[Music] [Applause]
[Applause]
[Applause] humans love to pretend that objects are
humans love to pretend that objects are
humans love to pretend that objects are like people we give them names we talk
like people we give them names we talk
like people we give them names we talk to them we get attached to them like Tom
to them we get attached to them like Tom
to them we get attached to them like Tom hangs here in castaway talking to his
hangs here in castaway talking to his
hangs here in castaway talking to his friend of olive oil called Wilson but
friend of olive oil called Wilson but
friend of olive oil called Wilson but when it comes to computers how do you
when it comes to computers how do you
when it comes to computers how do you feel about machines thinking like people
feel about machines thinking like people
feel about machines thinking like people and if machines can think like people
and if machines can think like people
and if machines can think like people what if one day they begin to think
what if one day they begin to think
what if one day they begin to think better than us these are the sorts of
better than us these are the sorts of
better than us these are the sorts of questions that people keep asking me how
questions that people keep asking me how
questions that people keep asking me how far away are we from being outsmarted by
far away are we from being outsmarted by
far away are we from being outsmarted by computers how far is the development of
computers how far is the development of
computers how far is the development of artificial intelligence I’m a researcher
artificial intelligence I’m a researcher
artificial intelligence I’m a researcher in artificial intelligence at Cambridge
in artificial intelligence at Cambridge
in artificial intelligence at Cambridge University and it’s been my life’s work
University and it’s been my life’s work
University and it’s been my life’s work to address these questions artificial
to address these questions artificial
to address these questions artificial intelligence is making the news
intelligence is making the news
intelligence is making the news headlines almost daily we read about the
headlines almost daily we read about the
headlines almost daily we read about the computer deep mind beating the world
computer deep mind beating the world
computer deep mind beating the world grandmaster in the game of go driverless
grandmaster in the game of go driverless
grandmaster in the game of go driverless cars are being tested on our streets
cars are being tested on our streets
cars are being tested on our streets drones are going to be delivering our
drones are going to be delivering our
drones are going to be delivering our parcels films like ex machina and Blade
parcels films like ex machina and Blade
parcels films like ex machina and Blade Runner are portraying our future
Runner are portraying our future
Runner are portraying our future populated with robots that are
populated with robots that are
populated with robots that are indistinguishable from us but what does
indistinguishable from us but what does
indistinguishable from us but what does the future of AI really hoped for us
the future of AI really hoped for us
the future of AI really hoped for us when I was a child I loved maths what I
when I was a child I loved maths what I
when I was a child I loved maths what I really really was fascinated about was
really really was fascinated about was
really really was fascinated about was the beauty and the elegance of the
the beauty and the elegance of the
the beauty and the elegance of the solutions that I saw and sometimes even
solutions that I saw and sometimes even
solutions that I saw and sometimes even found by myself and I was also
found by myself and I was also
found by myself and I was also interested in people so I wondered how
interested in people so I wondered how
interested in people so I wondered how do you people come up with such
do you people come up with such
do you people come up with such intuitive answers
intuitive answers
intuitive answers as I went through my education and work
as I went through my education and work
as I went through my education and work journey I began to investigate this kind
journey I began to investigate this kind
journey I began to investigate this kind of human thinking and try to make
of human thinking and try to make
of human thinking and try to make computers do the same I want computers
computers do the same I want computers
computers do the same I want computers to find solutions that are intuitive to
to find solutions that are intuitive to
to find solutions that are intuitive to humans let me give you an example what I
humans let me give you an example what I
humans let me give you an example what I mean by intuitive human thinking here’s
mean by intuitive human thinking here’s
mean by intuitive human thinking here’s a checkerboard it’s called a mutilated
a checkerboard it’s called a mutilated
a checkerboard it’s called a mutilated checkerboard because two diagonal
checkerboard because two diagonal
checkerboard because two diagonal corners have been removed now a famous
corners have been removed now a famous
corners have been removed now a famous problem asks can you cover this mutilate
problem asks can you cover this mutilate
