[Music] you
you
you [Music]
[Music]
[Music] the first part of this talk is intended
the first part of this talk is intended
the first part of this talk is intended to scare you really scare you okay and
to scare you really scare you okay and
to scare you really scare you okay and what I want you to think about is what
what I want you to think about is what
what I want you to think about is what you do in your job or what you may well
you do in your job or what you may well
you do in your job or what you may well be doing in your future career or your
be doing in your future career or your
be doing in your future career or your future job and ask yourself this
future job and ask yourself this
future job and ask yourself this question could a machine do what you do
question could a machine do what you do
question could a machine do what you do that’s the question now I want to yeah I
that’s the question now I want to yeah I
that’s the question now I want to yeah I mean let’s think about like some things
mean let’s think about like some things
mean let’s think about like some things that you could potentially do that you
that you could potentially do that you
that you could potentially do that you would think these are things that no
would think these are things that no
would think these are things that no machine could possibly do so let’s say
machine could possibly do so let’s say
machine could possibly do so let’s say what do you think you think if you’re a
what do you think you think if you’re a
what do you think you think if you’re a doctor do you think a machine could do
doctor do you think a machine could do
doctor do you think a machine could do what a doctor does hands up anyone who
what a doctor does hands up anyone who
what a doctor does hands up anyone who thinks that’s possible okay
thinks that’s possible okay
thinks that’s possible okay anyone who thinks okay an artist anyone
anyone who thinks okay an artist anyone
anyone who thinks okay an artist anyone think that machine could do what an
think that machine could do what an
think that machine could do what an artist does okay anyone there’s a lot
artist does okay anyone there’s a lot
artist does okay anyone there’s a lot less people anyone who thinks that a
less people anyone who thinks that a
less people anyone who thinks that a machine could do what the prime minister
machine could do what the prime minister
machine could do what the prime minister does okay so there are huge numbers of
does okay so there are huge numbers of
does okay so there are huge numbers of of reasons to be afraid about this right
of reasons to be afraid about this right
of reasons to be afraid about this right this is the possibility that in the
this is the possibility that in the
this is the possibility that in the future that will note there will no
future that will note there will no
future that will note there will no longer be any jobs for humans all right
longer be any jobs for humans all right
longer be any jobs for humans all right this is not a future that is completely
this is not a future that is completely
this is not a future that is completely mad yes a lot of people believe that
mad yes a lot of people believe that
mad yes a lot of people believe that this is a almost a sure thing to the
this is a almost a sure thing to the
this is a almost a sure thing to the point that you see headlines which say
point that you see headlines which say
point that you see headlines which say things like hey I’m making huge strides
things like hey I’m making huge strides
things like hey I’m making huge strides performs dermatologist level skin cancer
performs dermatologist level skin cancer
performs dermatologist level skin cancer classification or I’ll forego for
classification or I’ll forego for
classification or I’ll forego for example beats the world player more
example beats the world player more
example beats the world player more robots fewer jobs yes these sort of
robots fewer jobs yes these sort of
robots fewer jobs yes these sort of headlines now I don’t know if there’s
headlines now I don’t know if there’s
headlines now I don’t know if there’s going to be a robot teacher remote robot
going to be a robot teacher remote robot
going to be a robot teacher remote robot professor like me in the future and I
professor like me in the future and I
professor like me in the future and I wanted to set out to see whether that
wanted to set out to see whether that
wanted to set out to see whether that was really going to be true and my
was really going to be true and my
was really going to be true and my process of setting this of of setting
process of setting this of of setting
process of setting this of of setting out on this quest so I asked his
out on this quest so I asked his
out on this quest so I asked his question
question
question about whether or not this is really true
about whether or not this is really true
about whether or not this is really true it’s what I’m going to be talking to you
it’s what I’m going to be talking to you
it’s what I’m going to be talking to you about today to understand where we got
about today to understand where we got
about today to understand where we got to this point that machines could
to this point that machines could
to this point that machines could potentially take over what humans do we
potentially take over what humans do we
potentially take over what humans do we have to go back and have a look at a
have to go back and have a look at a
have to go back and have a look at a little bit of history next slide please
little bit of history next slide please
little bit of history next slide please so this has really been developing for
so this has really been developing for
so this has really been developing for over 200 years but I’d start with the
over 200 years but I’d start with the
over 200 years but I’d start with the Victorian era and this is these people
Victorian era and this is these people
Victorian era and this is these people Charles Babbage and ADA Lovelace and
Charles Babbage and ADA Lovelace and
Charles Babbage and ADA Lovelace and Charles Babbage was the first person to
Charles Babbage was the first person to
Charles Babbage was the first person to invent what might call computer hardware
invent what might call computer hardware
invent what might call computer hardware really an Ada Lovelace was the first
really an Ada Lovelace was the first
really an Ada Lovelace was the first recognizable computer programmer now the
recognizable computer programmer now the
recognizable computer programmer now the thing is that it’s taken a long time
thing is that it’s taken a long time
thing is that it’s taken a long time nearly a century had to go passed before
nearly a century had to go passed before
nearly a century had to go passed before Alonzo Church and Alan Turing came up
Alonzo Church and Alan Turing came up
Alonzo Church and Alan Turing came up with this the idea of a general
with this the idea of a general
with this the idea of a general theoretical basis for computing because
