what is knowledge in the age of big data it sounds like rather big ponderous
it sounds like rather big ponderous
it sounds like rather big ponderous question just now so I’m going to start
question just now so I’m going to start
question just now so I’m going to start with something a bit more tangible what
with something a bit more tangible what
with something a bit more tangible what do you know do you know what this is I’m
do you know do you know what this is I’m
do you know do you know what this is I’m going to give you a clue it’s the same
going to give you a clue it’s the same
going to give you a clue it’s the same thing photographed from both sides any
thing photographed from both sides any
thing photographed from both sides any thought
thought
thought it’s the shin bone of a young wolf so
it’s the shin bone of a young wolf so
it’s the shin bone of a young wolf so don’t feel bad if you didn’t recognize
don’t feel bad if you didn’t recognize
don’t feel bad if you didn’t recognize that and if you were thinking it’s a
that and if you were thinking it’s a
that and if you were thinking it’s a tally stick you are also right because
tally stick you are also right because
tally stick you are also right because about 30,000 years ago in a cave in what
about 30,000 years ago in a cave in what
about 30,000 years ago in a cave in what is now in the Czech Republic in the Ice
is now in the Czech Republic in the Ice
is now in the Czech Republic in the Ice Age somebody scratched 57 notches into
Age somebody scratched 57 notches into
Age somebody scratched 57 notches into this wolf bone 57 notches in groups of
this wolf bone 57 notches in groups of
this wolf bone 57 notches in groups of five making it therefore the oldest
five making it therefore the oldest
five making it therefore the oldest surviving example of digital data data
surviving example of digital data data
surviving example of digital data data you could count on your fingers now
you could count on your fingers now
you could count on your fingers now today of course we think of digital data
today of course we think of digital data
today of course we think of digital data as being something you could put into a
as being something you could put into a
as being something you could put into a computer in a form that a computer can
computer in a form that a computer can
computer in a form that a computer can store and process and give back so it
store and process and give back so it
store and process and give back so it would be in binary so 57 in binary is 1
would be in binary so 57 in binary is 1
would be in binary so 57 in binary is 1 1 1 0 0 1 satisfyingly that’s just under
1 1 0 0 1 satisfyingly that’s just under
1 1 0 0 1 satisfyingly that’s just under a byte making a byte roughly equivalent
a byte making a byte roughly equivalent
a byte making a byte roughly equivalent to a wolf’s bone thank you that’s a pond
to a wolf’s bone thank you that’s a pond
to a wolf’s bone thank you that’s a pond that only I enjoy it also means that the
that only I enjoy it also means that the
that only I enjoy it also means that the one terabyte hard drive on which I back
one terabyte hard drive on which I back
one terabyte hard drive on which I back up my entire life is equivalent to a
up my entire life is equivalent to a
up my entire life is equivalent to a million million wolf bones so when
million million wolf bones so when
million million wolf bones so when people say big data that’s what they’re
people say big data that’s what they’re
people say big data that’s what they’re talking about a million million wolf
talking about a million million wolf
talking about a million million wolf bones so much for data what about facts
bones so much for data what about facts
bones so much for data what about facts which of these facts do you know which
which of these facts do you know which
which of these facts do you know which these questions could you answer where
these questions could you answer where
these questions could you answer where is Toronto very good excellent I’m glad
is Toronto very good excellent I’m glad
is Toronto very good excellent I’m glad you are joining in how old is Harvard
you are joining in how old is Harvard
you are joining in how old is Harvard University for older than the US very
University for older than the US very
University for older than the US very good you’re going to get top marks in
good you’re going to get top marks in
good you’re going to get top marks in this quiz
this quiz
this quiz it is indeed older than the country in
it is indeed older than the country in
it is indeed older than the country in which it now sits what is the French
which it now sits what is the French
which it now sits what is the French word for werewolf excellent
word for werewolf excellent
word for werewolf excellent do you know why you know that ah cuz
do you know why you know that ah cuz
do you know why you know that ah cuz he’s French okay well that’s a head
he’s French okay well that’s a head
he’s French okay well that’s a head start I it’s a fact that I know and
start I it’s a fact that I know and
start I it’s a fact that I know and never knew I knew until somebody asked
never knew I knew until somebody asked
never knew I knew until somebody asked me in a bar so good these are facts
me in a bar so good these are facts
me in a bar so good these are facts there and if you didn’t know them what
there and if you didn’t know them what
there and if you didn’t know them what you you do now and and also you could
you you do now and and also you could
you you do now and and also you could easily find them out without this
easily find them out without this
easily find them out without this question though how many cats are there
question though how many cats are there
question though how many cats are there in the world now this question last
in the world now this question last
in the world now this question last Tuesday just gone I was hosting an event
Tuesday just gone I was hosting an event