problem asks can you cover this mutilate the checkerboard with Domino’s there are
the checkerboard with Domino’s there are
the checkerboard with Domino’s there are many ways that we could try to do that
many ways that we could try to do that
many ways that we could try to do that like this
still no now consider this idea let’s
still no now consider this idea let’s color this board black and white like a
color this board black and white like a
color this board black and white like a chess board and now it’s immediately
chess board and now it’s immediately
chess board and now it’s immediately obvious that we have more black squares
obvious that we have more black squares
obvious that we have more black squares than white squares because when we
than white squares because when we
than white squares because when we mutilated the board we removed two white
mutilated the board we removed two white
mutilated the board we removed two white squares but we need there to be the same
squares but we need there to be the same
squares but we need there to be the same number of black squares and white
number of black squares and white
number of black squares and white squares because a domino covers one
squares because a domino covers one
squares because a domino covers one black and one white square so it’s
black and one white square so it’s
black and one white square so it’s obvious we will never be able to cover
obvious we will never be able to cover
obvious we will never be able to cover this mutilated checkerboard with
this mutilated checkerboard with
this mutilated checkerboard with dominoes
dominoes
dominoes this is something so easy and simple for
this is something so easy and simple for
this is something so easy and simple for people to see and understand no symbols
people to see and understand no symbols
people to see and understand no symbols no algebra no numbers I’m done now what
no algebra no numbers I’m done now what
no algebra no numbers I’m done now what do you think how does a computer go
do you think how does a computer go
do you think how does a computer go about solving this problem this is how
about solving this problem this is how
about solving this problem this is how lots of symbols
lots of symbols
lots of symbols lots of algebra lots of mathematical
lots of algebra lots of mathematical
lots of algebra lots of mathematical concepts it’s completely unintelligible
concepts it’s completely unintelligible
concepts it’s completely unintelligible and the intuition behind
and the intuition behind
and the intuition behind human solution is completely lost in it
human solution is completely lost in it
human solution is completely lost in it computers can’t capture this kind of
computers can’t capture this kind of
computers can’t capture this kind of human thinking yet this is what
human thinking yet this is what
human thinking yet this is what fascinates me how do we emulate this
fascinates me how do we emulate this
fascinates me how do we emulate this kind of human thinking on machines now
kind of human thinking on machines now
kind of human thinking on machines now artificial intelligence has long strived
artificial intelligence has long strived
artificial intelligence has long strived to achieve computer thinking and
to achieve computer thinking and
to achieve computer thinking and behavior that is indistinguishable from
behavior that is indistinguishable from
behavior that is indistinguishable from a human so why is it that AI is such a
a human so why is it that AI is such a
a human so why is it that AI is such a hot topic now
hot topic now
hot topic now well there are three main advances there
well there are three main advances there
well there are three main advances there are enabling us to make huge progress in
are enabling us to make huge progress in
are enabling us to make huge progress in AI research and they are the
AI research and they are the
AI research and they are the availability of masses of data that we
availability of masses of data that we
availability of masses of data that we all generate all the time the massive
all generate all the time the massive
all generate all the time the massive computing power that we have access to
computing power that we have access to
computing power that we have access to today and the new and more sophisticated
today and the new and more sophisticated
today and the new and more sophisticated machine learning algorithms that we
machine learning algorithms that we
machine learning algorithms that we haven’t considered before so in terms of
haven’t considered before so in terms of
haven’t considered before so in terms of data every click you make is recorded
data every click you make is recorded
data every click you make is recorded somewhere every video you watch every
somewhere every video you watch every
somewhere every video you watch every item you buy online everything you say
item you buy online everything you say
item you buy online everything you say to your friends on social media and this
to your friends on social media and this
to your friends on social media and this data is used to learn something and to
data is used to learn something and to
data is used to learn something and to gain insights about you individually so
gain insights about you individually so
gain insights about you individually so that you can get personalized
