodic
sequences log in over N.

quasiperiodic as log and squared over N.

random is like that and
you should realize by the way

that sometimes the Kolmogorov Complexity
compress is mud.

Much smaller than the Shannon entropy for
instance.

Is affectively a random number there's
no analysis anybody's ever done

on that which tells you that it's other
than a random number on the other

hand you can write a short computer
code which should will generate for

you pipe to as many integers as
you want so it's cut Kolmogorov

complexity is small this shows you OK for

a case think we've looked at what this
extrapolation looks like for a random

sequence which I'll explain to in a minute
we're just going to take a set of.

Boxes and randomly throw particles
in them and calculate what

the compression is and we know for that
problem exactly what the entropy is and

here's a plot of how much how big a.

House a lurch and number of boxes you
have to have or particles in order to get

into the region where you can extrapolate
and here is going like a log log and

you can extrapolate reasonably
well to a couple percent

to what the actual answer is what it looks
like is this if you stop with maybe.

Ten to the fourth ten to the fifth
particle something like that you get this

for the levels the entropy if you
like you can extrapolate using

this log log business OK back to
find out when you get an infinity

here's what you extract to and
this is the exact value so this isn't so

bad if you do an extrapolation You could
also do this another way which for

instance the tell of Reeve group did which
also works really well which is simply to

balance this and normalize it and
that gives you a very good answer as well.

Here's what happens the natural thing
to do is what happens with to the I.C.

right you take to the eye.

Saying if you do the extrapolation with
a number eight over to a Over here in

doing the extrapolation using in a number
a over here which is what you expect for

a completely random sequence you get this
curve which is off by a couple percent

if you just fit one point here to
find to use a different value for

a you get this the so the block
there is the exact to the icing and

Shippey and this is what you get by
fitting so it works pretty good the other

thing we can show is if when you do the
extrapolation you get extrapolated value

which is higher than another extrapolated
value that you using the actual entropy

is is is monotonic that way right
you don't get in the first OK.

Suppose you want to do Not one of these
sequences to these sequences like low over

here you have to figure out how to scan
you can do a wrester scan like that you

can do a rain scan like that it turns out
the best scan that people are found is

a scan Hilbert's scan is
sort of a fractal sation

of this problem so you see you're
more likely to have in a local

region a short distance to go to find the
similar thing rather the rest are scare

on the other hand we take a picture of the
camera most of the time it's arrestor scan

because that's the horizon right
OK It turns out you pay for

this if you buy a Canon
camera OK Anything buy for

under four hundred dollars does not have
lost this compression and anything you buy

above four hundred dollars has their
own loss this compression Tina OK

now everybody knows the G.I.
Taylor experiment right I don't have to

show this movie is that right right OK so
he showed that that.

At moguls them for if you take a fluid.

Is.

What I show it.

It's OK I will show the movie because
it's nice I also see this show

this movie you know if you haven't seen
this movie this is this is a coed cell

to concentric cylinders a very viscous
fluid in the middle of it OK and

you inject thinking they are and you want
it around and what I should tell you is.

If you haven't seen
this movie movie before

it's the only thing you'll
remember from the stock.

And it may be the only thing you
remember from this conference so

you surly in shop he's whirling around is
this appeared he's not doing it you know

this is actually G.I. Taylor's finger OK
he's going around he was in a certain And

now he's going to wind it back now
he's not at the same rate OK he's not

trying to go the same speed or whatever OK
but when the under unwinds at four turns.

It comes back.

Now that is a beautiful.

OK so.

Now you remember if you haven't seen it
before you've seen it all you have to do

is see it once you will remember it
forever is just a great movie OK now.

They fine injury Ghaleb whose
name should be on here isn't

this great experiment really great
experiment to see what happens

if you not just have littles number
fluid but you put particles in there

this is still holding you have particles
in there that when you reverse the flow

you go back by the way I should have said
what that's supposed to show you is that

low rentals number flow is time
reversible in a space in a real specific

sense that is that the motion of the fluid
is slave to the boundary conditions so

all you have to do is reverse
the direction reverse what's going on

with the Ballantrae because that's the
only place forces are in this case OK And

you completely reverse the flow and
it's like playing the movie backwards

OK they want to know what happens if you
put particles in so they did the G.I.

Taylor experiment but with particles
in the air in a cool wet cell and

they're just going to go back and forth OK
and what they found is sort of remarkable

here is what happens if you
this is actually a movie this

one you can tell is a move because things
are moving this is also a movie it's

a strobe OK so you're going back
to the same point in every cycle

as you're oscillating turning it back and
forth OK And the question is if G.I.

