[Music] so I’m gonna start with a quote the
so I’m gonna start with a quote the
so I’m gonna start with a quote the intuitive mind is a sacred gift the
intuitive mind is a sacred gift the
intuitive mind is a sacred gift the irrational mind is a faithful servant
irrational mind is a faithful servant
irrational mind is a faithful servant we’ve created a society that has honest
we’ve created a society that has honest
we’ve created a society that has honest servant and has forgotten the gift I
servant and has forgotten the gift I
servant and has forgotten the gift I found this quote from when I was looking
found this quote from when I was looking
found this quote from when I was looking around for other people’s perspective on
around for other people’s perspective on
around for other people’s perspective on intuition because it’s really
intuition because it’s really
intuition because it’s really fascinating how our brain comes up with
fascinating how our brain comes up with
fascinating how our brain comes up with these gut feelings to tell you stuff and
these gut feelings to tell you stuff and
these gut feelings to tell you stuff and I found this one attributed to Albert
I found this one attributed to Albert
I found this one attributed to Albert Einstein and it really resonated with me
Einstein and it really resonated with me
Einstein and it really resonated with me particular this slightly ominous part
particular this slightly ominous part
particular this slightly ominous part that I think was never as timely as
that I think was never as timely as
that I think was never as timely as today in a world where we are deeply
today in a world where we are deeply
today in a world where we are deeply enamored with AI machine learning and
enamored with AI machine learning and
enamored with AI machine learning and all things data I remember I’m the first
all things data I remember I’m the first
all things data I remember I’m the first one to plead guilty I fell in love with
one to plead guilty I fell in love with
one to plead guilty I fell in love with data as a 21 year old socially awkward
data as a 21 year old socially awkward
data as a 21 year old socially awkward exchange student in Boulder Colorado
exchange student in Boulder Colorado
exchange student in Boulder Colorado during high school I had no idea what to
during high school I had no idea what to
during high school I had no idea what to do with my life I was good at math but
do with my life I was good at math but
do with my life I was good at math but have no true convictions otherwise and
have no true convictions otherwise and
have no true convictions otherwise and so I talked to my dad and he said they
so I talked to my dad and he said they
so I talked to my dad and he said they will use computers everywhere to study
will use computers everywhere to study
will use computers everywhere to study computer science and you can do anything
computer science and you can do anything
computer science and you can do anything and that’s how I found myself enrolled
and that’s how I found myself enrolled
and that’s how I found myself enrolled in the course in artificial intelligence
in the course in artificial intelligence
in the course in artificial intelligence and artificial neural networks now what
and artificial neural networks now what
and artificial neural networks now what was it about data well for the introvert
was it about data well for the introvert
was it about data well for the introvert itself was a revelation you had this
itself was a revelation you had this
itself was a revelation you had this virtual window on the world through
virtual window on the world through
virtual window on the world through which you could observe and understand
which you could observe and understand
which you could observe and understand the world and the people without having
the world and the people without having
the world and the people without having to talk to them
and the words of my dad should prove
and the words of my dad should prove truly prophetic a good 17 years later
truly prophetic a good 17 years later
truly prophetic a good 17 years later because not only are computers
because not only are computers
because not only are computers everywhere today data science is
everywhere today data science is
everywhere today data science is everywhere and I had the great luxury
everywhere and I had the great luxury
everywhere and I had the great luxury and pleasure to work with incredibly
and pleasure to work with incredibly
and pleasure to work with incredibly smart people from all kinds of
smart people from all kinds of
smart people from all kinds of industries medicine finance and
industries medicine finance and
industries medicine finance and advertising but as I progressed in my
advertising but as I progressed in my
advertising but as I progressed in my career and I started teaching more and
career and I started teaching more and
career and I started teaching more and mentoring there was this nagging thing
mentoring there was this nagging thing
mentoring there was this nagging thing that couldn’t be taught something else
that couldn’t be taught something else
that couldn’t be taught something else than algorithms and math it was this gut
than algorithms and math it was this gut
than algorithms and math it was this gut feeling thing and that brings us back to
feeling thing and that brings us back to
feeling thing and that brings us back to the quote that I started with now I have
the quote that I started with now I have