that you can get personalized
that you can get personalized recommendations such as what kind of
recommendations such as what kind of
recommendations such as what kind of film you might like but more than that
film you might like but more than that
film you might like but more than that it’s also used to learn about groups of
it’s also used to learn about groups of
it’s also used to learn about groups of people and their behavior computing
people and their behavior computing
people and their behavior computing power well you can rent it really
power well you can rent it really
power well you can rent it really cheaply from anyone of for example
cheaply from anyone of for example
cheaply from anyone of for example Amazon’s data centers and gain access to
Amazon’s data centers and gain access to
Amazon’s data centers and gain access to its 80,000 or so computer servers for
its 80,000 or so computer servers for
its 80,000 or so computer servers for comparison the Watson computer that beat
comparison the Watson computer that beat
comparison the Watson computer that beat a human in jeopardy
a human in jeopardy
a human in jeopardy in 2011 used 90 servers so we have
in 2011 used 90 servers so we have
in 2011 used 90 servers so we have instant access to massive computing
instant access to massive computing
instant access to massive computing power
power
power but if the neumes and more sophisticated
but if the neumes and more sophisticated
but if the neumes and more sophisticated machine learning algorithms that are at
machine learning algorithms that are at
machine learning algorithms that are at the heart of the AI revolution unlike in
the heart of the AI revolution unlike in
the heart of the AI revolution unlike in the past when we build these specialized
the past when we build these specialized
the past when we build these specialized systems that knew about problems and
systems that knew about problems and
systems that knew about problems and went about solving these specific
went about solving these specific
went about solving these specific problems the machine learning algorithms
problems the machine learning algorithms
problems the machine learning algorithms of today know nothing about the problems
of today know nothing about the problems
of today know nothing about the problems they only know about learning they only
they only know about learning they only
they only know about learning they only know about learning
know about learning
know about learning take for example autocorrect on your
take for example autocorrect on your
take for example autocorrect on your phone
phone
phone previously your spelling mistake would
previously your spelling mistake would
previously your spelling mistake would rarely be picked up and even then only
rarely be picked up and even then only
rarely be picked up and even then only if it was misspelled in a particular way
if it was misspelled in a particular way
if it was misspelled in a particular way and this was because these were
and this was because these were
and this was because these were specialized system that had very
specialized system that had very
specialized system that had very specific rules today I can type into my
specific rules today I can type into my
specific rules today I can type into my phone in Slovene and even though my
phone in Slovene and even though my
phone in Slovene and even though my phone knows nothing about this foreign
phone knows nothing about this foreign
phone knows nothing about this foreign language it will be able to learn how I
language it will be able to learn how I
language it will be able to learn how I spell surveen words and even how I
spell surveen words and even how I
spell surveen words and even how I misspell them now this is interesting
misspell them now this is interesting
misspell them now this is interesting because it seems rather counterintuitive
because it seems rather counterintuitive
because it seems rather counterintuitive doesn’t it so these machine learning
doesn’t it so these machine learning
doesn’t it so these machine learning algorithms know nothing about the
algorithms know nothing about the
algorithms know nothing about the problem instead they are general in that
problem instead they are general in that
problem instead they are general in that they can take any data and crunch it and
they can take any data and crunch it and
they can take any data and crunch it and spot patterns in fact any problem that
spot patterns in fact any problem that
spot patterns in fact any problem that can be recast as a statistical algorithm
can be recast as a statistical algorithm
can be recast as a statistical algorithm running on huge datasets can now be
running on huge datasets can now be
running on huge datasets can now be revisited and tackled again using these
revisited and tackled again using these
revisited and tackled again using these new creative approaches so for example
new creative approaches so for example
new creative approaches so for example the modern dating apps use new machine
the modern dating apps use new machine
the modern dating apps use new machine learning algorithms and the result is
learning algorithms and the result is
learning algorithms and the result is your ideal partner did you know
your ideal partner did you know
your ideal partner did you know apparently one in three of us use them
