From AI to Computational Thinking | Fredrik Heintz | TEDxNorrköping

so I guess I’m one of those good old

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