The following is a conversation with Jürgen Schmidhuber.

He's the codirector of its CS Swiss AI lab

and a cocreator of long short term memory networks.

LSDMs are used in billions of devices today

for speech recognition, translation, and much more.

Over 30 years, he has proposed a lot of interesting

out of the box ideas, a meta learning, adversarial networks,

computer vision, and even a formal theory of quote,

creativity, curiosity, and fun.

This conversation is part of the MIT course

on artificial general intelligence

and the artificial intelligence podcast.

If you enjoy it, subscribe on YouTube, iTunes,

or simply connect with me on Twitter

at Lex Friedman spelled F R I D.

And now here's my conversation with Jürgen Schmidhuber.

Early on you dreamed of AI systems

that self improve recursively.

When was that dream born?

When I was a baby.

No, that's not true.

When I was a teenager.

And what was the catalyst for that birth?

What was the thing that first inspired you?

When I was a boy, I...

I was thinking about what to do in my life

and then I thought the most exciting thing

is to solve the riddles of the universe.

And that means you have to become a physicist.

However, then I realized that there's something even grander.

You can try to build a machine.

That isn't really a machine any longer.

That learns to become a much better physicist

than I could ever hope to be.

And that's how I thought maybe I can multiply

my tiny little bit of creativity into infinity.

But ultimately, that creativity will be multiplied

to understand the universe around us.

That's the curiosity for that mystery that drove you.

Yes, so if you can build a machine

that learns to solve more and more complex problems

and more and more general problems over,

then you basically have solved all the problems.

At least all the solvable problems.

So how do you think...

What is the mechanism for that kind of general solver look like?

Obviously, we don't quite yet have one or know

how to build one boy of ideas

and you have had throughout your career several ideas about it.

So how do you think about that mechanism?

So in the 80s, I thought about how to build this machine

that learns to solve all these problems

that I cannot solve myself.

And I thought it is clear, it has to be a machine

that not only learns to solve this problem here

and this problem here,

but it also has to learn to improve

the learning algorithm itself.

So it has to have the learning algorithm

in a representation that allows it to inspect it

and modify it so that it can come up

with a better learning algorithm.

So I called that meta learning, learning to learn

and recursive self improvement.

That is really the pinnacle of that,

where you then not only learn how to improve

on that problem and on that,

but you also improve the way the machine improves

and you also improve the way it improves the way

it improves itself.

And that was my 1987 diploma thesis,

which was all about that hierarchy of meta learners

that have no computational limits

except for the well known limits

that Gödel identified in 1931

and for the limits of physics.

In the recent years, meta learning has gained popularity

in a specific kind of form.

You've talked about how that's not really meta learning

with neural networks, that's more basic transfer learning.

Can you talk about the difference

between the big general meta learning

and a more narrow sense of meta learning

the way it's used today, the way it's talked about today?

Let's take the example of a deep neural network

that has learned to classify images.

And maybe you have trained that network

on 100 different databases of images.

And now a new database comes along

and you want to quickly learn the new thing as well.

So one simple way of doing that is you take the network

which already knows 100 types of databases

and then you just take the top layer of that

and you retrain that using the new labeled data

that you have in the new image database.

And then it turns out that it really, really quickly

can learn that too, one shot basically,

because from the first 100 data sets,

it already has learned so much about computer vision

that it can reuse that and that is then almost good enough

to solve the new tasks except you need a little bit

of adjustment on the top.

So that is transfer learning

and it has been done in principle for many decades.

People have done similar things for decades.

Meta learning, true meta learning is about

having the learning algorithm itself

open to introspection by the system that is using it

and also open to modification

such that the learning system has an opportunity

to modify any part of the learning algorithm

and then evaluate the consequences of that modification

and then learn from that to create a better learning algorithm

and so on recursively.

So that's a very different animal

where you are opening the space of possible learning algorithms

to the learning system itself.

Right, so you've like in the 2004 paper,

you describe Gatal machines and programs

that rewrite themselves, right?

Philosophically and even in your paper mathematically,

these are really compelling ideas,

but practically, do you see these self referential programs

being successful in the near term to having an impact

where sort of it demonstrates to the world

that this direction is a good one to pursue in the near term?

Yes, we had these two different types

of fundamental research,

how to build a universal problem solver,

one basically exploiting proof search

and things like that that you need to come up

with asymptotically optimal, theoretically optimal

self improvers and problem solvers.

However, one has to admit that through this proof search

comes in an additive constant,

an overhead, an additive overhead

that vanishes in comparison to what you have to do

to solve large problems.

However, for many of the small problems

that we want to solve in our everyday life,

we cannot ignore this constant overhead.

And that's why we also have been doing other things,

non universal things such as recurrent neural networks

which are trained by gradient descent

and local search techniques which aren't universal at all,

which aren't provably optimal at all

like the other stuff that we did,

but which are much more practical

as long as we only want to solve the small problems

that we are typically trying to solve in this environment here.

So the universal problem solvers like the Gödel machine

but also Markus Hutter's fastest way

of solving all possible problems,

which he developed around 2002 in my lab,

they are associated with these constant overheads

for proof search, which guarantees

that the thing that you're doing is optimal.

For example, there is this fastest way

of solving all problems with a computable solution

which is due to Markus Hutter.

And to explain what's going on there,

let's take traveling salesman problems.

With traveling salesman problems,

you have a number of cities, N cities,

and you try to find the shortest path

through all these cities without visiting any city twice.

And nobody knows the fastest way

of solving traveling salesman problems, TSPs,

but let's assume there is a method of solving them

within N to the five operations

where N is the number of cities.