Tuesday just gone I was hosting an event called maths inspiration which is talks
called maths inspiration which is talks
called maths inspiration which is talks about maths for school students and one
about maths for school students and one
about maths for school students and one of the speakers Rob east away was
of the speakers Rob east away was
of the speakers Rob east away was talking about estimation and he used the
talking about estimation and he used the
talking about estimation and he used the example of Fermi questions where you
example of Fermi questions where you
example of Fermi questions where you don’t know the answer and you can’t
don’t know the answer and you can’t
don’t know the answer and you can’t really find that exact answer but you
really find that exact answer but you
really find that exact answer but you can make a good estimate from what you
can make a good estimate from what you
can make a good estimate from what you do know and he invited the audience to
do know and he invited the audience to
do know and he invited the audience to contribute questions that you could use
contribute questions that you could use
contribute questions that you could use as a fermi question so this appeared in
as a fermi question so this appeared in
as a fermi question so this appeared in the question box in green ink how many
the question box in green ink how many
the question box in green ink how many cats are there in the world and Rob
cats are there in the world and Rob
cats are there in the world and Rob started from something he did no he
started from something he did no he
started from something he did no he thought well I know how many people
thought well I know how many people
thought well I know how many people there are in the world was about seven
there are in the world was about seven
there are in the world was about seven billion people so if I estimate the
billion people so if I estimate the
billion people so if I estimate the average number of cats corresponding to
average number of cats corresponding to
average number of cats corresponding to each person the the mean number would be
each person the the mean number would be
each person the the mean number would be one he decided because some people don’t
one he decided because some people don’t
one he decided because some people don’t have a cat some people have several cats
have a cat some people have several cats
have a cat some people have several cats so he estimated there are seven billion
so he estimated there are seven billion
so he estimated there are seven billion cats in the world but before he’s even
cats in the world but before he’s even
cats in the world but before he’s even finished a hand went up on the second
finished a hand went up on the second
finished a hand went up on the second row so I said to the two school student
row so I said to the two school student
row so I said to the two school student yes what’s your question and he said I
yes what’s your question and he said I
yes what’s your question and he said I don’t have a question I have the answer
don’t have a question I have the answer
don’t have a question I have the answer six hundred million I’ve just googled it
six hundred million I’ve just googled it
six hundred million I’ve just googled it on my phone so they do say that in the
on my phone so they do say that in the
on my phone so they do say that in the Internet age all human knowledge is just
Internet age all human knowledge is just
Internet age all human knowledge is just three clicks away but where does that
three clicks away but where does that
three clicks away but where does that figure come from not that figure 600
figure come from not that figure 600
figure come from not that figure 600 million comes off the internet and I
million comes off the internet and I
million comes off the internet and I haven’t been able to find any reason why
haven’t been able to find any reason why
haven’t been able to find any reason why it’s more convincing than the seven
it’s more convincing than the seven
it’s more convincing than the seven billion estimate that Rob came up with
billion estimate that Rob came up with
billion estimate that Rob came up with the figure of three clicks away I can
the figure of three clicks away I can
the figure of three clicks away I can tell you where that comes from
tell you where that comes from
tell you where that comes from that comes from a 2012 Stanford research
that comes from a 2012 Stanford research
that comes from a 2012 Stanford research paper by West and less kvetch in which
paper by West and less kvetch in which
paper by West and less kvetch in which they got some human beings to play a
they got some human beings to play a
they got some human beings to play a game called Wikipedia to see how quickly
game called Wikipedia to see how quickly
game called Wikipedia to see how quickly they could get from one piece of
they could get from one piece of
they could get from one piece of information to another on Wikipedia and
information to another on Wikipedia and
information to another on Wikipedia and while the human beings
while the human beings
while the human beings doing this West and less kovitch
doing this West and less kovitch
doing this West and less kovitch calculated mathematically the shortest
calculated mathematically the shortest
calculated mathematically the shortest possible route the minimum number of
possible route the minimum number of
possible route the minimum number of clicks that you would need to do the
clicks that you would need to do the
clicks that you would need to do the same road now I realize not everybody
same road now I realize not everybody
same road now I realize not everybody spends as much time looking at graphs
spends as much time looking at graphs
spends as much time looking at graphs with as much love as I do so let me talk
with as much love as I do so let me talk
with as much love as I do so let me talk you through this a little the number of
