Good afternoon everyone, it's a pleasure to see you in
person and to put faces to names and see you at different
heights, which we usually have more of than we do, because
most of you don't share, you don't open the video and in
that there's no difference, it's probably your students and our students.
But you know us and you've seen us,
because we've already been in sessions.
So today we are fortunate to have here in person an event
where we will give the final touch, hopefully a golden
one, to the Artificial Intelligence course, with
activities for all ages and subjects.
Well, I won't introduce myself because you already know me.
I am Gregorio Roble, I am a professor here at the Juan Carlos University.
I welcome you and thank you for coming here, I know that
many of you have come from very, very far away.
We have teachers from every remote corner of the
Community of Madrid, and that makes this 4th edition
of the course a source of satisfaction for us.
Well, and without further delay, I'm going to give the
floor to Beatriz Rodríguez Merchán, who is the Director
General of Bilingualism and Quality of Education of
the Community of Madrid, who has come here and who is
going to give us the official welcome.
Thank you.
Okay, thanks.
Okay, that's enough.
Thank you Bego, thank you very much.
Well, good afternoon everyone.
First of all, I want to thank all the teachers,
speakers, organizers, and all the collaborators
who make this 4th edition of this course possible,
which I believe is so important.
An initiative promoted by the Community of Madrid in collaboration
with this wonderful university that always supports us in many
training courses and as I mentioned Gregorio, I think it is important
that behind the training of teachers there are universities
that endorse the content and the courses that we do.
This edition reflects something very important: the enormous interest
that currently exists in understanding and applying artificial
intelligence in the educational field. The fact that you are here today,
and all those who are not here but have been in the other sessions, says a lot
about your teaching work.
As we were discussing earlier with the organizers,
we had 150 places available, but we have received
more than 530 applications, which is always a sign of success.
We would have loved to be able to attend to those more than 500 requests for
organizational reasons.
It's not always possible, but it says a lot about the interest this course
generates among all teachers, so I thank you for it.
I believe that artificial intelligence should not be understood
solely as a technological tool, although it is one, because it
is also an opportunity to develop new methodologies, to foster
critical thinking above all, which I believe is one of the main
tasks we have as teachers with such a powerful tool as this, and
to personalize it so that it helps us personalize learning and
prepare our students for a world that obviously changes much
faster than we can sometimes adapt in schools.
But along with the opportunities, we must also address challenges.
That is why it is essential that both teachers and students
understand what artificial intelligence really is, how it
evolves, what its benefits are, but also, and above all, what its risks are and
where its limits may lie.
Talking about artificial intelligence in education, I believe,
necessarily involves talking about ethics in that artificial
intelligence, privacy, critical thinking, responsible use,
and above all, teacher training, which is why we are here today.
We need teachers like you who are prepared not only
to use these artificial intelligence tools, but
to teach your students to live with them in a natural,
but also safe, critical and conscious way.
Because educating today is not just about knowledge, which we all
know how to do, but also about helping to interpret the world around us.
So, well, I don't want to take up any more of your time from these
wonderful presentations that we're going to have next.
I just want to wish you a good session.
Thank you all so much for your interest and especially for the commitment you
show to your students every day.
So thank you all very much.
Congratulations and I hope to see you in many more courses.
We at the School of Computer Science are preparing a project called
Impacta URJC, in which we want to link students from 4th grade of
Primary Education to 4th year of Secondary Education in
technological projects that have a social impact.
Teams will be required to have girls among their members.
No, it's not closed to just girls, there have to be girls present.
We believe that linking technology with more social
action can attract girls to the technological field.
So, well, we're also going to add something else, which is that
each team will have a mentor, like, sorry, they will have a
research professor from our school as a mentor, so that she can
also serve as a role model for these potential
future students and help to reduce gender bias.
Nothing else.
Congratulations on your participation in this course and congratulations to the
organization for
the great work they have done and to the Community of Madrid for collaborating on
this initiative.
Thank you so much.
Very well, thank you very much for your words.
And well, to use the cooking analogy, it's my second
passion after artificial intelligence.
Today we have a tasting menu with six courses,
so let's go, they are very diverse dishes.
It is important to know that there are more technological dishes, more
educational dishes, and some teaching experiences.
Well, a little bit of everything, right?
Perhaps not all the talks will be for everyone, in the sense
that what also happens when one goes to a tasting menu,
sometimes I like one dish more than another, we are going to
start with a very technological dish and the first talk will
be given by Jesús María González Barahona, from the Rey Juan
Carlos University, and he will tell us how to have generative models on your
computer.
Note that whenever we have been talking about generative models up to now, we
have
been talking about using generative models that were in the cloud,
which were services offered by the big technology companies.
It usually works, and Jesus is going to tell
us how to do that on our own computers.
So let's take advantage of the welcoming applause for Jesus also for the
farewell applause here at the table, for the welcome they have given us.
Thank you all so much. No!
Director, Vice-Rector, Director, and I now give the floor to Jesus.
Thank you all very much.
First of all, I have no choice but to thank you for inviting me back here
again,
Estefanía, Gregorio and everyone else involved in the organization.
Thank you so much.
Which also gives me the opportunity to keep updating the talk, as you can
imagine.
At the speeds we're going, it's needed not every year, but every month.
And in fact, this is a slightly shorter version of a longer
version in English, which is the one I've been using
lately to talk a little about this type of model.
I like to put generative artificial intelligence running on your own
infrastructure because it's not something we necessarily want to run in our own
home.
Maybe so, but it's also something that can be moved.
For example, a company within the company itself or in a public administration, as
is ultimately the case with public education.
It could be something that the Ministry will offer us, for example, or in our
case, the
university could be infrastructure deployed within the university itself.
The key is that you control the infrastructure and how you control it.
Are you sure about what you're doing?
Think for a moment about what this implies.
I'm sure you've thought about it many times, but let's begin.
This implies using any artificial intelligence tool today.
So you enter, for example, Chile GPT and you start writing things from that
moment, you start pressing a key.
You can rest assured that everything you're going through there is being
recorded and stored somewhere.
And since you normally had to authenticate first, it's being
stored and saved along with your data, so it's perfectly known.
And now is there someone somewhere, I don't know, in California, for example,
who, if you want to know what
you asked yesterday, what you asked a month ago, what you asked two months ago,
what the AI ​​answered you?
What did you do from there?
Well, you can know that with complete peace of mind, which in itself isn't
necessarily a bad thing.
In fact, it helps improve systems and other things, but it's completely out of
your control.
And there are two things that are fundamentally out of your control.