Then the universal method of Markus

is going to solve the same traveling salesman problem

also within N to the five steps,

plus O of one, plus a constant number of steps

that you need for the proof searcher,

which you need to show that this particular

class of problems that traveling salesman problems

can be solved within a certain time bound,

within order N to the five steps, basically.

And this additive constant doesn't care for N,

which means as N is getting larger and larger,

as you have more and more cities,

the constant overhead pales and comparison.

And that means that almost all large problems are solved

in the best possible way already today.

We already have a universal problem solved like that.

However, it's not practical because the overhead,

the constant overhead is so large

that for the small kinds of problems

that we want to solve in this little biosphere.

By the way, when you say small,

you're talking about things that fall

within the constraints of our computational systems.

So they can seem quite large to us mere humans.

That's right, yeah.

So they seem large and even unsolvable

in a practical sense today,

but they are still small compared to almost all problems

because almost all problems are large problems,

which are much larger than any constant.

Do you find it useful as a person

who is dreamed of creating a general learning system,

has worked on creating one,

has done a lot of interesting ideas there

to think about P versus NP,

this formalization of how hard problems are,

how they scale,

this kind of worst case analysis type of thinking.

Do you find that useful?

Or is it only just a mathematical,

it's a set of mathematical techniques

to give you intuition about what's good and bad?

So P versus NP, that's super interesting

from a theoretical point of view.

And in fact, as you are thinking about that problem,

you can also get inspiration

for better practical problem solvers.

On the other hand, we have to admit

that at the moment,

the best practical problem solvers

for all kinds of problems

that we are now solving through what is called AI at the moment,

they are not of the kind

that is inspired by these questions.

There we are using general purpose computers,

such as recurrent neural networks,

but we have a search technique,

which is just local search gradient descent

to try to find a program

that is running on these recurrent networks,

such that it can solve some interesting problems,

such as speech recognition or machine translation

and something like that.

And there is very little theory

behind the best solutions that we have at the moment

that can do that.

Do you think that needs to change?

Do you think that will change or can we go,

can we create a general intelligence systems

without ever really proving

that that system is intelligent

in some kind of mathematical way,

solving machine translation perfectly

or something like that,

within some kind of syntactic definition of a language?

Or can we just be super impressed

by the thing working extremely well and that's sufficient?

There's an old saying,

and I don't know who brought it up first,

which says there's nothing more practical

than a good theory.

And a good theory of problem solving

under limited resources like here in this universe

or on this little planet

has to take into account these limited resources.

And so probably there is locking a theory

which is related to what we already have,

these asymptotically optimal problem solvers,

which tells us what we need in addition to that

to come up with a practically optimal problem solver.

So I believe we will have something like that

and maybe just a few little tiny twists

are necessary to change what we already have

to come up with that as well.

As long as we don't have that,

we admit that we are taking suboptimal ways

and recurrent neural networks and long short term memory

for equipped with local search techniques

and we are happy that it works better

than any competing methods,

but that doesn't mean that we think we are done.

You've said that an AGI system will ultimately be a simple one,

a general intelligence system will ultimately be a simple one,

maybe a pseudo code of a few lines

will be able to describe it.

Can you talk through your intuition behind this idea,

why you feel that at its core intelligence

is a simple algorithm?

Experience tells us that the stuff that works best

is really simple.

So the asymptotically optimal ways of solving problems,

if you look at them,

they're just a few lines of code, it's really true.

Although they are these amazing properties,

just a few lines of code,

then the most promising and most useful practical things

maybe don't have this proof of optimality associated with them.

However, they are also just a few lines of code.

The most successful recurrent neural networks,

you can write them down and five lines of pseudo code.

That's a beautiful, almost poetic idea,

but what you're describing there

is the lines of pseudo code

are sitting on top of layers and layers of abstractions,

in a sense.

So you're saying at the very top,

it'll be a beautifully written sort of algorithm,

but do you think that there's many layers of abstractions

we have to first learn to construct?

Yeah, of course.

We are building on all these great abstractions

that people have invented over the millennia,

such as matrix multiplications and drill numbers

and basic arithmetics and calculus and derivations

of error functions and derivatives of error functions

and stuff like that.

So without that language that greatly simplifies

our way of thinking about these problems,

we couldn't do anything.

So in that sense, as always,

we are standing on the shoulders of the giants

who in the past simplified the problem of problem solving

so much that now we have a chance to do the final step.

So the final step will be a simple one.

If we take a step back through all of human civilization

and just the universe in general,

how do you think about evolution?

And what if creating a universe is required

to achieve this final step?

What if going through the very painful

and inefficient process of evolution is needed

to come up with this set of abstractions

that ultimately lead to intelligence?

Do you think there's a shortcut

or do you think we have to create something like our universe

in order to create something like human level intelligence?

So far, the only example we have is this one,

this universe in which we are living.

You think you can do better?

Maybe not, but we are part of this whole process.

So apparently, so it might be the case

that the code that runs the universe

is really, really simple.

Everything points to that possibility

because gravity and other basic forces

are really simple laws that can be easily described,

also in just a few lines of code, basically.

And then there are these other events

that the apparently random events

in the history of the universe,

which as far as we know at the moment

don't have a compact code,

but who knows, maybe somebody in the near future

is going to figure out the pseudo random generator,

which is computing whether the measurement of that

spin up or down thing here

is going to be positive or negative.

Underline quantum mechanics.

Yes, so.

Do you ultimately think quantum mechanics

is a pseudo random number generator?

So it's all deterministic.

There's no randomness in our universe.

Does God play dice?

So a couple of years ago,

a famous physicist, quantum physicist, Anton Zeilinger,

he wrote an essay in Nature,

and it started more or less like that.