the quote that I started with now I have a confession to make it’s actually not
a confession to make it’s actually not
a confession to make it’s actually not by Albert Einstein
by Albert Einstein
by Albert Einstein sorry guys which is a lesson in
sorry guys which is a lesson in
sorry guys which is a lesson in intuition itself because he was known to
intuition itself because he was known to
intuition itself because he was known to have a deep appreciation for intuition
have a deep appreciation for intuition
have a deep appreciation for intuition but he was not known to be a very
but he was not known to be a very
but he was not known to be a very religious man and so that word of the
religious man and so that word of the
religious man and so that word of the sacred gift should have raised some red
sacred gift should have raised some red
sacred gift should have raised some red flag but then my intuition for data not
flag but then my intuition for data not
flag but then my intuition for data not language sorry I quote I can actually
language sorry I quote I can actually
language sorry I quote I can actually attribute this by my friend and
attribute this by my friend and
attribute this by my friend and colleague Hilary Mason and she talks
colleague Hilary Mason and she talks
colleague Hilary Mason and she talks about this fallacy that there’s some
about this fallacy that there’s some
about this fallacy that there’s some supposed to be some tension between data
supposed to be some tension between data
supposed to be some tension between data and intuition and she claims the data as
and intuition and she claims the data as
and intuition and she claims the data as a tool to enhance intuition and I’m
a tool to enhance intuition and I’m
a tool to enhance intuition and I’m taking a slightly different perspective
taking a slightly different perspective
taking a slightly different perspective on this and saying intuition is an
on this and saying intuition is an
on this and saying intuition is an absolutely crucial tool for data science
absolutely crucial tool for data science
absolutely crucial tool for data science and the reason it’s so important is
and the reason it’s so important is
and the reason it’s so important is because data is extremely compelling
because data is extremely compelling
because data is extremely compelling many of you know this code by Deming
many of you know this code by Deming
many of you know this code by Deming that In God We Trust and all other bring
that In God We Trust and all other bring
that In God We Trust and all other bring data but that implies there are some
data but that implies there are some
data but that implies there are some divine truth to data but it’s there
divine truth to data but it’s there
divine truth to data but it’s there really may be the facts are true but
really may be the facts are true but
really may be the facts are true but often our interpretation and
often our interpretation and
often our interpretation and understanding
understanding
understanding it’s wrong the rational mind is really
it’s wrong the rational mind is really
it’s wrong the rational mind is really easily fooled by data an intuition is
easily fooled by data an intuition is
easily fooled by data an intuition is the only safeguard
the only safeguard
the only safeguard standing in between since then I have
standing in between since then I have
standing in between since then I have moved on I work today in advertising now
moved on I work today in advertising now
moved on I work today in advertising now you may wonder why well if you love data
you may wonder why well if you love data
you may wonder why well if you love data working in advertising is like living in
working in advertising is like living in
working in advertising is like living in Disneyland it’s it’s like this huge
Disneyland it’s it’s like this huge
Disneyland it’s it’s like this huge playground where you can do almost
playground where you can do almost
playground where you can do almost anything you want and you can really
anything you want and you can really
anything you want and you can really push these technologies and see how far
push these technologies and see how far
push these technologies and see how far they work what they can do based on this
they work what they can do based on this
they work what they can do based on this incredible richness of data and for the
incredible richness of data and for the
incredible richness of data and for the better other words one of the things
better other words one of the things
better other words one of the things that marketers care about is whether you
that marketers care about is whether you
that marketers care about is whether you click on ads because that means that
click on ads because that means that
click on ads because that means that you’re interested in the product and may
you’re interested in the product and may
you’re interested in the product and may actually want to buy it now technically
actually want to buy it now technically
actually want to buy it now technically that’s a simple question was there an
that’s a simple question was there an
that’s a simple question was there an event that was recorded by the software
event that was recorded by the software
event that was recorded by the software of your browser has happened clicking on
of your browser has happened clicking on
of your browser has happened clicking on an ad but does it actually mean that the
an ad but does it actually mean that the
an ad but does it actually mean that the person clicked on an ad now this might
