Intelligence, what is it?
If we take a look back at the history
of how intelligence is being viewed,
one seminal example has been
Edsger Dijkstra's famous quote
that the question of
whether a machine can think
is about as interesting as the question of
whether a submarine can swim.
Now, Edsger Dijkstra, when he wrote this,
intended it as a criticism
of early pioneers of computer science
like Alan Turing.
However, if you take a look back
and think about what have been
the most empowering innovations
that enable us to build
artificial machines that swim
and artificial machines that think,
you find that it was only through
understanding the underlying
physical mechanisms of swimming
and flight that we were able
to build these machines.
And so, several years ago,
I undertook a program
to try to understand the fundamental
physical mechanisms
underlying intelligence.
Let's take a step back.
Let's first begin
with a thought experiment.
Pretend that you're an alien race
that doesn't know anything
about Earth biology or Earth neuroscience
or Earth intelligence, but you have
amazing telescopes
and you're able to watch the Earth
and you have amazingly long lives
so you're able to watch the Earth
over millions, even billions of years.
And you observe a really strange effect,
you observe that over the course
of the millennia,
Earth is continually bombarded
with asteroids up until a point
and that at some point,
corresponding roughly
to our year 2000 AD, asteroids that are
on a collision course with the Earth,
that otherwise would have collided,
mysteriously get deflected
or detonate before they can hit the Earth.
Now, of course, as Earthlings,
we know the reason would be
that we're trying to save ourselves,
we're trying to prevent an impact.
But if you're an alien race
that doesn't know any of this,
that doesn't have any concept
of Earth intelligence,
you'd be forced to put together
a physical theory that explains how,
up until a certain point in time,
asteroids thad would demolish
the surface of the planet,
mysteriously stop doing that.
So, I claim that this is the same question
as understanding the physical
nature of intelligence.
So, in this program that I undertook
years ago, I've looked at a variety
of different threads in crossed science
across a variety of disciplines,
pointing, I think, towards a single
underlying mechanism for intelligence.
In cosmology, for example,
there has been a variety
of different threads of evidence
that our universe appears to be
finely tuned for the development
of intelligence, and in particular,
for the development
of universal states that maximize
the diversity of possible futures.
In gameplay, for example in Go,
everyone remembers in 1997
when IBM's Deep Blue beat
Gary Kasparov at chess.
Fewer people are aware
that in the past ten year or so,
the game of Go, arguably a much more
challenging game because it has
a much higher branching factor,
has also started to succumb to computer
game players for the same reason.
The best techniques, right now,
for computers playing Go,
are techniques that try to maximize
future options during gameplay.
Finally, in robotic motion planning,
there has been a variety
of recent techniques
that have tried to take advantage
of abilities of robots to maximize
future freedom of action in order
to accomplish complex tasks.
And so, taking all of these different
threads and putting them together,
I asked, starting several years ago,
is there an underlying mechanism
for intelligence that we can factor out
of all of these different threads?
Is there, as it were,
a single equation for intelligence?
And the answer, I believe, is yes.
What you're seeing is probably the closest
equivalent to an E=mc2 for intelligence
that I certainly have ever seen.
So, what you're seeing here
is a statement of correspondence
that intelligence is a Force (F)
that acts so as to maximize
future freedom of action;
It acts to maximize future freedom
of action or keep options open
with some strength (T),
with the amount of the diversity
of possible accessible futures (S),
up to some future time horizon (Ƭ).
In short, intelligence doesn't like
to get trapped, intelligence tries
to maximize future freedom of action
and keep options open.
And so, given this one equation
it's natural to ask:
So, what can you do with this?
How predictive is it? Does it predict
human-level intelligence?
Does it predict artificial intelligence?
So, I'm going to show you now a video
that will, I think, demonstrate
some of the amazing applications
of just this single equation.
Recent research in cosmology
has suggested that universes
that produce more disorder or "entropy"
over their lifetimes should tend
to have more favorable conditions
for the existence of intelligent beings
such as ourselves.
But what if that tentative
cosmological connection
between entropy and intelligence
hints at a deeper relationship?
What if intelligent behavior
doesn't just correlate
with the production of long-term entropy,
but actually emerges directly from it?
