so can machines think this idea the singularity the time in history where a
singularity the time in history where a
singularity the time in history where a computer program will be so smart that
computer program will be so smart that
computer program will be so smart that it will be able to create another
it will be able to create another
it will be able to create another computer program even smarter and so on
computer program even smarter and so on
computer program even smarter and so on and so forth until humanity’s demise
and so forth until humanity’s demise
and so forth until humanity’s demise ensues it’s not likely Alan Turing
ensues it’s not likely Alan Turing
ensues it’s not likely Alan Turing devised the test to test this idea ask
devised the test to test this idea ask
devised the test to test this idea ask the question can machines think his test
the question can machines think his test
the question can machines think his test was let’s have a computer trying to fool
was let’s have a computer trying to fool
was let’s have a computer trying to fool a human into thinking it itself is human
a human into thinking it itself is human
a human into thinking it itself is human by means of conversation a simple and
by means of conversation a simple and
by means of conversation a simple and you may think ill-defined test but would
you may think ill-defined test but would
you may think ill-defined test but would it surprise you to know that almost 70
it surprise you to know that almost 70
it surprise you to know that almost 70 years on this test has not yet been
years on this test has not yet been
years on this test has not yet been passed not close generic AI strong AI
passed not close generic AI strong AI
passed not close generic AI strong AI the artificial intelligence that can do
the artificial intelligence that can do
the artificial intelligence that can do everything humans can do and more and
everything humans can do and more and
everything humans can do and more and better it’s nowhere near here however
better it’s nowhere near here however
better it’s nowhere near here however arguably one of the biggest leaps in
arguably one of the biggest leaps in
arguably one of the biggest leaps in that direction is a technology that’s
that direction is a technology that’s
that direction is a technology that’s called deep learning in fact when in the
called deep learning in fact when in the
called deep learning in fact when in the news you hear about developments in AI
news you hear about developments in AI
news you hear about developments in AI more often than not you are hearing
more often than not you are hearing
more often than not you are hearing about developments or applications of
about developments or applications of
about developments or applications of deep learning
deep learning
deep learning you see whilst other AIS are capable of
you see whilst other AIS are capable of
you see whilst other AIS are capable of solving small problems such as playing a
solving small problems such as playing a
solving small problems such as playing a game or finding the path in a maze
game or finding the path in a maze
game or finding the path in a maze what’s exciting and new about deep
what’s exciting and new about deep
what’s exciting and new about deep learning is that it’s one technology
learning is that it’s one technology
learning is that it’s one technology that can solve a range of different
that can solve a range of different
that can solve a range of different problems
it is what we call a classifier it’s one
it is what we call a classifier it’s one of the best classifiers yet but what is
of the best classifiers yet but what is
of the best classifiers yet but what is that it can be boiled down to cats and
that it can be boiled down to cats and
that it can be boiled down to cats and dogs
imagine you want a computer to be able
imagine you want a computer to be able to distinguish between cats and dogs you
to distinguish between cats and dogs you
to distinguish between cats and dogs you could teach it about the structure about
could teach it about the structure about
could teach it about the structure about what they look like you could teach them
what they look like you could teach them
what they look like you could teach them specifically to recognize specific
specifically to recognize specific
specifically to recognize specific patterns but what’s revolutionary about
patterns but what’s revolutionary about
patterns but what’s revolutionary about deep learning is that it’s entirely
deep learning is that it’s entirely
deep learning is that it’s entirely based on samples the only thing you need
based on samples the only thing you need
based on samples the only thing you need is many many samples many many pictures
is many many samples many many pictures
is many many samples many many pictures of cats and dogs and the Internet has
of cats and dogs and the Internet has
of cats and dogs and the Internet has plenty of errors and the system will
plenty of errors and the system will
plenty of errors and the system will learn on its own to distinguish between
learn on its own to distinguish between
learn on its own to distinguish between them and you may think well this is a
them and you may think well this is a
them and you may think well this is a very simple example and you’d be
very simple example and you’d be
very simple example and you’d be surprised the number of different
surprised the number of different
surprised the number of different problems in the world that can be