theoretical basis for computing because
theoretical basis for computing because there was hardware and software but no
there was hardware and software but no
there was hardware and software but no theory for how to sort might all fit
theory for how to sort might all fit
theory for how to sort might all fit together and in principle at least they
together and in principle at least they
together and in principle at least they came up with this idea of a machine
came up with this idea of a machine
came up with this idea of a machine called the cheering machine that could
called the cheering machine that could
called the cheering machine that could potentially do all calculations that any
potentially do all calculations that any
potentially do all calculations that any human could do that’s the idea it’s a
human could do that’s the idea it’s a
human could do that’s the idea it’s a thesis it’s not a proof that there are
thesis it’s not a proof that there are
thesis it’s not a proof that there are machines out there that you can
machines out there that you can
machines out there that you can demonstrably show can do all
demonstrably show can do all
demonstrably show can do all calculations they’re humans could do now
calculations they’re humans could do now
calculations they’re humans could do now the thing is that’s in theory the
the thing is that’s in theory the
the thing is that’s in theory the practice of it only really started
practice of it only really started
practice of it only really started taking off when will Shockley and others
taking off when will Shockley and others
taking off when will Shockley and others at AT&T Labs invented the transistor and
at AT&T Labs invented the transistor and
at AT&T Labs invented the transistor and invented there the microchip as well
invented there the microchip as well
invented there the microchip as well which came along with that invention
which came along with that invention
which came along with that invention they kicked off the era of so-called
they kicked off the era of so-called
they kicked off the era of so-called exponentially increasing miniaturization
exponentially increasing miniaturization
exponentially increasing miniaturization of computing machines and this is
of computing machines and this is
of computing machines and this is famously summed up in a law named after
famously summed up in a law named after
famously summed up in a law named after Gordon Moore who was the co-founder of
Gordon Moore who was the co-founder of
Gordon Moore who was the co-founder of Intel Corporation Intel that make all
Intel Corporation Intel that make all
Intel Corporation Intel that make all the chips that go into your pcs for
the chips that go into your pcs for
the chips that go into your pcs for example known as Moore’s law after
example known as Moore’s law after
example known as Moore’s law after obviously that’s right at the same time
obviously that’s right at the same time
obviously that’s right at the same time in 1950s pioneers such as Frank
in 1950s pioneers such as Frank
in 1950s pioneers such as Frank Rosenblatt here who’s actually a
Rosenblatt here who’s actually a
Rosenblatt here who’s actually a psychologist started experimenting
psychologist started experimenting
psychologist started experimenting seriously with the idea of mimicking how
seriously with the idea of mimicking how
seriously with the idea of mimicking how the brain is wired up yes so this is
the brain is wired up yes so this is
the brain is wired up yes so this is really the beginning of the notion of
really the beginning of the notion of
really the beginning of the notion of artificial intelligence you came up with
artificial intelligence you came up with
artificial intelligence you came up with this idea for the artificial neural
this idea for the artificial neural
this idea for the artificial neural network and later it became clear that
network and later it became clear that
network and later it became clear that to make substantial progress artificial
to make substantial progress artificial
to make substantial progress artificial intelligence was going to have to cope
intelligence was going to have to cope
intelligence was going to have to cope with uncertainty because computers are
with uncertainty because computers are
with uncertainty because computers are not very good with uncertainty but the
not very good with uncertainty but the
not very good with uncertainty but the real world is always uncertain right but
real world is always uncertain right but
real world is always uncertain right but this idea has been essentially solved in
this idea has been essentially solved in
this idea has been essentially solved in theory by this chap do they a pearl in
theory by this chap do they a pearl in
theory by this chap do they a pearl in 1980s and this brings us up to the
1980s and this brings us up to the
1980s and this brings us up to the modern era the modern era of artificial
modern era the modern era of artificial
modern era the modern era of artificial intelligence with these two people
intelligence with these two people
intelligence with these two people Janna Kuhn and Geoffrey Hinton and they
Janna Kuhn and Geoffrey Hinton and they
Janna Kuhn and Geoffrey Hinton and they came back this idea of called deep
came back this idea of called deep
came back this idea of called deep learning which is basically artificial
learning which is basically artificial
learning which is basically artificial neural networks which are very very deep
neural networks which are very very deep
neural networks which are very very deep layers and this supports us things like
layers and this supports us things like
layers and this supports us things like serious speech recognition yes and
serious speech recognition yes and
serious speech recognition yes and things like Google’s automated photo
things like Google’s automated photo
things like Google’s automated photo labeling apps right this is all powered
labeling apps right this is all powered
labeling apps right this is all powered by the ideas that have been invented by
by the ideas that have been invented by
by the ideas that have been invented by by these two pretty much now that we
by these two pretty much now that we
by these two pretty much now that we understand how we got here we need to
understand how we got here we need to
understand how we got here we need to know what the current state of the art
know what the current state of the art
know what the current state of the art is yes that’s the past now let’s look at
is yes that’s the past now let’s look at
is yes that’s the past now let’s look at the current where are we now
the current where are we now
the current where are we now with this hardware and it leads us to
with this hardware and it leads us to
with this hardware and it leads us to ask the first question which is what is
ask the first question which is what is
ask the first question which is what is intelligence it’s what I mean by that
intelligence it’s what I mean by that
intelligence it’s what I mean by that this is a very vexed question it is a
