[Music] today I’m going to talk about AI why
today I’m going to talk about AI why
today I’m going to talk about AI why it’s important why it’s important for
it’s important why it’s important for
it’s important why it’s important for everybody in the room and why diversity
everybody in the room and why diversity
everybody in the room and why diversity matters in AI and so imagine a world
matters in AI and so imagine a world
matters in AI and so imagine a world where you can just think of something
where you can just think of something
where you can just think of something and it happens you’re kind of won and
and it happens you’re kind of won and
and it happens you’re kind of won and won with computers and they
won with computers and they
won with computers and they automatically do what you’re thinking so
automatically do what you’re thinking so
automatically do what you’re thinking so maybe you’re hungry and you want to
maybe you’re hungry and you want to
maybe you’re hungry and you want to order food and it automatically comes to
order food and it automatically comes to
order food and it automatically comes to your door and it might even be prepared
your door and it might even be prepared
your door and it might even be prepared by machines and flown with drones to
by machines and flown with drones to
by machines and flown with drones to your house because it knows where you
your house because it knows where you
your house because it knows where you are and gets the exact dish the way you
are and gets the exact dish the way you
are and gets the exact dish the way you like it
like it
like it prepared for you right when you need it
prepared for you right when you need it
prepared for you right when you need it stuff like that is not too far off
stuff like that is not too far off
stuff like that is not too far off and it needs to learn from you and learn
and it needs to learn from you and learn
and it needs to learn from you and learn from data to be able to do that kind of
from data to be able to do that kind of
from data to be able to do that kind of thing and so I was struck by this video
thing and so I was struck by this video
thing and so I was struck by this video this got me into machine learning so
this got me into machine learning so
this got me into machine learning so this supposed to be a video and being
this supposed to be a video and being
this supposed to be a video and being playing but it’s completely generated by
playing but it’s completely generated by
playing but it’s completely generated by artificial intelligence and I saw this
artificial intelligence and I saw this
artificial intelligence and I saw this in right after my second year in this
in right after my second year in this
in right after my second year in this engineering science program which is a
engineering science program which is a
engineering science program which is a pretty cool program the first two years
pretty cool program the first two years
pretty cool program the first two years you take every discipline of engineering
you take every discipline of engineering
you take every discipline of engineering and the last two you pick one out of
and the last two you pick one out of
and the last two you pick one out of eight different options and when I was
eight different options and when I was
eight different options and when I was deciding one of Jeff Hinton’s PhD
deciding one of Jeff Hinton’s PhD
deciding one of Jeff Hinton’s PhD students showed me this so Jeff is kind
students showed me this so Jeff is kind
students showed me this so Jeff is kind of the pioneer of a field of artificial
of the pioneer of a field of artificial
of the pioneer of a field of artificial intelligence called neural networks and
intelligence called neural networks and
intelligence called neural networks and I’ll teach you how those work in this
I’ll teach you how those work in this
I’ll teach you how those work in this talk but when I went to this PhD student
talk but when I went to this PhD student
talk but when I went to this PhD student who happened to be my resident advisor
who happened to be my resident advisor
who happened to be my resident advisor on the floor I was living in at
on the floor I was living in at
on the floor I was living in at University of Toronto he showed me this
University of Toronto he showed me this
University of Toronto he showed me this and said it was completely generated by
and said it was completely generated by
and said it was completely generated by neural networks and I was blown away
neural networks and I was blown away
neural networks and I was blown away because I knew how to program at that
because I knew how to program at that
because I knew how to program at that time and there was no way I could write
time and there was no way I could write
time and there was no way I could write functions and loops or any kind of
functions and loops or any kind of
functions and loops or any kind of regular programming to create something
regular programming to create something
regular programming to create something like this create a realistic video so I
like this create a realistic video so I
like this create a realistic video so I had to learn more and I took the
had to learn more and I took the
had to learn more and I took the computer option and took Jeff Hinton’s
computer option and took Jeff Hinton’s
computer option and took Jeff Hinton’s machine learning course to learn more
machine learning course to learn more
machine learning course to learn more about it and did my undergrad thesis
about it and did my undergrad thesis
about it and did my undergrad thesis with Geoff Hinton and that was a great
with Geoff Hinton and that was a great
with Geoff Hinton and that was a great learning experience I got a little taste
learning experience I got a little taste
learning experience I got a little taste of machine learning but I didn’t know
of machine learning but I didn’t know
of machine learning but I didn’t know enough to start a company and it’s been
enough to start a company and it’s been
enough to start a company and it’s been my childhood dream to start a company in
my childhood dream to start a company in
my childhood dream to start a company in the tech space but I knew I was hooked
the tech space but I knew I was hooked
the tech space but I knew I was hooked on machine learning and so I took a PhD
on machine learning and so I took a PhD
on machine learning and so I took a PhD at New York University and that’s what
at New York University and that’s what
at New York University and that’s what brought me down to the United States and
brought me down to the United States and
brought me down to the United States and I’m down there right now in New York
I’m down there right now in New York