problems in the world that can be
problems in the world that can be reduced to just classify for example
reduced to just classify for example
reduced to just classify for example classifying between a good chess
classifying between a good chess
classifying between a good chess position and a bad chess position
position and a bad chess position
position and a bad chess position classifying between an empty road and a
classifying between an empty road and a
classifying between an empty road and a road with pedestrians on it if you’re
road with pedestrians on it if you’re
road with pedestrians on it if you’re building a self-driving car so deep
building a self-driving car so deep
building a self-driving car so deep learning is versatile and yes it can be
learning is versatile and yes it can be
learning is versatile and yes it can be used to solve and play games better than
used to solve and play games better than
used to solve and play games better than humans you may have heard recently about
humans you may have heard recently about
humans you may have heard recently about alphago defeating the world’s best go
alphago defeating the world’s best go
alphago defeating the world’s best go player and then recently again defeating
player and then recently again defeating
player and then recently again defeating the world’s best go player again the
the world’s best go player again the
the world’s best go player again the other world best brain deep learning has
other world best brain deep learning has
other world best brain deep learning has another very important characteristic
another very important characteristic
another very important characteristic which is because we only need samples to
which is because we only need samples to
which is because we only need samples to train it
train it
train it we don’t need expert
we don’t need expert
we don’t need expert the engineers that built alphago are not
the engineers that built alphago are not
the engineers that built alphago are not expert go players they barely needs to
expert go players they barely needs to
expert go players they barely needs to know the rules and the system will learn
know the rules and the system will learn
know the rules and the system will learn on its own and is this same technology
on its own and is this same technology
on its own and is this same technology that can be applied to a number of
that can be applied to a number of
that can be applied to a number of problems which capture the imagination
problems which capture the imagination
problems which capture the imagination of the media from lip-reading to medical
of the media from lip-reading to medical
of the media from lip-reading to medical diagnosis and what is fascinating is
diagnosis and what is fascinating is
diagnosis and what is fascinating is that even though it can solve all these
that even though it can solve all these
that even though it can solve all these problems deep learning has a number of
problems deep learning has a number of
problems deep learning has a number of limitations we’ve been here today about
limitations we’ve been here today about
limitations we’ve been here today about how to future-proof your life we’ve been
how to future-proof your life we’ve been
how to future-proof your life we’ve been hearing from our first speaker about
hearing from our first speaker about
hearing from our first speaker about having to learn things and about other
having to learn things and about other
having to learn things and about other speakers about being involved with new
speakers about being involved with new
speakers about being involved with new technology and the additional
technology and the additional
technology and the additional contribution I like to make is that
contribution I like to make is that
contribution I like to make is that sometimes new technology can be
sometimes new technology can be
sometimes new technology can be overrated deep learning is powerful it’s
overrated deep learning is powerful it’s
overrated deep learning is powerful it’s exciting it also has some limitations as
exciting it also has some limitations as
exciting it also has some limitations as I said you need lots of pictures of cats
I said you need lots of pictures of cats
I said you need lots of pictures of cats and dogs and we have plenty of those but
and dogs and we have plenty of those but
and dogs and we have plenty of those but when your problem becomes more complex
when your problem becomes more complex
when your problem becomes more complex you need more and more data and big data
you need more and more data and big data
you need more and more data and big data quickly becomes the need for a big
quickly becomes the need for a big
quickly becomes the need for a big computing system which requires a lot of
computing system which requires a lot of
computing system which requires a lot of energy and a lot of money and that’s not
energy and a lot of money and that’s not
energy and a lot of money and that’s not the only limitation it was shown
the only limitation it was shown
the only limitation it was shown recently that you can trick deep
recently that you can trick deep
recently that you can trick deep learning you can show it an image of a
learning you can show it an image of a
learning you can show it an image of a cat we love this and then we can change
cat we love this and then we can change
cat we love this and then we can change every pixel in the image by a very small
every pixel in the image by a very small