this is a very vexed question it is a
this is a very vexed question it is a very difficult question to answer but
very difficult question to answer but
very difficult question to answer but let’s put I just want to put it to you a
let’s put I just want to put it to you a
let’s put I just want to put it to you a few possibilities about what
few possibilities about what
few possibilities about what intelligence could possibly be one
intelligence could possibly be one
intelligence could possibly be one possibility is that intelligence is just
possibility is that intelligence is just
possibility is that intelligence is just about memory yes it’s just about
about memory yes it’s just about
about memory yes it’s just about memorizing lots and lots of things
memorizing lots and lots of things
memorizing lots and lots of things really really well right so if that’s
really really well right so if that’s
really really well right so if that’s the case we would have to pit nature’s
the case we would have to pit nature’s
the case we would have to pit nature’s memory against computer memory now what
memory against computer memory now what
memory against computer memory now what is nature’s memory
is nature’s memory
is nature’s memory well nature’s memory is essentially DNA
well nature’s memory is essentially DNA
well nature’s memory is essentially DNA now that represents
now that represents
now that represents all the information in your genome which
all the information in your genome which
all the information in your genome which is the record in history of all the
is the record in history of all the
is the record in history of all the things that all animals before you that
things that all animals before you that
things that all animals before you that led to you encountered in their
led to you encountered in their
led to you encountered in their environment and led to you as as the
environment and led to you as as the
environment and led to you as as the winner you are the success story of that
winner you are the success story of that
winner you are the success story of that massive evolutionary history which is
massive evolutionary history which is
massive evolutionary history which is recorded in your DNA and of course there
recorded in your DNA and of course there
recorded in your DNA and of course there have been trillions and trillions of
have been trillions and trillions of
have been trillions and trillions of individuals and there are countless DNA
individuals and there are countless DNA
individuals and there are countless DNA strings out there yes so but these
strings out there yes so but these
strings out there yes so but these things have survived for billions and
things have survived for billions and
things have survived for billions and billions of years yes for billions of
billions of years yes for billions of
billions of years yes for billions of years now that means really that the
years now that means really that the
years now that means really that the amount of information that’s stored in
amount of information that’s stored in
amount of information that’s stored in DNA versus the amount of information
DNA versus the amount of information
DNA versus the amount of information that’s stored in computer memory that’s
that’s stored in computer memory that’s
that’s stored in computer memory that’s the winner
the winner
the winner so DNA is very much definitely winning
so DNA is very much definitely winning
so DNA is very much definitely winning currently so intelligence really isn’t
currently so intelligence really isn’t
currently so intelligence really isn’t just about memory is it right
just about memory is it right
just about memory is it right intelligence could be more than that you
intelligence could be more than that you
intelligence could be more than that you could be married plus you have to have a
could be married plus you have to have a
could be married plus you have to have a lot of experience yes you’ve got to see
lot of experience yes you’ve got to see
lot of experience yes you’ve got to see a lot of things if so what that’s going
a lot of things if so what that’s going
a lot of things if so what that’s going to do is Pitt for example I’ll just give
to do is Pitt for example I’ll just give
to do is Pitt for example I’ll just give an example of what mean by experience
an example of what mean by experience
an example of what mean by experience like sensory experience things that you
like sensory experience things that you
like sensory experience things that you see with your eyes yes
see with your eyes yes
see with your eyes yes so that’ll Pitt say your eye that’s your
so that’ll Pitt say your eye that’s your
so that’ll Pitt say your eye that’s your retina against a digital camera right
retina against a digital camera right
retina against a digital camera right look these are broadly similar things
look these are broadly similar things
look these are broadly similar things yes and we’d have to ask how much
yes and we’d have to ask how much
yes and we’d have to ask how much information is this thing capturing
information is this thing capturing
information is this thing capturing versus how much information is a digital
versus how much information is a digital
versus how much information is a digital camera capturing yes and by one estimate
camera capturing yes and by one estimate
camera capturing yes and by one estimate retina has about 74 million dots on it
retina has about 74 million dots on it
retina has about 74 million dots on it light-sensitive dots right now camera
light-sensitive dots right now camera
light-sensitive dots right now camera digital camera sensors approach that
digital camera sensors approach that
digital camera sensors approach that there are camera sensors you can get
there are camera sensors you can get
there are camera sensors you can get they’re even more detailed than that but
they’re even more detailed than that but
they’re even more detailed than that but the thing about the hue about the human
the thing about the hue about the human
the thing about the hue about the human retina is you’ve had one ever since
retina is you’ve had one ever since
retina is you’ve had one ever since you’re a fetus and you’ve this has been
you’re a fetus and you’ve this has been
you’re a fetus and you’ve this has been collating images once every twenty five
collating images once every twenty five
collating images once every twenty five times a second from the moment you were
times a second from the moment you were
times a second from the moment you were born yes and possibly earlier so the
born yes and possibly earlier so the
born yes and possibly earlier so the thing is that this has been amassing a
thing is that this has been amassing a
thing is that this has been amassing a vast amount of information and it’s been
vast amount of information and it’s been
vast amount of information and it’s been somehow filtered into your filters into
somehow filtered into your filters into
somehow filtered into your filters into your perception