person clicked on an ad now this might
person clicked on an ad now this might be a bit of a straumann but I do have a
be a bit of a straumann but I do have a
be a bit of a straumann but I do have a cat that when she gets bored actually
cat that when she gets bored actually
cat that when she gets bored actually walks over my keyboard it is really fun
walks over my keyboard it is really fun
walks over my keyboard it is really fun if you’re in a video conference but we
if you’re in a video conference but we
if you’re in a video conference but we have a lot of bots and fraudulence
have a lot of bots and fraudulence
have a lot of bots and fraudulence player that have written software or
player that have written software or
player that have written software or programs that pretend to click on ads
programs that pretend to click on ads
programs that pretend to click on ads and that way make huge amounts of money
and that way make huge amounts of money
and that way make huge amounts of money selling those ads and also benefiting a
selling those ads and also benefiting a
selling those ads and also benefiting a lot from things like fake news that we
lot from things like fake news that we
lot from things like fake news that we see a lot these days but ok fine let’s
see a lot these days but ok fine let’s
see a lot these days but ok fine let’s assume that it actually is a person
assume that it actually is a person
assume that it actually is a person anybody remembers last time you clicked
anybody remembers last time you clicked
anybody remembers last time you clicked on an ad all right let me rephrase do
on an ad all right let me rephrase do
on an ad all right let me rephrase do you remember last time something popped
you remember last time something popped
you remember last time something popped up out of nowhere and you realize that
up out of nowhere and you realize that
up out of nowhere and you realize that instead of closing it you must have
instead of closing it you must have
instead of closing it you must have accidentally clicked on something there
accidentally clicked on something there
accidentally clicked on something there you go so one of the things where my
you go so one of the things where my
you go so one of the things where my expectation and iteration got concerned
expectation and iteration got concerned
expectation and iteration got concerned is the models that are we
is the models that are we
is the models that are we be good at predicting who clicks on ads
be good at predicting who clicks on ads
be good at predicting who clicks on ads always addressed that people who are
always addressed that people who are
always addressed that people who are using the flashlight app are really into
using the flashlight app are really into
using the flashlight app are really into credit cards and pizzas and hotels we’re
credit cards and pizzas and hotels we’re
credit cards and pizzas and hotels we’re really only the more or less found is
really only the more or less found is
really only the more or less found is there fumbling in the dark in chances of
there fumbling in the dark in chances of
there fumbling in the dark in chances of accidentally clicking up pretty high so
accidentally clicking up pretty high so
accidentally clicking up pretty high so there may be some notion of technical
there may be some notion of technical
there may be some notion of technical truth to that event but the reality is
truth to that event but the reality is
truth to that event but the reality is the interpretation of this is certainly
the interpretation of this is certainly
the interpretation of this is certainly wrong so there is this paradox that were
wrong so there is this paradox that were
wrong so there is this paradox that were having now that on the one hand side as
having now that on the one hand side as
having now that on the one hand side as a society we have a possibly misplaced
a society we have a possibly misplaced
a society we have a possibly misplaced reverence for data and then by the same
reverence for data and then by the same
reverence for data and then by the same token the one topic that data scientists
token the one topic that data scientists
token the one topic that data scientists love to lament about is all the tedious
love to lament about is all the tedious
love to lament about is all the tedious work they have to do cleaning up data
work they have to do cleaning up data
work they have to do cleaning up data but here’s the thing if you don’t like
but here’s the thing if you don’t like
but here’s the thing if you don’t like cleaning data you shouldn’t be data
cleaning data you shouldn’t be data
cleaning data you shouldn’t be data scientist that is your job and there’s
scientist that is your job and there’s
scientist that is your job and there’s nothing trivial about it in fact there’s
nothing trivial about it in fact there’s
nothing trivial about it in fact there’s nothing dirty about data data often just
nothing dirty about data data often just
nothing dirty about data data often just wants you to understand where it came
wants you to understand where it came
wants you to understand where it came from and it’s our job as a data
from and it’s our job as a data
from and it’s our job as a data scientist to listen one of the parts of
scientist to listen one of the parts of
scientist to listen one of the parts of cleaning often means replacing missing
cleaning often means replacing missing
cleaning often means replacing missing values and yes it’s tedious if you don’t
