so I guess I’m one of those good old fashioned a I people so when I started
fashioned a I people so when I started
fashioned a I people so when I started to do research in artificial
to do research in artificial
to do research in artificial intelligence we were really about doing
intelligence we were really about doing
intelligence we were really about doing this knowledge representation so I
this knowledge representation so I
this knowledge representation so I actually come from the knowledge
actually come from the knowledge
actually come from the knowledge representation kind of laboratory at the
representation kind of laboratory at the
representation kind of laboratory at the in Chipping University so I actually
in Chipping University so I actually
in Chipping University so I actually would argue that we have a lot to to
would argue that we have a lot to to
would argue that we have a lot to to learn from this traditional techniques
learn from this traditional techniques
learn from this traditional techniques to artificial intelligence as as well
to artificial intelligence as as well
to artificial intelligence as as well but of course today we have a lot of
but of course today we have a lot of
but of course today we have a lot of machine learning to assist and to
machine learning to assist and to
machine learning to assist and to improve on those technologies so what
improve on those technologies so what
improve on those technologies so what I’m interested in in sharing here the
I’m interested in in sharing here the
I’m interested in in sharing here the idea that I think is worth cheering here
idea that I think is worth cheering here
idea that I think is worth cheering here today is that how do we go from all this
today is that how do we go from all this
today is that how do we go from all this exciting and very fascinating results
exciting and very fascinating results
exciting and very fascinating results that we see in artificial intelligence
that we see in artificial intelligence
that we see in artificial intelligence which is really about understanding how
which is really about understanding how
which is really about understanding how can mechanize intelligence or thought
can mechanize intelligence or thought
can mechanize intelligence or thought processes cognitions and so on and how
processes cognitions and so on and how
processes cognitions and so on and how do we leverage that when it comes to our
do we leverage that when it comes to our
do we leverage that when it comes to our own probably problem-solving
own probably problem-solving
own probably problem-solving capabilities and abilities and I would
capabilities and abilities and I would
capabilities and abilities and I would argue that we can actually both learn a
argue that we can actually both learn a
argue that we can actually both learn a lot from artificial intelligence but
lot from artificial intelligence but
lot from artificial intelligence but also take advantage of the fact that we
also take advantage of the fact that we
also take advantage of the fact that we have extremely powerful computers when
have extremely powerful computers when
have extremely powerful computers when we solve problems so what we really need
we solve problems so what we really need
we solve problems so what we really need to do is to develop and cultivate our
to do is to develop and cultivate our
to do is to develop and cultivate our computational thinking and leveraging
computational thinking and leveraging
computational thinking and leveraging and this computational power that exists
and this computational power that exists
and this computational power that exists while solving problems so I mean what is
while solving problems so I mean what is
while solving problems so I mean what is artificial intelligence so I asked the
artificial intelligence so I asked the
artificial intelligence so I asked the president of Swedish area society I do
president of Swedish area society I do
president of Swedish area society I do get asked this question quite a lot but
get asked this question quite a lot but
get asked this question quite a lot but I think from the purpose of this talk at
I think from the purpose of this talk at
I think from the purpose of this talk at least I would just say that artificial
least I would just say that artificial
least I would just say that artificial intelligence is really kind of the study
intelligence is really kind of the study
intelligence is really kind of the study of the algorithmic mechanization or
of the algorithmic mechanization or
of the algorithmic mechanization or cognition where cognition is how do we
cognition where cognition is how do we
cognition where cognition is how do we think how do we solve problems how do we
think how do we solve problems how do we
think how do we solve problems how do we plan how do we reason and so on so how
plan how do we reason and so on so how
plan how do we reason and so on so how do we get understand it well enough
do we get understand it well enough
do we get understand it well enough and our being able to write algorithms
and our being able to write algorithms
and our being able to write algorithms that can actually carry
that can actually carry
that can actually carry the same kind of problem solving
the same kind of problem solving
the same kind of problem solving processes for example so we have seen
processes for example so we have seen
processes for example so we have seen that in in the previous examples that we
that in in the previous examples that we
that in in the previous examples that we have made quite a good progress and we
have made quite a good progress and we
have made quite a good progress and we have come quite far so I mean one
have come quite far so I mean one
have come quite far so I mean one question we can ask is why now why are