your perception
your perception raining somewhere so I estimate it’s
raining somewhere so I estimate it’s
raining somewhere so I estimate it’s just a simple calculation would be that
just a simple calculation would be that
just a simple calculation would be that there’s about five times ten to the
there’s about five times ten to the
there’s about five times ten to the eight number of bits of information so
eight number of bits of information so
eight number of bits of information so it’s very five times ten to the 18 bits
it’s very five times ten to the 18 bits
it’s very five times ten to the 18 bits of information that you’ll experience
of information that you’ll experience
of information that you’ll experience over your lifetime now to give you some
over your lifetime now to give you some
over your lifetime now to give you some idea 10 to the 12 is a trillion right so
idea 10 to the 12 is a trillion right so
idea 10 to the 12 is a trillion right so it’s a lot bigger than that and the
it’s a lot bigger than that and the
it’s a lot bigger than that and the thing is that that is about the scale of
thing is that that is about the scale of
thing is that that is about the scale of the size of all the information on
the size of all the information on
the size of all the information on YouTube so that’s what you’ve been
YouTube so that’s what you’ve been
YouTube so that’s what you’ve been experiencing over you that’s what you
experiencing over you that’s what you
experiencing over you that’s what you will experience over your entire life
will experience over your entire life
will experience over your entire life right but there are billions of us of
right but there are billions of us of
right but there are billions of us of course so that gives you some idea that
course so that gives you some idea that
course so that gives you some idea that this the the human retina is probably
this the the human retina is probably
this the the human retina is probably experiencing more information than all
experiencing more information than all
experiencing more information than all these cameras are currently so it can’t
these cameras are currently so it can’t
these cameras are currently so it can’t just be about the amount of information
just be about the amount of information
just be about the amount of information we’re amassing yes it could also be
we’re amassing yes it could also be
we’re amassing yes it could also be about computational speed yeah so how
about computational speed yeah so how
about computational speed yeah so how fast can you think someone who can think
fast can you think someone who can think
fast can you think someone who can think faster than somebody else is presumably
faster than somebody else is presumably
faster than somebody else is presumably more intelligent yes that’s how we would
more intelligent yes that’s how we would
more intelligent yes that’s how we would suggest that’s what we’d suggest now
suggest that’s what we’d suggest now
suggest that’s what we’d suggest now here the story is quite different yes
here the story is quite different yes
here the story is quite different yes this is a neuron okay it switches about
this is a neuron okay it switches about
this is a neuron okay it switches about 10 to 100 pulses per second if you’re
10 to 100 pulses per second if you’re
10 to 100 pulses per second if you’re lucky it takes about a second for you to
lucky it takes about a second for you to
lucky it takes about a second for you to stub your toe and then for you to feel
stub your toe and then for you to feel
stub your toe and then for you to feel the pain right so it gives you how long
the pain right so it gives you how long
the pain right so it gives you how long that gives you an idea about how fast
that gives you an idea about how fast
that gives you an idea about how fast the information transfer is transferred
the information transfer is transferred
the information transfer is transferred in neurons yes this thing here that’s a
in neurons yes this thing here that’s a
in neurons yes this thing here that’s a transistor and there are now all tens to
transistor and there are now all tens to
transistor and there are now all tens to a hundreds of billions of these on there
a hundreds of billions of these on there
a hundreds of billions of these on there on a smartphone’s processor and so but
on a smartphone’s processor and so but
on a smartphone’s processor and so but this thing here can make about one to
this thing here can make about one to
this thing here can make about one to five billion decisions per second yes
five billion decisions per second yes
five billion decisions per second yes so clearly transistors win hands down
so clearly transistors win hands down
so clearly transistors win hands down computers can currently think far far
computers can currently think far far
computers can currently think far far faster than we possibly can so if we
faster than we possibly can so if we
faster than we possibly can so if we just sum this up and you ask what’s
just sum this up and you ask what’s
just sum this up and you ask what’s intelligence at the moment yes the
intelligence at the moment yes the
intelligence at the moment yes the current state of humans versus machines
current state of humans versus machines
current state of humans versus machines well they’re not equal right there’s a
well they’re not equal right there’s a
well they’re not equal right there’s a it’s it’s not clear which who’s the
it’s it’s not clear which who’s the
it’s it’s not clear which who’s the winner here you know machines are
winner here you know machines are
winner here you know machines are certainly faster but nature stores much
certainly faster but nature stores much
certainly faster but nature stores much more well but nature is also more
more well but nature is also more
more well but nature is also more efficient can process more information
efficient can process more information
efficient can process more information with
with
with less energy for example so it’s not a
less energy for example so it’s not a
less energy for example so it’s not a slam-dunk either way next line so let’s
slam-dunk either way next line so let’s
slam-dunk either way next line so let’s now look to the future so you’ve looked
now look to the future so you’ve looked
now look to the future so you’ve looked at the past the history look at the
at the past the history look at the
at the past the history look at the current state of the art and let’s see
current state of the art and let’s see
current state of the art and let’s see where this is going
where this is going
where this is going we can try to extrapolate into the
we can try to extrapolate into the
we can try to extrapolate into the future a bit so this is how a future of
future a bit so this is how a future of
future a bit so this is how a future of AI could work and I’m going to give you
AI could work and I’m going to give you
AI could work and I’m going to give you like a really simple idea that could
like a really simple idea that could
