http://www.wired.com/2009/04/newtonai/ http://www.wired.com/2009/04/newtonai/
http://www.theguardian.com/science/2009/apr/02/eureka-laws-
nature-artificial-intelligence-ai
http://www.theguardian.com/science/2009/apr/02/eureka-laws-nature-artificial-intelligence-ai
Data scientist Michael schmidt sees the world filled with
intricate beauty -- the flowering of a rose, the veins
branching on a leaf, the flight of a Bumblebee.
But below the surface of nature's wonders, Michael also sees
a treasure trove of uncharted mathematical complexity.
Schmidt: Well, I love coming out here.
Nature is beautiful.
There are equations hidden in every plant and every bee and
the ecosystems involved in this garden.
And part of science is figuring out what causes those things
to happen.
Freeman: Science is our effort to make sense of nature, and
this quest has given us some very famous discoveries.
In Newton's time, he was able to figure out a very important
rule in physics, which is the law of gravity.
It predicts how this apple falls and the forces that act
upon this apple.
Today in science, we're interested in similar problems but
not just about how the apple falls but the massive
complexity that follows from this very simple dynamic to the
world around us.
For example, when I drop this apple, the apple stirs up
dust.
This dust could hit a flower, and a bee may be less likely
to pollinate that flower.
And the entire ecosystem in this garden could change
dramatically from that single event.
Freeman: Scientists understand the basic forces of nature,
but making precise predictions about what will happen in the
real world with its staggering complexity is overwhelming to
the human mind.
So, one of the reasons why it's extremely difficult for
humans to understand and figure out the equations and the
laws of nature is literally the number of variables that are
at play.
There could be thousands of variables that influence a
system that we're only just beginning to tease apart.
In fact, there are so many of these equations, we'll never
be able to finish analyzing them if we do it by hand.
Freeman: In 2006, Michael began developing intelligent
computer software that could observe complex natural systems
and derive meaning from what seems like chaos.
So, what I have here is a double pendulum.
If you look at it, it consists of two arms.
One arm swings along the top axis, and the second arm is
attached to the bottom of the first arm, and it's two
pendulums that are hooked together, one pendulum at the end
of the other.
Now, the pendulum is a great example of complexity because
it exhibits some of the most complex behavior that we're
aware of, which is called chaos.
So, when you collect data from this sort of device, it looks
almost completely random, and there doesn't appear to be any
sort of pattern.
But because this is a physical deterministic system, a
pattern does exist.
Freeman: Finding a pattern amidst the chaos of the double
pendulum has stumped scientists for decades.
But then Michael had a flash of inspiration.
Why not grow new ideas the same way nature created us, using
evolution? He called his program Eureka.
Eureka starts with a primordial soup of random equations and
checks how closely they fit the behavior of the double
pendulum.
If they don't fit, the computer kills them.
If they do, the computer moves them into the next
generation, where they mutate and try to get an even closer
fit.
Eventually, a winning equation emerges, one that Archimedes
would be proud of. Eureka!
Schmidt: And I'm running our algorithm now.
On the left pane are the lists of the equations that Eureka
has thought up for this double pendulum.
Walking up, we can see we increase the complexity, and we're
also increasing the agreement with the data.
And eventually, as you go up, you start to get an extremely
close agreement with the data, and eventually you snap on to
a truth where you get a large improvement in the accuracy.
And we can actually look in here and see exactly what pops
out.
For example here, you might notice we have a 9.
8, and if you remember from physics courses, that is the
coefficient of gravity on earth.
What's very important is the difference between the two
angles of the double pendulum.
This pops out.
Essentially, we've used this software and the data we've
collected to model chaos, and we've teased out the solution
directly from the data.
Freeman: Eureka has not only discovered a single equation to
explain how a double pendulum moves.
It has found meaning in what looks like chaos -- something
no human or machine has done before.
Schmidt: So, we could collect an entirely new data set, run
this process again, and even though the data is completely
different -- we could have different observations -- we can
still identify the underlying truth, the underlying pattern,
which is this equation.
Freeman: To Michael, the future of scientific exploration
isn't inside our heads