Taylor was right just like that plot comes
back well a part of this company should

come back to the same position and
over here they do they sensually when

you strobe it they essentially come back
which White is why this movie looks still

worse above some threshold which
is volume fraction dependent

concentration dependent
this is the amplitude

not the rate the amplitude of the motion
over here if you're above some threshold.

It's chaotic and diffuse.

And below.

That threshold is reversible just
like G.I. Taylor said OK So here's

what it looks like there's essentially
no diffusion the particles come back

every cycle periodic way they come back to
exactly the same place until you get to

the stress shoal and then there's motion
and it's an isotropic in this case but

that doesn't matter now the question
is how the hell do you get that and.

They sort of had a quasi explanation
their quasi explanation was it's known

that if you have no Reynolds number
flow and you have two particles OK and

you share them past one another they
go around one another they end up on

the same stream wall and you come
back they do the same thing OK and

that problem's completely deterministic in
mono on the other hand what source unknown

is the have three particles in
the motion is exponentially sensitive

to the position of the particles
that is it's chaotic OK And so

what their explanation was was
that the bigger displacement

you make the more likely you are to
get three particle interactions.

And that sort of sounded OK the first time
I heard one I thought about I didn't like

it very much because the only thing I
sort of know about random systems is that

random and occasionally have two particles
close together but occasionally will serve

three four holes together or four as many
as you want so it's not a matter of how

far you go OK it's simply how many
you're going to have and that would say

you can't possibly get a threshold from
this or you get this a crossover so

I tried to convince him of this OK and
I sort of gave up.

Because I couldn't get him instead so
I said OK I have to convince you so

I made a simulation in the simulation
because I really don't know how to

do simulations and in the how to do.

Hydra nomics is the simplest simulation
you've ever seen in your life OK It

consists of the following you take you
through particles down the box you put

an African defamation on to represent
the Shia are under that the particles

make lie they actually won't of course
if they're high genomics involved but

since I don't know how to do I can Hydra
nomics I let them collide if they collide

I'll say something happens I don't know
what happens involves friction involves

Hydra nomics but when I bring it back then
I'll say I'll take account of the fact

that they collided by bringing them back
to their original position ology I tell it

but then giving them each a slight
displacement OK so they move

that's the idea and then I'm just going
to repeat this OK so I set this thing up.

And I let it go and
this is what the strobe looks like so

the bridge guys here have made contact and
I move them slightly and

the same with the red and the blue
guys haven't contacted in things so

I don't move them such every time I
strobe it I didn't do anything and

it sort of looks like things
over here are moving but

they're sort of going to get
a confected overall in a while I think.

If I follow that eventually
everything here is moving right but

you'll notice this is a ball
of a critical strain.

This is below a critical strain is
above saying the activity here.

Was dying is a function of time.

And here it stays on and
it never stops so.

Since I'm mostly since
I'm an experimentalist

the first thing I did is they
hit the computer like that.

And it didn't start didn't restart OK so
that's good so then I ran it again and

it did the same thing because I'm not sure
on the how to write computer programs and

it's the same thing and as soon as that
happened I said off I don't know what

happened since there are colliding in
there moving around OK they explore

new configurations and if Explorer
configurations eventually they may find

a configuration where none of
the particles is colliding the others and

then the problem just stops it just stops
because there's no more activity right.

So that's kind of cool cause I didn't
know the systems could do that OK.

OK So the question and
the neat thing about it is in

the simulations we do this above threshold
what you find is there is an activity

this is the number of active particles
precise call it starts out high and

then it goes to a steady state and if you
below the threshold has a characteristic

time and beyond that time it eventually
just stops in this new activity and

now if you plot that time as
a function of the amplitude of

the strain you find that it diverges
on both sides of their transition

which means that this is a second order
phase transition it's not a third

of that hammock face transition it's
a dynamic face transition so then.

With Dave we went after that why have
we didn't really go back to the lab

with similar on back to the way
I did all the experiments and

he looked at this in the real difference
between the idea that has to do with

chaos and stuff where that has to do
with this organization which we called

random organization because we didn't
know what the organization was

is whether it depends on time or
you get whether you get it immediately so

it develops after a time the things
are all moving and then they come to rest

that's when the Morgan ization if it
happens from the start it's maybe chaos or

something like that Here's essentially
what happens this is a lot of our plot of

the activity for a different strain for
different amplitudes of the sheer And

you can see there's a time here
that it takes in order to stop or

whatever and you can measure that and
indeed what you find in the experiment is

divergence of both sides of the transition
so that seems to be the answer now

to me this came all sorts of
questions that I just didn't

understand didn't know how to answer
at all like why in the world does this

happen in this particular threshold strain
it's not the geometric place that you'd

want geometric you could always
move things around until you got to

some packing density.