like a really simple idea that could mean that all of this the idea of
mean that all of this the idea of
mean that all of this the idea of putting all of you out of work all of us
putting all of you out of work all of us
putting all of you out of work all of us out of work in the future is totally
out of work in the future is totally
out of work in the future is totally possible yeah
possible yeah
possible yeah totally possible on some really simple
totally possible on some really simple
totally possible on some really simple principle and it’s the idea which I like
principle and it’s the idea which I like
principle and it’s the idea which I like to call the giant lookup table
to call the giant lookup table
to call the giant lookup table hypothesis I’ll explain what I mean by
hypothesis I’ll explain what I mean by
hypothesis I’ll explain what I mean by that this is the big data dream for AI
that this is the big data dream for AI
that this is the big data dream for AI right this is about collecting vast
right this is about collecting vast
right this is about collecting vast amounts of information and let me give
amounts of information and let me give
amounts of information and let me give you a simple idea about where this could
you a simple idea about where this could
you a simple idea about where this could work and how this would this might work
work and how this would this might work
work and how this would this might work basically maybe many of you will
basically maybe many of you will
basically maybe many of you will probably know about things like a.m. PR
probably know about things like a.m. PR
probably know about things like a.m. PR so automatic number plate recognition if
so automatic number plate recognition if
so automatic number plate recognition if you go to your garage and you fill up
you go to your garage and you fill up
you go to your garage and you fill up your car there’s not somebody sitting
your car there’s not somebody sitting
your car there’s not somebody sitting there looking at a CCTV and writing down
there looking at a CCTV and writing down
there looking at a CCTV and writing down your number plate yes it’s actually
your number plate yes it’s actually
your number plate yes it’s actually machine learning that recognizes what
machine learning that recognizes what
machine learning that recognizes what the alum number plate is and then
the alum number plate is and then
the alum number plate is and then records that information automatically
records that information automatically
records that information automatically so that’s already happening right now
so that’s already happening right now
so that’s already happening right now yes now the question is how could that
yes now the question is how could that
yes now the question is how could that work not how does it work but how could
work not how does it work but how could
work not how does it work but how could that work the answer is pretty simple
that work the answer is pretty simple
that work the answer is pretty simple right all we do is we get a list of all
right all we do is we get a list of all
right all we do is we get a list of all the possible digital images that could
the possible digital images that could
the possible digital images that could exist so we’d have a table that has all
exist so we’d have a table that has all
exist so we’d have a table that has all the possible lists of values of pixels
the possible lists of values of pixels
the possible lists of values of pixels for example for a B C D etc for all the
for example for a B C D etc for all the
for example for a B C D etc for all the numbers yes and let me just have this
numbers yes and let me just have this
numbers yes and let me just have this big lookup table that tells us what the
big lookup table that tells us what the
big lookup table that tells us what the label is associated with that yes so
label is associated with that yes so
label is associated with that yes so what letter what number is it based on
what letter what number is it based on
what letter what number is it based on that image the thing is that then all
that image the thing is that then all
that image the thing is that then all the camera has to do is it just has to
the camera has to do is it just has to
the camera has to do is it just has to ask the computer I’ve seen a new image
ask the computer I’ve seen a new image
ask the computer I’ve seen a new image so what’s the label for it yes I’ve seen
so what’s the label for it yes I’ve seen
so what’s the label for it yes I’ve seen this these these pixels here
this these these pixels here
this these these pixels here I’ve seen these pixels what’s the label
I’ve seen these pixels what’s the label
I’ve seen these pixels what’s the label for it you just have a program that just
for it you just have a program that just
for it you just have a program that just looks it up and gives you the answer
looks it up and gives you the answer
looks it up and gives you the answer that’s perfect right and it will always
that’s perfect right and it will always
that’s perfect right and it will always work it’s completely infallible and it’s
work it’s completely infallible and it’s
work it’s completely infallible and it’s completely possible to solve every
completely possible to solve every
completely possible to solve every possible AI problem that way yes now
possible AI problem that way yes now
possible AI problem that way yes now this is actually quite close to how a
this is actually quite close to how a
this is actually quite close to how a lot of AI systems work these days but
lot of AI systems work these days but
lot of AI systems work these days but the question is is there a catch to this
the question is is there a catch to this
the question is is there a catch to this with this with this possibility you can
with this with this possibility you can
with this with this possibility you can see how simple it is right would it work
see how simple it is right would it work
see how simple it is right would it work would it work at all in practice I’m
would it work at all in practice I’m
would it work at all in practice I’m gonna say fortunately answer is no it’s
gonna say fortunately answer is no it’s
gonna say fortunately answer is no it’s complete bunkum yes it’s nonsense why is
complete bunkum yes it’s nonsense why is
complete bunkum yes it’s nonsense why is it nonsense because even if I take a
it nonsense because even if I take a
it nonsense because even if I take a really small image like this yeah this
really small image like this yeah this
really small image like this yeah this is only 20 by 20 pixels yeah and it’s
is only 20 by 20 pixels yeah and it’s
is only 20 by 20 pixels yeah and it’s got Iowa so it’s got 8 bits per pixel so
got Iowa so it’s got 8 bits per pixel so
got Iowa so it’s got 8 bits per pixel so that tells me how many how the values of
that tells me how many how the values of
that tells me how many how the values of the gray scales in here there’s 2 to the
the gray scales in here there’s 2 to the