The first is what happens to your data, and that's important, but the
second is perhaps even more important, and that's how you're actually
interacting with artificial intelligence, with the model behind it all.
Because between you and the artificial intelligence model there are
many layers of software that can do things like change the answer it gives
you, not give you certain answers, or even give you different answers
because it is in the interest of the provider that does it.
Furthermore, it should be noted that in our case, the supplier is
most likely subject to different regulations than those we have in
Spain or Europe, which means that they may be applying California
laws to us, and that may not seem beneficial to us, since
we did not participate in the enactment of those laws.
And there are things that, for example, are perfectly legal in Spain, but we
can't do them with AI for that reason.
Conversely, if you put all of this together, what happens is that we are in
a
situation where we have very little agency, very little control over how we
handle artificial intelligence in general, and as has already been said, I
think we are all aware of it.
The use of artificial intelligence will become increasingly important, not only
in education, but in all facets of life.
So, having the possibility of maximizing that control we have over
it, at least a priori, seems like an interesting option.
And this is an option that allows us to not have absolute control.
I will explain why later, but in some cases, it does require a higher level of
control than usual.
Furthermore, it may make sense from an economic point of view, as you know that
all this is not free.
The fact that we can now access some services for free does not mean that they
are free.
We are now paying in other ways, but that doesn't mean they will always be free or
at least at that price.
So, from an economic point of view, in many cases it might also make sense, but
today I'm
not going to focus on the economic aspect, I'm going to focus on other
aspects and above
all I'm going to try to explain it in a somewhat practical way, not so that you
can set
this up in your house, because we wouldn't have time, would we?
You'll see later with some of the technologies that will
be discussed, but above all so that if possible you leave
with a little concern that maybe I should care a little
about having control over the AI ​​model in this version.
As I was saying, it's a Spanish version translated from English with one of
those models that we've installed in my house in particular.
I haven't installed this one, I haven't used it with my home
hardware, let's say, but later I'll explain one of the possibilities
we have of using these models without having to, as I would say,
without having to use one of the usual places where you work in a
place that gives us more direct access to the infrastructure.
That also means the translation has rats.
It's been checked a little, but it has some errors, especially in the
translation of English
words, which sometimes makes it a bit strange to translate terms that we use
differently in Spanish.
In any case, let's first review what a generative model has.
Well, naturally I'm referring throughout the presentation to AI models,
let's say, of the latest generation, so to speak, which is what we normally call
generative models.
And I'm going to give a very quick summary.
Most of you know that a model is fundamentally numbers in a neural
network.
To put it very simply, each neuron communicates with the next ones, with the
parameters, and depending on those parameters it does some things or others.
Those parameters are nothing more than numbers.
So, if someone gives you a neural network, if someone gives you a
generative
language model, what they give you is a bunch of numbers.
And when I say a lot, I usually mean thousands and millions of numbers.
But that's all there is to it.
That, plus information on how to turn those numbers into something
that works, which is basically the software that animates those numbers.
Therefore, we have what we call the architecture, which is the way neurons
are arranged.
To put it very simply, we also have the pesos, the numbers in English and in
Spanish.
Sometimes we call them weights, sometimes high, other names, and the software that
makes it all work.
The software is usually free software, so I can download it safely from anywhere.
Almost all manufacturers, the big manufacturers, are currently using free
programs to do it.
Therefore, that part is not very complicated.
The architecture is usually published; it's part of the technical information,
and
they give it to you for various reasons. The key is whether or not they give me
the weights.
If they give me the weights, and I have the right software and I know the
architecture,
I can install it on my own infrastructure. Sometimes they give me
the weights, and sometimes they don't.
But if there are very popular models that you can no longer access, we still have a
little bit of a return.
Okay, let's see, they can fix it.
Meanwhile, he said that sometimes you can only use it on the provider's site, let's
say
the one that's giving you the service, but sometimes they also give you the
possibility,
if you want, to download those files and install them on your own machine, on your
own infrastructure.
Those are the models I'm going to refer to.
There are other things that I won't go into much detail about today so that the
models are truly free, reproducible, etc. In other words, I have total control over
them.
I also need other things like training data, which are essential,
for example, to detect biases, but also, for example, to detect
backdoors or to detect certain intentional intentions.
During training.
I need the full description of the training process so I can replicate it.
Then I need the software that allows training and comparison, because the
models
are, let's say, trained in small steps, and each time there is a new step it is
compared with the previous one to see if we have improved or not. I need all of
that, and therefore I also need all those intermediate models.
And I need documentation that explains all of that.
When we have all that bottom part, we can say that we have virtually total
control over the model, because I could reproduce that unique model; all I need
is the right hardware.
You've probably heard that the right hardware is expensive, and only big
companies make it.
It is true that a lot of hardware is needed, but it is also true that, for
example, in Spain or in the European Union we have more than enough hardware
to do that kind of thing, paid for in many cases with public money.
Whether we want to dedicate it to these things or whether it's profitable, given
that
other people have already done it in companies, well, it could be done.
The calculations can be done, and you can see how if we eventually wanted to have
a model similar to the big models and we had been given all that data, we could
reproduce it, and what is very interesting, reproduce it with or without
the
biases we wanted, and reproduce it understanding exactly how the model is
going
to work, to the point that we understand it today, although you know that we still
don't fully understand how these models work, but we do know that there is a
certain
relationship, for example, between the learning set, the data
used to train them, and the results that the model gives.
We're not going to focus on the ones at the top, where we mainly have weights,
although
I'll talk a little bit about the others later, because with those at the top my
control situation improves a lot.
Even though I can't do everything with them or know everything about them.
So we have a wide range of models that meet these conditions in one way or
another.
You know the first ones at the top because they are, as we usually use
models, not the majority of people.
We have an application, usually a web application, and the model is behind that
application, so to speak.
How do we use the web application?
We don't know exactly what the model does.
Then we have the models that are directly or almost directly accessible. We also
have some kind of application, but it's a very lightweight
projection where what I write practically goes into the model.
They're usually GPT-style things, where I practically talk to the model.
The ones you have above refer more to more elaborate things like Notebook, LM.
For
example, I give it information and it generates, for example, a podcast and it
knows how to do that very well, but there are other things that I can't do with it.
Then I have the models with available weights; these are the ones that will
start to interest me from then on. These are the ones I've talked about before,
where the manufacturer, the one who trained the model, has given me the
weights.
And then I have several categories, which are where the information I had
before, where I was saying that I can also reproduce what I have, the set of
elements, etc. Well, depending on how many of those characteristics I have,
the others below in the complete presentation have a description of all of
them.
But I'm not going in today.