you through this a little the number of
you through this a little the number of clicks along the lot the x-axis here is
clicks along the lot the x-axis here is
clicks along the lot the x-axis here is a logarithmic scale which means that the
a logarithmic scale which means that the
a logarithmic scale which means that the numbers get closer together as you come
numbers get closer together as you come
numbers get closer together as you come this way so it starts off with one in
this way so it starts off with one in
this way so it starts off with one in the middle is 10 over this side is a
the middle is 10 over this side is a
the middle is 10 over this side is a hundred so you can see that the the
hundred so you can see that the the
hundred so you can see that the the colored lines which are the human roots
colored lines which are the human roots
colored lines which are the human roots the roots the human beings found are
the roots the human beings found are
the roots the human beings found are sometimes as long as 70 or 80 clicks
sometimes as long as 70 or 80 clicks
sometimes as long as 70 or 80 clicks whereas the shortest possible route that
whereas the shortest possible route that
whereas the shortest possible route that mathematically optimum route that a
mathematically optimum route that a
mathematically optimum route that a perfect machine would get are never more
perfect machine would get are never more
perfect machine would get are never more than ten clicks and you can also see I
than ten clicks and you can also see I
than ten clicks and you can also see I think the the left-hand scale is the
think the the left-hand scale is the
think the the left-hand scale is the percentage so roughly how many how many
percentage so roughly how many how many
percentage so roughly how many how many how many roots were that long that the
how many roots were that long that the
how many roots were that long that the frequency so the most popular length of
frequency so the most popular length of
frequency so the most popular length of route is three and this is a bit clearer
route is three and this is a bit clearer
route is three and this is a bit clearer actually if I show you that the table of
actually if I show you that the table of
actually if I show you that the table of results you can see the shortest
results you can see the shortest
results you can see the shortest possible paths the mode the median that
possible paths the mode the median that
possible paths the mode the median that are both three and the mean is 2.9 I’m
are both three and the mean is 2.9 I’m
are both three and the mean is 2.9 I’m obviously using a working assumption
obviously using a working assumption
obviously using a working assumption here that you know what mode median and
here that you know what mode median and
here that you know what mode median and mean that mean they need average so you
mean that mean they need average so you
mean that mean they need average so you could say that this that all human
could say that this that all human
could say that this that all human knowledge in the Internet age is only
knowledge in the Internet age is only
knowledge in the Internet age is only three clicks away on average if you’re a
three clicks away on average if you’re a
three clicks away on average if you’re a machine if you’re a human being it’s
machine if you’re a human being it’s
machine if you’re a human being it’s four clicks away but nevertheless it’s
four clicks away but nevertheless it’s
four clicks away but nevertheless it’s out there so does this mean that if you
out there so does this mean that if you
out there so does this mean that if you have an internet connection you know all
have an internet connection you know all
have an internet connection you know all human knowledge of course not because
human knowledge of course not because
human knowledge of course not because you only have a finite amount of time in
you only have a finite amount of time in
you only have a finite amount of time in your life to click links on the internet
your life to click links on the internet
your life to click links on the internet and if you remember nothing else from my
and if you remember nothing else from my
and if you remember nothing else from my talk today please take away that one
talk today please take away that one
talk today please take away that one fact you only have a finite amount of
fact you only have a finite amount of
fact you only have a finite amount of time in your life to click links on the
time in your life to click links on the
time in your life to click links on the internet if however you are a machine
internet if however you are a machine
internet if however you are a machine that can click links at superhuman speed
that can click links at superhuman speed
that can click links at superhuman speed then you can amass a lot of knowledge if
then you can amass a lot of knowledge if
then you can amass a lot of knowledge if you are for example IBM what
you are for example IBM what
you are for example IBM what an algorithm developed to understand
an algorithm developed to understand
an algorithm developed to understand human language which was let loose onto
human language which was let loose onto
human language which was let loose onto the internet to acquire masses and
the internet to acquire masses and
the internet to acquire masses and masses of facts and information and then
masses of facts and information and then
masses of facts and information and then entered for the u.s. game show Jeopardy
entered for the u.s. game show Jeopardy
entered for the u.s. game show Jeopardy where it beats the to human champions at
where it beats the to human champions at
where it beats the to human champions at a quiz show there it is in the middle
a quiz show there it is in the middle
a quiz show there it is in the middle although it did get some things wrong
although it did get some things wrong
although it did get some things wrong it did answer a category of questions US
