True Artificial Intelligence will change everything | Juergen Schmidhuber | TEDxLakeComo

[Music]

When I was a boy, I wanted to
maximise my impact on the world,

and I was smart enough to realise
that I am not very smart.

And that I have to build a machine that
learns to become much smarter than myself,

such that it can solve all the problems
that I cannot solve myself,

and I can retire.

And my first publication on
that dates back 30 years: 1987.

My diploma thesis, where I already try
to solve the grand problem of AI,

not only build a machine that learns a
little bit here, learns a little bit there,

but also learns to improve
the learning algorithm itself.

And the way it learns, the way
it learns and so on recursively,

without any limits except
the limits of logics and physics.

And, I'm still working
on the same old thing,

and I'm still pretty much
saying the same thing,

except, that now, more people
are listening.

Because, the learning algorithms that we
have developed on the way to this goal,

they are now on three thousand
million smartphones.

And all of you have them
in your pockets.

What you see here are the five most
valuable companies of the Western world:

Apple, Google, Facebook,
Microsoft, and Amazon.

And all of them are emphasising that AI,
artificial intelligence,

is central to what they are doing.

And all of them,
are using heavily,

the deep learning methods that my team
has developed since the early nineties,

in Munich and in Switzerland.

Especially something which is called:
the long short-term memory.

Has anybody in this room ever heard
of the long short-term memory?

Or the LSTM? Hands up,
anybody ever heard of that?

Okay. Has anybody never heard of
the LSTM?

I see we have a third group in this room:
who didn't understand the question.

The LSTM is a little bit like your brain:

it's an artificial neural network
which also has neurons,

and in your brain, you've got about
100 billion neurons.

And each of them is connected to
roughly 10,000 other neurons on average,

Which means that you have got
a million billion connections.

And each of these connections has
a strength which says,

how much this neuron over here, influences
that one over there at the next time step,

And in the beginning all these connections
are random and the system knows nothing,

but then, through a smart learning algorithm,
it learns from lots of examples,

to translate the incoming data,
such as video through the cameras,

or audio through the microphones,

or pain signals through the
pain sensors.

It learns to translate that
into output actions,

because some of these neurons are
output neurons,

that control speech muscles
and finger muscles.

And only through experience,
it can learn to solve

all kinds of interesting problems,
such as driving a car

or, do the speech recognition
on your smartphone.

Because, whenever you
take out your smartphone,

an Android phone, for example,
and you speak to it,

and you say: "Ok Google, show me
the shortest way to Milano."

Then it understands your speech,

because there is a LSTM in there,

which has learned to understand speech.

Every 10 milliseconds,
100 times a second,

new inputs are coming
from the microphone,

and then translates it

after thinking into letters which is then
question to the search engine and it has

long to do that by listening to lots of
speech from women from me all kinds of

people and that's how since

2015 Google speech recognition is
now much better than it used to be

the basic lsdm cell looks like that I
don't have the time to explain that but at

least I can list the names of
the brilliant students in my lab who made

that possible and what are
the big companies doing with

that well speech recognition is only
one example if you are on Facebook is

anybody on Facebook okay I
use sometimes clicking at

the translate button because somebody sent
you something in a foreign language and

then you can translate it is anybody
doing that yeah whatever you do that you

are waking up again
a long short term memory and lsdm which

has learned to translate text in
one language into translated text and

Facebook is doing that four billion times
a day so every 50 every second

50,000 sentences are being translated by
an LST am working for

Facebook and another 50,000 in the
second and another

50,000 and to see how much this thing
is now permitting the modern world

just note that almost 30 percent of
the awesome computational power for

interference and all these Google Data

Centers all these data centers of Google
are all over the world is used for LST on

almost 30 percent if you have an

Amazon echo you can ask a questions and it
answers you and the voice that you hear

it's not a recording it's
an LS TM network which has learned from

training examples to sound like
a female voice if you have an iPhone and

you're using
the quick type it's trying to predict what

you want to do next given
all the previous context of what you did

so far again that's an LS DM which
has to do that so it's on

a billion iPhones you are a large audience
by my standards but when we started

this work decades ago in the early
90s only few people who were interested

in that because computers were so slow and
you couldn't do so much with it and I

remember I gave a talk at
a conference and there was just

one single person in the audience
a young lady I said young lady it's

very embarrassing but apparently
today I'm going to give this talk just to

you and she said okay but please hurry I
am the next speaker since then we

have greatly profited from the fact
that every five years computers again in

ten times cheaper which is
an old trend that has held since 1941 at

least since this man Conrad Susan built
the first working program control computer