But I would like you to understand that what we are going to talk about when we
say to
arrange the weights, as I said before, is only a certain improvement in control
that
may be very interesting to me but that is not absolute, that there are still more
things we can achieve and that suits me.
This would be the complete scheme in English, where what we have in the
middle, the greenest part, is what we would love to have. That is, models
that we control almost totally where we know how the model is trained, we
know what has happened to it, we can reproduce it from scratch if we want,
etc. And at the top end we have the models that we use behind applications
where we can only interact with them in very limited and very controlled ways.
In this context we can see that we have three fundamental things that
matter to us in determining the openness of a model.
On the one hand we can say that the model itself is open.
To sum it up, just give me the weights and the information I need to be able to
handle it.
But we also have the software.
As we've said, the software itself can be open source software, right?
And I'm referring to the software for making inferences, that is, for
interacting with
the model when it is already trained, but also for training it, for example.
And finally, we have what we might call open science characteristics, which are
things like everything being reproducible from scratch.
Each of those things you have there can be studied separately and in each model.
If I want to know exactly what I can and cannot do with the model, I need to know
for each of them what characteristics it has, what they give me in return, and
under what license.
Because although the legal status of the models is still under much legal
discussion, for now licenses very similar to those of software are being used.
Therefore, they can give me the model but without permission, for example for
commercial use or without
permission for I don't know what or for I don't know how many, for example, without
permission to retrain, which is something that may interest me a lot.
So sometimes I have the technical ability to do something, but I don't have the
legal ability
because the manufacturer who made the model, the one who trained it, doesn't
give me that possibility.
In any case, as I said, doing a detailed analysis involves looking at all those
things.
I just want you to understand that talking about open models is a huge
oversimplification, and I'm going to do that from now on.
But. But it's very big.
There are many details I need to look at to see exactly what it means for the
model to be open, because for each of those things we can apply the four
freedoms
of free software that you may know, which were defined by Richard Stallman
back in the 80s and which are the four things that are looked at to know if a
program is free: that I can use it as I want, that I can study it as I want, that
I can modify it, if I am interested in modifying it, and that I can redistribute
it, give it to others, etc., etc.
I can apply the same thing to models, but I can also apply it to all the things
we've seen before about models.
I can apply it to the weights, I can apply it to the inference software, it
can
be applied to training software, I can only apply the dataset, etc., etc.
So when I want to know if a model is, let's say, completely free, it would have
to meet all those conditions.
For all of the above.
There are some of those models, but they are very few for primarily
practical reasons, as I will also discuss a little later.
All of this ultimately boils down to a few things that are what I might be more
or
less interested in knowing, such as whether or not I can have a model, for
example, what level of access I have.
I can run the model however I want.
That's technically called making inferences.
I can ask any question, so to speak, the model, or there will be certain questions
that I
won't be able to ask or that the model won't answer, but not because the model
doesn't know, but because they are restricted.
Because in my access to the model, those types of questions aren't allowed,
and there's something in between that intercepts the response and removes
parts of it so I don't see them. Or, as I said before, it changes them.
Everyone here thinks about sex and weapons.
I don't know what to think, about more mundane things, like you asking "Where do
I give?"
This summer I'm going to Valencia on vacation where I can go for a walk.
Imagine that the model captures the response and where it says "Well, go for
a walk on the beach."
I don't know what he's telling me.
Go get a coffee at Starbucks because the model is programmed that way, because
it's software that interacts with the model, and every time it finds a pattern
like that, it changes it for someone who has sponsored it, for example.
But these are things we're not yet seeing from a sponsorship standpoint, but I can
assure you we will see them very soon.
And above all, it's very difficult to know when they are happening, when they
are not happening.
So that level of access is, let's say, the first thing I'd like to know.
I have real access to the model and the limitations I'm seeing are those of the
model
itself, not those of the software or things that have been put in front of me.
Second control: control has to do with being able
to modify the way it works technically.
There are many ways to do it.
One of the most common is Hay Tunes, which has been translated for me here in
a very, very funny way.
Finally, "tuning"—which I don't really know what "tuning" means—is like refining
the model.
This means taking the model as it is and partially retraining it to perform
specific tasks.
It's a very interesting thing because it allows a generalist model to be very good
at certain things.
I give him some documentation.
For example, imagine a medical exam for something or other,
and the model learns that documentation specifically and
is able to answer questions related to that documentation much better.
And is that something we can all do? Can we give him our course
notes, for example? Can I do that kind of thing, or not?
I can evolve the model in ways that interest me or not; this is fundamental
to being able to use the model realistically and, above all, more
reliably.
What control do we have afterwards?
I've already talked about the data, and before, I can control what I pass to the
model.
Can I monitor the results? Yes, if they are being saved.
I don't know if those are the ones I'm actually putting in.
Autonomy.
How dependent are we?
For example, if you start using a model in class, you will be able to use it
throughout the
academic year, or perhaps halfway through the year they will change it because the
manufacturer is interested and what was previously free now becomes a paid
service, or
they simply remove access to the model because it is no longer commercially
viable for them.
If I don't have a minimum level of independence that allows me to decide
when
I want to use the model, but instead I depend on the manufacturer, my autonomy
is very limited, and mind you, autonomy is also linked to the economy.
The autonomy might be related to the fact that it's free for me now, but halfway
through the course,
they charge me a certain amount because the manufacturer's policy has changed,
for example.
So my autonomy is very limited, especially if I don't have those
resources
to spend on the model or if my students don't have them.
And finally, trust. I've missed a line here.
I'm sorry.
Basically, I can make sure that the model does what I think it does.
Or as I said before, that they are changing the behavior of the model in
some way.
Why does all this matter?
Well, you can imagine.
Access to the model matters to me because it enables or prevents many use
cases, or there are things I won't be able to do if I don't have the access I
want. The model.
But it also has a lot to do with the possibilities for innovation and the
possibilities for integration with other tools.
In other words, realize that it's not just a matter of whether or not I'm
deceived by the
answers, it's a matter of whether or not I can develop applications from within a
company.
For example.
Control of the model also has a lot to do with what use cases it can utilize,
whether it can be used, for example, by companies or public institutions in
certain cases, and also with innovation and integration.
Data control obviously has a huge impact on privacy, but also
on our dependence on who provides us with those models.
Also about this asymmetry of knowledge, that they are going to have a
lot of knowledge about us, much more than they have now.
And that, depending on the circumstances, might not interest us.
And again, remember that this affects us not only as individuals, but also, for
example, as organizations.
Is there a place where they will know everything my company or organization is
doing
because it's receiving all the requests from my employees and often directly
linked
to the company name, for example, which is the one who pays for that, and in
certain contexts that can be very important?