the gray scales in here there’s 2 to the 3200 possible in that yes now that
3200 possible in that yes now that
3200 possible in that yes now that number is more than a number of
number is more than a number of
number is more than a number of particles in the universe so that means
particles in the universe so that means
particles in the universe so that means it is totally impossible to do that
it is totally impossible to do that
it is totally impossible to do that dream yes this big data dream for AI is
dream yes this big data dream for AI is
dream yes this big data dream for AI is never gonna happen because you’d need as
never gonna happen because you’d need as
never gonna happen because you’d need as much hardware as there is why you need
much hardware as there is why you need
much hardware as there is why you need the whole of the universe to be that
the whole of the universe to be that
the whole of the universe to be that computing hardware to make that possible
computing hardware to make that possible
computing hardware to make that possible yes convert all the stars all the
yes convert all the stars all the
yes convert all the stars all the planets you everything into big bit into
planets you everything into big bit into
planets you everything into big bit into into hardware to make that happen and
into hardware to make that happen and
into hardware to make that happen and it’s still not feasible right so but a
it’s still not feasible right so but a
it’s still not feasible right so but a child can easily read digits like this
child can easily read digits like this
child can easily read digits like this and recognize them so how does a child
and recognize them so how does a child
and recognize them so how does a child do it if AI can’t in principle do this
do it if AI can’t in principle do this
do it if AI can’t in principle do this well in essence the reason why this
well in essence the reason why this
well in essence the reason why this isn’t isn’t the case why why this is not
isn’t isn’t the case why why this is not
isn’t isn’t the case why why this is not just looking up information yeah it’s
just looking up information yeah it’s
just looking up information yeah it’s because of the fact that knowledge
because of the fact that knowledge
because of the fact that knowledge requires more than just remembering lots
requires more than just remembering lots
requires more than just remembering lots of data yes ok and next example here’s
of data yes ok and next example here’s
of data yes ok and next example here’s another thing that a I could do so we
another thing that a I could do so we
another thing that a I could do so we just have a really simple idea here
just have a really simple idea here
just have a really simple idea here which
which
which that we want to solve some tasks some
that we want to solve some tasks some
that we want to solve some tasks some tasks in your job involves processing
tasks in your job involves processing
tasks in your job involves processing information and coming up with some
information and coming up with some
information and coming up with some particular answer yeah there’s a
particular answer yeah there’s a
particular answer yeah there’s a solution to every problem presumably you
solution to every problem presumably you
solution to every problem presumably you just have to try them all out yes let’s
just have to try them all out yes let’s
just have to try them all out yes let’s try out all possible solutions given a
try out all possible solutions given a
try out all possible solutions given a problem yeah now given how fast
problem yeah now given how fast
problem yeah now given how fast computers really are you could quite
computers really are you could quite
computers really are you could quite easily see that this could be done right
easily see that this could be done right
easily see that this could be done right so a computer can be completely done all
so a computer can be completely done all
so a computer can be completely done all it has to do is search through all the
it has to do is search through all the
it has to do is search through all the possible answers yes I call that the
possible answers yes I call that the
possible answers yes I call that the giant hypothesis search engine yes it’s
giant hypothesis search engine yes it’s
giant hypothesis search engine yes it’s like a search engine that tries out all
like a search engine that tries out all
like a search engine that tries out all possible explanations for your problem
possible explanations for your problem
possible explanations for your problem and then it picks the best one yeah so
and then it picks the best one yeah so
and then it picks the best one yeah so let me give an example of this I don’t
let me give an example of this I don’t
let me give an example of this I don’t know if you know about the logistics
know if you know about the logistics
know if you know about the logistics network the global logistics networks
network the global logistics networks
network the global logistics networks global so-called containerized transport
global so-called containerized transport
global so-called containerized transport network now if you don’t know about this
network now if you don’t know about this
network now if you don’t know about this this is absolutely amazing yes because
this is absolutely amazing yes because
this is absolutely amazing yes because containerized logistics network move
containerized logistics network move
containerized logistics network move pretty much everything you buy around
pretty much everything you buy around
pretty much everything you buy around the world yes on shipping containers and
the world yes on shipping containers and
the world yes on shipping containers and trucks and rail etcetera it’s actually
trucks and rail etcetera it’s actually
trucks and rail etcetera it’s actually mind bogglingly vast and the thing is
mind bogglingly vast and the thing is
mind bogglingly vast and the thing is that the number of problems that you
that the number of problems that you
that the number of problems that you have to solve are pretty large in this
have to solve are pretty large in this
have to solve are pretty large in this in this system for example one of the
in this system for example one of the
in this system for example one of the hardest problems and it may seem like an
hardest problems and it may seem like an
hardest problems and it may seem like an easy problem but it’s not is the
easy problem but it’s not is the
easy problem but it’s not is the so-called Traveling Salesman problem
so-called Traveling Salesman problem
so-called Traveling Salesman problem right this is there that computer
right this is there that computer
right this is there that computer scientist call it that is what you have
scientist call it that is what you have
scientist call it that is what you have to find is the shortest circular route
to find is the shortest circular route
to find is the shortest circular route that visits a number of cities yes
that visits a number of cities yes