values and yes it’s tedious if you don’t
values and yes it’s tedious if you don’t know the age of the patient but imagine
know the age of the patient but imagine
know the age of the patient but imagine you don’t have the measurement for the
you don’t have the measurement for the
you don’t have the measurement for the white blood cell count is that missing
white blood cell count is that missing
white blood cell count is that missing do you need to replace that or is it not
do you need to replace that or is it not
do you need to replace that or is it not there for very good reason the doctor
there for very good reason the doctor
there for very good reason the doctor actually thinks everything’s fine and
actually thinks everything’s fine and
actually thinks everything’s fine and didn’t even bother to measure so the
didn’t even bother to measure so the
didn’t even bother to measure so the absence of evidence is in fact evidence
absence of evidence is in fact evidence
absence of evidence is in fact evidence that everything is all right so in this
that everything is all right so in this
that everything is all right so in this tension in medicine you also have a deep
tension in medicine you also have a deep
tension in medicine you also have a deep understanding that intuition brings to
understanding that intuition brings to
understanding that intuition brings to the place it’s not that they
the place it’s not that they
the place it’s not that they has the truth but you need to think as a
has the truth but you need to think as a
has the truth but you need to think as a data scientist about what it means so
data scientist about what it means so
data scientist about what it means so data really isn’t dirty
data really isn’t dirty
data really isn’t dirty it’s just misunderstood intuition also
it’s just misunderstood intuition also
it’s just misunderstood intuition also helps you to find order of randomness
helps you to find order of randomness
helps you to find order of randomness should be now that sounds weird okay let
should be now that sounds weird okay let
should be now that sounds weird okay let me give you an example I have a
me give you an example I have a
me give you an example I have a thirteen-year-old
thirteen-year-old
thirteen-year-old and if I come home and his room looks
and if I come home and his room looks
and if I come home and his room looks like this I’m worried I mean that’s the
like this I’m worried I mean that’s the
like this I’m worried I mean that’s the thing about thirteen year olds they’re
thing about thirteen year olds they’re
thing about thirteen year olds they’re not good with order and this usually
not good with order and this usually
not good with order and this usually means that either he has something
means that either he has something
means that either he has something really bad to confess so he’s trying to
really bad to confess so he’s trying to
really bad to confess so he’s trying to beg for that new video game
beg for that new video game
beg for that new video game either way I’m a bit concerned here’s
either way I’m a bit concerned here’s
either way I’m a bit concerned here’s another story um he has a very good
another story um he has a very good
another story um he has a very good friend who lives around the block and
friend who lives around the block and
friend who lives around the block and they often hang out and they afternoon
they often hang out and they afternoon
they often hang out and they afternoon do homework and then one week Monday
do homework and then one week Monday
do homework and then one week Monday Tuesday he came home at exactly 810 two
Tuesday he came home at exactly 810 two
Tuesday he came home at exactly 810 two days maybe that’s a coincidence comes
days maybe that’s a coincidence comes
days maybe that’s a coincidence comes Thursday he still comes home and when I
Thursday he still comes home and when I
Thursday he still comes home and when I take him outside he actually confesses
take him outside he actually confesses
take him outside he actually confesses that well they weren’t exactly doing
that well they weren’t exactly doing
that well they weren’t exactly doing homework the whole time they actually
homework the whole time they actually
homework the whole time they actually went to a carnival that closed at
went to a carnival that closed at
went to a carnival that closed at exactly eight o’clock thus the very
exactly eight o’clock thus the very
exactly eight o’clock thus the very predictable pattern of him throwing up
predictable pattern of him throwing up
predictable pattern of him throwing up at home because usually teenagers are
at home because usually teenagers are
at home because usually teenagers are not terribly predictable so be concerned
not terribly predictable so be concerned
not terribly predictable so be concerned when things are not random enough
when things are not random enough
when things are not random enough talking of prediction the human kind has
talking of prediction the human kind has
talking of prediction the human kind has for the longest time being absolutely
for the longest time being absolutely
for the longest time being absolutely obsessed with predicting the futures
obsessed with predicting the futures
obsessed with predicting the futures Oracle’s prophecies and there is now a
Oracle’s prophecies and there is now a
Oracle’s prophecies and there is now a field in AI a machine learning that
field in AI a machine learning that