Imagine the context of defense, for example.
Then I'll explain why there are so many defense companies, because the use of
these
models is prohibited, for example, although people then use ways to try to
continue using them.
Well, autonomy has already been mentioned and it has to do with very basic
aspects such as competence and competitiveness and trust, which has to
do
not only with everything we have talked about, but also with transparency,
how much we know about what the model is doing versus what we believe it is doing.
Okay, now you have several slides that I'm going to skip over, and the
laws afterwards, if you want, are each of the types of access to the model that
I've put.
So what are the implications from these points of view that I've mentioned?
I'm going to skip them because we have a chart down here and you have, by the way,
a lot of links.
You can download the presentation later if you want, and there are
many links and sites where you can use all these things.
But I'm going to skip ahead to a table that's not here, because I'll arrive on
time in the end.
Remember, this was the diagram with all the models and this is the table I was
referring to.
This is a very simplified table regarding the type of access to the model.
What can I do then?
One first thing we can do is, for example, when I am working with
her or when I am asking my students to work with her.
I can see which of those parts I'm in, and if I haven't made a mistake
in the classification, then we immediately see what characteristics that
has.
For example, when we are asking our students to use, for example,
an automatic translator based on generative AI models, then the
access is to translate as defined by the app, nothing more.
And in all other dimensions we have nothing, we have no control over the
model, we don't know if it's doing other things or any other story.
We have no control over the data, what we ask you to translate, or what you return
to us. Translated.
We have no autonomy.
If they cut off my access to the model, there's nothing I can do.
And finally, I can only be confident or not that the translation is good
depending on how I look at the translation, but it is impossible to be
sure that specific translations will be done correctly, because no,
I have no real information about how the model works at the opposite
extreme, which is the one you have at the very bottom, which would be,
so to speak, the most open use than what we call reproducible, because
it means that I could use it however I want because I would use it in my
infrastructure.
Furthermore, I would control all the software, I would control the model, I
knew how the model was trained,
so I also have complete control of the model, I also have complete control of
the data, because I know
everything that happens to the model and everything that the model returns to me.
It's all within my environment, so to speak.
Is the autonomy complete because I am the provider myself, or is it someone I trust
who is doing it for me?
The provision of the service and the confidence that the model does what it
should do,
because somehow the model does exactly what it has been trained to do, which I
also know it is, is total.
So of course, keep in mind that all our models will fall between those two
extremes, and as I mentioned before, in the time I have left, I'm going to
focus on those of you who have models with lower available weights.
In other words, let's at least assume that I can run the model on our own
infrastructure and realize that this already gives me a few
interesting things compared to what we had at the top.
I suspect, for example, that access will be somewhat restricted,
but I'll already have many interesting features.
I don't know everything the model does, but I know that I put in the software
that manages the model, for example, so I can trust it.
The model control isn't perfect either, because I don't know exactly if the model
does what I've been told it does.
It may be conditioned, it may have backdoors, but again
it's on my infrastructure running with my software.
So I have considerably more control than I did before.
Data control is complete, and that is fundamental for issues such as privacy,
and especially for the privacy of our students, which is particularly
protected.
Instead of sending the data to a company that I have to trust or not to do who
knows what with it, I send it myself.
Imagine in Madrid, for example, we send it to the Ministry, whatever
infrastructure
they have there and we directly trust the Ministry because they have set up
infrastructure precisely so that this can be done without any problems of privacy
and autonomy and trust, without being perfect, well in terms of autonomy I have
a
certain autonomy because I provide the service and so on and I have the weights,
so I can do quite a few things.
I have very little confidence because I don't know exactly what's in the model.
Remember that these are models whose training methods I don't know; they give
me the weights
and the model's characteristics, but I don't know how they were trained.
Therefore, I can have less confidence there, and in between we have several
degrees of open models, some with more interesting features and others with
fewer.
Many problems remain unresolved.
Anyway, with this I just want to introduce you a little to the problem.
For example, we have a very complex relationship between the training data.
The model.
Reproducing it is not trivial for many reasons, not only because of the cost and
infrastructure, and in many cases, it is also not easy to know exactly which
categories to look at in a model. Many details and not always.
It's not always easy to do.
And perhaps the most important point is the one at the very bottom.
If I don't have absolutely all the data for the model and nobody has reproduced
it, I
can believe what the manufacturer tells me or not, but it's very difficult to
know that the data they are giving me is correct.
And with this we are now going to talk very quickly about some examples
and some direct implications of these models, and these are the ones I can
install.
I repeat, in terms of infrastructure at least they have to give me the money for
the
software and, let's say, the recipe, the architecture to be able to run it.
So, what's the minimum?
So we go back again to reviewing the inference code with all the necessary
libraries, which fortunately is usually free software.
Therefore, most or all of the models I know of give me the weights, that's not a
problem.
Then the weights, including some parameters, etc., which they also have to
give me to use the model, basically so that I can run it with the software I
wrote earlier.
So if they give it to me, perfect, I usually also need at least a technical
report, at least a summary, where they explain how I have to do the
prompts, what kind of capabilities the model has, things like that.
For example, whether the model understands a graph or not, whether the
model is capable of producing graphs or only text, things like that.
And usually there's something called the model card, which is the technical
specifications
of the model, which gives me some more detailed information that helps me run it
with confidence.
Let's say the minimum, I repeat once again, is a model with
available weights, just as we classified it before.
I'm skipping this because I don't think it says anything new.
Let's go back to the previous chart and look at some examples of where
these models live. First, the place where they usually live is called Aging Face.
For those of you who know GitHub, the world of software, well, it's a bit like
the GitHub of models.
There are literally tens of thousands of models hosted on face.
I can download the weights of all of them.
Not all of them are models of what they call foundations,
models that someone has trained from scratch and so on.
Many of them are simply refinements of other models, adaptations,
etc. But there are models of all kinds for all kinds of
tasks and for all kinds of activities.
So, if you go to Facebook and look for "Models,"
you can find a very complete list of all of them.
And the main advantages of these models have to
do with the features we have seen before.
Perhaps the first one is the most important.
We know that once I have the model, they can't take it away from me.
I will always have it available, and that's essential.
If I want to ensure that I can do things with the model, I can execute them.
Infrastructure, as I said, I can integrate them very easily with other
applications and in
fact, one of the most remarkable things that is happening now in this world is
precisely that
they are integrating with programs that do lots of different things, so that even
if you don't
know anything about computers, you can simply be the model, sorry, the program
that is very fast.
You download the template, which is usually transparent because the program
itself creates it, and you can start using it directly.