One of the fundamental insights of the 20th century

was that the universe is fundamentally random

on the quantum level, and that whenever

you measure spin up or down or something like that,

a new bit of information enters the history of the universe.

And while I was reading that,

I was already typing the response

and they had to publish it because I was right,

that there is no evidence, no physical evidence for that.

So there's an alternative explanation

where everything that we consider random

is actually pseudo random,

such as the decimal expansion of pi, 3.141 and so on,

which looks random, but isn't.

So pi is interesting because every three digit sequence,

every sequence of three digits appears roughly

one in a thousand times, and every five digit sequence

appears roughly one in 10,000 times.

What do you expect?

If it was random, but there's a very short algorithm,

a short program that computes all of that.

So it's extremely compressible.

And who knows, maybe tomorrow somebody,

some grad student at CERN goes back

over all these data points, better decay,

and whatever, and figures out, oh,

it's the second billion digits of pi or something like that.

We don't have any fundamental reason at the moment

to believe that this is truly random

and not just a deterministic video game.

If it was a deterministic video game,

it would be much more beautiful

because beauty is simplicity.

And many of the basic laws of the universe

like gravity and the other basic forces are very simple.

So very short programs can explain what these are doing.

And it would be awful and ugly.

The universe would be ugly.

The history of the universe would be ugly

if for the extra things, the random,

the seemingly random data points that we get all the time

that we really need a huge number of extra bits

to describe all these extra bits of information.

So as long as we don't have evidence

that there is no short program

that computes the entire history of the entire universe,

we are, as scientists, compelled to look further

for that shortest program.

Your intuition says there exists a program

that can backtrack to the creation of the universe.

So it can take the shortest path to the creation of the universe.

Yes, including all the entanglement things

and all the spin up and down measurements

that have been taken place since 13.8 billion years ago.

So we don't have a proof that it is random.

We don't have a proof that it is compressible to a short program.

But as long as we don't have that proof,

we are obliged as scientists to keep looking

for that simple explanation.

Absolutely.

So you said simplicity is beautiful or beauty is simple.

Either one works.

But you also work on curiosity, discovery.

The romantic notion of randomness, of serendipity,

of being surprised by things that are about you,

kind of in our poetic notion of reality,

we think as humans require randomness.

So you don't find randomness beautiful.

You find simple determinism beautiful.

Yeah.

Okay.

So why?

Why?

Because the explanation becomes shorter.

A universe that is compressible to a short program

is much more elegant and much more beautiful

than another one,

which needs an almost infinite number of bits to be described.

As far as we know,

many things that are happening in this universe are really simple

in terms of short programs that compute gravity

and the interaction between elementary particles and so on.

So all of that seems to be very, very simple.

Every electron seems to reuse the same subprogram all the time

as it is interacting with other elementary particles.

If we now require an extra oracle

injecting new bits of information all the time

for these extra things which are currently not understood,

such as better decay,

then the whole description length of the data that we can observe

of the history of the universe would become much longer.

And therefore, uglier.

And uglier.

Again, the simplicity is elegant and beautiful.

All the history of science is a history of compression progress.

Yeah.

So you've described sort of as we build up abstractions

and you've talked about the idea of compression.

How do you see this, the history of science,

the history of humanity, our civilization and life on Earth

as some kind of path towards greater and greater compression?

What do you mean by that?

How do you think about that?

Indeed, the history of science is a history of compression progress.

What does that mean?

Hundreds of years ago, there was an astronomer

whose name was Kepler.

And he looked at the data points that he got by watching planets move.

And then he had all these data points and suddenly it turned out

that he can greatly compress the data by predicting it through an ellipse law.

So it turns out that all these data points are more or less on ellipses around the sun.

And another guy came along whose name was Newton and before him Hook.

And they said the same thing that is making these planets move like that

is what makes the apples fall down.

And it also holds for stones and for all kinds of other objects.

And suddenly many, many of these observations became much more compressible

because as long as you can predict the next thing,

given what you have seen so far, you can compress it.

But you don't have to store that data extra.

This is called predictive coding.

And then there was still something wrong with that theory of the universe

and you had deviations from these predictions of the theory.

And 300 years later another guy came along whose name was Einstein

and he was able to explain away all these deviations from the predictions of the old theory

through a new theory which was called the general theory of relativity

which at first glance looks a little bit more complicated

and you have to warp space and time but you can't phrase it within one single sentence

which is no matter how fast you accelerate and how fast or how hard you decelerate

and no matter what is the gravity in your local framework,

light speed always looks the same.

And from that you can calculate all the consequences.

So it's a very simple thing and it allows you to further compress all the observations

because certainly there are hardly any deviations any longer

that you can measure from the predictions of this new theory.

So art of science is a history of compression progress.

You never arrive immediately at the shortest explanation of the data

but you're making progress.

Whenever you are making progress you have an insight.

You see, oh, first I needed so many bits of information to describe the data,

to describe my falling apples, my video of falling apples,

I need so many data, so many pixels have to be stored

but then suddenly I realize, no, there is a very simple way of predicting the third frame

in the video from the first two and maybe not every little detail can be predicted

but more or less most of these orange blots that are coming down,

I'm sorry, in the same way, which means that I can greatly compress the video

and the amount of compression, progress,

that is the depth of the insight that you have at that moment.

That's the fun that you have, the scientific fun,

the fun in that discovery and we can build artificial systems that do the same thing.

They measure the depth of their insights as they are looking at the data

through their own experiments and we give them a reward,

an intrinsic reward and proportion to this depth of insight.

And since they are trying to maximize the rewards they get,

they are suddenly motivated to come up with new action sequences,

with new experiments that have the property that the data that is coming in

as a consequence of these experiments has the property

that they can learn something about, see a pattern in there

which they hadn't seen yet before.