it did answer a category of questions US
it did answer a category of questions US cities onion it in the category US
cities onion it in the category US
cities onion it in the category US cities it answered one of the questions
cities it answered one of the questions
cities it answered one of the questions with Toronto which to us seems like a
with Toronto which to us seems like a
with Toronto which to us seems like a really basic mistake but to IBM Watson
really basic mistake but to IBM Watson
really basic mistake but to IBM Watson it can collect all this information but
it can collect all this information but
it can collect all this information but it doesn’t have the context that we have
it doesn’t have the context that we have
it doesn’t have the context that we have it doesn’t have the real-world
it doesn’t have the real-world
it doesn’t have the real-world experience that we have sometimes data
experience that we have sometimes data
experience that we have sometimes data and facts and not enough so going back
and facts and not enough so going back
and facts and not enough so going back to our wolf bone for example we can
to our wolf bone for example we can
to our wolf bone for example we can count the notches we know there are 57
count the notches we know there are 57
count the notches we know there are 57 notches but we don’t know what the
notches but we don’t know what the
notches but we don’t know what the notches were counting we don’t know who
notches were counting we don’t know who
notches were counting we don’t know who scratched them or what for we don’t know
scratched them or what for we don’t know
scratched them or what for we don’t know what they mean I like watching the TV
what they mean I like watching the TV
what they mean I like watching the TV programs where they dig up human
programs where they dig up human
programs where they dig up human skeletons and then they examine them
skeletons and then they examine them
skeletons and then they examine them forensically and tell us something about
forensically and tell us something about
forensically and tell us something about what they can tell about the person who
what they can tell about the person who
what they can tell about the person who lived so they can say oh we know that
lived so they can say oh we know that
lived so they can say oh we know that this woman lived with chronic toothache
this woman lived with chronic toothache
this woman lived with chronic toothache for 50 years or we know that this man
for 50 years or we know that this man
for 50 years or we know that this man was attacked with a sword but he
was attacked with a sword but he
was attacked with a sword but he survived and he lived on with a scarred
survived and he lived on with a scarred
survived and he lived on with a scarred hand and face for the rest of his life I
hand and face for the rest of his life I
hand and face for the rest of his life I like it partly because it reminds me how
like it partly because it reminds me how
like it partly because it reminds me how much medical care has come on in the
much medical care has come on in the
much medical care has come on in the last few hundred years but it doesn’t
last few hundred years but it doesn’t
last few hundred years but it doesn’t really tell us what’s important about
really tell us what’s important about
really tell us what’s important about those people it doesn’t tell us that
those people it doesn’t tell us that
those people it doesn’t tell us that this woman with a toothache was actually
this woman with a toothache was actually
this woman with a toothache was actually Queen Elizabeth the first of England it
Queen Elizabeth the first of England it
Queen Elizabeth the first of England it doesn’t tell us that this man who
doesn’t tell us that this man who
doesn’t tell us that this man who survived the sword attack was President
survived the sword attack was President
survived the sword attack was President Andrew Jackson of the United States of
Andrew Jackson of the United States of
Andrew Jackson of the United States of America you could dig my skeleton up in
America you could dig my skeleton up in
America you could dig my skeleton up in 500 years and you could work out that I
500 years and you could work out that I
500 years and you could work out that I had a wisdom tooth removed by a surgeon
had a wisdom tooth removed by a surgeon
had a wisdom tooth removed by a surgeon who was particularly skilled by the low
who was particularly skilled by the low
who was particularly skilled by the low standards of the 21st century but it
standards of the 21st century but it
standards of the 21st century but it wouldn’t really tell you anything
wouldn’t really tell you anything
wouldn’t really tell you anything important about me or what I think or
important about me or what I think or
important about me or what I think or what it’s like to be me it wouldn’t tell
what it’s like to be me it wouldn’t tell
what it’s like to be me it wouldn’t tell you anything about my life you could
you anything about my life you could
you anything about my life you could capture my data now
capture my data now
capture my data now you could fit me with a heart rate
you could fit me with a heart rate
you could fit me with a heart rate monitor and put that together with other
monitor and put that together with other
monitor and put that together with other data about me like my location or what I
data about me like my location or what I
data about me like my location or what I just used my credit card for or even
just used my credit card for or even
just used my credit card for or even what other people were in the same
what other people were in the same
what other people were in the same location as me at that time and you
location as me at that time and you
location as me at that time and you could find some associations even if you
could find some associations even if you
could find some associations even if you were a machine you could find
were a machine you could find
