- 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)
Nice video! I'm super into this topic so and it's a decent overview of what's out there. Plus the evolution of technology stuff is something I'm thinking about a lot recently.
I think some of the arguments don't work and there's some misleading points though. She talks about how you get incredibly complexity out without putting any in but the patterns she shows were designed by people. I suspect Gemini may have required algorithms to aid in the design even. I think her point is valid but the evidence (except for maybe the glider) isn't.
Very good video.