field in AI a machine learning that focuses erect Lee on this being able to
focuses erect Lee on this being able to
focuses erect Lee on this being able to teach machines based on historical data
teach machines based on historical data
teach machines based on historical data to predict the future or to diagnose
to predict the future or to diagnose
to predict the future or to diagnose things
now what do people who like to do this
now what do people who like to do this do for fun
do for fun
do for fun anybody here preparing for the Boston
anybody here preparing for the Boston
anybody here preparing for the Boston Marathon so we have kind of marathons –
Marathon so we have kind of marathons –
Marathon so we have kind of marathons – not quite
not quite
not quite prize money is about the same about 20
prize money is about the same about 20
prize money is about the same about 20 years ago some geeks in machine learning
years ago some geeks in machine learning
years ago some geeks in machine learning because back then it wasn’t data science
because back then it wasn’t data science
because back then it wasn’t data science came up with the idea of data mining
came up with the idea of data mining
came up with the idea of data mining competitions where they take data set
competitions where they take data set
competitions where they take data set and then they hand it out to the
and then they hand it out to the
and then they hand it out to the participants and they try to build the
participants and they try to build the
participants and they try to build the best possible model on the second half
best possible model on the second half
best possible model on the second half of the data set they keep the truth of
of the data set they keep the truth of
of the data set they keep the truth of what actually happened and then they
what actually happened and then they
what actually happened and then they will declare the winner as whoever has
will declare the winner as whoever has
will declare the winner as whoever has the most predictive model so you’ve
the most predictive model so you’ve
the most predictive model so you’ve heard of these maybe as the Netflix
heard of these maybe as the Netflix
heard of these maybe as the Netflix prize we could make a million bucks so
prize we could make a million bucks so
prize we could make a million bucks so there’s some money in this now but most
there’s some money in this now but most
there’s some money in this now but most of us are in it for the glory one of
of us are in it for the glory one of
of us are in it for the glory one of these competitions was organized by
these competitions was organized by
these competitions was organized by Siemens medical and was around detecting
Siemens medical and was around detecting
Siemens medical and was around detecting breast cancer they shared with us the
breast cancer they shared with us the
breast cancer they shared with us the fMRI data of 2000 patients and the task
fMRI data of 2000 patients and the task
fMRI data of 2000 patients and the task was to identify either a group of people
was to identify either a group of people
was to identify either a group of people who were fou-fou healthy or a good risk
who were fou-fou healthy or a good risk
who were fou-fou healthy or a good risk score so doing the most likely people
score so doing the most likely people
score so doing the most likely people who had a malignant tumor to the least
who had a malignant tumor to the least
who had a malignant tumor to the least and we won this competition not because
and we won this competition not because
and we won this competition not because we knew anything about medicine not
we knew anything about medicine not
we knew anything about medicine not because we actually detected some secret
because we actually detected some secret
because we actually detected some secret signal from this data in fact it had
signal from this data in fact it had
signal from this data in fact it had nothing to do with the algorithm what we
nothing to do with the algorithm what we
nothing to do with the algorithm what we found was something wrong with the data
found was something wrong with the data
found was something wrong with the data so let me show you what it looked like
so let me show you what it looked like
so let me show you what it looked like so this is the data set where you have a
so this is the data set where you have a
so this is the data set where you have a patient identifier which is a random
patient identifier which is a random
patient identifier which is a random number and this random ID was assigned
number and this random ID was assigned
number and this random ID was assigned to protect the identity of the patient
to protect the identity of the patient
to protect the identity of the patient now anything strikes me as interesting
now anything strikes me as interesting
now anything strikes me as interesting well the first thing is that the sting
well the first thing is that the sting
well the first thing is that the sting is indeed sorted which is fine but if it
is indeed sorted which is fine but if it
is indeed sorted which is fine but if it is sorted then what exactly is going on
is sorted then what exactly is going on
is sorted then what exactly is going on here I mean what happened to all these
here I mean what happened to all these
here I mean what happened to all these people between half a million and four
people between half a million and four
people between half a million and four million the plot thickens when you look
