- This episode was made possible by CuriosityStream.
- Hi there, welcome to Up and Atom,
I'm Jade and I'd like to show you something if I may.
This is a toy Ferris wheel made out
of 429 individually cut pieces of wood.
This took me and my dad 15 hours to assemble by hand
and that was the easy part.
Let's not forget everything that went into this creation
before we even started building it,
like the hours or days of carefully planning the design
so that every cog would work perfectly with the others.
The advanced technology used to cut each individual piece,
someone had to write out all the instructions
so that people like me who have never done something
like this before could understand it.
But even though a lot of work went into the making
of this wooden contraption,
you're probably not very surprised,
it's a fairly sophisticated piece of machinery
so this seems pretty normal to us.
The more complicated a piece of work is,
the more planning went into its design,
and the more manpower went into its building.
And, you know, even though this is clever and complicated,
there are a lot of things it can't do.
It can't, for example, make a copy of itself.
It can't protect itself from an attack.
It can't grow or react to its environment.
In fact, compared to even the simplest processes we see
in nature, it can't do much at all.
A quick walk in the garden exposes us
to behaviors infinitely more complex than the wooden wheel,
autonomous creatures with a wheel of their own,
effortless growth and reproduction cycles,
huge colonies of individual interacting parts,
and creatures made up of thousands
of moving parts and processes.
But how did all of this complexity come into being?
Where are the blueprints for these impressive structures?
Who designed the mechanics by which they move?
What I'm trying to say is,
how does anything complex arise in nature?
This is the question we'll be exploring in this video
and we're going to start by playing a game,
The Game of Life.
This game was invented in the 1970s
by a mathematician named John Conway.
Him and his buddies at Los Alamos National Laboratory
were messing around on a go board, just like this one.
Each square on the board is called a cell
and each cell can be either dead or alive.
We'll put a marker to represent a living cell,
and all the empty cells are dead cells.
Each cell is surrounded by eight neighboring cells.
Now whether or not a cell stays alive in the next turn
or as we call it in the game, the next generation,
depends on how many of its neighbors are alive.
The rules are, any live cell with fewer
than two live neighbors dies, as if by under population.
Any live cell with two or three live neighbors lives
on to the next generation.
Any live cell with more than three live neighbors dies,
as if by overpopulation.
And any dead cell with exactly three live neighbors
becomes a live cell, as if by pre-production.
Why don't we play a game?
Let's start with this configuration.
So looking at this live cell,
we can see that of its eight neighbors two are alive.
So according to the rules,
it lives on to the next generation.
Now looking at this one,
two of its neighbors are also alive so it too lives on.
And the same with this one.
Now this empty square has three live neighbors,
so it becomes a live cell and that is our first generation.
If we follow the rules again,
we can see that all of the live cells live
on to the next generation and no new cells are born.
In Game of Life jargon, this pattern is called
as still life as it remains unchanged forever.
Let's try a slightly more exciting configuration.
This pattern oscillates back and forth
so it's called an oscillator.
I think it's safe to say that neither of the patterns
that we've seen are very interesting,
but what were we expecting?
We put in very simple rules so it's only natural
that we get out very simple behaviors,
as they say you get out what you put in, right?
Well, let me show you just one more pattern.
This fun little guy is called a glider
thus appearance of gliding across the board.
Now this behavior is somewhat exciting.
We've put in the same simple rules
but we're getting out surprising
and seemingly complex behavior.
To some extent it even appears lifelike
as if it's moving with agency but of course it's not.
The discovery of the glider caused a surge of interest
in the game and programmers from all
over the world went nuts in search for new
and interesting patterns.
I'd like to show you some of the most interesting ones.
The Game of Life is much easier to play now
than it was in the 70s because of the evolution
of computers and programs.
This is a website with the rules
of The Game of Life already pre-programmed into it.
This is called the Gosper glided gun,
it generates a continuous stream of gliders,
the cool little guys we just saw on the go board.
Remember, we're still using
the same simple rules from before.
Yeah, we seem to have created a self-organizing pattern
which produces a limitless amounts of complexity.
This cool glider is called a backrake ejecting gliders
as it moves along the board.
This type of pattern is known as a breeder
for its apparent rapid population growth.
Now, this particular pattern caused a lot
of ruckus when it was discovered.
It's called Gemini because it creates an exact copy
of itself while destroying its parent.
This was the first instance of self-replication found
in the game.
There are so many more patterns
that have been found displaying extremely complex,
intricate and unpredictable behavior and they all arise
from the simple rules we started with on the go board.
This goes to show that sometimes you
can get out a lot more than what you put in.
That the level of complexity displayed
by a system isn't always proportional to the amount
of complexity put in contrary to our human intuition.
There's no doubt that The Game of Life is really cool,
but it's not unique.
In fact, it's just one of a logic class
of something called cellular automata.
Cellular automata are a grid of cells
which behave automatically,
that is with pre-programmed rules.
The Game of Life is the most famous cellular automaton,
but there are many more with different sets of rules.
So coming back to our question,
what does this have to do with nature?
Sure, it shows us that very complex behaviors can arise
from simple rules but our world is not a computer simulation
with rules programmed into it by someone.
Well, that we know of.
But although our universe is much more complicated
than a cellular automata, it does follow rules,
the laws of physics.
A snowflake is a perfect example of how complex structure
can arise from simple physical laws.