were a machine you could find associations between my heart beating a
associations between my heart beating a
associations between my heart beating a little faster and where I was and who
little faster and where I was and who
little faster and where I was and who with and what I was doing but it
with and what I was doing but it
with and what I was doing but it wouldn’t actually tell you what I was
wouldn’t actually tell you what I was
wouldn’t actually tell you what I was thinking and feeling at that point an
thinking and feeling at that point an
thinking and feeling at that point an algorithm couldn’t tell that because it
algorithm couldn’t tell that because it
algorithm couldn’t tell that because it doesn’t have any subjective experience
doesn’t have any subjective experience
doesn’t have any subjective experience an algorithm could look at a scan of my
an algorithm could look at a scan of my
an algorithm could look at a scan of my brain and this is a scan of my brain not
brain and this is a scan of my brain not
brain and this is a scan of my brain not actual size
actual size
actual size now now Gregor would look at that it
now now Gregor would look at that it
now now Gregor would look at that it could categorize it as a female brain it
could categorize it as a female brain it
could categorize it as a female brain it could possibly go through and find some
could possibly go through and find some
could possibly go through and find some disorder in my brain that I’m unaware of
disorder in my brain that I’m unaware of
disorder in my brain that I’m unaware of and that even a trained neurologist
and that even a trained neurologist
and that even a trained neurologist hadn’t spotted yet which could be handy
hadn’t spotted yet which could be handy
hadn’t spotted yet which could be handy but the algorithm would have no idea
but the algorithm would have no idea
but the algorithm would have no idea what that meant for me what impact it
what that meant for me what impact it
what that meant for me what impact it would have on me what choices it would
would have on me what choices it would
would have on me what choices it would face me with in my life for that I would
face me with in my life for that I would
face me with in my life for that I would need a human neurologist who might be
need a human neurologist who might be
need a human neurologist who might be assisted by a very clever algorithm but
assisted by a very clever algorithm but
assisted by a very clever algorithm but who could talk to me human to human and
who could talk to me human to human and
who could talk to me human to human and say well these are the implications I
say well these are the implications I
say well these are the implications I understand how you’re feeling I can
understand how you’re feeling I can
understand how you’re feeling I can empathize with you what do you want to
empathize with you what do you want to
empathize with you what do you want to do when we say we know a person it’s not
do when we say we know a person it’s not
do when we say we know a person it’s not the same as knowing a fact because we
the same as knowing a fact because we
the same as knowing a fact because we are thinking not only about the outside
are thinking not only about the outside
are thinking not only about the outside things we observe the data we’ve
things we observe the data we’ve
things we observe the data we’ve collected on that person what we
collected on that person what we
collected on that person what we observed about their behavior we’re also
observed about their behavior we’re also
observed about their behavior we’re also using our subjective experience to
using our subjective experience to
using our subjective experience to empathize to project ourselves and
empathize to project ourselves and
empathize to project ourselves and imagine what it’s like to be them in
imagine what it’s like to be them in
imagine what it’s like to be them in French it’s not even the same word
French it’s not even the same word
French it’s not even the same word savoir to know a fact Canet to know a
savoir to know a fact Canet to know a
savoir to know a fact Canet to know a person and to be familiar with a person
person and to be familiar with a person
person and to be familiar with a person is perhaps a better translation in those
is perhaps a better translation in those
is perhaps a better translation in those words connects were familiar there are
words connects were familiar there are
words connects were familiar there are associations of kinship of
associations of kinship of
associations of kinship of fellow-feeling so if we say a machine
fellow-feeling so if we say a machine
fellow-feeling so if we say a machine knows a person that
knows a person that
knows a person that that makes no sense a machine has no
that makes no sense a machine has no
that makes no sense a machine has no subjective experience I may once have
subjective experience I may once have
subjective experience I may once have been guilty of writing big data knows
been guilty of writing big data knows
been guilty of writing big data knows you better than you know yourself that
you better than you know yourself that
you better than you know yourself that was hyperbole I had a book to sell it’s
was hyperbole I had a book to sell it’s
was hyperbole I had a book to sell it’s not actually true
not actually true
not actually true big data cannot know a person so meaning
big data cannot know a person so meaning
big data cannot know a person so meaning who knows what this means l mg TF why no
who knows what this means l mg TF why no
who knows what this means l mg TF why no I get this sometimes if I’m having a
I get this sometimes if I’m having a
I get this sometimes if I’m having a discussion on the Internet
discussion on the Internet
discussion on the Internet and I asked somebody what what do you
and I asked somebody what what do you
and I asked somebody what what do you mean by the age of reason and this they