I’m down there right now in New York where I started clarified
where I started clarified
where I started clarified and so if you think of artificial
and so if you think of artificial
and so if you think of artificial intelligence it’s everywhere around us
intelligence it’s everywhere around us
intelligence it’s everywhere around us already
already
already it’s an apps you use every single day
it’s an apps you use every single day
it’s an apps you use every single day whether it’s Siri when you talk into
whether it’s Siri when you talk into
whether it’s Siri when you talk into your phone and it turns it into text
your phone and it turns it into text
your phone and it turns it into text it’s automatically understanding that
it’s automatically understanding that
it’s automatically understanding that with neural networks type of artificial
with neural networks type of artificial
with neural networks type of artificial intelligence or I heard Spotify playing
intelligence or I heard Spotify playing
intelligence or I heard Spotify playing at lunch those playlists that are
at lunch those playlists that are
at lunch those playlists that are automatically created that’s another use
automatically created that’s another use
automatically created that’s another use case for artificial intelligence which
case for artificial intelligence which
case for artificial intelligence which is based on your past listening habits
is based on your past listening habits
is based on your past listening habits and other people that are like you
and other people that are like you
and other people that are like you similarly for buying products on Amazon
similarly for buying products on Amazon
similarly for buying products on Amazon other retailers they understand in
other retailers they understand in
other retailers they understand in aggregate from multiple users buying
aggregate from multiple users buying
aggregate from multiple users buying stuff over time how you might be
stuff over time how you might be
stuff over time how you might be interested in other products as you’re
interested in other products as you’re
interested in other products as you’re shopping and then with image recognition
shopping and then with image recognition
shopping and then with image recognition and video recognition we can now
and video recognition we can now
and video recognition we can now understand who’s in the image or what’s
understand who’s in the image or what’s
understand who’s in the image or what’s in the image the dog the tree the
in the image the dog the tree the
in the image the dog the tree the mountain etc all automatically just from
mountain etc all automatically just from
mountain etc all automatically just from the pixels and that’s what we do at
the pixels and that’s what we do at
the pixels and that’s what we do at clarify and so let’s get it fast so
clarify and so let’s get it fast so
clarify and so let’s get it fast so there’s a lot of really big problems for
there’s a lot of really big problems for
there’s a lot of really big problems for humanity that artificial intelligence
humanity that artificial intelligence
humanity that artificial intelligence can also help out with maybe it can
can also help out with maybe it can
can also help out with maybe it can understand how you like your temperature
understand how you like your temperature
understand how you like your temperature in your office or in a place like this
in your office or in a place like this
in your office or in a place like this the optimize how everybody feels while
the optimize how everybody feels while
the optimize how everybody feels while minimizing energy to affect climate
minimizing energy to affect climate
minimizing energy to affect climate change or optimize the crop yields that
change or optimize the crop yields that
change or optimize the crop yields that farmers are producing in order to
farmers are producing in order to
farmers are producing in order to minimize world hunger or even drug
minimize world hunger or even drug
minimize world hunger or even drug discovery finding new drugs that can
discovery finding new drugs that can
discovery finding new drugs that can help cure global health issues and all
help cure global health issues and all
help cure global health issues and all these other important issues like human
these other important issues like human
these other important issues like human trafficking gun violence human rights
trafficking gun violence human rights
trafficking gun violence human rights they can all be improved
they can all be improved
they can all be improved with artificial intelligence so it’s a
with artificial intelligence so it’s a
with artificial intelligence so it’s a broadly applicable new way of
broadly applicable new way of
broadly applicable new way of programming computers to act as if they
programming computers to act as if they
programming computers to act as if they were humans or like humans or even
were humans or like humans or even
were humans or like humans or even smarter than humans well why No
smarter than humans well why No
smarter than humans well why No why is it so important why are you
why is it so important why are you
why is it so important why are you hearing so much about it in the news and
hearing so much about it in the news and
hearing so much about it in the news and hopefully in school well it’s actually
hopefully in school well it’s actually
hopefully in school well it’s actually starting to work these algorithms have
starting to work these algorithms have
starting to work these algorithms have been in progress since the 70s and 80s
been in progress since the 70s and 80s
been in progress since the 70s and 80s when people like chef Hinton were
when people like chef Hinton were
when people like chef Hinton were actually in their school in their PhD is
actually in their school in their PhD is
actually in their school in their PhD is coming up with these algorithms they’re
coming up with these algorithms they’re
coming up with these algorithms they’re almost exactly the same algorithms from
almost exactly the same algorithms from
almost exactly the same algorithms from back then many decades ago and they
back then many decades ago and they
back then many decades ago and they actually work today in real world
actually work today in real world
actually work today in real world applications and that happens because of
applications and that happens because of
applications and that happens because of two main factors one is lots of data
two main factors one is lots of data
two main factors one is lots of data everyone
everyone
everyone has a cell phone in their pocket right
has a cell phone in their pocket right
has a cell phone in their pocket right now and they can take pictures they can