So there's an idea of power play that you've described,

a training and general problem solver in this kind of way of looking for the unsolved problems.

Can you describe that idea a little further?

It's another very simple idea.

Normally what you do in computer science, you have some guy who gives you a problem

and then there is a huge search space of potential solution candidates

and you somehow try them out and you have more or less sophisticated ways

of moving around in that search space

until you finally found a solution which you consider satisfactory.

That's what most of computer science is about.

Power play just goes one little step further and says,

let's not only search for solutions to a given problem,

but let's search to pairs of problems and their solutions

where the system itself has the opportunity to phrase its own problem.

So we are looking suddenly at pairs of problems and their solutions

or modifications of the problem solver

that is supposed to generate a solution to that new problem.

And this additional degree of freedom

allows us to build career systems that are like scientists

in the sense that they not only try to solve and try to find answers

to existing questions, no, they are also free to pose their own questions.

So if you want to build an artificial scientist,

you have to give it that freedom and power play is exactly doing that.

So that's a dimension of freedom that's important to have,

how hard do you think that, how multi dimensional and difficult the space of

then coming up with your own questions is.

So it's one of the things that as human beings we consider to be

the thing that makes us special, the intelligence that makes us special

is that brilliant insight that can create something totally new.

Yes. So now let's look at the extreme case.

Let's look at the set of all possible problems that you can formally describe,

which is infinite, which should be the next problem

that a scientist or power play is going to solve.

Well, it should be the easiest problem that goes beyond what you already know.

So it should be the simplest problem that the current problems

that you have which can already solve 100 problems that he cannot solve yet

by just generalizing.

So it has to be new.

So it has to require a modification of the problem solver such that the new

problem solver can solve this new thing, but the old problem solver cannot do it.

And in addition to that, we have to make sure that the problem solver

doesn't forget any of the previous solutions.

Right.

And so by definition, power play is now trying always to search in this pair of

in the set of pairs of problems and problems over modifications

for a combination that minimize the time to achieve these criteria.

Power is trying to find the problem which is easiest to add to the repertoire.

So just like grad students and academics and researchers can spend their whole

career in a local minima stuck trying to come up with interesting questions,

but ultimately doing very little.

Do you think it's easy in this approach of looking for the simplest

problem solver problem to get stuck in a local minima is not never really discovering

new, you know, really jumping outside of the hundred problems that you've already

solved in a genuine creative way.

No, because that's the nature of power play that it's always trying to break

its current generalization abilities by coming up with a new problem which is

beyond the current horizon, just shifting the horizon of knowledge a little bit

out there, breaking the existing rules such that the new thing becomes solvable

but wasn't solvable by the old thing.

So like adding a new axiom, like what Gödel did when he came up with these

new sentences, new theorems that didn't have a proof in the formal system,

which means you can add them to the repertoire, hoping that they are not

going to damage the consistency of the whole thing.

So in the paper with the amazing title, Formal Theory of Creativity,

Fun and Intrinsic Motivation, you talk about discovery as intrinsic reward.

So if you view humans as intelligent agents, what do you think is the purpose

and meaning of life for us humans?

You've talked about this discovery.

Do you see humans as an instance of power play agents?

Yeah, so humans are curious and that means they behave like scientists,

not only the official scientists but even the babies behave like scientists

and they play around with their toys to figure out how the world works

and how it is responding to their actions.

And that's how they learn about gravity and everything.

And yeah, in 1990, we had the first systems like that

who would just try to play around with the environment

and come up with situations that go beyond what they knew at that time

and then get a reward for creating these situations

and then becoming more general problem solvers

and being able to understand more of the world.

So yeah, I think in principle that curiosity,

strategy or more sophisticated versions of what I just described,

they are what we have built in as well because evolution discovered

that's a good way of exploring the unknown world and a guy who explores

the unknown world has a higher chance of solving problems

that he needs to survive in this world.

On the other hand, those guys who were too curious,

they were weeded out as well.

So you have to find this trade off.

Evolution found a certain trade off apparently in our society.

There is a certain percentage of extremely explorative guys

and it doesn't matter if they die because many of the others are more conservative.

And so yeah, it would be surprising to me

if that principle of artificial curiosity wouldn't be present

in almost exactly the same form here in our brains.

So you're a bit of a musician and an artist.

So continuing on this topic of creativity,

what do you think is the role of creativity in intelligence?

So you've kind of implied that it's essential for intelligence,

if you think of intelligence as a problem solving system,

as ability to solve problems.

But do you think it's essential, this idea of creativity?

We never have a subprogram that is called creativity or something.

It's just a side effect of what our problems always do.

They are searching a space of candidates, of solution candidates,

until they hopefully find a solution to a given problem.

But then there are these two types of creativity

and both of them are now present in our machines.

The first one has been around for a long time,

which is human gives problem to machine.

Machine tries to find a solution to that.

And this has been happening for many decades.

And for many decades, machines have found creative solutions

to interesting problems where humans were not aware

of these particularly creative solutions,

but then appreciated that the machine found that.

The second is the pure creativity.

What I just mentioned, I would call the applied creativity,

like applied art, where somebody tells you,

now make a nice picture of this pope,

and you will get money for that.

So here is the artist and he makes a convincing picture of the pope

and the pope likes it and gives him the money.

And then there is the pure creativity,

which is more like the power play and the artificial curiosity thing,

where you have the freedom to select your own problem,

like a scientist who defines his own question to study.

And so that is the pure creativity, if you will,

as opposed to the applied creativity, which serves another.

In that distinction, there's almost echoes of narrow AI versus general AI.