question we can ask is why now why are
question we can ask is why now why are we here today showing these kind of
we here today showing these kind of
we here today showing these kind of advances well first of all software is
advances well first of all software is
advances well first of all software is eating the world so basically software
eating the world so basically software
eating the world so basically software is taking over where we previously had
is taking over where we previously had
is taking over where we previously had hardware for example one just bring in
hardware for example one just bring in
hardware for example one just bring in one example is the Ericsson telecom
one example is the Ericsson telecom
one example is the Ericsson telecom networks where previously you had
networks where previously you had
networks where previously you had specialized hardware and specialized the
specialized hardware and specialized the
specialized hardware and specialized the base stations and if you want to change
base stations and if you want to change
base stations and if you want to change the feature of your telecom network
the feature of your telecom network
the feature of your telecom network someone has to physically go there
someone has to physically go there
someone has to physically go there replace the hardware today they just
replace the hardware today they just
replace the hardware today they just have a standard computer and they just
have a standard computer and they just
have a standard computer and they just change the software so if you want a new
change the software so if you want a new
change the software so if you want a new feature in your telecom network you just
feature in your telecom network you just
feature in your telecom network you just change the software and everything works
change the software and everything works
change the software and everything works the same and the same is for cars where
the same and the same is for cars where
the same and the same is for cars where you can have the same engine in several
you can have the same engine in several
you can have the same engine in several models of the car the only difference is
models of the car the only difference is
models of the car the only difference is that if you pay a hundred thousand more
that if you pay a hundred thousand more
that if you pay a hundred thousand more you get the sports Edition which means
you get the sports Edition which means
you get the sports Edition which means that it has a different software than in
that it has a different software than in
that it has a different software than in the standard edition so software is
the standard edition so software is
the standard edition so software is continually eating the world and it’s
continually eating the world and it’s
continually eating the world and it’s also the fact it’s not only the digital
also the fact it’s not only the digital
also the fact it’s not only the digital world but those are the analog word are
world but those are the analog word are
world but those are the analog word are actually merging so what we see is that
actually merging so what we see is that
actually merging so what we see is that things that we do in the digital world
things that we do in the digital world
things that we do in the digital world has consequences in the analog world of
has consequences in the analog world of
has consequences in the analog world of the physical world and of course the
the physical world and of course the
the physical world and of course the other way around that the things that we
other way around that the things that we
other way around that the things that we do in the physical world have
do in the physical world have
do in the physical world have consequences in the digital world that’s
consequences in the digital world that’s
consequences in the digital world that’s kind of easier to see but this actually
kind of easier to see but this actually
kind of easier to see but this actually been the case that there was a reporter
been the case that there was a reporter
been the case that there was a reporter who had epilepsy and there was someone
who had epilepsy and there was someone
who had epilepsy and there was someone who was angry at him they didn’t like
who was angry at him they didn’t like
who was angry at him they didn’t like what he’s writing and actually sent him
what he’s writing and actually sent him
what he’s writing and actually sent him a file that they opened his file and he
a file that they opened his file and he
a file that they opened his file and he started to blink and it’s like a
started to blink and it’s like a
started to blink and it’s like a stroboscope effect and they actually
stroboscope effect and they actually
stroboscope effect and they actually caused a seizure for him so it’s
caused a seizure for him so it’s
caused a seizure for him so it’s actually a way of attacking a person
actually a way of attacking a person
actually a way of attacking a person through a digital media so the digital
through a digital media so the digital
through a digital media so the digital world and the analog world are truly
world and the analog world are truly
world and the analog world are truly becoming one so that’s another
becoming one so that’s another
becoming one so that’s another interesting fact of course then we also
interesting fact of course then we also
interesting fact of course then we also have this development computational
have this development computational
have this development computational powers by geographical processing units
powers by geographical processing units
powers by geographical processing units so
so
so thanks to all the kids playing a lot of
thanks to all the kids playing a lot of