now and they can take pictures they can
now and they can take pictures they can create text messages they can talk into
create text messages they can talk into
create text messages they can talk into it that’s all data that can feed into
it that’s all data that can feed into
it that’s all data that can feed into these algorithms to teach them how
these algorithms to teach them how
these algorithms to teach them how people interact and computation so now
people interact and computation so now
people interact and computation so now these same algorithms that were created
these same algorithms that were created
these same algorithms that were created back then with really slow computers now
back then with really slow computers now
back then with really slow computers now have orders of magnitude more
have orders of magnitude more
have orders of magnitude more computation and we even use these
computation and we even use these
computation and we even use these graphics cards which are made for video
graphics cards which are made for video
graphics cards which are made for video games to process these machine learning
games to process these machine learning
games to process these machine learning algorithms because they’re really
algorithms because they’re really
algorithms because they’re really parallel and a lot of the things you do
parallel and a lot of the things you do
parallel and a lot of the things you do when you’re thinking are really parallel
when you’re thinking are really parallel
when you’re thinking are really parallel so they’re perfectly suited for
so they’re perfectly suited for
so they’re perfectly suited for artificial intelligence so data and
artificial intelligence so data and
artificial intelligence so data and computation make these old algorithms
computation make these old algorithms
computation make these old algorithms actually work and they work really
actually work and they work really
actually work and they work really really well so how does it work it’s not
really well so how does it work it’s not
really well so how does it work it’s not that much magic it’s actually pretty
that much magic it’s actually pretty
that much magic it’s actually pretty structured mathematical operations which
structured mathematical operations which
structured mathematical operations which I’ll get into now and it works just like
I’ll get into now and it works just like
I’ll get into now and it works just like a human does so does anybody know what
a human does so does anybody know what
a human does so does anybody know what this little guy is called I think I
this little guy is called I think I
this little guy is called I think I heard somebody shove it yes okay so one
heard somebody shove it yes okay so one
heard somebody shove it yes okay so one person said axolotl and that’s correct
person said axolotl and that’s correct
person said axolotl and that’s correct so does everybody can everybody repeat
so does everybody can everybody repeat
so does everybody can everybody repeat that axolotl awesome okay
that axolotl awesome okay
that axolotl awesome okay what’s this called awesome and how about
what’s this called awesome and how about
what’s this called awesome and how about these
these
these all right so you learned from one
all right so you learned from one
all right so you learned from one picture what this creature is and you
picture what this creature is and you
picture what this creature is and you realize that even though there’s you
realize that even though there’s you
realize that even though there’s you know black one’s different colored one’s
know black one’s different colored one’s
know black one’s different colored one’s different poses different lighting
different poses different lighting
different poses different lighting conditions different viewpoints from the
conditions different viewpoints from the
conditions different viewpoints from the camera they’re all axolotls so it didn’t
camera they’re all axolotls so it didn’t
camera they’re all axolotls so it didn’t take much to teach a human how to learn
take much to teach a human how to learn
take much to teach a human how to learn a new concept and this is a visual
a new concept and this is a visual
a new concept and this is a visual concept but you can hear new things you
concept but you can hear new things you
concept but you can hear new things you can read new things you can taste the
can read new things you can taste the
can read new things you can taste the things you learn very quickly as humans
things you learn very quickly as humans
things you learn very quickly as humans and so to simulate that this is the type
and so to simulate that this is the type
and so to simulate that this is the type of algorithm we use in computers and
of algorithm we use in computers and
of algorithm we use in computers and this is a type of neural network that is
this is a type of neural network that is
this is a type of neural network that is perfectly suited for images and video
perfectly suited for images and video
perfectly suited for images and video understanding and there’s different
understanding and there’s different
understanding and there’s different types different architectures of neural
types different architectures of neural
types different architectures of neural networks that are good for audio and
networks that are good for audio and
networks that are good for audio and good for text reading and good for
good for text reading and good for
good for text reading and good for financial data and so forth but neural
financial data and so forth but neural
financial data and so forth but neural network is just a computer algorithm
network is just a computer algorithm
network is just a computer algorithm that’s all it is that tries to simulate
that’s all it is that tries to simulate
that’s all it is that tries to simulate how the brain works nobody really knows
how the brain works nobody really knows
how the brain works nobody really knows exactly how the brain works but this is
exactly how the brain works but this is
exactly how the brain works but this is a good approximation and it shares a lot
a good approximation and it shares a lot
a good approximation and it shares a lot of the same properties and so a neural
of the same properties and so a neural
of the same properties and so a neural network is a bunch of layers of
network is a bunch of layers of
network is a bunch of layers of processing and you get to configure them
processing and you get to configure them
processing and you get to configure them in however you want to map some input in
in however you want to map some input in
in however you want to map some input in this case the pixels of an image to some