mean by the age of reason and this they
mean by the age of reason and this they respond with this l mg TF why let me
respond with this l mg TF why let me
respond with this l mg TF why let me google that for you its sarcastic is not
google that for you its sarcastic is not
google that for you its sarcastic is not the rudest thing they could say they
the rudest thing they could say they
the rudest thing they could say they could say Jo G I and if you don’t know
could say Jo G I and if you don’t know
could say Jo G I and if you don’t know what that means
what that means
what that means just flippin google it now this annoys
just flippin google it now this annoys
just flippin google it now this annoys me because I don’t want to know what a
me because I don’t want to know what a
me because I don’t want to know what a machine thinks this means I don’t want
machine thinks this means I don’t want
machine thinks this means I don’t want to know what Google thinks this means I
to know what Google thinks this means I
to know what Google thinks this means I want to know what they mean by it in
want to know what they mean by it in
want to know what they mean by it in this context if you ask a machine what a
this context if you ask a machine what a
this context if you ask a machine what a word means a machine or the Internet is
word means a machine or the Internet is
word means a machine or the Internet is probably using something called word
probably using something called word
probably using something called word embedding where they model words in 200
embedding where they model words in 200
embedding where they model words in 200 dimensional vector space hold on to your
dimensional vector space hold on to your
dimensional vector space hold on to your seats do the demonstration so when it’s
seats do the demonstration so when it’s
seats do the demonstration so when it’s only three dimensions so it’s the same
only three dimensions so it’s the same
only three dimensions so it’s the same as the room we’re sitting in with that
as the room we’re sitting in with that
as the room we’re sitting in with that we’ve got an x axis y axis and the z
we’ve got an x axis y axis and the z
we’ve got an x axis y axis and the z axis this way so you won’t get vertigo
axis this way so you won’t get vertigo
axis this way so you won’t get vertigo but the way the way a machine would use
but the way the way a machine would use
but the way the way a machine would use Wert word embedding to give you the
Wert word embedding to give you the
Wert word embedding to give you the meaning of a word is it would put a word
meaning of a word is it would put a word
meaning of a word is it would put a word into this mathematical space close to
into this mathematical space close to
into this mathematical space close to words that are close to it in meaning so
words that are close to it in meaning so
words that are close to it in meaning so the word data would be up here close to
the word data would be up here close to
the word data would be up here close to words like knowledge and information the
words like knowledge and information the
words like knowledge and information the word Toronto be down here near other
word Toronto be down here near other
word Toronto be down here near other Canadian cities Watson and the word
Canadian cities Watson and the word
Canadian cities Watson and the word werewolf might be up there close to the
werewolf might be up there close to the
werewolf might be up there close to the word for werewolf in other languages for
word for werewolf in other languages for
word for werewolf in other languages for example they could all be close together
example they could all be close together
example they could all be close together but if you ask a human what data means
but if you ask a human what data means
but if you ask a human what data means they might give all sorts of different
they might give all sorts of different
they might give all sorts of different answers they might say well it’s not
answers they might say well it’s not
answers they might say well it’s not just scratched on a wolf’s bone or it’s
just scratched on a wolf’s bone or it’s
just scratched on a wolf’s bone or it’s it’s bites or bit
it’s bites or bit
it’s bites or bit inside a computer or it’s the Latin word
inside a computer or it’s the Latin word
inside a computer or it’s the Latin word for something that’s given and it’s a
for something that’s given and it’s a
for something that’s given and it’s a plural word of datum those are the
plural word of datum those are the
plural word of datum those are the particularly pedantic people that I do
particularly pedantic people that I do
particularly pedantic people that I do work with sometimes I but it also people
work with sometimes I but it also people
work with sometimes I but it also people we use the word in different ways you
we use the word in different ways you
we use the word in different ways you might use the word data to close down a
might use the word data to close down a
might use the word data to close down a conversation and people often do these
conversation and people often do these
conversation and people often do these days they say well this is this is the
days they say well this is this is the
days they say well this is this is the data or these are the data if they’re
data or these are the data if they’re
data or these are the data if they’re that kind of person this is this is the
that kind of person this is this is the
that kind of person this is this is the evidence that’s it there’s no more
evidence that’s it there’s no more
evidence that’s it there’s no more discussion here I have finished the
discussion here I have finished the
discussion here I have finished the conversation by giving you something
conversation by giving you something
conversation by giving you something incontrovertible I prefer to use data to
incontrovertible I prefer to use data to
incontrovertible I prefer to use data to open up a conversation to say well this