thanks to all the kids playing a lot of video games at home we today have the
video games at home we today have the
video games at home we today have the massive computational powers that we
massive computational powers that we
massive computational powers that we have today that we need for all this
have today that we need for all this
have today that we need for all this machine learning and so on we also have
machine learning and so on we also have
machine learning and so on we also have a lot of labeled data we actually have
a lot of labeled data we actually have
a lot of labeled data we actually have examples so again it’s basically us here
examples so again it’s basically us here
examples so again it’s basically us here using our mobile devices or other things
using our mobile devices or other things
using our mobile devices or other things to collect data continuously that allows
to collect data continuously that allows
to collect data continuously that allows these machine learning algorithms to to
these machine learning algorithms to to
these machine learning algorithms to to learn from the data that we have
learn from the data that we have
learn from the data that we have available and finally of course it’s
available and finally of course it’s
available and finally of course it’s also improvement in algorithms due to
also improvement in algorithms due to
also improvement in algorithms due to research but it’s interesting that what
research but it’s interesting that what
research but it’s interesting that what we see is this kind of convergence of a
we see is this kind of convergence of a
we see is this kind of convergence of a number of different factors that makes
number of different factors that makes
number of different factors that makes all of this possible so why is this
all of this possible so why is this
all of this possible so why is this important them why should we care well
important them why should we care well
important them why should we care well first of all what we’ve seen is this
first of all what we’ve seen is this
first of all what we’ve seen is this machine learning can actually find these
machine learning can actually find these
machine learning can actually find these complex patterns and extract them so
complex patterns and extract them so
complex patterns and extract them so previously we had to spend a lot of
previously we had to spend a lot of
previously we had to spend a lot of effort in in defining these ourselves
effort in in defining these ourselves
effort in in defining these ourselves now they can be extracted much more
now they can be extracted much more
now they can be extracted much more efficiently and at scale and it also
efficiently and at scale and it also
efficiently and at scale and it also allows for adaptation so it’s not a
allows for adaptation so it’s not a
allows for adaptation so it’s not a fixed system but rather it can change
fixed system but rather it can change
fixed system but rather it can change and evolve and adapt at runtime so it
and evolve and adapt at runtime so it
and evolve and adapt at runtime so it becomes much more flexible and of course
becomes much more flexible and of course
becomes much more flexible and of course then much more useful but we also see is
then much more useful but we also see is
then much more useful but we also see is that the interface to computers is
that the interface to computers is
that the interface to computers is becoming much more based on natural
becoming much more based on natural
becoming much more based on natural language so I mean in some sense you can
language so I mean in some sense you can
language so I mean in some sense you can see it I mean the Google search box is
see it I mean the Google search box is
see it I mean the Google search box is kind of interest it’s just it’s just a
kind of interest it’s just it’s just a
kind of interest it’s just it’s just a box enter anything you like and it will
box enter anything you like and it will
box enter anything you like and it will answer any kind of questions you have
answer any kind of questions you have
answer any kind of questions you have and what we see today is that of course
and what we see today is that of course
and what we see today is that of course that we are evolving this so previously
that we are evolving this so previously
that we are evolving this so previously it was this kind of one question system
it was this kind of one question system
it was this kind of one question system or question answering system of course
or question answering system of course
or question answering system of course Google is one example this IBM Watson is
Google is one example this IBM Watson is
Google is one example this IBM Watson is a different example you you ask you the
a different example you you ask you the
a different example you you ask you the question it comes back with an answer so
question it comes back with an answer so
question it comes back with an answer so what we’re starting to see now is
what we’re starting to see now is
what we’re starting to see now is dialogues so you actually have a
dialogues so you actually have a
dialogues so you actually have a conversation with your computer or your
conversation with your computer or your
conversation with your computer or your your system and it understands you and
your system and it understands you and
your system and it understands you and keeps a track of history and can respond
keeps a track of history and can respond
keeps a track of history and can respond in a meaningful manner so the the kind
in a meaningful manner so the the kind
in a meaningful manner so the the kind of low-hanging fruit there is all
of low-hanging fruit there is all
of low-hanging fruit there is all customer support functions that
customer support functions that