apparently one in three of us use them
apparently one in three of us use them but there are huge challenges in trying
but there are huge challenges in trying
but there are huge challenges in trying to achieve what sometimes is referred to
to achieve what sometimes is referred to
to achieve what sometimes is referred to as general AI a flexible adaptable
as general AI a flexible adaptable
as general AI a flexible adaptable learning self improving system that does
learning self improving system that does
learning self improving system that does all the things that the human does in
all the things that the human does in
all the things that the human does in ways that are indistinguishable from a
ways that are indistinguishable from a
ways that are indistinguishable from a human computer systems today are super
human computer systems today are super
human computer systems today are super fast are dealing with lots of data
fast are dealing with lots of data
fast are dealing with lots of data that far exceeds human capabilities but
that far exceeds human capabilities but
that far exceeds human capabilities but they do so in ways that are machine
they do so in ways that are machine
they do so in ways that are machine oriented and most of the time
oriented and most of the time
oriented and most of the time incomprehensible to humans one of the
incomprehensible to humans one of the
incomprehensible to humans one of the things that they lack is the ability to
things that they lack is the ability to
things that they lack is the ability to think intuitively like humans not yet
think intuitively like humans not yet
think intuitively like humans not yet now this brings me back to my love of
now this brings me back to my love of
now this brings me back to my love of maths and people and in my work I look
maths and people and in my work I look
maths and people and in my work I look at how people solve mathematical
at how people solve mathematical
at how people solve mathematical problems and then I make computers do
problems and then I make computers do
problems and then I make computers do the same in particular I’m interested in
the same in particular I’m interested in
the same in particular I’m interested in how people solve problems with using
how people solve problems with using
how people solve problems with using intuitive methods like using diagrams
intuitive methods like using diagrams
intuitive methods like using diagrams here’s an example I’m going to add up
here’s an example I’m going to add up
here’s an example I’m going to add up all the numbers from 1 to any number
all the numbers from 1 to any number
all the numbers from 1 to any number that you give me yes any number so any
that you give me yes any number so any
that you give me yes any number so any number from 0 to 100 let’s say so I I
number from 0 to 100 let’s say so I I
number from 0 to 100 let’s say so I I have a chance even so if look can I hear
have a chance even so if look can I hear
have a chance even so if look can I hear a number ok I hear 53 so the sum of all
a number ok I hear 53 so the sum of all
a number ok I hear 53 so the sum of all the numbers from 1 to 53 is 1431 how did
the numbers from 1 to 53 is 1431 how did
the numbers from 1 to 53 is 1431 how did I do that
I do that
I do that well I used the theorem this one so I
well I used the theorem this one so I
well I used the theorem this one so I didn’t really add up all the numbers for
didn’t really add up all the numbers for
didn’t really add up all the numbers for 1 to 53 instead I multiplied it 53 times
1 to 53 instead I multiplied it 53 times
1 to 53 instead I multiplied it 53 times 54 divided by 2 because I used this
54 divided by 2 because I used this
54 divided by 2 because I used this theorem it’s much easier and faster that
theorem it’s much easier and faster that
theorem it’s much easier and faster that way so this is the kind of problem that
way so this is the kind of problem that
way so this is the kind of problem that you may remember from your maths classes
you may remember from your maths classes
you may remember from your maths classes in school so and my children are now
in school so and my children are now
in school so and my children are now learning about it so let me see show of
learning about it so let me see show of
learning about it so let me see show of hands how many of you remember this from
hands how many of you remember this from
hands how many of you remember this from school ah some some not all let’s see if
school ah some some not all let’s see if
school ah some some not all let’s see if I can jog your memory and I will show
I can jog your memory and I will show
I can jog your memory and I will show you how I was taught this problem in
you how I was taught this problem in
you how I was taught this problem in school we used a diagram
school we used a diagram
school we used a diagram so my job is to add up all the numbers
so my job is to add up all the numbers
so my job is to add up all the numbers from to show that adding up all the
from to show that adding up all the
from to show that adding up all the numbers from 1 to any number n equals to
numbers from 1 to any number n equals to
numbers from 1 to any number n equals to n times n plus 1 divided by 2 so I’m
n times n plus 1 divided by 2 so I’m