that visits a number of cities yes that’s a simple idea just visits these
that’s a simple idea just visits these
that’s a simple idea just visits these cities and how might we solve this
cities and how might we solve this
cities and how might we solve this problem using this big computer idea
problem using this big computer idea
problem using this big computer idea this big search engine well it’s simple
this big search engine well it’s simple
this big search engine well it’s simple we just try all the possible routes yeah
we just try all the possible routes yeah
we just try all the possible routes yeah and pick the best one
and pick the best one
and pick the best one yeah that’s not that’s how they solve
yeah that’s not that’s how they solve
yeah that’s not that’s how they solve that problem it’s that gonna work
that problem it’s that gonna work
that problem it’s that gonna work fortunately for us the answer is no that
fortunately for us the answer is no that
fortunately for us the answer is no that doesn’t work either right why can’t that
doesn’t work either right why can’t that
doesn’t work either right why can’t that work well because of the fact of a
work well because of the fact of a
work well because of the fact of a really nice idea a really nice phrase
really nice idea a really nice phrase
really nice idea a really nice phrase that computer science is called use
that computer science is called use
that computer science is called use called combinatorial explosion
called combinatorial explosion
called combinatorial explosion combinatorial explosion so what does
combinatorial explosion so what does
combinatorial explosion so what does that mean if you have 15 cities and you
that mean if you have 15 cities and you
that mean if you have 15 cities and you want to find the shortest route through
want to find the shortest route through
want to find the shortest route through yes
yes
yes it turns out there’s over over a
it turns out there’s over over a
it turns out there’s over over a trillion possible routes yes so that’s a
trillion possible routes yes so that’s a
trillion possible routes yes so that’s a lot
lot
lot we just assert just 15 cities and
we just assert just 15 cities and
we just assert just 15 cities and imagining containerized network you have
imagining containerized network you have
imagining containerized network you have to solve problems with hundreds of
to solve problems with hundreds of
to solve problems with hundreds of cities or thousands of them right the
cities or thousands of them right the
cities or thousands of them right the reason why is because it’s factorial
reason why is because it’s factorial
reason why is because it’s factorial that exclamation mark just means 1 times
that exclamation mark just means 1 times
that exclamation mark just means 1 times 2 times 3 times 4 all the way up to 215
2 times 3 times 4 all the way up to 215
2 times 3 times 4 all the way up to 215 yes and that gets bigger and bigger and
yes and that gets bigger and bigger and
yes and that gets bigger and bigger and bigger it gets astronomically large as
bigger it gets astronomically large as
bigger it gets astronomically large as this number is even like up to 20 years
this number is even like up to 20 years
this number is even like up to 20 years ludicrously large ok but the kicker is
ludicrously large ok but the kicker is
ludicrously large ok but the kicker is that human planners can actually come up
that human planners can actually come up
that human planners can actually come up with pretty good routes that are close
with pretty good routes that are close
with pretty good routes that are close to optimal just by plotting this out you
to optimal just by plotting this out you
to optimal just by plotting this out you say yes that’s a pretty good route that
say yes that’s a pretty good route that
say yes that’s a pretty good route that we’ll do and most of the time when you
we’ll do and most of the time when you
we’ll do and most of the time when you do that it works really well it’s not
do that it works really well it’s not
do that it works really well it’s not optimal but it works for you well so how
optimal but it works for you well so how
optimal but it works for you well so how do we do that
do we do that
do we do that well in essence you can set up
well in essence you can set up
well in essence you can set up understanding a problem is not just
understanding a problem is not just
understanding a problem is not just searching for all the dump through all
searching for all the dump through all
searching for all the dump through all the dumb possible solutions you
the dumb possible solutions you
the dumb possible solutions you understand something about this problem
understand something about this problem
understand something about this problem right and that’s how you’re able to come
right and that’s how you’re able to come
right and that’s how you’re able to come up with a good route this is about
up with a good route this is about
up with a good route this is about predicting the future to see what we are
predicting the future to see what we are
predicting the future to see what we are we’re machine versus human intelligence
we’re machine versus human intelligence
we’re machine versus human intelligence and try and make predictions it’s
and try and make predictions it’s
and try and make predictions it’s helpful to look at one particular area
helpful to look at one particular area
helpful to look at one particular area of human ingenuity yes to see where this
of human ingenuity yes to see where this
of human ingenuity yes to see where this is going this figure here is called
is going this figure here is called
is going this figure here is called Moore’s Law that I mentioned earlier on
Moore’s Law that I mentioned earlier on
Moore’s Law that I mentioned earlier on and it charts the decrease in time
and it charts the decrease in time
and it charts the decrease in time needed to solve a fixed mathematical
needed to solve a fixed mathematical
needed to solve a fixed mathematical problem due to improvements in computers
problem due to improvements in computers
problem due to improvements in computers alone
alone
alone improvements just in computer hardware
improvements just in computer hardware
improvements just in computer hardware right and that since time has gone by
right and that since time has gone by
right and that since time has gone by since 1960s and it’s a particular area
since 1960s and it’s a particular area
since 1960s and it’s a particular area it’s the area that I do a lot of work in
it’s the area that I do a lot of work in
it’s the area that I do a lot of work in it’s called mathematical is just
it’s called mathematical is just
it’s called mathematical is just mathematical algorithms numerical
mathematical algorithms numerical
mathematical algorithms numerical problems and you can see here that
problems and you can see here that
problems and you can see here that Moore’s Law follows the straight line
Moore’s Law follows the straight line