million the plot thickens when you look
million the plot thickens when you look at the other column which is whether or
at the other column which is whether or
at the other column which is whether or not that patient had cancer so remember
not that patient had cancer so remember
not that patient had cancer so remember what I said about missingness trying to
what I said about missingness trying to
what I said about missingness trying to tell you about the story how the data
tell you about the story how the data
tell you about the story how the data was created won’t turns out you really
was created won’t turns out you really
was created won’t turns out you really have two separate groups of patients
have two separate groups of patients
have two separate groups of patients here there were the lucky ones was the
here there were the lucky ones was the
here there were the lucky ones was the low numbers and the unlucky ones but of
low numbers and the unlucky ones but of
low numbers and the unlucky ones but of course it had nothing to do with luck
course it had nothing to do with luck
course it had nothing to do with luck one group came from a treatment facility
one group came from a treatment facility
one group came from a treatment facility in the other one from a screening center
in the other one from a screening center
in the other one from a screening center now of course if you observe that
now of course if you observe that
now of course if you observe that winning the competition wasn’t that hard
winning the competition wasn’t that hard
winning the competition wasn’t that hard and again it had nothing to do with the
and again it had nothing to do with the
and again it had nothing to do with the algorithm but there’s a deeper important
algorithm but there’s a deeper important
algorithm but there’s a deeper important meaning here this data is completely
meaning here this data is completely
meaning here this data is completely useless to build any model that detects
useless to build any model that detects
useless to build any model that detects cancer because all the algorithm does it
cancer because all the algorithm does it
cancer because all the algorithm does it finds out what the average greyscale is
finds out what the average greyscale is
finds out what the average greyscale is and therefore which of the two centers
and therefore which of the two centers
and therefore which of the two centers it was it doesn’t learn anything about
it was it doesn’t learn anything about
it was it doesn’t learn anything about cancer and that is an important skill
cancer and that is an important skill
cancer and that is an important skill for data scientists to have to tell you
for data scientists to have to tell you
for data scientists to have to tell you that this is not working the interesting
that this is not working the interesting
that this is not working the interesting thing here is that the model actually
thing here is that the model actually
thing here is that the model actually looked too good to be true
looked too good to be true
looked too good to be true so one form of intuition is to warn you
so one form of intuition is to warn you
so one form of intuition is to warn you if things are actually looking too good
if things are actually looking too good
if things are actually looking too good the leading cause of death in intensive
the leading cause of death in intensive
the leading cause of death in intensive care units is one
care units is one
care units is one Kamille pramanya that means while the
Kamille pramanya that means while the
Kamille pramanya that means while the person is in hospital they contract form
person is in hospital they contract form
person is in hospital they contract form of ramonja
of ramonja
of ramonja that sometimes it’s even resistant
that sometimes it’s even resistant
that sometimes it’s even resistant strain and it would be incredibly
strain and it would be incredibly
strain and it would be incredibly important and really save lives if we
important and really save lives if we
important and really save lives if we could understand how these infections
could understand how these infections
could understand how these infections work and identify people at risk so we
work and identify people at risk so we
work and identify people at risk so we build the model to detect this using all
build the model to detect this using all
build the model to detect this using all of the patient information and diagnosis
of the patient information and diagnosis
of the patient information and diagnosis and medication and the model was
and medication and the model was
and medication and the model was incredibly good it founds this pocket of
incredibly good it founds this pocket of
incredibly good it founds this pocket of people that it was certain would get
people that it was certain would get
people that it was certain would get this infection a smoking gun
this infection a smoking gun
this infection a smoking gun and then you looked into the model and
and then you looked into the model and
and then you looked into the model and here’s what it said people who have
here’s what it said people who have
here’s what it said people who have nothing else wrong with them will get
nothing else wrong with them will get
nothing else wrong with them will get this infection now why are they in
this infection now why are they in
this infection now why are they in hospital in the first place well they
hospital in the first place well they
hospital in the first place well they aren’t Hospital in the first place