There is a lot of competition in the market because in the end any company
can install them and provide access to them via the Internet.
And if you decide, you're the one who decides whether you want to run them
either in the cloud on some infrastructure that someone can see, or
on
your computer, or sometimes even on your mobile phone, depending on the
model; they also have disadvantages, it takes a little more work to use them.
They are usually not as advanced as the most advanced ones available.
Right now, people are usually talking about a delay, like between a year and a
year and a half.
If you mean that the open models of today, well, maybe they are equivalent to
those of, I
don't know, January of 25, although other people say that this has been reduced a
lot and that now we are between six and nine months.
But it depends a lot on the model and the task you're interested in. And we've
already seen the advantages of self-hosting, that is, setting it up on
your own infrastructure.
So I'm going to skip those.
I'm going to focus for a moment on the disadvantages, which also exist,
remembering
that they are running on infrastructure that I provide.
So, access isn't going to be free for me; I'm going to have to buy or pay for the
infrastructure
somehow. And I'm wondering how it's possible that if the infrastructure costs
so
much—and it's true that it costs so much—they're now giving me free access to
it because
everyone who uses, for example, that GPT, knows that within certain limits, you get
it for free.
But start thinking right away if it hasn't already happened to you, or if you
know
people who have, that you've already gone beyond those limits and started buying
it, because when you start using it, you use it very little, but over time you use
it more and you probably need more things.
And above all, consider whether prices will remain that way or if
there are incentives for prices not to remain that way.
In any case, we have to maintain it and we have to pay for it.
And besides, the available hardware is expensive, especially now,
when there is a great need for many companies to acquire that hardware,
so its price is not decreasing at the same rate as other hardware.
In IT, you have to take care of maintenance, even if it's small,
and above all, you have to make many decisions to stay up to date.
This world moves very fast, and the model that was better a year ago is now one
of the worst, so you have to be very aware of how things are evolving.
In other words, right now this is still a world where you'll need mostly
knowledge and a few resources—not a lot of resources without a lot of
knowledge, mind you, not special technical knowledge like knowing a lot
about computers, but understanding very well which tools are the best here right
now.
And which models are the best, and then how to use them.
But understand that there are many people involved in this who don't have specific
training
in computer science; what they do have is specific training in how these types of
models work.
If we have time, I'll give an example later; we'll get into the
equipment requirements, as they depend a lot on what you want to do.
A high-end computer with a gaming GPU like the ones kids use
for gaming is probably more than enough to run most of the
games and do some pretty interesting things.
Little by little things are changing too, and less and less hardware is needed.
There are now models that even run on mobile phones.
The thing is, they might not give you the same capabilities as a model that runs
the cloud with a bunch of GPUs.
But in principle, as technology advances.
We are also getting smaller models that fit in smaller devices,
with less specific hardware and are cheaper.
I'll skip over a bit of this, I'll just stick with the last line.
In the end, you have to do the math if you want to set it up at home, but
nowadays
this is, I would say, especially designed, as I said before, for those who
like the subject and turn it into their hobby and want to do these things, or
for organizations that see it as a way to improve either the economy or
autonomy, or any of these other values ​​we have been talking about before.
And we have a very interesting option which
is to use auto-talk models via APIs.
What does this mean?
This means that some company sets up the model as a model that can be hosted
anywhere; they take it and host it and give me the service to access it.
I don't have to use their infrastructure, but I have to deploy my own
infrastructure, I use theirs, and they charge me a fee for doing so.
But these amounts are usually very reasonable and allow me to use many
of the applications I mentioned earlier without having to do any deployment on my
own infrastructure.
Since there's very little time left, Gregorio has already gotten up here.
I'm going to mention a couple of examples very quickly so
you can see that this is much simpler than it seems.
Then, if you want, you can download the slides and look at all the links.
But I wanted to mention Civitas.
Civitas is a site where people, mostly from the computer science
field, exchange models for producing images, pictures, and video.
Take a look, if you like.
There is a program that everyone in that community uses.
Well, in fact there are several that allow me to download a model and do fine
tuning for certain tasks.
For example, to make manga-style drawings for me, for example, and they take models
and specialize them in that and upload the model they have generated
again, so that people share models and do things with them.
And I repeat, these are people who, for example, are very interested in
image or video generation, but who do not necessarily have computer training.
And for another very interesting example, this is how
you install a model on your own infrastructure.
If you want, there's an application called Llama or Lama, which is
an interface that you install on Windows or Linux with the command
above, and with the command in the middle you basically run it. What
it gives you is a browser, a server that you connect your browser to
and you can start making inferences very similar to how you would do with GPS.
Obviously, if you don't have a GPU, if you have a limited infrastructure, it's
going
to be slow, but it can be used and at least it's good as a demo if you
want to show how it works with that specific model you have there.
Gema 3D uses 31V, which is a very small 1 million parameter model
compared to what we deal with now, which is usually
hundreds or thousands of millions of parameters.
However, he does things like you can talk to him, you can say hello and he'll say,
"Can I tell you a joke?" and he'll tell it to you.
Well, the joke will be like that, but he tells it to you and you can interact with
him.
And really, if you look at it, you can do that on your own computer for about ten
minutes if you want.
If you also have a slightly more powerful CPU or
a slightly better GPU, you can do a lot more.
The other slides contain many more examples of this, but we don't have time
to look
at the treaty today, so we want you to look at the rest of the slides.
I don't know if you've noticed.
So if you want to download them, and of course, feel free to ask me any questions
you may have about it.
But I think the main thing, before you kick me out, is
that you believe that anyone can do this.
It's much easier than it looks.
The people doing this out there aren't people with amazing technical knowledge;
they're just people who like making videos and want to find a way
to make videos with artificial intelligence, for example.
It's possible. It's a matter of getting started, and like any young person, you
get into it, learn what you need to learn, and you do it.
Thank you so much.
He asks himself.
Okay, we have time for a comment with a question if you want to
do it, otherwise we'll leave something for you the next day.
I wanted to ask a little about Asian models, do you know anything about them?
Their infrastructure is very similar.
The models that everyone is making are very similar.
I mean that each, each, each supplier, let's say of models, usually makes
some kind of change with their model in different ways, but they don't have
any special characteristics, at least not those that come from China or
Korea, which we also have some of in Europe, most of them come, well,
most, many others come from the United States as well.
Let's say that the basic architecture in all of them is very similar.
They have their own quirks, but the basic architecture is very similar across the
board, and they all basically use the same software with minor variations.
And there are more
powerful in that they become.
For example, using a professional point of view.