open up a conversation to say well this
open up a conversation to say well this is a given but this means it’s the
is a given but this means it’s the
is a given but this means it’s the beginning of a process of reasoning
beginning of a process of reasoning
beginning of a process of reasoning given that given that there are seven
given that given that there are seven
given that given that there are seven billion people in the world how many
billion people in the world how many
billion people in the world how many cats are there let’s embark on a process
cats are there let’s embark on a process
cats are there let’s embark on a process of reasoning reason itself has different
of reasoning reason itself has different
of reasoning reason itself has different meanings in different contexts there is
meanings in different contexts there is
meanings in different contexts there is the age of reason which is a historical
the age of reason which is a historical
the age of reason which is a historical period but why that’s important I’m sure
period but why that’s important I’m sure
period but why that’s important I’m sure historians are going to argue until the
historians are going to argue until the
historians are going to argue until the end of whatever age were in now there’s
end of whatever age were in now there’s
end of whatever age were in now there’s without good reason which to a lawyer
without good reason which to a lawyer
without good reason which to a lawyer means I might be able to get you
means I might be able to get you
means I might be able to get you compensation for losing your job and to
compensation for losing your job and to
compensation for losing your job and to a philosopher means do we really have
a philosopher means do we really have
a philosopher means do we really have free will or if you’re a Blaise Pascal
free will or if you’re a Blaise Pascal
free will or if you’re a Blaise Pascal the heart has its reasons which reason
the heart has its reasons which reason
the heart has its reasons which reason knows not although as the the French
knows not although as the the French
knows not although as the the French person in the audience will know what
person in the audience will know what
person in the audience will know what was he wrote it in French and the words
was he wrote it in French and the words
was he wrote it in French and the words he chose were liquor I see those old
he chose were liquor I see those old
he chose were liquor I see those old color plays on the conic one he used the
color plays on the conic one he used the
color plays on the conic one he used the word for knowing a person for being
word for knowing a person for being
word for knowing a person for being familiar with being acquainted with so
familiar with being acquainted with so
familiar with being acquainted with so reason is not acquainted with the
reason is not acquainted with the
reason is not acquainted with the reasons of the heart which has a rather
reasons of the heart which has a rather
reasons of the heart which has a rather different meaning and I asked myself why
different meaning and I asked myself why
different meaning and I asked myself why did Pascal choose those particular words
did Pascal choose those particular words
did Pascal choose those particular words what did he mean what did he intend what
what did he mean what did he intend what
what did he mean what did he intend what did he want me as the reader to get from
did he want me as the reader to get from
did he want me as the reader to get from that particular choice of words meaning
that particular choice of words meaning
that particular choice of words meaning is not something that is embodied in a
is not something that is embodied in a
is not something that is embodied in a word like the color red in these
word like the color red in these
word like the color red in these balloons meaning is something that goes
balloons meaning is something that goes
balloons meaning is something that goes from the speaker or the right
from the speaker or the right
from the speaker or the right to the listener or the reader so he has
to the listener or the reader so he has
to the listener or the reader so he has an intention about what I should get but
an intention about what I should get but
an intention about what I should get but in order to choose the words for that
in order to choose the words for that
in order to choose the words for that intention he has to project himself
intention he has to project himself
intention he has to project himself imaginatively into my mind his uses
imaginatively into my mind his uses
imaginatively into my mind his uses empathy to think I’m a subjective human
empathy to think I’m a subjective human
empathy to think I’m a subjective human being how is a subjective human being
being how is a subjective human being
being how is a subjective human being reading this going to respond are they
reading this going to respond are they
reading this going to respond are they going to get from it because the writer
going to get from it because the writer
going to get from it because the writer knows that the meaning is created by the
knows that the meaning is created by the
knows that the meaning is created by the person reading the meaning doesn’t exist
person reading the meaning doesn’t exist
person reading the meaning doesn’t exist objectively in the words the meaning is
objectively in the words the meaning is
objectively in the words the meaning is created in the process of reading and
created in the process of reading and
created in the process of reading and thinking and understanding and that’s
thinking and understanding and that’s
thinking and understanding and that’s why a machine cannot mean anything
why a machine cannot mean anything
why a machine cannot mean anything because it has no subjective mind in
because it has no subjective mind in
because it has no subjective mind in 1636 when Harvard was founded you would