this case the pixels of an image to some
this case the pixels of an image to some output in this case the categories
output in this case the categories
output in this case the categories people romance grass and house and so to
people romance grass and house and so to
people romance grass and house and so to teach this algorithm how to recognize
teach this algorithm how to recognize
teach this algorithm how to recognize these different things you have to feed
these different things you have to feed
these different things you have to feed it data data that people have labeled
it data data that people have labeled
it data data that people have labeled like axolotl manually and so that’s the
like axolotl manually and so that’s the
like axolotl manually and so that’s the pixel and in this case person our people
pixel and in this case person our people
pixel and in this case person our people as category and these different layers
as category and these different layers
as category and these different layers of operations sound complicated but
of operations sound complicated but
of operations sound complicated but they’re pretty quite simple so
they’re pretty quite simple so
they’re pretty quite simple so convolution so this is a big word but it
convolution so this is a big word but it
convolution so this is a big word but it all it means is template matching so
all it means is template matching so
all it means is template matching so it’s gonna look at that little white
it’s gonna look at that little white
it’s gonna look at that little white patch that’s the template and it’s gonna
patch that’s the template and it’s gonna
patch that’s the template and it’s gonna slide it over every location in the
slide it over every location in the
slide it over every location in the image and when it matches very closely
image and when it matches very closely
image and when it matches very closely to the pixels at that location it gives
to the pixels at that location it gives
to the pixels at that location it gives a strong activation in that feature map
a strong activation in that feature map
a strong activation in that feature map so that that blue square that is the
so that that blue square that is the
so that that blue square that is the output of the convolution layer creates
output of the convolution layer creates
output of the convolution layer creates a feature map the same size as the image
a feature map the same size as the image
a feature map the same size as the image so it goes every location the stronger
so it goes every location the stronger
so it goes every location the stronger the activation the higher the value you
the activation the higher the value you
the activation the higher the value you have in that feature map you can see
have in that feature map you can see
have in that feature map you can see there’s a stack of those feature Maps
there’s a stack of those feature Maps
there’s a stack of those feature Maps it’s not just one
it’s not just one
it’s not just one because you want to recognize different
because you want to recognize different
because you want to recognize different patterns different templates and most
patterns different templates and most
patterns different templates and most importantly those templates are learned
importantly those templates are learned
importantly those templates are learned from the data they start off at complete
from the data they start off at complete
from the data they start off at complete random and they start updating in a
random and they start updating in a
random and they start updating in a training algorithm that I’m going to
training algorithm that I’m going to
training algorithm that I’m going to describe in a second the second layer
describe in a second the second layer
describe in a second the second layer there is called pooling and this is
there is called pooling and this is
there is called pooling and this is meant to help with some variability in
meant to help with some variability in
meant to help with some variability in the network so that it doesn’t care if
the network so that it doesn’t care if
the network so that it doesn’t care if the person’s face is in the middle of
the person’s face is in the middle of
the person’s face is in the middle of the image or on the left hand side or
the image or on the left hand side or
the image or on the left hand side or the right hand side is still a face so
the right hand side is still a face so
the right hand side is still a face so to do that in the pooling layer it looks
to do that in the pooling layer it looks
to do that in the pooling layer it looks at a small region and takes the
at a small region and takes the
at a small region and takes the strongest activation out of it that’s
strongest activation out of it that’s
strongest activation out of it that’s why it’s called max pooling in many
why it’s called max pooling in many
why it’s called max pooling in many cases and there’s different types of
cases and there’s different types of
cases and there’s different types of booing but max works great and that’s
booing but max works great and that’s
booing but max works great and that’s also why these maps get smaller cuz
also why these maps get smaller cuz
also why these maps get smaller cuz you’re taking one out of a set a small
you’re taking one out of a set a small
you’re taking one out of a set a small region and then you do alternating
region and then you do alternating
region and then you do alternating layers of convolution pooling and so
layers of convolution pooling and so
layers of convolution pooling and so forth remember the convolution has
forth remember the convolution has
forth remember the convolution has templates that are going to be learned
templates that are going to be learned
templates that are going to be learned the pooling is just a fixed math
the pooling is just a fixed math
the pooling is just a fixed math function and then eventually you get to
function and then eventually you get to
function and then eventually you get to the end of the network these fully
the end of the network these fully
the end of the network these fully connected layers are just a fancy word
connected layers are just a fancy word
connected layers are just a fancy word for convolution template matching where
for convolution template matching where
for convolution template matching where there’s only one pixel all of the
there’s only one pixel all of the
there’s only one pixel all of the pooling has reduced the image size down
pooling has reduced the image size down
pooling has reduced the image size down to one pixel and the number of output
to one pixel and the number of output
to one pixel and the number of output maps is corresponding to how many
maps is corresponding to how many