customer support functions that companies
companies
companies this is very much in the process of
this is very much in the process of
this is very much in the process of being taken over by these kind of
being taken over by these kind of
being taken over by these kind of avatars but then if we look at more
avatars but then if we look at more
avatars but then if we look at more physical entities like robots so what I
physical entities like robots so what I
physical entities like robots so what I would say is the really interesting
would say is the really interesting
would say is the really interesting thing with a robot it’s that it allows
thing with a robot it’s that it allows
thing with a robot it’s that it allows physical presence anywhere at any time
physical presence anywhere at any time
physical presence anywhere at any time that I mean previously you had to be
that I mean previously you had to be
that I mean previously you had to be somewhere to physically interact with
somewhere to physically interact with
somewhere to physically interact with that particular location now by having a
that particular location now by having a
that particular location now by having a robot you can actually be at any place
robot you can actually be at any place
robot you can actually be at any place any place at least that where there is a
any place at least that where there is a
any place at least that where there is a robot and you can be at many places
robot and you can be at many places
robot and you can be at many places physical places at the same time so I
physical places at the same time so I
physical places at the same time so I think that or those things are what’s
think that or those things are what’s
think that or those things are what’s really interesting with robotics and I
really interesting with robotics and I
really interesting with robotics and I mean taking all this together is that
mean taking all this together is that
mean taking all this together is that what we have seen previously is like
what we have seen previously is like
what we have seen previously is like relatively what is a simple or
relatively what is a simple or
relatively what is a simple or repetitive repetitive work being taking
repetitive repetitive work being taking
repetitive repetitive work being taking over by automation but what we see today
over by automation but what we see today
over by automation but what we see today especially if you look at systems like
especially if you look at systems like
especially if you look at systems like IBM Watson is that knowledge work it’s
IBM Watson is that knowledge work it’s
IBM Watson is that knowledge work it’s very much right for automation and it’s
very much right for automation and it’s
very much right for automation and it’s actually the kind of professional work
actually the kind of professional work
actually the kind of professional work which requires a lot of knowledge a
which requires a lot of knowledge a
which requires a lot of knowledge a person to gather a lot a lot of
person to gather a lot a lot of
person to gather a lot a lot of knowledge and then being able to ask
knowledge and then being able to ask
knowledge and then being able to ask questions do diagnosis or suggest
questions do diagnosis or suggest
questions do diagnosis or suggest treatments and so on those are kind of
treatments and so on those are kind of
treatments and so on those are kind of jobs which are currently being targeted
jobs which are currently being targeted
jobs which are currently being targeted by these kind of systems so think for
by these kind of systems so think for
by these kind of systems so think for example lawyers they’re currently
example lawyers they’re currently
example lawyers they’re currently systems that are giving legal advice
systems that are giving legal advice
systems that are giving legal advice much more efficient much faster much
much more efficient much faster much
much more efficient much faster much cheaper and also with higher accuracy
cheaper and also with higher accuracy
cheaper and also with higher accuracy than than humans and it’s also the fact
than than humans and it’s also the fact
than than humans and it’s also the fact that you have again if you take the Iban
that you have again if you take the Iban
that you have again if you take the Iban what’s example what they’re trying to do
what’s example what they’re trying to do
what’s example what they’re trying to do now is of course go into the medical
now is of course go into the medical
now is of course go into the medical business as supporting doctors in making
business as supporting doctors in making
business as supporting doctors in making medical diagnosis so it’s actually very
medical diagnosis so it’s actually very
medical diagnosis so it’s actually very interesting to see what are the
interesting to see what are the
interesting to see what are the consequences and then it is important
consequences and then it is important
consequences and then it is important for all of us so on this note I think
for all of us so on this note I think
for all of us so on this note I think it’s very interesting to look at the
it’s very interesting to look at the
it’s very interesting to look at the game of chess
game of chess
game of chess so the game of chess has been a game
so the game of chess has been a game
so the game of chess has been a game where we humans don’t have a chance
where we humans don’t have a chance
where we humans don’t have a chance so since deep blue beat Garry Kasparov
so since deep blue beat Garry Kasparov
so since deep blue beat Garry Kasparov in 1997 I believe it was actually 20
in 1997 I believe it was actually 20
in 1997 I believe it was actually 20 years ago people haven’t had a chance in
years ago people haven’t had a chance in
