[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