So this kind of constrained painting of a pope seems like

the approaches of what people are calling narrow AI.

And pure creativity seems to be,

maybe I'm just biased as a human,

but it seems to be an essential element of human level intelligence.

Is that what you're implying?

To a degree.

If you zoom back a little bit and you just look at a general problem solving machine,

which is trying to solve arbitrary problems,

then this machine will figure out in the course of solving problems

that it's good to be curious.

So all of what I said just now about this pre wild curiosity

and this will to invent new problems that the system doesn't know how to solve yet,

should be just a byproduct of the general search.

However, apparently evolution has built it into us

because it turned out to be so successful, a pre wiring, a bias,

a very successful exploratory bias that we are born with.

And you've also said that consciousness in the same kind of way

may be a byproduct of problem solving.

Do you find this an interesting byproduct?

Do you think it's a useful byproduct?

What are your thoughts on consciousness in general?

Or is it simply a byproduct of greater and greater capabilities of problem solving

that's similar to creativity in that sense?

We never have a procedure called consciousness in our machines.

However, we get a side effects of what these machines are doing,

things that seem to be closely related to what people call consciousness.

So for example, already 1990 we had simple systems

which were basically recurrent networks and therefore universal computers

trying to map incoming data into actions that lead to success.

Maximizing reward in a given environment, always finding the charging station in time

whenever the battery is low and negative signals are coming from the battery,

always find the charging station in time without bumping against painful obstacles on the way.

So complicated things but very easily motivated.

And then we give these little guys a separate recurrent network

which is just predicting what's happening if I do that and that.

What will happen as a consequence of these actions that I'm executing

and it's just trained on the long and long history of interactions with the world.

So it becomes a predictive model of the world basically.

And therefore also a compressor of the observations of the world

because whatever you can predict, you don't have to store extra.

So compression is a side effect of prediction.

And how does this recurrent network compress?

Well, it's inventing little subprograms, little subnetworks

that stand for everything that frequently appears in the environment.

Like bottles and microphones and faces, maybe lots of faces in my environment.

So I'm learning to create something like a prototype face

and a new face comes along and all I have to encode are the deviations from the prototype.

So it's compressing all the time the stuff that frequently appears.

There's one thing that appears all the time

that is present all the time when the agent is interacting with its environment,

which is the agent itself.

So just for data compression reasons,

it is extremely natural for this recurrent network

to come up with little subnetworks that stand for the properties of the agents,

the hand, the other actuators,

and all the stuff that you need to better encode the data,

which is influenced by the actions of the agent.

So there, just as a side effect of data compression during primal solving,

you have internal self models.

Now you can use this model of the world to plan your future.

And that's what we also have done since 1990.

So the recurrent network, which is the controller,

which is trying to maximize reward,

can use this model of the network of the world,

this model network of the world, this predictive model of the world

to plan ahead and say, let's not do this action sequence.

Let's do this action sequence instead

because it leads to more predicted rewards.

And whenever it's waking up these little subnetworks that stand for itself,

then it's thinking about itself.

Then it's thinking about itself.

And it's exploring mentally the consequences of its own actions.

And now you tell me why it's still missing.

Missing the gap to consciousness.

There isn't. That's a really beautiful idea that, you know,

if life is a collection of data

and life is a process of compressing that data to act efficiently.

In that data, you yourself appear very often.

So it's useful to form compressions of yourself.

And it's a really beautiful formulation of what consciousness is,

is a necessary side effect.

It's actually quite compelling to me.

We've described RNNs, developed LSTMs, long short term memory networks.

They're a type of recurrent neural networks.

They've gotten a lot of success recently.

So these are networks that model the temporal aspects in the data,

temporal patterns in the data.

And you've called them the deepest of the neural networks, right?

What do you think is the value of depth in the models that we use to learn?

Yeah, since you mentioned the long short term memory and the LSTM,

I have to mention the names of the brilliant students who made that possible.

Yes, of course, of course.

First of all, my first student ever, Sepp Hochreiter,

who had fundamental insights already in his diploma thesis.

Then Felix Giers, who had additional important contributions.

Alex Gray is a guy from Scotland who is mostly responsible for this CTC algorithm,

which is now often used to train the LSTM to do the speech recognition

on all the Google Android phones and whatever, and Siri and so on.

So these guys, without these guys, I would be nothing.

It's a lot of incredible work.

What is now the depth?

What is the importance of depth?

Well, most problems in the real world are deep

in the sense that the current input doesn't tell you all you need to know

about the environment.

So instead, you have to have a memory of what happened in the past

and often important parts of that memory are dated.

They are pretty old.

So when you're doing speech recognition, for example,

and somebody says 11,

then that's about half a second or something like that,

which means it's already 50 time steps.

And another guy or the same guy says 7.

So the ending is the same, even.

But now the system has to see the distinction between 7 and 11,

and the only way it can see the difference is it has to store

that 50 steps ago there was an S or an L, 11 or 7.

So there you have already a problem of depth 50,

because for each time step you have something like a virtual layer

and the expanded, unrolled version of this recurrent network

which is doing the speech recognition.

So these long time lags, they translate into problem depth.

And most problems in this world are such that you really

have to look far back in time to understand what is the problem

and to solve it.

But just like with LSTMs, you don't necessarily need to,

when you look back in time, remember every aspect.

You just need to remember the important aspects.

That's right.

The network has to learn to put the important stuff into memory

and to ignore the unimportant noise.

But in that sense, deeper and deeper is better?

Or is there a limitation?

I mean LSTM is one of the great examples of architectures

that do something beyond just deeper and deeper networks.

There's clever mechanisms for filtering data for remembering and forgetting.

So do you think that kind of thinking is necessary?

