Intelligence -- what is it?

If we take a look back at the history

of how intelligence has been 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 the 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 enabled us to build

artificial machines that swim

and artificial machines that [fly],

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 they 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

who doesn't know any of this,

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 that would demolish the surface of a planet

mysteriously stop doing that.

And so I claim that this is the same question

as understanding the physical nature of intelligence.

So in this program that I
undertook several years ago,

I looked at a variety of different threads

across science, across a variety of disciplines,

that were pointing, I think,

towards a single, underlying mechanism

for intelligence.

In cosmology, for example,

there have 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 game play, for example, in Go --

everyone remembers in 1997

when IBM's Deep Blue beat 
Garry Kasparov at chess --

fewer people are aware

that in the past 10 years 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 game play.

Finally, in robotic motion planning,

there have 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 a single equation for intelligence?

And the answer, I believe, is yes.
["F = T ∇ Sτ"]

What you're seeing is probably

the closest equivalent to an E = mc²

for intelligence that I've 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 diversity of possible accessible futures, S,

up to some future time horizon, tau.

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.

(Video) Narrator: 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 will have appliactions

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 use ability will have applications

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

will have broad applications in finance

and insurance.

Alex Wissner-Gross: So what you've just seen

is that a variety of signature human intelligent

cognitive behaviors

such as 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 developed machine intelligence,

there would be a cybernetic revolt.

The 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 sort of framework?

And the answer is, the ability to seek goals

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, Richard Feynman, famous physicist,

once wrote that if human civilization were destroyed

and you could pass only a single concept

on to our descendants

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 of that statement

to pass on to descendants

to help them build artificial intelligences

or to help them 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)