And then he just reduced set
a little you can move it more and

this was nothing like the packing and
said the other question is when it's in

the active phase over here is it regarding
this is sample all states what's going

on right it obviously doesn't
the sample all states in the.

In the absorbing region.

OK So it turns out unbeknown to me
there are lots of absorbing state

models that came before we did for
in the organization Here's a simple one

this is called conserve lattice chaos and
this isn't one dimension so

it's really easy to conceive of
how you take the sequence and

you compress it questions just
the string it's a string.

Zeros and ones the ones are occupied
sites in the zeros are occupied sites

in conserved a lot of scarce you throw the
particles down the particles down randomly

and if there's a neighbor you say a sacked
of and if there's a neighbor you move it

right you take an active say the next step
you move it to a knock on occupied site

and so this is going to go until it finds
a state like this where there's no more

activity that's sort of the simplest
model now you do this OK

you say again what we're going to do now
is the compression to look at this so

you do as you generate the random
states you compress them you find

the length of the file or
you do the extrapolation either way and

what you find is a curve that
looks like this OK and now you

let the dynamics happen at the end of
the day what you find out is anything less

than a half the critical point here is
the density of a half which is unusual cos

that's actually the geometrical limit here
and what you find is when you compress

the absorbing States when you look
at the absorbing states you get

the configurations you find
this blue curve over here.

And then you take a month to Carlo
calculation that you can do and

you find all of the states we
have no nearest neighbors and

you compare that to right so
if the system were godlike or

something like it you would find
all of the absorbing States and

it turns out comparing these two if you
like the information here is less or

the entropy years less which means
it hasn't found all the states

which means to some sense in some
sense it must be more organized or

more ordered than if I just
threw them down randomly.

This one.

No even for
this old model conserve flatus gas but

once you find this you can go and
you can look at what happens and

sure enough what you find which wasn't
long before we did this simple calculation

is when you look from the dynamics of
the correlation function you find out

that the states that you find at the same
density for the conserved lattice chaos.

This these are for different densities
this is the correlation function

the correlation length grows much much
larger than it does if you just exclude

neighbors so you do find a much
shorter word say here why it turns

out in conserved lot of scarce when you
start out with clusters they spread and

that spreading gives you longer range
correlations So already we've discovered

something here that we didn't know
simply by doing the compression for

an active system here's an example
I showed you before which is more

interesting system courses and
two dimensions which is the man a model

OK And again what you do is you
throw down particles in the two

dimensional law to see here they're
active if you have more than one person

you move them to neighboring sites and
you keep on doing this until you have no

double occupancy Here's what it looks like
it doesn't look to that too dissimilar

from the random organization model
I showed you this will of all of.

And stop eventually if it's
sort of Reince facts and

then whatever but
this will eventually stop.

The OK And what that looks like is the
following Here are the initial states to

get you through things down randomly after
you let are valid to ten to the six steps

OK you find the black are now the black
curve is mostly hidden over here

by this red curve which is all
the absorbing space again that.

Just find all the possible configurations
OK where I don't openly occupy a site and

now if I blow this up in this
region the states that are found by

doing this compression algorithm or
layer or so again it's more organized.

We don't know why that is yet
but it's more organized OK

here's something else that's kind of
interesting over here it's active so

anything from here up here is by the way
if you plot the activity OK over here.

It's not the you have to have those
orbit says during spaces overstays and

then that this critical value you start
to get activity just like in there

in the morning the station and
of course these guys match right on

without believe they just match
right now what I would have expected

is when you're in the active phase it's
going to be organic as you get more and

more activity it's going to sample more
and more of the space suit expect that

this guy should convert conversion to
what your initial random states were

that would tell you that you're a god and
it doesn't seem to be doing that so

again this is something that we looked
at him said this is funky really

expects this maybe it's true but why OK so
then it turns out we go in the book

Stefano had been doing these simulations
and he'd been doing them with the power

level update OK So
he finds all of the active States and

in one move he moves all
of them right OK I would

have done that that way not very good at
computing I would have just taken one

of the sack of moved it and
then look for another one and

move there are ten cents a minute that
turns out when you do parallel updates

swears this what happens is.

Checkerboard.

OK And it's like a spinor movie

compositional machine there are two phases
here they're out of phase one another but

it's going to in order to
face is going back and

forth between these two word phrase
nobody will study man the models for

the past ten fifteen twenty years however
long they've been around has ever found

anything like this before but
you just do the compression and

you find discover stuff that
you haven't seen before and

once you get rid of that OK
this curve goes through that

curve once you do random updates you
find out that it is go in your gob and

you've got a quantitative way of
seeing that it's going to go OK.