every pixel in the image by a very small amount in a very clever way in such a
amount in a very clever way in such a
amount in a very clever way in such a way that the resulting image to us still
way that the resulting image to us still
way that the resulting image to us still looks like a cat but the deep learning
looks like a cat but the deep learning
looks like a cat but the deep learning system will think it’s a dog
or a table or a Mac or whatever you want
or a table or a Mac or whatever you want to trick it into believing deep learning
to trick it into believing deep learning
to trick it into believing deep learning is limited and it’s exciting and it’s
is limited and it’s exciting and it’s
is limited and it’s exciting and it’s versatile and it’s powerful but all
versatile and it’s powerful but all
versatile and it’s powerful but all those adjectives are shared I contend by
those adjectives are shared I contend by
those adjectives are shared I contend by pretty much every disruptive technology
pretty much every disruptive technology
pretty much every disruptive technology we’ve encountered here’s an example it’s
we’ve encountered here’s an example it’s
we’ve encountered here’s an example it’s called a computer or a personal computer
called a computer or a personal computer
called a computer or a personal computer and let me ask you this do you
and let me ask you this do you
and let me ask you this do you understand every single electronic
understand every single electronic
understand every single electronic component and its function inside your
component and its function inside your
component and its function inside your computer or your phone I know I don’t
computer or your phone I know I don’t
computer or your phone I know I don’t and that doesn’t stop me using them it
and that doesn’t stop me using them it
and that doesn’t stop me using them it doesn’t stop me
doesn’t stop me
doesn’t stop me learning about them every day more and
learning about them every day more and
learning about them every day more and more as I need if computers were
more as I need if computers were
more as I need if computers were invented today and people were saying
invented today and people were saying
invented today and people were saying they will make your job obsolete
they will make your job obsolete
they will make your job obsolete they will render your role irrelevant
they will render your role irrelevant
they will render your role irrelevant well how would you react panic
well how would you react panic
well how would you react panic complacency I contend and this is my
complacency I contend and this is my
complacency I contend and this is my challenge to you that we treat deep
challenge to you that we treat deep
challenge to you that we treat deep learning in the same way that we treat
learning in the same way that we treat
learning in the same way that we treat our computers we learn it and it will be
our computers we learn it and it will be
our computers we learn it and it will be difficult but so was using spreadsheets
difficult but so was using spreadsheets
difficult but so was using spreadsheets the first time around it may not even be
the first time around it may not even be
the first time around it may not even be that efficient but then again neither
that efficient but then again neither
that efficient but then again neither were word processes when they first
were word processes when they first
were word processes when they first appeared the good news is that because
appeared the good news is that because
appeared the good news is that because the only thing we need to train a deep
the only thing we need to train a deep
the only thing we need to train a deep learning system is examples samples of
learning system is examples samples of
learning system is examples samples of data it is relatively nice Treacher
data it is relatively nice Treacher
data it is relatively nice Treacher relatively simple to actually implement
how simple nine nine lines of code
how simple nine nine lines of code that’s how many lines of code it takes
that’s how many lines of code it takes
that’s how many lines of code it takes me to write a deep learning program so
me to write a deep learning program so
me to write a deep learning program so here is the idea that I think it’s worth
here is the idea that I think it’s worth
here is the idea that I think it’s worth sharing let us do away with this elitism
sharing let us do away with this elitism
sharing let us do away with this elitism of skill let us make the machine
of skill let us make the machine
of skill let us make the machine learning experts less unique
learning experts less unique
learning experts less unique let’s democratize the use of technology
let’s democratize the use of technology
let’s democratize the use of technology including machine learning go away and
including machine learning go away and
including machine learning go away and find out what those nine lines of code
find out what those nine lines of code
find out what those nine lines of code are and if you don’t know how to code
are and if you don’t know how to code
are and if you don’t know how to code learn how to code and if you can do it
learn how to code and if you can do it
learn how to code and if you can do it in fewer than nine lines get in touch my
in fewer than nine lines get in touch my
in fewer than nine lines get in touch my point is don’t panic learn to code and
you
you [Applause]
”
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