n times n plus 1 divided by 2 so I’m gonna represent because we’re talking
gonna represent because we’re talking
gonna represent because we’re talking about numbers I’m gonna represent them
about numbers I’m gonna represent them
about numbers I’m gonna represent them with using counters and in fact I’m
with using counters and in fact I’m
with using counters and in fact I’m gonna start showing one side of the
gonna start showing one side of the
gonna start showing one side of the equation n times n plus 1 and so I will
equation n times n plus 1 and so I will
equation n times n plus 1 and so I will take a rectangle in fact I’m gonna take
take a rectangle in fact I’m gonna take
take a rectangle in fact I’m gonna take n to be a particular number to be a
n to be a particular number to be a
n to be a particular number to be a number 5 so I’m gonna draw a diagram a
number 5 so I’m gonna draw a diagram a
number 5 so I’m gonna draw a diagram a rectangle that is five counters wide and
rectangle that is five counters wide and
rectangle that is five counters wide and six counters tall and that’s my n times
six counters tall and that’s my n times
six counters tall and that’s my n times n plus 1 now imagine cutting this
n plus 1 now imagine cutting this
n plus 1 now imagine cutting this rectangle down the diagonal like this
rectangle down the diagonal like this
rectangle down the diagonal like this and you can see that what we get is two
and you can see that what we get is two
and you can see that what we get is two identical triangles and each is five
identical triangles and each is five
identical triangles and each is five counters tall but my problem halfs my
counters tall but my problem halfs my
counters tall but my problem halfs my rectangle it says n times n plus 1
rectangle it says n times n plus 1
rectangle it says n times n plus 1 divided by 2 so I’m just gonna take one
divided by 2 so I’m just gonna take one
divided by 2 so I’m just gonna take one of these triangles and this now
of these triangles and this now
of these triangles and this now represents one side of my equation now
represents one side of my equation now
represents one side of my equation now my job is to show that that equals to
my job is to show that that equals to
my job is to show that that equals to the sum of all increasing numbers now
the sum of all increasing numbers now
the sum of all increasing numbers now imagine cutting this triangle into rows
imagine cutting this triangle into rows
imagine cutting this triangle into rows like this and now you will notice that I
like this and now you will notice that I
like this and now you will notice that I get a sequence of increasing numbers so
get a sequence of increasing numbers so
get a sequence of increasing numbers so my triangle shows that 1 plus 2 Plus 3
my triangle shows that 1 plus 2 Plus 3
my triangle shows that 1 plus 2 Plus 3 plus 4 plus 5 equals 2 5 times 6 divided
plus 4 plus 5 equals 2 5 times 6 divided
plus 4 plus 5 equals 2 5 times 6 divided by 2 which is 15 but more than that it
by 2 which is 15 but more than that it
by 2 which is 15 but more than that it also we can see that my solution is
also we can see that my solution is
also we can see that my solution is gonna hold for any number number 5 or
gonna hold for any number number 5 or
gonna hold for any number number 5 or number 3 that you gave me or 99 or 1,001
number 3 that you gave me or 99 or 1,001
number 3 that you gave me or 99 or 1,001 it will be the same and we can also see
it will be the same and we can also see
it will be the same and we can also see that it’s a correct solution so I wanted
that it’s a correct solution so I wanted
that it’s a correct solution so I wanted to capture this on computers and wanted
to capture this on computers and wanted
to capture this on computers and wanted to make them solve problems in exactly
to make them solve problems in exactly
to make them solve problems in exactly this way
this way
this way which is which is exactly what I did so
which is which is exactly what I did so
which is which is exactly what I did so I built a system where these spatial
I built a system where these spatial
I built a system where these spatial manipulations are the inferences of the
manipulations are the inferences of the
manipulations are the inferences of the solution I then used formal methods to
solution I then used formal methods to
solution I then used formal methods to justify why a specific a concrete case
justify why a specific a concrete case
justify why a specific a concrete case like for number five can capture a
like for number five can capture a
like for number five can capture a solution for any number and the system
solution for any number and the system
solution for any number and the system shows them that the solution is correct
shows them that the solution is correct
shows them that the solution is correct so I showed that computers can solve
so I showed that computers can solve
so I showed that computers can solve problems in similar ways that people
problems in similar ways that people
problems in similar ways that people solve them but I also wondered could
solve them but I also wondered could
solve them but I also wondered could they discover new unseen solutions
they discover new unseen solutions
they discover new unseen solutions things that people have not understood