years ago people haven’t had a chance in a game of chess but what’s interesting
a game of chess but what’s interesting
a game of chess but what’s interesting is that even if humans cannot be
is that even if humans cannot be
is that even if humans cannot be computers it’s actually the fact that
computers it’s actually the fact that
computers it’s actually the fact that humans are becoming increasingly much
humans are becoming increasingly much
humans are becoming increasingly much better at chess and the reason they are
better at chess and the reason they are
better at chess and the reason they are becoming better is because they are
becoming better is because they are
becoming better is because they are using the chess programs to train so
using the chess programs to train so
using the chess programs to train so they get more effective training through
they get more effective training through
they get more effective training through these computerized chess players and the
these computerized chess players and the
these computerized chess players and the second is that the interest per purchase
second is that the interest per purchase
second is that the interest per purchase is still very high so I mean now that –
is still very high so I mean now that –
is still very high so I mean now that – Carlson is playing and so on that
Carlson is playing and so on that
Carlson is playing and so on that generates tremendous interest even
generates tremendous interest even
generates tremendous interest even though that humans are not the best but
though that humans are not the best but
though that humans are not the best but to kind of make the third point which is
to kind of make the third point which is
to kind of make the third point which is the most interesting one is that even
the most interesting one is that even
the most interesting one is that even though computers are better than humans
though computers are better than humans
though computers are better than humans if you combine humans and computers
if you combine humans and computers
if you combine humans and computers together in teams which to do in
together in teams which to do in
together in teams which to do in something called freestyle chess where
something called freestyle chess where
something called freestyle chess where you have one or more humans playing
you have one or more humans playing
you have one or more humans playing together with one or more chess
together with one or more chess
together with one or more chess computers and they are better than both
computers and they are better than both
computers and they are better than both the humans and the computers so of
the humans and the computers so of
the humans and the computers so of course there is very interesting that we
course there is very interesting that we
course there is very interesting that we complement each other that humans have
complement each other that humans have
complement each other that humans have some strengths and the computers have
some strengths and the computers have
some strengths and the computers have some strengths let’s leverage that and
some strengths let’s leverage that and
some strengths let’s leverage that and become even better together so in my in
become even better together so in my in
become even better together so in my in my mind it’s not either computers or
my mind it’s not either computers or
my mind it’s not either computers or humans it’s more both it’s computers and
humans it’s more both it’s computers and
humans it’s more both it’s computers and humans together collaborating and since
humans together collaborating and since
humans together collaborating and since since I think this is very important I
since I think this is very important I
since I think this is very important I have also been heavily involved in
have also been heavily involved in
have also been heavily involved in education so how do we take this
education so how do we take this
education so how do we take this understanding and move it into education
understanding and move it into education
understanding and move it into education so the concept that I have been
so the concept that I have been
so the concept that I have been promoting is the concept of
promoting is the concept of
promoting is the concept of computational thinking which is
computational thinking which is
computational thinking which is basically a problem-solving process to
basically a problem-solving process to
basically a problem-solving process to describe analyze and solve problems in
describe analyze and solve problems in
describe analyze and solve problems in such a way that computers can assist and
such a way that computers can assist and
such a way that computers can assist and often we use techniques from computer
often we use techniques from computer
often we use techniques from computer science because in my mind
science because in my mind
science because in my mind computer science is really about how do
computer science is really about how do
computer science is really about how do we mechanize problem-solving I mean
we mechanize problem-solving I mean
we mechanize problem-solving I mean already from start when Alan Turing
already from start when Alan Turing
already from start when Alan Turing basically invented computer science
basically invented computer science
basically invented computer science during during the Second World War
during during the Second World War
during during the Second World War his goal was we need to be better at
his goal was we need to be better at
his goal was we need to be better at solving this problem namely cracking the
solving this problem namely cracking the
solving this problem namely cracking the determined code and we need help with
determined code and we need help with