To find out, we developed
a software engine called ENTROPICA
designed to maximize the production
of long-term entropy of any system
that it finds itself in.
Amazingly, ENTROPICA was able to pass
multiple animal intelligence tests,
play human games
and even earn money trading stocks;
all without being instructed to do so.
Here are some examples
of ENTROPICA in action:
just like a human standing upright
without falling over, here we see
ENTROPICA automatically
balancing a pole using a cart.
This behavior is remarkable, in part,
because we never gave ENTROPICA a goal,
it simply decided on its own
to balance the pole.
This balancing ability would have
applications for humanoid robotics
and human assistive technologies.
Just as some animals can use
objects in their environments
as tools to reach into narrow spaces,
here we see that ENTROPICA,
again on its own initiative,
was able to move a large disk,
representing an animal,
around so as to cause a small disk,
representing a tool,
to reach into a confined space
holding a third disk
and release the third disk
from its initially fixed position.
This tool usability would have application
for smart manufacturing and agriculture.
In addition, just as some other animals
are able to cooperate
by pulling opposite ends of a rope
at the same time to release food,
here we see that ENTROPICA
is able to accomplish
a model version of that task.
This cooperative ability has interesting
implications for economic planning
and a variety of other fields.
ENTROPICA is broadly applicable
to a variety of domains.
For example, here we see it successfully
playing a game of pong against itself
illustrating its potential for gaming.
Here, we see ENTROPICA orchestrating
new connections on a social network
where friends are constantly
falling out of touch and successfully
keeping the network well connected.
This same network orchestration ability
also has applications in health care,
energy and intelligence.
Here we see ENTROPICA directing
the paths of a fleet of ships
successfully discovering and utilizing
the Panama Canal to globally extend
its reach from the Atlantic
to the Pacific.
By the same token, ENTROPICA
is broadly applicable to problems
in autonomous defense,
logistics and transportation.
Finally, here we see ENTROPICA
spontaneously discovering and executing
a buy low, sell high strategy
on a simulated range traded stock
successfully growing assets
under management exponentially.
This risk management ability
would have broad applications
in finance and insurance.
So, what you've just seen
is that a variety
of signature human
intelligent cognitive behavior
such us tool use and walking upright
and social cooperation, all follow
from a single equation
which drives a system to maximize
its future freedom of action.
Now, there's a profound irony here.
Going back to the beginning
of the usage of the term robot,
the play RUR,
there was always a concept
that if we develop machine, intelligence,
there will be a cybernetic revolt,
that machines would rise up against us.
One major consequence of this work
is that maybe all of these decades
we've had the whole concept
of cybernetic revolt in reverse.
It's not that machines
first become intelligent
and then megalomaniacal,
and try to take over the world.
It's quite the opposite:
that the urge to take control
of all possible futures
is a more fundamental principle
than that of intelligence;
that general intelligence may, in fact,
emerge directly from this sort
of control grabbing,
rather than vice versa.
Another important consequence
is goal seeking.
I'm often asked how does the ability
to seek goals follow from this framework
and the answer is:
the ability to seek goals, for example
if you're playing the game of chess,
to try to win that game of chess
in order to accomplish worldly goods
and accomplishments outside of that game,
will follow directly from this
in the following sense:
Just like you would travel
through a tunnel, a bottleneck,
in your future path space
in order to achieve many other
diverse objectives later on
or just like you would invest
in a financial security reducing
your short term liquidity
in order to increase your wealth
over the long term,
goal seeking emerges directly
from a long term drive
to increase future freedom of action.
Finally, the famous physicist
Richard Feynman once wrote
that if human civilization were destroyed
and you could pass only a single concept
on to our descendents
to help them rebuild civilization,
that concept should be
that all matter around us
is made out of tiny elements
that attract each other
when they're far apart,
but repel each other
when they're close together.
My equivalent to that statement
to pass on to descendents
to help them build
artificial intelligence,
or to help them to understand
human intelligence, is the following:
Intelligence should be viewed
as a physical process
that tries to maximize
future freedom of action
and avoid constraints in its own future.
Thank you very much.
(Applause)