aren’t Hospital in the first place
aren’t Hospital in the first place because they showed up on a Friday
because they showed up on a Friday
because they showed up on a Friday evening in the emergency with an acute
evening in the emergency with an acute
evening in the emergency with an acute pneumonia and because of all the chaos
pneumonia and because of all the chaos
pneumonia and because of all the chaos it didn’t get recorded until Monday
it didn’t get recorded until Monday
it didn’t get recorded until Monday so the timestamp of the diagnosis wasn’t
so the timestamp of the diagnosis wasn’t
so the timestamp of the diagnosis wasn’t the time some of the I diagnosis but
the time some of the I diagnosis but
the time some of the I diagnosis but when a person actually got around to
when a person actually got around to
when a person actually got around to type into the system and now it looked
type into the system and now it looked
type into the system and now it looked as if they actually contracted it while
as if they actually contracted it while
as if they actually contracted it while they were in hospital so the lesson here
they were in hospital so the lesson here
they were in hospital so the lesson here is if your model is too good to be true
is if your model is too good to be true
is if your model is too good to be true maybe you want to go back and check the
maybe you want to go back and check the
maybe you want to go back and check the data but it also means in the bigger
data but it also means in the bigger
data but it also means in the bigger picture that the models we built and all
picture that the models we built and all
picture that the models we built and all of these AIS are really only as good as
of these AIS are really only as good as
of these AIS are really only as good as the data intuition of their creators and
the data intuition of their creators and
the data intuition of their creators and they are only to human when you look at
they are only to human when you look at
they are only to human when you look at for instance modern culture themes like
for instance modern culture themes like
for instance modern culture themes like person of interest there’s a machine
person of interest there’s a machine
person of interest there’s a machine here that can tell with certainty who in
here that can tell with certainty who in
here that can tell with certainty who in the next 24 hours will die of a violent
the next 24 hours will die of a violent
the next 24 hours will die of a violent death and this is science fiction today
death and this is science fiction today
death and this is science fiction today and will always be
these machines will never get that good
these machines will never get that good and not just because we aren’t that good
and not just because we aren’t that good
and not just because we aren’t that good but in fact I refuse to believe that the
but in fact I refuse to believe that the
but in fact I refuse to believe that the world will ever be predictable because I
world will ever be predictable because I
world will ever be predictable because I do wish to believe in the power of free
do wish to believe in the power of free
do wish to believe in the power of free will and if I can decide what to do with
will and if I can decide what to do with
will and if I can decide what to do with my next 24 hours there will always be
my next 24 hours there will always be
my next 24 hours there will always be limits to what we can predict but that
limits to what we can predict but that
limits to what we can predict but that poses a different question if they
poses a different question if they
poses a different question if they aren’t perfect when shall we trust them
aren’t perfect when shall we trust them
aren’t perfect when shall we trust them with our lives now even if they’re not
with our lives now even if they’re not
with our lives now even if they’re not perfect they’re pretty damn good they
perfect they’re pretty damn good they
perfect they’re pretty damn good they have beaten us and tress trêpa d poker
have beaten us and tress trêpa d poker
have beaten us and tress trêpa d poker turns out poker players think that they
turns out poker players think that they
turns out poker players think that they are not predictable in fact they are but
are not predictable in fact they are but
are not predictable in fact they are but if you know what to look for or if a
if you know what to look for or if a
if you know what to look for or if a machine knows what to look for and today
machine knows what to look for and today
machine knows what to look for and today even our current versions of
even our current versions of
even our current versions of self-driving cars are a lot better than
self-driving cars are a lot better than
self-driving cars are a lot better than the average teenage driver so 3 years
the average teenage driver so 3 years
the average teenage driver so 3 years from now I will sleep very well when my
from now I will sleep very well when my
from now I will sleep very well when my son comes home in a self-driving car
son comes home in a self-driving car
son comes home in a self-driving car even if on occasion accidentally it
even if on occasion accidentally it
even if on occasion accidentally it breaks for traffic because I thought it
breaks for traffic because I thought it
breaks for traffic because I thought it was a cat thank you
was a cat thank you
was a cat thank you [Applause]
”
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