maps is corresponding to how many categories you want to recognize so here
categories you want to recognize so here
categories you want to recognize so here there’s only four so that’s how the math
there’s only four so that’s how the math
there’s only four so that’s how the math works going through you take the pixels
works going through you take the pixels
works going through you take the pixels template match pool template match and
template match pool template match and
template match pool template match and so forth you have many many more layers
so forth you have many many more layers
so forth you have many many more layers than this and even different types but
than this and even different types but
than this and even different types but these are the most important and so
these are the most important and so
these are the most important and so since the templates start off at random
since the templates start off at random
since the templates start off at random off the start it goes through and makes
off the start it goes through and makes
off the start it goes through and makes a random guess it doesn’t know anything
a random guess it doesn’t know anything
a random guess it doesn’t know anything about the world and then you compare it
about the world and then you compare it
about the world and then you compare it to what was labeled by a person in this
to what was labeled by a person in this
to what was labeled by a person in this case people and you would get your error
case people and you would get your error
case people and you would get your error signal this is the mistakes you’re
signal this is the mistakes you’re
signal this is the mistakes you’re making so just like the interlude was
making so just like the interlude was
making so just like the interlude was talking about the mistakes we make and
talking about the mistakes we make and
talking about the mistakes we make and learn from is exactly how a eye learns
learn from is exactly how a eye learns
learn from is exactly how a eye learns as well so it compares to the ground
as well so it compares to the ground
as well so it compares to the ground truth sees the mistakes it’s making and
truth sees the mistakes it’s making and
truth sees the mistakes it’s making and then it goes back through the network to
then it goes back through the network to
then it goes back through the network to update those templates and it updates
update those templates and it updates
update those templates and it updates them a little bit for this image and
them a little bit for this image and
them a little bit for this image and then it goes looks at another image and
then it goes looks at another image and
then it goes looks at another image and another image and another and millions
another image and another and millions
another image and another and millions of images later it can start to
of images later it can start to
of images later it can start to recognize a bunch of different
recognize a bunch of different
recognize a bunch of different categories so that’s how these neural
categories so that’s how these neural
categories so that’s how these neural networks work and they are really good
networks work and they are really good
networks work and they are really good at image and video understanding but
at image and video understanding but
at image and video understanding but you can think of neural networks just
you can think of neural networks just
you can think of neural networks just mapping inputs to outputs so you can
mapping inputs to outputs so you can
mapping inputs to outputs so you can configure them in different ways for
configure them in different ways for
configure them in different ways for different problems so what do they
different problems so what do they
different problems so what do they actually learn it’s pretty amazing that
actually learn it’s pretty amazing that
actually learn it’s pretty amazing that these algorithms start off at random but
these algorithms start off at random but
these algorithms start off at random but the first layer those templates I was
the first layer those templates I was
the first layer those templates I was telling you about learn this so there is
telling you about learn this so there is
telling you about learn this so there is 96 templates here not just 3 like the
96 templates here not just 3 like the
96 templates here not just 3 like the previous diagram and these are all
previous diagram and these are all
previous diagram and these are all learned from data we never told it that
learned from data we never told it that
learned from data we never told it that edges would be important or colors would
edges would be important or colors would
edges would be important or colors would be important to recognize in the pixels
be important to recognize in the pixels
be important to recognize in the pixels it learns different edges vertical
it learns different edges vertical
it learns different edges vertical different angles different frequencies
different angles different frequencies
different angles different frequencies you see sharp ones and blurry ones and
you see sharp ones and blurry ones and
you see sharp ones and blurry ones and even different colored edges and colored
even different colored edges and colored
even different colored edges and colored blobs and when you look into a primates
blobs and when you look into a primates
blobs and when you look into a primates brain and people have done this they’ve
brain and people have done this they’ve
brain and people have done this they’ve probed into cat’s eyeballs and monkeys
probed into cat’s eyeballs and monkeys
probed into cat’s eyeballs and monkeys eyeballs and you see exactly this in the
eyeballs and you see exactly this in the
eyeballs and you see exactly this in the first layer of the visual system which
first layer of the visual system which
first layer of the visual system which is pretty amazing because we just came
is pretty amazing because we just came
is pretty amazing because we just came up with these algorithms over the years
up with these algorithms over the years
up with these algorithms over the years to replicate how the brain works and in
to replicate how the brain works and in
to replicate how the brain works and in practice it actually does learn to
practice it actually does learn to
practice it actually does learn to replicate how the brain works and what’s
replicate how the brain works and what’s
replicate how the brain works and what’s more exciting is in certain tasks ai is
more exciting is in certain tasks ai is
more exciting is in certain tasks ai is actually working better as a visual
actually working better as a visual
actually working better as a visual system than humans and primates and so