Are American models more expensive than Asian models?
They are more sorry, more than more expensive in terms of use,
which is in terms of the price they charge you.
Beautiful, you mean.
Well, keep in mind that the prices, as I was trying to explain before, the prices
they are charging us are not necessarily the cost prices, they are the prices
that companies are interested in setting so that we use them.
And that implies that if, for example, your company has a lot of capital and has
committed
to its shareholders to have 1,000,000,000 users next year, then it's going to
provide
a very cheap way to access it depending on how you want.
Even so, we found a bit of everything.
There are very cheap Chinese models and there are suppliers, in fact Chinese,
that use some very cheap unlocked models.
And they exist in Europe and the United States as well.
To give an example, one of the lesser-known ones, the very
cheap Nvidia, has several models that are also open.
In other words, you can download them to their infrastructure, they cost you
nothing, and you can use them up to certain reasonable limits at zero cost.
Obviously someone is paying for that, and in this case it's Nvidia, which has its
commitments to its shareholders to ensure that it has so many thousands or millions
of users next year or whatever.
But remember that there is no relationship between the cost they charge
us and the cost they have to provide the infrastructure.
Thank you, thank you.
Well, thank you all very much.
Very good.
The second presentation is by Natalia Galván, who comes from the El Molino
Special Education School in Navarra and will talk to us about technology
and pedagogy, computational thinking, artificial
intelligence at the service of inclusion, all of that.
Thank you. Okay.
Let's see about this.
Let's see, I think I've seen that it's there.
We have it, right? Well, thank you very much.
Good afternoon everyone, and well, first of all, thank you for the invitation,
Estefanía María, for inviting me and bringing me here to tell you a little bit
about
the educational innovation project that we discussed in a...
At the school where I work, which is a special education school called El Molino
de Pamplona.
So, I'll tell you about the innovation project, how we got there, and above
all, I'm interested in sharing the activities that helped us get started
in computational thinking and artificial intelligence.
But before that, I would like to share some concerns
about the current context in education.
As you can see, I suppose you all agree with me that we have increasingly
diverse classrooms to which we have to respond.
And it is true that we also constantly have very strong pressure regarding
digitization, new tools, and robots that give us all this.
Ultimately, it is true that it impacts student inclusion, because we do
have to try to ensure that students are integrated through progress,
that is, that they learn, that they participate, validate any form of
participation, and that this participation and this
achievement-oriented
progress makes them feel good and that they are part of the classroom like
any other student, regardless of their diversity.
So, what is the current challenge we face, or at least the one
that concerns us, at least as a center, is how to integrate
technology without creating another barrier to student
access to learning, regardless of whether they have a disability
or not? Ultimately, this is the key we have from the
perspective of our own teaching competence.
It's about creating a good instructional design, providing support from the
beginning and having a good foundation of pedagogical intent.
In other words, we want them to learn exactly what they need to learn,
not so much what tool we're going to use, but we want them to learn.
In all this you will see and agree that in the end, as teachers, we bear a very
heavy burden, because it is not only what we often cannot control in the
classroom, but also because outside we have a digitalization that is constantly
overwhelming us.
So, apart from the weight we have and the role we have as mediators between
technology and the students we have in the classroom, we are indeed
impacted by being able to make a learning situation accessible.
In other words, all students in the classroom, regardless of
their personal circumstances, can access, express themselves
and participate, not under the universal design of learning.
And all of this through a pedagogical intention, as I said before, of the
digital tools that we have or think are the most appropriate
for the point at which the students I have in question are.
Obviously, we're not all going to take on the burden, but we also have many
contextual barriers that will depend somewhat on the type of
center, what we're doing, and the training we have.
So, given this situation, how do we do it?
Well, for us, things did get a bit of a breakthrough when,
through computational thinking and under that STEM approach,
we saw that it was the starting point for any digital learning
with students and that, in turn, it would allow us to work on
the development of digital competence, because today it
has become a way of participating more, almost in many cases the only way.
And in the students we had, we did see that in the end,
working on computational thinking allowed us to work
with them on the reflective process of learning to learn,
which is fundamental not only for learning artificial
intelligence, but also for making it transferable to daily life, to autonomy.
In all this sense, we saw that computational thinking was becoming a
powerful tool for students, at whatever level they are, to exercise active
citizenship.
And as mentioned at the beginning, through logical and critical thinking.
We're talking about computational thinking.
But how does artificial intelligence work?
How do we fit him into all of this, or what does he have to do with it?
Well, the European Digital Competence Framework has recently been published.
This one you have here is from the 5th edition of last year.
And well, you see that it talks about five skills that you have listed there.
Information search, evaluation and management, communication and
collaboration.
Content creation, safety, well-being and responsible use, and problem-solving.
These five dimensions or competencies have been transformed into 21
competencies
in which I don't know if you see it there, but the little arrow.
This computational thinking has become a fundamental skill with its own identity.
And the novelty is that in the end, artificial intelligence is becoming a
completely cross-cutting skill among the rest, and it basically focuses
on the fact that we have to educate our students in data processing or in
knowing what data is, and above all, in interacting with AI in a critical and
responsible way.
Well, I don't know if you can see it, but the QR code was left there.
Okay, so, how else not?
For example, in my case with students with intellectual disabilities,
how do we integrate computational thinking, artificial intelligence, and
these special needs?
Well, as I was saying before, through computational thinking, through the
development of computational thinking skills, we are talking about
decomposition, pattern recognition, abstraction, algorithmic thinking,
and error evaluation and debugging, which allow us to work with many of them on the
development of executive functions.
It is true that there is evidence and studies that confirm that many
of the profiles we have in attention to diversity do show many
challenges in these functions. They will allow us to work on
metacognition, not always at the level they have, but so that they
are able to reflect on their own learning and therefore, the development of
adaptive skills.
And on the other hand, we approach the concept of artificial intelligence,
understanding the algorithm as a learning sequence, as a sequence of
itself, as a routine that will ultimately allow it to access
learning and be more autonomous and able to participate.
In our case, we also approach it from one of the branches of artificial
intelligence, which is machine learning, for which a manipulative or very
costly task is fundamental and which is not usually liked, or at least we were
told that it is the data one.
We're talking about classifying and labeling a dataset and reviewing
it according to the instructions they give us. They are.
These are structured, repetitive tasks with very strict rules and very little
cognitive load.
Ultimately, it's a very basic level of Bloom's
taxonomy, and as I said, these are all the machine
learning models we have available today.
These tasks are required in order to train the model, which is why it
is within the artificial intelligence value chain.
It is a very powerful value and that is where we saw a niche to be able to start
with the social and labor integration of students with intellectual disabilities.