If you think about LSTMs as a leap, a big leap forward

over traditional vanilla RNNs, what do you think is the next leap

within this context?

So LSTM is a very clever improvement, but LSTMs still don't

have the same kind of ability to see far back in the past

as humans do, the credit assignment problem across way back,

not just 50 time steps or 100 or 1,000, but millions and billions.

It's not clear what are the practical limits of the LSTM

when it comes to looking back.

Already in 2006, I think, we had examples where not only

looked back tens of thousands of steps, but really millions of steps.

And Juan Perez Ortiz in my lab, I think was the first author of a paper

where we really, was it 2006 or something, had examples where it

learned to look back for more than 10 million steps.

So for most problems of speech recognition, it's not

necessary to look that far back, but there are examples where it does.

Now, the looking back thing, that's rather easy because there is only

one past, but there are many possible futures.

And so a reinforcement learning system, which is trying to maximize

its future expected reward and doesn't know yet which of these

many possible futures should I select, given this one single past,

is facing problems that the LSTM by itself cannot solve.

So the LSTM is good for coming up with a compact representation

of the history so far, of the history and of observations and actions so far.

But now, how do you plan in an efficient and good way among all these,

how do you select one of these many possible action sequences

that a reinforcement learning system has to consider to maximize

reward in this unknown future.

So again, we have this basic setup where you have one recon network,

which gets in the video and the speech and whatever, and it's

executing the actions and it's trying to maximize reward.

So there is no teacher who tells it what to do at which point in time.

And then there's the other network, which is just predicting

what's going to happen if I do that and then.

And that could be an LSTM network, and it learns to look back

all the way to make better predictions of the next time step.

So essentially, although it's predicting only the next time step,

it is motivated to learn to put into memory something that happened

maybe a million steps ago because it's important to memorize that

if you want to predict that at the next time step, the next event.

Now, how can a model of the world like that,

a predictive model of the world be used by the first guy,

let's call it the controller and the model, the controller and the model.

How can the model be used by the controller to efficiently select

among these many possible futures?

The naive way we had about 30 years ago was

let's just use the model of the world as a stand in,

as a simulation of the world.

And millisecond by millisecond we plan the future

and that means we have to roll it out really in detail

and it will work only if the model is really good

and it will still be inefficient

because we have to look at all these possible futures

and there are so many of them.

So instead, what we do now since 2015 in our CN systems,

controller model systems, we give the controller the opportunity

to learn by itself how to use the potentially relevant parts

of the model network to solve new problems more quickly.

And if it wants to, it can learn to ignore the M

and sometimes it's a good idea to ignore the M

because it's really bad, it's a bad predictor

in this particular situation of life

where the controller is currently trying to maximize reward.

However, it can also learn to address and exploit

some of the subprograms that came about in the model network

through compressing the data by predicting it.

So it now has an opportunity to reuse that code,

the algorithmic information in the model network

to reduce its own search space,

search that it can solve a new problem more quickly

than without the model.

Compression.

So you're ultimately optimistic and excited

about the power of reinforcement learning

in the context of real systems.

Absolutely, yeah.

So you see RL as a potential having a huge impact

beyond just sort of the M part is often developed

on supervised learning methods.

You see RL as a, for problems of cell driving cars

or any kind of applied side robotics,

that's the correct, interesting direction for researching you.

I do think so.

We have a company called Nasense,

which has applied reinforcement learning to little Audis.

Little Audis.

Which learn to park without a teacher.

The same principles were used, of course.

So these little Audis, they are small, maybe like that,

so much smaller than the RL Audis.

But they have all the sensors that you find in the RL Audis.

You find the cameras, the LIDAR sensors.

They go up to 120 kilometers an hour if they want to.

And they have pain sensors, basically.

And they don't want to bump against obstacles and other Audis.

And so they must learn like little babies to park.

Take the raw vision input and translate that into actions

that lead to successful parking behavior,

which is a rewarding thing.

And yes, they learn that.

So we have examples like that.

And it's only in the beginning.

This is just a tip of the iceberg.

And I believe the next wave of AI is going to be all about that.

So at the moment, the current wave of AI is about

passive pattern observation and prediction.

And that's what you have on your smartphone

and what the major companies on the Pacific Rim are using

to sell you ads to do marketing.

That's the current sort of profit in AI.

And that's only one or two percent of the wild economy,

which is big enough to make these companies

pretty much the most valuable companies in the world.

But there's a much, much bigger fraction of the economy

going to be affected by the next wave,

which is really about machines that shape the data

through their own actions.

Do you think simulation is ultimately the biggest way

that those methods will be successful in the next 10, 20 years?

We're not talking about 100 years from now.

We're talking about sort of the near term impact of RL.

Do you think really good simulation is required?

Or is there other techniques like imitation learning,

observing other humans operating in the real world?

Where do you think this success will come from?

So at the moment we have a tendency of using

physics simulations to learn behavior for machines

that learn to solve problems that humans also do not know how to solve.

However, this is not the future,

because the future is in what little babies do.

They don't use a physics engine to simulate the world.

They learn a predictive model of the world,

which maybe sometimes is wrong in many ways,

but captures all kinds of important abstract high level predictions

which are really important to be successful.

And that's what was the future 30 years ago

when we started that type of research,

but it's still the future,

and now we know much better how to move forward

and to really make working systems based on that,

where you have a learning model of the world,

a model of the world that learns to predict what's going to happen

if I do that and that,

and then the controller uses that model

to more quickly learn successful action sequences.

And then of course always this curiosity thing,

in the beginning the model is stupid,

so the controller should be motivated

to come up with experiments, with action sequences

that lead to data that improve the model.