determined code and we need help with that and therefore he invented this and
that and therefore he invented this and
that and therefore he invented this and we have been studying the computational
we have been studying the computational
we have been studying the computational properties or problem solving or
properties or problem solving or
properties or problem solving or computational computation in general so
computational computation in general so
computational computation in general so I will really argue that this is the
I will really argue that this is the
I will really argue that this is the kind of skills that people need to
kind of skills that people need to
kind of skills that people need to develop so what is it I mean what are
develop so what is it I mean what are
develop so what is it I mean what are the actual kind of concrete components
the actual kind of concrete components
the actual kind of concrete components of course it’s giving step-by-step
of course it’s giving step-by-step
of course it’s giving step-by-step instruction actually instructing a
instruction actually instructing a
instruction actually instructing a computer or a person to do things it’s
computer or a person to do things it’s
computer or a person to do things it’s decomposing problems into smaller parts
decomposing problems into smaller parts
decomposing problems into smaller parts breaking problems down finding patterns
breaking problems down finding patterns
breaking problems down finding patterns again this is what machine learning is
again this is what machine learning is
again this is what machine learning is very good at and how do we then taking
very good at and how do we then taking
very good at and how do we then taking these patterns and creating abstractions
these patterns and creating abstractions
these patterns and creating abstractions or more general models from the
or more general models from the
or more general models from the specifics and finally designing
specifics and finally designing
specifics and finally designing algorithms to actually mechanize parts
algorithms to actually mechanize parts
algorithms to actually mechanize parts or all of the problem-solving process so
or all of the problem-solving process so
or all of the problem-solving process so these are usually the basic components
these are usually the basic components
these are usually the basic components of computational thinking so to take a
of computational thinking so to take a
of computational thinking so to take a concrete example so since I’ve worked
concrete example so since I’ve worked
concrete example so since I’ve worked with the National Agency for Education
with the National Agency for Education
with the National Agency for Education we actually have the government just
we actually have the government just
we actually have the government just decided a or had made a new curriculum
decided a or had made a new curriculum
decided a or had made a new curriculum for the K to 9 education here in Sweden
for the K to 9 education here in Sweden
for the K to 9 education here in Sweden which now involves programming and as
which now involves programming and as
which now involves programming and as I’ve been part of that process in
I’ve been part of that process in
I’ve been part of that process in developing this new curriculum I’ve been
developing this new curriculum I’ve been
developing this new curriculum I’ve been using this example to to show what we
using this example to to show what we
using this example to to show what we mean so assume you want to teach the
mean so assume you want to teach the
mean so assume you want to teach the kids to do regular polygons so in this
kids to do regular polygons so in this
kids to do regular polygons so in this case do a square and one way to free
case do a square and one way to free
case do a square and one way to free kids to show that to understand what a
kids to show that to understand what a
kids to show that to understand what a square is make a program that draws a
square is make a program that draws a
square is make a program that draws a square and in one example would be to
square and in one example would be to
square and in one example would be to use scratch which is one particular
use scratch which is one particular
use scratch which is one particular programming language for doing that and
programming language for doing that and
programming language for doing that and to do that in scratch you paste
to do that in scratch you paste
to do that in scratch you paste would write a program like this you you
would write a program like this you you
would write a program like this you you you have to understand that the square
you have to understand that the square
you have to understand that the square consists of four sides and to draw a
consists of four sides and to draw a
consists of four sides and to draw a side you move 100 step and then you turn
side you move 100 step and then you turn
side you move 100 step and then you turn 90 degrees and you’d go 100 F you turn
90 degrees and you’d go 100 F you turn
90 degrees and you’d go 100 F you turn 90 degrees it go 1 and step turn 90
90 degrees it go 1 and step turn 90
90 degrees it go 1 and step turn 90 degrees go one on the steps and then you
degrees go one on the steps and then you
degrees go one on the steps and then you have done your your square but then you
have done your your square but then you
have done your your square but then you will what is the commonality here you
will what is the commonality here you
will what is the commonality here you want to find the patterns so of course
want to find the patterns so of course
want to find the patterns so of course the pattern is you repeat something four
the pattern is you repeat something four