Something else if you look instead
of the whole curve here as it's

progressing in time if you take
a particular density and you follow

it as a function of time you find that
it goes through the absorbing state or

goes to an active state OK you can't
tell if Zorin are active from this or

you can the tell is that
the information OK or

the length of the data file is going
down versus time you can look at that.

And evaluate what it looks like
as a function of time in the same

way you would look at an order parameter
so if you want to know how things

scale with time you get a divergence
OK in either side amount of time.

OK.

OK good so if you want to do that you
can look at it as almost like a similar

parameter and do the scaling is going
to be won over time to a power and

then the sex potential which gives
you the correlation time and

this is a comparison of what you
get if you look at the activity or

if you just look at the information this
is below the transition this is above

the transitions the just right on so you
can get critical exponents here with the.

Even knowing what the order parameter
is for the system just by looking at

the information and they say well what
if I choose a different algorithm for

my compression answer you
get a different answer but

it's not a very different answer and
it shows you essentially the same physics

OK this is random organization
again you see the change

here in entropy you see that the states
the random organization states why below

all the absorbing states are running at a
time so I won't go through this very much

again you can try different algorithms and
here they lie on top of another and

again you can do the time scale me you can
look not just at absorbing systems these

are swimmers this is a model OK from.

Christina.

Mock headache OK of particles which
in don't interact with each other

unless they get within the diameter of one
another in which case they repel with K.

harmonically otherwise their swimming and

their motion changes they have
wrecked Brownian the fusion in the OR

in the ant in their
annular dependence OK and

if you look at that system OK as a
function of density you put in the initial

configuration it looks like this OK and
now you let them start swimming and

when you find they see it's better doing
it here what you find this if there's

a low density even though they're
swimming that information doesn't change

the configurations they find do the same
until you get to a certain density and

when you get to that certain density what
you find is at that density they're going

they're going their information isn't
changing and then it changes and

goes down OK and you look at
the same pole and that's true for

every value above here and you look
at what your images look like now and

you find that over here
there are sentient.

Uniformly distributed and
over here they claim and

so what you find here is another active
system and mind you I should say here this

also is not on the last where the more
musician is also not on the last so

you have to figure out how to describe
ties this which you can do here you see

a dynamic first quarter
face transition right and

you can tell by the jump rather than just
the costs OK here we change the value and

you find the cost of the transitions
into for place and you can compare that

with Christina found by looking because
there really isn't an order parameter for

the system but other things that
would identify when you get face

separation things like this but here
you find this transition by compression

without knowing what you
were primitives at all.

You can do this for an experimental system
that's we're doing now here are some

swimmers we have if you trace them as
a function of time when they're swimming

These are light activated so you can turn
a light and you can see the information or

the compressed file going down we haven't
analyzed this for lots of different that.

Densities or speeds yet but when you turn
off the light you can see the information

go up so we have to do now for an
experimental system with Christina did for

the model system you can look at
regular face transitions as well OK.

Thermal thermal dynamics face
transitions where you normally

would a parameter of stuff for
instance the cells help and

also a young male thing which
is defect mediated OK And

what happens in that case is you go from
having lots of the facts to few defects

in terms of the inverse temperature
of the strength of the interaction.

This transition happens very quickly
you can do the compression of that

the red curve here is just taking.

A picture of the structure and
compressing it OK.

And you find that you get a roll off
that looks like that we couldn't find

a good reference for what the entropy of
the system is but you can calculate it but

one minus the order parameter in this case
square to something like the entropy and

that's what this red curve is and

the other curves are where you get from
compression so even for thermodynamics

face transition where you know what
the water pressure is you can find it and

if you don't know the order parameter
you can still find the south.

They have ways who've left told us
if we thought this was important

we should name it OK and we can call it
computer Bill information then city but

he said you should pick out a good
name for right so it turns out Prince.

Changed his name he became a symbol and

he was that became
The Artist Formerly Known as Prince so

we thought that sounds like a really
good idea will do is take a symbol and

call this a quantity formerly known
as entropy OK Anyway I'm done so

I just want to tell you that I think this
is a new way of looking at things if

you've got a system where you want to know
whether it's organizing you she just stood

there this is the city OK depending
how much information you want out

of it it's harder OK but the lowest order
it's easy and it's a good way of finding

out with your systems order or not even if
you don't know what the ordering is and

we're going to do next is look at
classes in correlation lengths

We'll look at the sun Sky Survey
which has redshift information so

we can do the entropy of
the universe as a function of time.

Maybe we're going to do some
memory with said I hope.

Something like that thanks.