Consequently, we have been able to see how we can generate a
new professional competence, which we have named in our
context, which is the professional competence in auxiliary tasks and data
review.
And this has been the final result of the educational
innovation project that we have carried out in my school.
Well, as I was saying, I come from Pamplona.
That's a picture of my school, which is very close to the old town. It's a
special
education school, subsidized by the Government of Navarre, and belongs to the
Ferrer Citizen Foundation.
We have an educational service, an occupational service,
a Family Guidance Center and a Sports Club, which is also
very important and currently has 76 students.
You can see that we have agreements with the stages from Early Childhood Education
to Special
Vocational Training, and as I was saying, we have turned that professional
competence into a
specialization course that the Department of Education has told us about, which is
the Specialization
Course in Artificial Intelligence for people with intellectual disabilities who
have just
entered the system, and I don't know what's happening at the parent company,
but at least in Navarre
they finish the app and there is very little accessible training for people
with intellectual disabilities.
Well, you see there it's for people over 22 and it lasts for a year.
Okay.
How did I get into this specialization course?
Well, through the educational innovation project that I mentioned, which we
started in the 21-22 school year, that is, just after the pandemic.
We got quite a scare when they told us that they were proposing that our school
participate in the Artificial Intelligence week for educational centers
that the Navarre government is piloting, and also, precisely, we were starting
with a new education law and through the Lomloe they were already explicitly
talking about computational thinking in the socio-labor field.
Our foundation, to which we belong as a school, was beginning to organize itself
together with
another Navarrese entity, which is in the same vein, that has special employment
centers for people with intellectual disabilities.
They were beginning to form the Teresa Foundation, which is a foundation that
tries to find
employment niches in technology for people with intellectual disabilities.
Therefore, the need was there.
We had to respond to a training demand for employment for people
with intellectual disabilities in the digital field and digital technologies.
So how do we begin?
Now it's true, five years ago.
It may not seem like that much time has passed, but I remember five years ago
when they came to tell us about this.
I know that some of my colleagues in the force would have run away and said,
'We're done with this,
we don't want to know anything about it,' because it wasn't that loud anyway.
We were just starting to explore the topic of artificial intelligence; we
didn't know where to
begin. We saw that there was computational thinking, the curriculum,
but no, we had no idea where to start.
So, well, two of us colleagues were training in computational thinking
in order to later replicate the training to the faculty and to start
generating preliminary activities that would prepare the students for
the AI ​​week, that we were going to have an AI week, that we had clear
objectives, that the students would have an active role that they would leave.
It's great to watch YouTube videos and have them, but...
But we wanted them to be technology generators with very
basic introductory concepts of what an algorithm is and block programming.
And also very importantly, with our students.
In terms of cybersecurity, the students we have at the
school are very vulnerable to any scam, anything at all.
So, well, we felt the issue of fake accounts and digital identity was
fundamental.
And we developed all of this through digital applications, primarily with
Code.
Well, if you don't know, well, I'm sure you do, okay.
This is a screenshot of the Symbaloo we prepared with these activities for each
pair of students.
This one, this one is bad.
It was dedicated to the students of Special Vocational Training
and it was, as you can see, very sequenced at the end.
We started with a simple code course.
There was something for every skill, even just getting close to the screen and
dragging from there.
We were working on digital skills to then move on,
and that was scaling up to small block programs.
Then it scales.
Let's go to the concept of AI through a very simple course they have about and
for the oceans, which is classifying garbage.
And then we were already getting into the cybersecurity part,
mainly trying to decrypt, but not so much anymore.
We wanted them to decipher it, but oh well, it's an app.
The phase is an application that shows you two images, and we were trying to
play a game to
see if he could tell which one was made by AI and which one wasn't, simply to
raise Andrew's awareness, since he skipped it.
Talking a little about pattern recognition and machine
learning to understand how they could quickly create their own machine learning
model.
Well, here you see some images and it is true that AI week was initially geared
towards older students, those in vocational training.
Yes, we are very proud because we managed to get it through all
the educational stages, from the preschool you see there, to secondary
school, etc.
It was also a very enriching week because, in addition to student training,
training was also provided to families and teachers.
And then it also came with a very interesting exhibition, a bit
about the key concepts of artificial intelligence and about the
people who had emerged with this technology, which was covered in the
local press.
The truth is that it had a big impact and it was a week of a lot of
learning, a lot of work, but one that was very enjoyable.
AI Week has passed and now we're wondering what to do next?
Well, we launched an evaluation for all the teachers so they could give us some
feedback, things to improve or what we could do from now on.
And well, we carried out another training that we felt was important on
curriculum integration and mathematical development.
So, computational thinking is fine, but where's the curriculum?
And then it is true that we felt it was important to be able to continue a little
with the work we had done during AI week.
Systematize at all stages, at all, from early childhood to
small sessions of computational thinking and artificial intelligence.
Therefore, it was important for us to understand where we saw computational
thinking. For our part, well, from the mathematical field and the field of
knowledge of the doctor of the environment, sorry, the social field, in
order
to work on digital competence and competence, learning to learn to learn
personal and social.
Regarding everything I was telling you, we put it on a weekly or
bi-weekly schedule depending on the needs of the group.
And we did think it was important that, as I mentioned before, for there
to be a real transfer, a positive and tangible transfer, they would do
something, that is, not just be on the screen, but in the end they had to
create some kind of mural or some kind of classification game and through
the transversality of key content elements that we work on at all stages,
such as money management, shopping, road safety education and centers
of interest specific to the students.
And yes, we do focus a lot on mathematical thinking, because in the end
our students are in different phases of learning and we saw that the
development of this thinking is the earliest, through games and the senses of
sensory development, you know.
And that the environment ultimately acts as a source of learning for all of them.
Continuously.
From a young age, we are constantly creating mental
representations that we ultimately use to try to observe
and understand the world around us, to solve everyday
situations, and this learning process we undertake
with these mental representations generates autonomous thinking.
For all of this, and as I mentioned, through the development of skills,
especially executive functions, and that's a little bit where we start.
We talked about logical reasoning, number measurement,
problem solving, geometric knowledge, and representation and communication.
And yes, we did separate.
As I mentioned, in the end we had students at different learning levels
and we did make a sort of classification of the students we had.
On one hand, we had students who were still in a very early stage of learning,
and for them
we were going to dedicate some activities called prerequisites, which in the end
is working on those cognitive and mathematical thinking skills.