system than humans and primates and so
system than humans and primates and so even though it’s learning in the same
even though it’s learning in the same
even though it’s learning in the same ways we can make it work better and
ways we can make it work better and
ways we can make it work better and faster and so now you know how a eye
faster and so now you know how a eye
faster and so now you know how a eye works but let’s shift gears and talk
works but let’s shift gears and talk
works but let’s shift gears and talk about why diversity is important in AI
about why diversity is important in AI
about why diversity is important in AI and how all you can have an impact on
and how all you can have an impact on
and how all you can have an impact on that so if you see an image like this
that so if you see an image like this
that so if you see an image like this you might be thinking black and white
you might be thinking black and white
you might be thinking black and white you might be thinking World War 2 or
you might be thinking World War 2 or
you might be thinking World War 2 or sailor or love or kissing everybody has
sailor or love or kissing everybody has
sailor or love or kissing everybody has a different mindset right now as they
a different mindset right now as they
a different mindset right now as they see this a different perspective and
see this a different perspective and
see this a different perspective and because of your diverse backgrounds
because of your diverse backgrounds
because of your diverse backgrounds growing up in different places different
growing up in different places different
growing up in different places different parents different nations perhaps and
parents different nations perhaps and
parents different nations perhaps and you’ve moved here you have a different
you’ve moved here you have a different
you’ve moved here you have a different way of thinking about the world and if
way of thinking about the world and if
way of thinking about the world and if you want to have a really powerful AI
you want to have a really powerful AI
you want to have a really powerful AI system that is fair to all different
system that is fair to all different
system that is fair to all different users and use cases we really want a
users and use cases we really want a
users and use cases we really want a diverse set of people training the AI
diverse set of people training the AI
diverse set of people training the AI and we view it as this critical period
and we view it as this critical period
and we view it as this critical period right now in artificial intelligence in
right now in artificial intelligence in
right now in artificial intelligence in the last three years we got it working
the last three years we got it working
the last three years we got it working really well and now there’s this period
really well and now there’s this period
really well and now there’s this period where we have to make it work for all
where we have to make it work for all
where we have to make it work for all different users all different use cases
different users all different use cases
different users all different use cases and there was this really interesting
and there was this really interesting
and there was this really interesting experiment by Blakemore and Cooper
experiment by Blakemore and Cooper
experiment by Blakemore and Cooper where they did some horrible stuff to
where they did some horrible stuff to
where they did some horrible stuff to cats but it was for science so it was
cats but it was for science so it was
cats but it was for science so it was good
good
good they they they made these drums that had
they they they made these drums that had
they they they made these drums that had on the inside only horizontal lines in
on the inside only horizontal lines in
on the inside only horizontal lines in some drums and only vertical lines in
some drums and only vertical lines in
some drums and only vertical lines in other drums and they put a newborn cat
other drums and they put a newborn cat
other drums and they put a newborn cat into the drum right when it was born so
into the drum right when it was born so
into the drum right when it was born so that was the only thing that’s ever seen
that was the only thing that’s ever seen
that was the only thing that’s ever seen in the world was in the inside of the
in the world was in the inside of the
in the world was in the inside of the drums and they left the cat in there and
drums and they left the cat in there and
drums and they left the cat in there and fed them and all that for weeks but even
fed them and all that for weeks but even
fed them and all that for weeks but even when they’re feeding they made sure not
when they’re feeding they made sure not
when they’re feeding they made sure not to expose it to any other of the
to expose it to any other of the
to expose it to any other of the different types of lines in the world
different types of lines in the world
different types of lines in the world that we all see every day and then they
that we all see every day and then they
that we all see every day and then they took the cats out of the drums after
took the cats out of the drums after
took the cats out of the drums after several weeks and the cats that were in
several weeks and the cats that were in
several weeks and the cats that were in the drum with a vertical line could see
the drum with a vertical line could see
the drum with a vertical line could see all vertical lines in the world so they
all vertical lines in the world so they
all vertical lines in the world so they would see like the chair legs and build
would see like the chair legs and build
would see like the chair legs and build a weed between them but they wouldn’t
a weed between them but they wouldn’t
a weed between them but they wouldn’t see any horizontal lines at all they
see any horizontal lines at all they
see any horizontal lines at all they couldn’t see the seat of the chair they
couldn’t see the seat of the chair they
couldn’t see the seat of the chair they would never they would even think the
would never they would even think the
would never they would even think the the floor or a table as they were being
the floor or a table as they were being
the floor or a table as they were being placed on a table was a weird thing they
placed on a table was a weird thing they
placed on a table was a weird thing they just hit it and then they could feel it
just hit it and then they could feel it
just hit it and then they could feel it they couldn’t see it at all and then the
they couldn’t see it at all and then the
they couldn’t see it at all and then the horizontal the cats that were in the
horizontal the cats that were in the