1636 when Harvard was founded you would
1636 when Harvard was founded you would have had to go to Harvard to get the
have had to go to Harvard to get the
have had to go to Harvard to get the benefit of all the knowledge that they
benefit of all the knowledge that they
benefit of all the knowledge that they have there you’d have have to go and sit
have there you’d have have to go and sit
have there you’d have have to go and sit in their library to read their books
in their library to read their books
in their library to read their books you’d have to sit in their lecture
you’d have to sit in their lecture
you’d have to sit in their lecture theatre to listen to their lecture and
theatre to listen to their lecture and
theatre to listen to their lecture and speaking today you don’t have to you
speaking today you don’t have to you
speaking today you don’t have to you don’t even have to get dressed if you
don’t even have to get dressed if you
don’t even have to get dressed if you have an internet connection you can
have an internet connection you can
have an internet connection you can download research papers from Harvard
download research papers from Harvard
download research papers from Harvard you can sign up to a free online course
you can sign up to a free online course
you can sign up to a free online course you can listen to their lecturers you
you can listen to their lecturers you
you can listen to their lecturers you can even do quizzes to find out how much
can even do quizzes to find out how much
can even do quizzes to find out how much you’ve understood well that is marvelous
you’ve understood well that is marvelous
you’ve understood well that is marvelous it’s a wonderful thing that the internet
it’s a wonderful thing that the internet
it’s a wonderful thing that the internet and data have opened up so much of the
and data have opened up so much of the
and data have opened up so much of the field of human knowledge to so many more
field of human knowledge to so many more
field of human knowledge to so many more people but it’s not a good thing if we
people but it’s not a good thing if we
people but it’s not a good thing if we lose track of what knowledge is if we
lose track of what knowledge is if we
lose track of what knowledge is if we start to think of knowledge as a
start to think of knowledge as a
start to think of knowledge as a substance like porridge that you scoop
substance like porridge that you scoop
substance like porridge that you scoop out with your facts ladle into the empty
out with your facts ladle into the empty
out with your facts ladle into the empty bowls of ignorance come on Charlie you
bowls of ignorance come on Charlie you
bowls of ignorance come on Charlie you all have a fact ladle ready to fill the
all have a fact ladle ready to fill the
all have a fact ladle ready to fill the empty bowls of ignorance knowledge is
empty bowls of ignorance knowledge is
empty bowls of ignorance knowledge is not something that exists in the bits of
not something that exists in the bits of
not something that exists in the bits of paper it’s not existing in the ink on
paper it’s not existing in the ink on
paper it’s not existing in the ink on the paper on the page of a book or on in
the paper on the page of a book or on in
the paper on the page of a book or on in the ones and zeros inside a computer
the ones and zeros inside a computer
the ones and zeros inside a computer knowledge is a field of human activity
knowledge is a field of human activity
knowledge is a field of human activity knowing is a verb it’s a thing that
knowing is a verb it’s a thing that
knowing is a verb it’s a thing that human beings do so yes it may be true
human beings do so yes it may be true
human beings do so yes it may be true that in the Internet age any fact on the
that in the Internet age any fact on the
that in the Internet age any fact on the Internet can be found in four clicks or
Internet can be found in four clicks or
Internet can be found in four clicks or in three clicks if you’re a machine but
in three clicks if you’re a machine but
in three clicks if you’re a machine but the machine won’t know
the machine won’t know
the machine won’t know it means the kid that googled and found
it means the kid that googled and found
it means the kid that googled and found the answer of 600 million cats had
the answer of 600 million cats had
the answer of 600 million cats had completely the wrong idea the point was
completely the wrong idea the point was
completely the wrong idea the point was never to learn how many cats there are
never to learn how many cats there are
never to learn how many cats there are in the world the point must learn how to
in the world the point must learn how to
in the world the point must learn how to estimate how to build your reasoning
estimate how to build your reasoning
estimate how to build your reasoning muscle by starting with something you do
muscle by starting with something you do
muscle by starting with something you do know a given there are seven billion
know a given there are seven billion
know a given there are seven billion people in the world and seeing if your
people in the world and seeing if your
people in the world and seeing if your reason can take you from that to
reason can take you from that to
reason can take you from that to understanding something completely new
understanding something completely new
understanding something completely new about the world
about the world
about the world so in the age of big data knowledge is
so in the age of big data knowledge is
so in the age of big data knowledge is as it has always been a field of human
as it has always been a field of human
as it has always been a field of human activity and the human activity is
activity and the human activity is
activity and the human activity is learning and reasoning and knowing thank
learning and reasoning and knowing thank
learning and reasoning and knowing thank you
”
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