Do you think improving the model,

constructing an understanding of the world in this connection

is now the popular approaches have been successful

or grounded in ideas of neural networks,

but in the 80s with expert systems there's symbolic AI approaches,

which to us humans are more intuitive

in the sense that it makes sense that you build up knowledge

in this knowledge representation.

What kind of lessons can we draw into our current approaches

from expert systems, from symbolic AI?

So I became aware of all of that in the 80s

and back then logic programming was a huge thing.

Was it inspiring to yourself that you find it compelling

that a lot of your work was not so much in that realm,

is more in the learning systems?

Yes and no, but we did all of that.

So my first publication ever actually was 1987,

was the implementation of a genetic algorithm

of a genetic programming system in Prolog.

So Prolog, that's what you learn back then,

which is a logic programming language,

and the Japanese, they had this huge fifth generation AI project,

which was mostly about logic programming back then,

although neural networks existed and were well known back then,

and deep learning has existed since 1965,

since this guy in the Ukraine, Ivak Nenko, started it,

but the Japanese and many other people,

they focused really on this logic programming,

and I was influenced to the extent that I said,

okay, let's take these biologically inspired algorithms

like evolution, programs,

and implement that in the language which I know,

which was Prolog, for example, back then.

And then in many ways this came back later,

because the Goudel machine, for example,

has a proof searcher on board,

and without that it would not be optimal.

Well, Markus Hutter's universal algorithm

for solving all well defined problems

has a proof search on board,

so that's very much logic programming.

Without that it would not be asymptotically optimal.

But then on the other hand, because we are very pragmatic guys also,

we focused on recurrent neural networks

and suboptimal stuff such as gradient based search

and program space rather than provably optimal things.

So logic programming certainly has a usefulness

when you're trying to construct something provably optimal

or provably good or something like that,

but is it useful for practical problems?

It's really useful for our theorem proving.

The best theorem proofers today are not neural networks.

No, they are logic programming systems

that are much better theorem proofers than most math students

in the first or second semester.

But for reasoning, for playing games of Go, or chess,

or for robots, autonomous vehicles that operate in the real world,

or object manipulation, you think learning...

Yeah, as long as the problems have little to do

with theorem proving themselves,

then as long as that is not the case,

you just want to have better pattern recognition.

So to build a self trying car, you want to have better pattern recognition

and pedestrian recognition and all these things,

and you want to minimize the number of false positives,

which is currently slowing down self trying cars in many ways.

And all of that has very little to do with logic programming.

What are you most excited about in terms of directions

of artificial intelligence at this moment in the next few years,

in your own research and in the broader community?

So I think in the not so distant future,

we will have for the first time little robots that learn like kids.

And I will be able to say to the robot,

look here robot, we are going to assemble a smartphone.

Let's take this slab of plastic and the screwdriver

and let's screw in the screw like that.

No, not like that, like that.

Not like that, like that.

And I don't have a data glove or something.

He will see me and he will hear me

and he will try to do something with his own actuators,

which will be really different from mine,

but he will understand the difference

and will learn to imitate me but not in the supervised way

where a teacher is giving target signals for all his muscles all the time.

No, by doing this high level imitation

where he first has to learn to imitate me

and to interpret these additional noises coming from my mouth

as helpful signals to do that pattern.

And then it will by itself come up with faster ways

and more efficient ways of doing the same thing.

And finally, I stop his learning algorithm

and make a million copies and sell it.

And so at the moment this is not possible,

but we already see how we are going to get there.

And you can imagine to the extent that this works economically and cheaply,

it's going to change everything.

Almost all our production is going to be affected by that.

And a much bigger wave,

a much bigger AI wave is coming

than the one that we are currently witnessing,

which is mostly about passive pattern recognition on your smartphone.

This is about active machines that shapes data through the actions they are executing

and they learn to do that in a good way.

So many of the traditional industries are going to be affected by that.

All the companies that are building machines

will equip these machines with cameras and other sensors

and they are going to learn to solve all kinds of problems.

Through interaction with humans, but also a lot on their own

to improve what they already can do.

And lots of old economy is going to be affected by that.

And in recent years I have seen that old economy is actually waking up

and realizing that this is the case.

Are you optimistic about that future? Are you concerned?

There's a lot of people concerned in the near term about the transformation

of the nature of work.

The kind of ideas that you just suggested

would have a significant impact on what kind of things could be automated.

Are you optimistic about that future?

Are you nervous about that future?

And looking a little bit farther into the future, there's people like Gila Musk

still wrestle concerned about the existential threats of that future.

So in the near term, job loss in the long term existential threat,

are these concerns to you or are you ultimately optimistic?

So let's first address the near future.

We have had predictions of job losses for many decades.

For example, when industrial robots came along,

many people predicted that lots of jobs are going to get lost.

And in a sense, they were right,

because back then there were car factories

and hundreds of people in these factories assembled cars.

And today the same car factories have hundreds of robots

and maybe three guys watching the robots.

On the other hand, those countries that have lots of robots per capita,

Japan, Korea, Germany, Switzerland, a couple of other countries,

they have really low unemployment rates.

Somehow all kinds of new jobs were created.

Back then nobody anticipated those jobs.

And decades ago, I already said,

it's really easy to say which jobs are going to get lost,

but it's really hard to predict the new ones.

30 years ago, who would have predicted all these people

making money as YouTube bloggers, for example?

200 years ago, 60% of all people used to work in agriculture.

Today, maybe 1%.

But still, only, I don't know, 5% unemployment.

Lots of new jobs were created.

And Homo Ludens, the playing man,

is inventing new jobs all the time.

Most of these jobs are not existentially necessary

for the survival of our species.