horizontal the cats that were in the horizontal drums they could see all
horizontal drums they could see all
horizontal drums they could see all horizontal things so they could jump up
horizontal things so they could jump up
horizontal things so they could jump up onto tables and and chair tops and all
onto tables and and chair tops and all
onto tables and and chair tops and all that but they would run into any of the
that but they would run into any of the
that but they would run into any of the chair legs so the visual system is in
chair legs so the visual system is in
chair legs so the visual system is in this critical period where it learns
this critical period where it learns
this critical period where it learns from when you’re born for a certain
from when you’re born for a certain
from when you’re born for a certain number of weeks and the the really scary
number of weeks and the the really scary
number of weeks and the the really scary thing is that no matter how long the
thing is that no matter how long the
thing is that no matter how long the cats were then left into the real world
cats were then left into the real world
cats were then left into the real world their system never came back and never
their system never came back and never
their system never came back and never learned the other types of lines and
learned the other types of lines and
learned the other types of lines and they showed several weeks later that
they showed several weeks later that
they showed several weeks later that they couldn’t see the horizontal lines
they couldn’t see the horizontal lines
they couldn’t see the horizontal lines or vertical lines depending on what drum
or vertical lines depending on what drum
or vertical lines depending on what drum they’re in so we view that as the
they’re in so we view that as the
they’re in so we view that as the critical period right now for AI and we
critical period right now for AI and we
critical period right now for AI and we don’t want that to affect how our
don’t want that to affect how our
don’t want that to affect how our algorithms work and how you can
algorithms work and how you can
algorithms work and how you can interface with AI in the future and so
interface with AI in the future and so
interface with AI in the future and so it’s not good vs. bad it’s not
it’s not good vs. bad it’s not
it’s not good vs. bad it’s not Terminator and Skynet and all this scary
Terminator and Skynet and all this scary
Terminator and Skynet and all this scary stuff that’s all in the movies it is
stuff that’s all in the movies it is
stuff that’s all in the movies it is important to think about but we view it
important to think about but we view it
important to think about but we view it as a couple different criteria that we
as a couple different criteria that we
as a couple different criteria that we look at smart versus dumb so we want AI
look at smart versus dumb so we want AI
look at smart versus dumb so we want AI to be incredibly smart because that’s
to be incredibly smart because that’s
to be incredibly smart because that’s what makes it useful and it could be
what makes it useful and it could be
what makes it useful and it could be less smart than a human worse more than
less smart than a human worse more than
less smart than a human worse more than human doesn’t really matter but does it
human doesn’t really matter but does it
human doesn’t really matter but does it provide a human some
provide a human some
provide a human some is it smart enough to do that that’s
is it smart enough to do that that’s
is it smart enough to do that that’s kind of one of the criteria we look at
kind of one of the criteria we look at
kind of one of the criteria we look at when pushing the limits of AI ethical
when pushing the limits of AI ethical
when pushing the limits of AI ethical versus unethical is artificial
versus unethical is artificial
versus unethical is artificial intelligence going to take people’s jobs
intelligence going to take people’s jobs
intelligence going to take people’s jobs or is it going to augment their jobs
or is it going to augment their jobs
or is it going to augment their jobs make them able to do 10 times more work
make them able to do 10 times more work
make them able to do 10 times more work or do it 10 times faster etc and is it
or do it 10 times faster etc and is it
or do it 10 times faster etc and is it gonna is AI gonna abide by the laws or
gonna is AI gonna abide by the laws or
gonna is AI gonna abide by the laws or what happens when AI gets so smart that
what happens when AI gets so smart that
what happens when AI gets so smart that it’s equivalent to a person or maybe
it’s equivalent to a person or maybe
it’s equivalent to a person or maybe smarter does the AI have more rights
smarter does the AI have more rights
smarter does the AI have more rights than a human or less rights and same
than a human or less rights and same
than a human or less rights and same rights there’s lots of different ethical
rights there’s lots of different ethical
rights there’s lots of different ethical implications of AI as well and then
implications of AI as well and then
implications of AI as well and then unbiased versus biased so this is a
unbiased versus biased so this is a
unbiased versus biased so this is a really important one because if you only
really important one because if you only
really important one because if you only teach just like the cat example if you
teach just like the cat example if you
teach just like the cat example if you only teach AI with a few small sets of
only teach AI with a few small sets of
only teach AI with a few small sets of the world it’s not going to understand
the world it’s not going to understand
the world it’s not going to understand the rest of the world and so right now
the rest of the world and so right now
the rest of the world and so right now we have to teach it with all of our
we have to teach it with all of our
we have to teach it with all of our backgrounds and all the things we know
backgrounds and all the things we know
backgrounds and all the things we know so that it doesn’t fall into case and
so that it doesn’t fall into case and
so that it doesn’t fall into case and this has happened very recently so this
this has happened very recently so this
this has happened very recently so this made a lot of press Microsoft had really
made a lot of press Microsoft had really
made a lot of press Microsoft had really cool technology chatbot that they let
cool technology chatbot that they let
cool technology chatbot that they let free on social media and what happened
free on social media and what happened
free on social media and what happened was a small community of people taught
was a small community of people taught