in Berlin and he could could do roughly
one operation per second one and then

ten years later for the same prize one
could do 100 operations 30 years later

1 million operations were
the same price and today after 75 years we

can do a million billion times as much for
the same price and the trend is not about

to stop because the physical limits are
much further out there rather soon and not

so many years or decades we will for
the first time have

little computational devices that
can compute as much as a human brain and

this a trend doesn't break 50 years
later there will be

a little computational device for
the same price that can compute as much as

all 10 billion human brains taken
together and there will not only be one of

those devices but
many many many everything

is going to change already in
2011 computers were fast enough such that

our deep learning methods for
the first time could achieve

a superhuman pattern-recognition result and
was the first superhuman result and

the history of
computer vision and back then computers

were 20 times more expensive than today so
today for the same price we can do

20 times as much and just a few five years
ago five years ago when computers were

10 times more expensive than today we
already could win for the first time

medical imaging competitions what you see
behind me is a slice through

the female breast and the tissue that you
see there has all kinds of

cells and normally you need
a trained doctor a trained the solid who

is able to detect
the dangerous cancer cells or

pre-cancer cells now our stupid network
knows nothing about cancer knows nothing

about vision it knows nothing in
the beginning but we can train it

to imitate the human teacher
the doctor and it became as good or better

than the best competitors and
very soon all of medical diagnosis

is going to be superhuman and
it's going to be mandatory because

it's going to be so much better than
the doctors after this all kinds of

medical imaging startups
were founded focusing just on this because

it's so important we can also use lsdm
to train robots one important thing I

want to say is that we not only have
systems that slavishly imitate what humans

show them no we also have a eyes that set
themselves their own goals and

like little babies invent
their own experiment to explore

the world and to figure out what you
can do in the world without a teacher and

becoming more and
more general problem solvers in

the process by learning new skills on top
of old skills and this is going to

scale we call that artificial curiosity or
a recent password is power plain

learning to become a more and
more general problems over by

learning to invent like a scientist
one new interesting goal after

Nathan and and it's going to scale and I
think in not so many years from now for

the first time we are going to have
an animal like

AI you don't have that yet on the level of
a little crowd which

already can learn to use two worlds for
example little monkey and once we have

that it may take just a few decades to do
the final step towards

human level intelligence because
technological evolution is about

a million times a million times faster
than biological evolution and

biological evolution needed
3.5 billion years to evolve a monkey

a monkey from scratch but then just
a few tens of millions of years

afterwards to evolve
human level intelligence we have

a company which is called Mason's
like birth in English

Mason's but spelled in
a different way which is trying to make

this a reality and build
the first true general and purpose AI at

the moment almost all research in AI is
very human centric and it's all about

making human lives longer and
healthier and easier and making humans

more addicted to their smartphones but
in the long run a eyes are going to

especially the smart ones are going to set
themselves their own goals and I have

no doubt in my mind that they
are going to become much smarter than we

are and what are they going to do of
course they are going to realize what we

have realized a long time ago namely that
most of the resources in

the solar system or in general are not in
our little biosphere they are out there in

space and so of course they
are going to emigrate and of course they

are going to use trillions of
self-replicating robot factories to expand

in form of growing

AI bubble which within a few
hundred thousand years is going to cover

the entire galaxy by senders and receivers
such that a eyes can travel the way they

are already traveling in my lab by radio
from sender to receiver Wireless so what

we are witnessing now is much more than
just another Industrial Revolution this is

something that transcends
humankind and even life itself

the last time something so important
has happened was maybe 3.5 billion years

ago when life was invented a new type of
life is going to emerge from

our little planet and
it's going to colonize and transform

the entire universe the universe is
still young it's only 13.8 billion years

old it's going to become much older than
that many times more many times older

than that so there's plenty of time
to reach all of it or all of

the visible parts totally within
the limits of light speed and physics

a new type of life is going to make
the universe intelligent now of course we

are not going to remain the crown of
creation of course not but there is still

beauty in seeing yourself as part of
a grander process that leads the cosmos

from low complexity towards
higher complexity it's a privilege to live

at a time where we can
witness the beginnings of that and where

we can contribute something to
that thank you for your patience