There are only very few existentially necessary jobs

such as farming and building houses and warming up the houses,

but less than 10% of the population is doing that.

And most of these newly invented jobs are about interacting with other people

in new ways, through new media and so on,

getting new types of kudos and forms of likes and whatever,

and even making money through that.

So, Homo Ludens, the playing man, doesn't want to be unemployed,

and that's why he's inventing new jobs all the time.

And he keeps considering these jobs as really important

and is investing a lot of energy and hours of work into those new jobs.

That's quite beautifully put.

We're really nervous about the future

because we can't predict what kind of new jobs will be created.

But you're ultimately optimistic that we humans are so restless

that we create and give meaning to newer and newer jobs,

telling you things that get likes on Facebook

or whatever the social platform is.

So, what about long term existential threat of AI

where our whole civilization may be swallowed up

by this ultra super intelligent systems?

Maybe it's not going to be swallowed up,

but I'd be surprised if we humans were the last step

in the evolution of the universe.

You've actually had this beautiful comment somewhere

that I've seen saying that artificial...

Quite insightful, artificial intelligence systems

just like us humans will likely not want to interact with humans.

They'll just interact amongst themselves,

just like ants interact amongst themselves

and only tangentially interact with humans.

And it's quite an interesting idea that once we create AGI

that will lose interest in humans

and have compete for their own Facebook likes

and their own social platforms.

So, within that quite elegant idea,

how do we know in a hypothetical sense

that there's not already intelligent systems out there?

How do you think broadly of general intelligence

greater than us, how do we know it's out there?

How do we know it's around us and could it already be?

I'd be surprised if within the next few decades

or something like that we won't have AIs

that are truly smart in every single way

and better problem solvers in almost every single important way.

And I'd be surprised if they wouldn't realize

what we have realized a long time ago,

which is that almost all physical resources are not here

in this biosphere, but throughout the rest of the solar system

gets two billion times more solar energy than our little planet.

There's lots of material out there that you can use

to build robots and self replicating robot factories and all this stuff.

And they are going to do that.

And they will be scientists and curious

and they will explore what they can do.

And in the beginning they will be fascinated by life

and by their own origins in our civilization.

They will want to understand that completely,

just like people today would like to understand how life works

and also the history of our own existence and civilization

and also the physical laws that created all of them.

So in the beginning they will be fascinated by life

once they understand it, they lose interest,

like anybody who loses interest in things he understands.

And then, as you said,

the most interesting sources of information for them

will be others of their own kind.

So, at least in the long run,

there seems to be some sort of protection

through lack of interest on the other side.

And now it seems also clear, as far as we understand physics,

you need matter and energy to compute

and to build more robots and infrastructure

and more AI civilization and AI ecologies

consisting of trillions of different types of AI's.

And so it seems inconceivable to me

that this thing is not going to expand.

Some AI ecology not controlled by one AI

but trillions of different types of AI's competing

in all kinds of quickly evolving

and disappearing ecological niches

in ways that we cannot fathom at the moment.

But it's going to expand,

limited by light speed and physics,

but it's going to expand and now we realize

that the universe is still young.

It's only 13.8 billion years old

and it's going to be a thousand times older than that.

So there's plenty of time

to conquer the entire universe

and to fill it with intelligence

and send us in receivers such that

AI's can travel the way they are traveling

in our labs today,

which is by radio from sender to receiver.

And let's call the current age of the universe one eon.

One eon.

Now it will take just a few eons from now

and the entire visible universe

is going to be full of that stuff.

And let's look ahead to a time

when the universe is going to be

one thousand times older than it is now.

They will look back and they will say,

look almost immediately after the Big Bang,

only a few eons later,

the entire universe started to become intelligent.

Now to your question,

how do we see whether anything like that

has already happened or is already in a more advanced stage

in some other part of the universe,

of the visible universe?

We are trying to look out there

and nothing like that has happened so far.

Or is that true?

Do you think we would recognize it?

How do we know it's not among us?

How do we know planets aren't in themselves intelligent beings?

How do we know ants seen as a collective

are not much greater intelligence than our own?

These kinds of ideas.

When I was a boy, I was thinking about these things

and I thought, hmm, maybe it has already happened.

Because back then I knew,

I learned from popular physics books,

that the structure, the large scale structure of the universe

is not homogeneous.

And you have these clusters of galaxies

and then in between there are these huge empty spaces.

And I thought, hmm, maybe they aren't really empty.

It's just that in the middle of that

some AI civilization already has expanded

and then has covered a bubble of a billion light years time

using all the energy of all the stars within that bubble

for its own unfathomable practices.

And so it always happened and we just failed to interpret the signs.

But then I learned that gravity by itself

explains the large scale structure of the universe

and that this is not a convincing explanation.

And then I thought maybe it's the dark matter

because as far as we know today

80% of the measurable matter is invisible.

And we know that because otherwise our galaxy

or other galaxies would fall apart.

They are rotating too quickly.

And then the idea was maybe all of these

AI civilizations that are already out there,

they are just invisible

because they are really efficient in using the energies

out their own local systems

and that's why they appear dark to us.

But this is also not a convincing explanation

because then the question becomes

why are there still any visible stars left in our own galaxy

which also must have a lot of dark matter.

So that is also not a convincing thing.

And today I like to think it's quite plausible

that maybe we are the first, at least in our local light cone

within the few hundreds of millions of light years

that we can reliably observe.

Is that exciting to you?

That we might be the first?

It would make us much more important

because if we mess it up through a nuclear war

then maybe this will have an effect

on the development of the entire universe.

So let's not mess it up.

Let's not mess it up.

Jürgen, thank you so much for talking today.

I really appreciate it.

It's my pleasure.