was a small community of people taught it a bunch of offensive stuff so when
it a bunch of offensive stuff so when
it a bunch of offensive stuff so when you communicated with the chat bot it
you communicated with the chat bot it
you communicated with the chat bot it would learn how to talk the way you talk
would learn how to talk the way you talk
would learn how to talk the way you talk and it would respond to you in that
and it would respond to you in that
and it would respond to you in that language and it learned very offensive
language and it learned very offensive
language and it learned very offensive stuff because only a subset of the
stuff because only a subset of the
stuff because only a subset of the planet really taught it whereas if the
planet really taught it whereas if the
planet really taught it whereas if the whole planet we don’t talk that way
whole planet we don’t talk that way
whole planet we don’t talk that way always and so the whole planet teaching
always and so the whole planet teaching
always and so the whole planet teaching it would be much better off and it would
it would be much better off and it would
it would be much better off and it would not have that bias another perspective
not have that bias another perspective
not have that bias another perspective there was recently a beauty contest that
there was recently a beauty contest that
there was recently a beauty contest that was judged by AI and it essentially
was judged by AI and it essentially
was judged by AI and it essentially picked only white girls as what it
picked only white girls as what it
picked only white girls as what it considered Beauty which is clearly not
considered Beauty which is clearly not
considered Beauty which is clearly not true and this is one of the cases where
true and this is one of the cases where
true and this is one of the cases where diversity is crucial because duty is
diversity is crucial because duty is
diversity is crucial because duty is really in the eye of the beholder and
really in the eye of the beholder and
really in the eye of the beholder and the same applies to AI so we have to
the same applies to AI so we have to
the same applies to AI so we have to teach it what everybody thinks is
teach it what everybody thinks is
teach it what everybody thinks is beautiful and that’s because AI is going
beautiful and that’s because AI is going
beautiful and that’s because AI is going to be everywhere you see it in products
to be everywhere you see it in products
to be everywhere you see it in products today you see it in the news almost
today you see it in the news almost
today you see it in the news almost every day and so how do you get involved
every day and so how do you get involved
every day and so how do you get involved how can you have this impact because the
how can you have this impact because the
how can you have this impact because the next generation is going to be driving
next generation is going to be driving
next generation is going to be driving they I field forward and you can think
they I field forward and you can think
they I field forward and you can think even
even
even a generation ahead your children
a generation ahead your children
a generation ahead your children whenever you have them probably will
whenever you have them probably will
whenever you have them probably will never have to get a driver’s license
never have to get a driver’s license
never have to get a driver’s license that’s how fast a eye is improving and
that’s how fast a eye is improving and
that’s how fast a eye is improving and it’s really exciting to think about it
it’s really exciting to think about it
it’s really exciting to think about it that way and so there’s lots of
that way and so there’s lots of
that way and so there’s lots of different ways you can get into AI you
different ways you can get into AI you
different ways you can get into AI you can work in AI at startups like clarify
can work in AI at startups like clarify
can work in AI at startups like clarify or large companies Google Microsoft
or large companies Google Microsoft
or large companies Google Microsoft Facebook they all have really big
Facebook they all have really big
Facebook they all have really big research labs and lots of universities
research labs and lots of universities
research labs and lots of universities are now teaching it even an undergrad so
are now teaching it even an undergrad so
are now teaching it even an undergrad so there’s lots of ways to learn about AI
there’s lots of ways to learn about AI
there’s lots of ways to learn about AI and contribute to it and as well
and contribute to it and as well
and contribute to it and as well contribute to open source projects
contribute to open source projects
contribute to open source projects there’s a huge flurry of open-source
there’s a huge flurry of open-source
there’s a huge flurry of open-source tool kits for neural networks and
tool kits for neural networks and
tool kits for neural networks and applications on top of those toolkits to
applications on top of those toolkits to
applications on top of those toolkits to apply ai in the real world and then you
apply ai in the real world and then you
apply ai in the real world and then you can think about the ethics of AI there’s
can think about the ethics of AI there’s
can think about the ethics of AI there’s think tanks like open AI which Elon Musk
think tanks like open AI which Elon Musk
think tanks like open AI which Elon Musk drove and even write your own blogs on
drove and even write your own blogs on
drove and even write your own blogs on how you view is gonna improve life and
how you view is gonna improve life and
how you view is gonna improve life and improve humanity and then bring AI into
improve humanity and then bring AI into
improve humanity and then bring AI into your world whether it’s your school as a
your world whether it’s your school as a
your world whether it’s your school as a science project start an AI club or you
science project start an AI club or you
science project start an AI club or you all be going a university soon perhaps
all be going a university soon perhaps
all be going a university soon perhaps started up there and bring it in to your
started up there and bring it in to your
started up there and bring it in to your everyday because AI is really for every
everyday because AI is really for every
everyday because AI is really for every human on the planet that’s why we need
human on the planet that’s why we need
human on the planet that’s why we need every single one of you teaching it
every single one of you teaching it
every single one of you teaching it thank you
thank you
thank you [Applause]
”
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