things that people have not understood
things that people have not understood before so in order to do that we could
before so in order to do that we could
before so in order to do that we could use AI methods like machine learning
use AI methods like machine learning
use AI methods like machine learning that I mentioned before I focused on
that I mentioned before I focused on
that I mentioned before I focused on problems in mathematics but the general
problems in mathematics but the general
problems in mathematics but the general principles of intuitive thinking can be
principles of intuitive thinking can be
principles of intuitive thinking can be applied to any area of human endeavor
applied to any area of human endeavor
applied to any area of human endeavor and this is where I’m going to be
and this is where I’m going to be
and this is where I’m going to be focusing next making machines discover
focusing next making machines discover
focusing next making machines discover new knowledge new intuitive human
new knowledge new intuitive human
new knowledge new intuitive human solutions so will AI outsmart humans
solutions so will AI outsmart humans
solutions so will AI outsmart humans will these intelligence systems pose a
will these intelligence systems pose a
will these intelligence systems pose a threat to humans one day I see the
threat to humans one day I see the
threat to humans one day I see the future in the hands of human experts but
future in the hands of human experts but
future in the hands of human experts but heavily supported and helped with these
heavily supported and helped with these
heavily supported and helped with these intelligent systems machines are gonna
intelligent systems machines are gonna
intelligent systems machines are gonna work and collaborate with us in order to
work and collaborate with us in order to
work and collaborate with us in order to help us work more effectively but also
help us work more effectively but also
help us work more effectively but also to do things that are beyond our
to do things that are beyond our
to do things that are beyond our capability alone should we be scared no
capability alone should we be scared no
capability alone should we be scared no should we restrict or slow down the
should we restrict or slow down the
should we restrict or slow down the development of artificial intelligence
development of artificial intelligence
development of artificial intelligence definitely not the positives far
definitely not the positives far
definitely not the positives far outweigh the negatives in fact I think
outweigh the negatives in fact I think
outweigh the negatives in fact I think it’s more risky not to pursue the
it’s more risky not to pursue the
it’s more risky not to pursue the development of artificial intelligence
development of artificial intelligence
development of artificial intelligence we are nowhere near a Terminator style
we are nowhere near a Terminator style
we are nowhere near a Terminator style future
future
future but we are only a decade or so away from
but we are only a decade or so away from
but we are only a decade or so away from the future where intelligent systems
the future where intelligent systems
the future where intelligent systems help doctors diagnose better where
help doctors diagnose better where
help doctors diagnose better where personalized drugs are used to treat
personalized drugs are used to treat
personalized drugs are used to treat diseases more effectively where AI
diseases more effectively where AI
diseases more effectively where AI systems help drivers drive more safely
systems help drivers drive more safely
systems help drivers drive more safely surgeons operate more accurately where
surgeons operate more accurately where
surgeons operate more accurately where these system enable us all ask people to
these system enable us all ask people to
these system enable us all ask people to converse across multiple languages this
converse across multiple languages this
converse across multiple languages this is perhaps the most exciting time in the
is perhaps the most exciting time in the
is perhaps the most exciting time in the history of mankind the rate at which
history of mankind the rate at which
history of mankind the rate at which technology is advancing is exponential
technology is advancing is exponential
technology is advancing is exponential and with rapid change there will always
and with rapid change there will always
and with rapid change there will always be winners and losers so now how what do
be winners and losers so now how what do
be winners and losers so now how what do we do how do we become winners in this
we do how do we become winners in this
we do how do we become winners in this listen learn to understand embrace the
listen learn to understand embrace the
listen learn to understand embrace the possibilities and make everything that
possibilities and make everything that
possibilities and make everything that we know today
we know today
we know today work for us so we can turn our planet
work for us so we can turn our planet
work for us so we can turn our planet into a better place thank you
into a better place thank you
into a better place thank you [Applause]
”
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