the pattern is you repeat something four times so you do draw the line four times
times so you do draw the line four times
times so you do draw the line four times and you turn and you repeat that four
and you turn and you repeat that four
and you turn and you repeat that four times then you say all great now you’re
times then you say all great now you’re
times then you say all great now you’re down to square how can you do a Pentagon
down to square how can you do a Pentagon
down to square how can you do a Pentagon of course what do you need to change
of course what do you need to change
of course what do you need to change well you have to change the four into a
well you have to change the four into a
well you have to change the four into a five everyone can understand that but
five everyone can understand that but
five everyone can understand that but what happens if you change the 100 into
what happens if you change the 100 into
what happens if you change the 100 into 200 well you get the square with twice
200 well you get the square with twice
200 well you get the square with twice as long sides and four times as much
as long sides and four times as much
as long sides and four times as much area so you can talk about the relation
area so you can talk about the relation
area so you can talk about the relation between circumference and an area so
between circumference and an area so
between circumference and an area so what we need to change this course how
what we need to change this course how
what we need to change this course how much we actually turn and then idea can
much we actually turn and then idea can
much we actually turn and then idea can be like me you can compute it or it can
be like me you can compute it or it can
be like me you can compute it or it can be lazy like me and just say oh well you
be lazy like me and just say oh well you
be lazy like me and just say oh well you have to do 360 degrees well you did four
have to do 360 degrees well you did four
have to do 360 degrees well you did four times ninth in the first case now you
times ninth in the first case now you
times ninth in the first case now you have to do four times five times what is
have to do four times five times what is
have to do four times five times what is the seventh 2.5 or something
the seventh 2.5 or something
the seventh 2.5 or something in order to get the full square and now
in order to get the full square and now
in order to get the full square and now you can generalize this even further and
you can generalize this even further and
you can generalize this even further and get construct an algorithm for drawing
get construct an algorithm for drawing
get construct an algorithm for drawing an arbitrary polygon basically just
an arbitrary polygon basically just
an arbitrary polygon basically just asking for how many sides and repeating
asking for how many sides and repeating
asking for how many sides and repeating this so in this particular simple
this so in this particular simple
this so in this particular simple example we’re going through all these
example we’re going through all these
example we’re going through all these different steps we have decomposed we
different steps we have decomposed we
different steps we have decomposed we have found a patterns we have
have found a patterns we have
have found a patterns we have generalized and we have designed an
generalized and we have designed an
generalized and we have designed an algorithm for solving this but it’s not
algorithm for solving this but it’s not
algorithm for solving this but it’s not only about skills it’s also about
only about skills it’s also about
only about skills it’s also about attitudes like confidence in dealing
attitudes like confidence in dealing
attitudes like confidence in dealing with complexity problems with many
with complexity problems with many
with complexity problems with many different components working with
different components working with
different components working with difficult problems and persistence I
difficult problems and persistence I
difficult problems and persistence I mean this is one of major challenges
mean this is one of major challenges
mean this is one of major challenges persistence how to get the students to
persistence how to get the students to
persistence how to get the students to not give up but actually continue in
not give up but actually continue in
not give up but actually continue in solving these problems tolerance for
solving these problems tolerance for
solving these problems tolerance for ambiguity the ability to deal with
ambiguity the ability to deal with
ambiguity the ability to deal with open-ended problems how do we take an
open-ended problems how do we take an
open-ended problems how do we take an open and a problem and narrow 8,000
open and a problem and narrow 8,000
open and a problem and narrow 8,000 something that we can actually solve and
something that we can actually solve and
something that we can actually solve and maybe we can even just by narrowing it
maybe we can even just by narrowing it
maybe we can even just by narrowing it down and juicing this factor we have the
down and juicing this factor we have the
down and juicing this factor we have the computational tools to our advantage
computational tools to our advantage
computational tools to our advantage and finally also the ability to
and finally also the ability to
and finally also the ability to communicate and work with others to
communicate and work with others to
communicate and work with others to achieve a common goal so it’s really
achieve a common goal so it’s really
achieve a common goal so it’s really about communicator and collaborating you
about communicator and collaborating you
about communicator and collaborating you cannot do things by your own you have to