Moore’s Law follows the straight line downwards right at the top that’s just
downwards right at the top that’s just
downwards right at the top that’s just computers getting better right this is a
computers getting better right this is a
computers getting better right this is a logarithmic scale and this is called
logarithmic scale and this is called
logarithmic scale and this is called exponential and exponentially decreasing
exponential and exponentially decreasing
exponential and exponentially decreasing line because
line because
line because right what you want is that that line
right what you want is that that line
right what you want is that that line goes down because that means things get
goes down because that means things get
goes down because that means things get faster basically yes now that’s just
faster basically yes now that’s just
faster basically yes now that’s just computer Harbor alone let’s have a look
computer Harbor alone let’s have a look
computer Harbor alone let’s have a look at this blue line instead what that is
at this blue line instead what that is
at this blue line instead what that is is improvements in speed in solving this
is improvements in speed in solving this
is improvements in speed in solving this problem due to human ingenuity yes due
problem due to human ingenuity yes due
problem due to human ingenuity yes due to people who are trained to understand
to people who are trained to understand
to people who are trained to understand these problems worked out ways to
these problems worked out ways to
these problems worked out ways to improve that find better algorithms for
improve that find better algorithms for
improve that find better algorithms for solving these problems and you can see
solving these problems and you can see
solving these problems and you can see that this vastly outstrips Moore’s Law
that this vastly outstrips Moore’s Law
that this vastly outstrips Moore’s Law yes what this means is that human
yes what this means is that human
yes what this means is that human ingenuity has long outstripped what
ingenuity has long outstripped what
ingenuity has long outstripped what computers have been able to do yes that
computers have been able to do yes that
computers have been able to do yes that the improvement is is vast you can see
the improvement is is vast you can see
the improvement is is vast you can see I’ll end on this point that back to my
I’ll end on this point that back to my
I’ll end on this point that back to my question I started off pretty scared and
question I started off pretty scared and
question I started off pretty scared and I’ve started off I think in a way by
I’ve started off I think in a way by
I’ve started off I think in a way by trying to make it clear to you that in
trying to make it clear to you that in
trying to make it clear to you that in some ways if you believe those headlines
some ways if you believe those headlines
some ways if you believe those headlines you should be scared
you should be scared
you should be scared you really should be scared because this
you really should be scared because this
you really should be scared because this could be worse than climate change this
could be worse than climate change this
could be worse than climate change this could be the end of the economy for us
could be the end of the economy for us
could be the end of the economy for us right no productive role for us
right no productive role for us
right no productive role for us whatsoever but I think that having gone
whatsoever but I think that having gone
whatsoever but I think that having gone through this this detail if you look
through this this detail if you look
through this this detail if you look behind the headlines and you look at
behind the headlines and you look at
behind the headlines and you look at what’s actually possible I start to feel
what’s actually possible I start to feel
what’s actually possible I start to feel a lot better start feel a lot less
a lot better start feel a lot less
a lot better start feel a lot less scared for my future a lot less scared
scared for my future a lot less scared
scared for my future a lot less scared for my children’s future yes I don’t
for my children’s future yes I don’t
for my children’s future yes I don’t think it’s going to happen in anytime
think it’s going to happen in anytime
think it’s going to happen in anytime soon that computers are going to take
soon that computers are going to take
soon that computers are going to take over our economy I don’t think that’s
over our economy I don’t think that’s
over our economy I don’t think that’s actually going to happen however there
actually going to happen however there
actually going to happen however there is one area where things could get worse
is one area where things could get worse
is one area where things could get worse for us and that’s because of us if some
for us and that’s because of us if some
for us and that’s because of us if some programmer over there we’re over there
programmer over there we’re over there
programmer over there we’re over there decides to make a to formulate the rules
decides to make a to formulate the rules
decides to make a to formulate the rules of this person’s job yeah then that
of this person’s job yeah then that
of this person’s job yeah then that programmer could write a program that
programmer could write a program that
programmer could write a program that puts that person out work yes but that’s
puts that person out work yes but that’s
puts that person out work yes but that’s people using computers to buy off
people using computers to buy off
people using computers to buy off against each other
against each other
against each other to come up with way better ways of
to come up with way better ways of
to come up with way better ways of putting each other out of work
putting each other out of work
putting each other out of work essentially so I think that there that
essentially so I think that there that
essentially so I think that there that it’s not really machines are going to be
it’s not really machines are going to be
it’s not really machines are going to be at fault for making this happen it’s us
at fault for making this happen it’s us
at fault for making this happen it’s us it’s us for using these machines in
it’s us for using these machines in
it’s us for using these machines in essentially in an unethical way yes so I
essentially in an unethical way yes so I
essentially in an unethical way yes so I can’t tell you what’s going to happen in
can’t tell you what’s going to happen in
can’t tell you what’s going to happen in future after all making predictions is
future after all making predictions is
future after all making predictions is hard particularly about the future but I
hard particularly about the future but I
hard particularly about the future but I think we’re going to be ok if we just
think we’re going to be ok if we just
think we’re going to be ok if we just think ahead thank you very much
”
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