Every snowflake begins as a water droplet freezing
onto a speck of dust or pollen.
As this droplet catches more water molecules,
they form themselves into a hexagon,
this is due to the shape of each water molecule
and to maximize attractive forces
and minimize repulsive ones.
From the more water molecules land on the corners,
simply because they stick out further than the sides
so it's just more likely for stuff to land there.
The shape of the arms are dictated by physical factors
like temperature and humidity
and other properties of the atmosphere.
The formation of the snowflake is dependent both
on the laws of physics,
and the state of each molecules neighboring molecules.
If we try to replicate these rules
with a cellular automaton,
we get very snowflake like patterns.
A biological example that has striking similarities
to cellular automata are the patterns on certain animals.
This cellular automaton called Rule 30 became famous
because of its resemblance to the pattern
on the textile cone snail shell.
The exact mechanisms by which the shell pattern
is formed are not fully understood but biologists suspect
that each cell secretes pigments,
according to the activating and inhibiting activity
of its neighbor pigment cells,
a chemical version of a mathematical role.
And this wouldn't be the first time
we've seen chemical reactions from intricate
and mesmerizing patterns,
as demonstrated by this Belousov-Zhabotinsky reaction.
Because of examples like these, many computer scientists
and theoretical biologists believe that cellular automata
are the key to understanding the complex patterns
and behaviors we see in nature.
They've been used to study crystal formation,
pattern formation in animals and plants, insect colonies,
fluid turbulence, galaxy formation and anything else
that can be modeled as a large number of simple agents
that interact to create complex results.
It's fascinating to wonder how far these models
could take us.
Might we one day have a cellular automata
for more advanced emergent phenomenon
like life or consciousness?
Only time will tell.
Now coming back to our more human endeavors,
wouldn't it be nice if you could just leave a bunch
of these parts here, put in some simple rules
and come back in 10 hours and have it assembled itself?
This would completely revolutionize IKEA's furniture model.
Maybe we should draw inspiration from cellular automata
and totally rethink engineering and design.
Who says that the more complex a structure is,
the more work needs to go into it?
Well, scientists and engineers are doing
just that by exploring the idea
of self-assembling materials and robots.
A complexity group at Stanford University
are experimenting with self-assembling wires.
By applying a large voltage to a bunch of ball bearings
in castor oil, they've managed to get the balls
to start forming chains as they try
to reach the outside of the dish.
Yes, snowflakes aren't the only things
that can use physics to their advantage.
A research group from MIT
are creating self-assembling origami robots triggered
by heat, they can then be controlled remotely by a person.
That's pretty cool.
Then there are the self-assembling cubes being developed
by the Swiss Institute of Technology Lausanne,
they can flat out and be programmed
to assemble into a variety of structures.
Whether uncovering nature's secrets
or building origami robots, cellular automata
are helping us understand that complexity of complexity.
Stephen Wolfram put it best when he said,
this discovery is helping us understand the world
of constructing things from dumb components.
So beautiful.
Wolfram also said something else that caught my attention
and that was, I can't help but wonder that if Rule 30
had been found in antiquity, a lot of ideas about nature
and science would have evolved differently.
This caught my attention for two reasons.
The first being it's a fascinating idea to think
that maybe ideas of science and nature
could have evolved differently so the way we go
about doing science now is very top down.
That is we discover some phenomenon and then try
to describe the final product
with an all encompassing mathematical law.
And it's been working pretty well for us,
but Wolfram is suggesting that perhaps
if Rule 30 were found in antiquity,
math and science
may have developed a more bottom up approach,
looking at the behavior of the smallest pieces first
and seeing how that produces the final results.
The second thing that caught my attention was the use
of the word evolve.
We tend to think of our scientific discoveries
as exactly that, discoveries,
like the laws and theories of our current science
couldn't be any other way.
But could it be that if we discovered these laws
in a different order,
our whole theory of science
could have turned out differently?
How much of our science is discovery
and how much is human intervention?
This question has been plaguing my mind for some time,
and I really wanted to explore in depth,
so I'm making a video about it over on Nebula.
Nebula is a streaming platform built
for creators by creators,
mainly your favorite educational creators like Adam Neely,
PolyMatter, Real Engineering, Wendover Productions,
Lindsay Ellis and many more.
We created Nebula for the reason that oftentimes we want
to experiment with different types of content
that may not perform so well on YouTube.
So in my case, I want to explore
the more philosophical aspect of math and science like
this question of how much of math is discovered
and how much is invented and this is quite different
to my usual content where I give you guys hard facts
and well developed theories.
It's going to be largely my own explorations
which I've gotta say I find quite daunting.
One reason I became a science communicator
and not a scientist,
it's because I feel a lot more comfortable presenting
other people's conclusions than my own.
I feel like it takes real competence and guts
so this is kind of my version of dipping my toes
in the water and coming up with my own conclusions
and presenting them.
Basically, Nebula is a platform where creators feel
like they can try out new things
without suffering horrible consequences
if it turns out terribly.
So if this is something that you'd like to support,
consider signing up with the link below.
Our friends at CuriosityStream,
a documentary streaming service,
are actually running a promotion where you get Nebula
for free when you sign up with them.
So if you are considering joining,
that's the deal you'll want to get
as it's two streaming services for the price of one.
All the links are in the description or you
can go to curiositystream.com/upandatom.
That's it for me and I will see you in the next episode.
Bye.
(upbeat music)