cannot do things by your own you have to
cannot do things by your own you have to work together whether you work together
work together whether you work together
work together whether you work together with people or with computers that does
with people or with computers that does
with people or with computers that does not matter that much I mean you have to
not matter that much I mean you have to
not matter that much I mean you have to collaborate because the interesting
collaborate because the interesting
collaborate because the interesting problems that we need to solve these
problems that we need to solve these
problems that we need to solve these days are not promised at a single
days are not promised at a single
days are not promised at a single individual can solve by the or by
individual can solve by the or by
individual can solve by the or by themselves so therefore I think that
themselves so therefore I think that
themselves so therefore I think that computational thing it really captures
computational thing it really captures
computational thing it really captures what we need to be good at in order to
what we need to be good at in order to
what we need to be good at in order to leverage all these artificial
leverage all these artificial
leverage all these artificial intelligence and other computational
intelligence and other computational
intelligence and other computational tools that we have so what’s interesting
tools that we have so what’s interesting
tools that we have so what’s interesting here is that as we learn more about
here is that as we learn more about
here is that as we learn more about artificial intelligence and as we
artificial intelligence and as we
artificial intelligence and as we develop these tools based on for example
develop these tools based on for example
develop these tools based on for example machine learning we need to be able to
machine learning we need to be able to
machine learning we need to be able to leverage them through our computational
leverage them through our computational
leverage them through our computational thinking by being able to understand how
thinking by being able to understand how
thinking by being able to understand how the computational processes work and how
the computational processes work and how
the computational processes work and how we can benefit from those when we saw
we can benefit from those when we saw
we can benefit from those when we saw problems so what we see today is
problems so what we see today is
problems so what we see today is actually a number of interesting cases
actually a number of interesting cases
actually a number of interesting cases where people use machine learning or
where people use machine learning or
where people use machine learning or other tools as part of their own
other tools as part of their own
other tools as part of their own problem-solving process and to just give
problem-solving process and to just give
problem-solving process and to just give the single concrete example was this
the single concrete example was this
the single concrete example was this writing these reports on what jobs are
writing these reports on what jobs are
writing these reports on what jobs are likely to be automated by actually
likely to be automated by actually
likely to be automated by actually learning and model of what are the
learning and model of what are the
learning and model of what are the factors that influence the risk of being
factors that influence the risk of being
factors that influence the risk of being taken over by automation and of course
taken over by automation and of course
taken over by automation and of course as we are getting better at this we can
as we are getting better at this we can
as we are getting better at this we can of course develop better artificial
of course develop better artificial
of course develop better artificial intelligence tools so by becoming better
intelligence tools so by becoming better
intelligence tools so by becoming better at using the tools we also become better
at using the tools we also become better
at using the tools we also become better at developing them further so I think
at developing them further so I think
at developing them further so I think this is really interesting
this is really interesting
this is really interesting interconnection between these two and
interconnection between these two and
interconnection between these two and also the fact that we as people can
also the fact that we as people can
also the fact that we as people can benefit from having this tool at our
benefit from having this tool at our
benefit from having this tool at our disposal and becoming better at solving
disposal and becoming better at solving
disposal and becoming better at solving problems so kind of to summarize I think
problems so kind of to summarize I think
problems so kind of to summarize I think it’s it’s not either/or it’s together
it’s it’s not either/or it’s together
it’s it’s not either/or it’s together together with the machines and together
together with the machines and together
together with the machines and together with these very sophisticated tools we
with these very sophisticated tools we
with these very sophisticated tools we can solve the problems that we face as a
can solve the problems that we face as a
can solve the problems that we face as a society and maybe you can see this as
society and maybe you can see this as
society and maybe you can see this as some next step in evolution
some next step in evolution
some next step in evolution taking this synergy
taking this synergy
taking this synergy between man and artificial intelligence
between man and artificial intelligence
between man and artificial intelligence thank you very much
”
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