And then another one that you will see later is computational thinking
as such, with artificial intelligence for the three requirements
of typical activities that we start from the applications of cause
and effect, series, recognition, identification, classification,
categorization, sequencing, toys, constructions, tangram, word
searches, mazes, Connect the Dots, Find the Differences, Inferences,
Intruder, Memory, lotto, Body Games, Not through imitation.
Table Tiles.
Little technology.
You can see what is needed to start working on that mathematical thinking,
that
computational thinking that is the basis of any digital technology.
A brief overview: In the end, these activities were intended for children
and, in fact, one of the primary school students and the transition to adulthood
program.
Older people with greater needs, with whom we started a little bit who might be
able to help us.
We downloaded this application, which is free on the CREA website,
which is the Resource Center for Educational Equity of Navarre,
meaning it is freely available for download.
And it's a great app because it also allows you
to work on any type of educational area.
And there is a part that does have computational thinking.
We also use the Pica app a lot, which is for iOS and
because it allows you to customize peer association,
categorization, and sequencing activities
with photos of the student or with objects.
They allow you to personalize them with very specific things that we know will
ultimately motivate the students.
And then with Symbaloo.
There you can see a bit of the structure from one to nine or from one to whatever
it may be.
Each item in the symbol directly opens to a digital resource.
So, as I was saying, with activities similar to those places
we've been talking about, we ultimately try to
work on that autonomous learning of the students.
For older students, between the "I owe one and I have two" stage, which
is primary and secondary school, and with the Vocational Training
cycle, for computational thinking we did about four categories of
activities: plugged-in floor board and table board activities, also on paper.
And then there were some that were directly with robots
and the panel that came with the robot, a tabletop directly
to the computer via tablet or software with applications,
and then joining the two parts, the robot and the computer.
Okay, here you can see a small example in all classrooms, and that's important.
Then we got scolded quite a bit, but well, in all the classrooms we painted
the
grids with a stamp, which I imagine you're already saying, but
in all of them we painted that grid to the scale we could.
So, we categorized them as movement because in the end it was the
part that most motivated the students, because we started
moving chairs and tables and we started moving around and working
on the grid that became the game board, the code we had to be able
to move on that game board that you can see.
The arrows are directions from top to bottom, right,
left, so that they could then build the sequence of steps
or the algorithm to achieve any challenge we proposed.
On the game board in this area, the.
The content we worked on depended a lot on the program each group
was in, but well, we worked on the solar system, money management,
recycling, vocabulary, skills and healthy habits and in the
end the role that one had was the programmer and another was the
robot and the others who were in class had to write down the code
that was written or debug the errors of the classmate who was
programming or another who was classifying a little or
completing that final product that they had to do and various support.
Each student, each student, needed a different kind of support,
which could be visual through oral gestural code.
Well, we were going, they were learning, but so were we.
This is another example of a moving activity where in
this case we had a student who, instead of simply leaving
the code on the floor, needed to put the code directly
onto the grid, or else the tutor would support him in building the map.
It's the same with the older ones, isn't it?
A bit of the same scheme and the opposite.
The truth is that in the end it allowed you to work on a lot of content,
and also here in the end what is needed is a lot of creativity and a lot of time.
Sometimes, in order to adapt everything and see a little bit where each
group is at, we can give them meaningful activities.
But well, under the scheme of my programming code, the type of dynamics
we did and a kind of challenge-solving, the truth is that they enjoyed
it a lot, they were getting hooked, getting more and more involved
because we chose that code that you see there in the background of the
arrows, because in the end we saw that it was the most significant
transfer that they will be able to make, for example, a visual
programming like Scratch and before, if not to the visual programming
that we use in Code, which I suppose you know are arrows that indicate arrows and
so on.
So, the idea was to start from scratch and then be able to
scale up to a more technological programming environment.
We also made several of these cards which are essentially
mazes that you have to overcome obstacles to get, in
this case cars, and allow you to write the code.
It is true that it allows for a more reflective approach, but I'm telling you,
it doesn't really impress us.
In other words, we gave out tokens and
little interest.
Okay, back
to review.
Forward
It is land
forward.
Don't you want to?
Well, in the end we involved the whole center and even the Physical Education
staff also work on computational thinking in the simplest way, instead of using
a grid with hoops to work on the whole issue of body schema and laterality.
In primary school, I am already, for example, in the example of a classroom
with an ASD profile.
The game board was already on the table with another robot,
but one of those simple ones that are like floor-standing.
And it is true that here the adaptation or adjustment we made was to give
the code through social history, which in the end also, because it is not
so much that many times they cannot do it, but that they can do it and we have
to mediate a little there.
Then we saw that in the end it simply presents a social
story with the color code and the typical 123 sequence.
The students quickly understood how the robot worked through direct modeling,
and in this way they could understand what activity was being proposed to them.
In addition, it will also allow us to work on social skills.
Finally, the turn, the wait.
And the truth is that this was a very, very interesting project
and it was very, very well received by the students.
And now, moving on to the older ones, well, this is what I was telling you
about
in the first photo, you can see the Codec programming there.
So, is it ultimately a visual program with arrows to create the
scale I mentioned earlier in the middle? See?
Maite is doing an activity that compresses, which I also suggested for
lower levels, but which can also be worked on in the end according to the
pedagogical sense we give it.
It is possible to work with other needs.
We also introduced the Blue bot robot, which is very similar to the
vivo one that I imagine you know, but with a slightly more adult aspect
that is sometimes difficult to find, allowing us to work from a tablet,
computer, and tabletop.
Here's another student, starting to work with Minecraft, with
some much more complicated programming, and another student with picto blogs.
Well, in the end we were pretty much doing what I was saying, they were
showing us the way.
It wasn't until this first year that we were working on this educational
innovation
project that we managed to formulate our own curriculum for the Vocational
Training
cycle specifically for the Artificial Intelligence module, which is what we
implemented the following year.
On the 22nd and 23rd.
We've already started this new module in the vocational training program, which
has been very successful.
We decided to participate in the Science and Technology Fair, which
is a fair held in Navarre, with different schools to showcase their STEM projects.
And we also felt it was important to give visibility to the work of people with
intellectual disabilities, who should not have a place in this framework.
And we managed to extend this integration that we had made in the app to
the rest of the educational stages in the 23-24 school year.
Seeing the great success, the great response from the students, we launched
the educational suggestion to create the specialization
course that I mentioned earlier, a specialization course.
Because it is true, the normal vocational training, misnamed, was
falling, it does have the option for students to take specialized
courses, so why won't people with intellectual disabilities also
have the option of having a specialized training course?
Well, we launched the course and it was accepted by the Department of Education.
We are very, very grateful and it was generated through the Department of
Education.
A network of computational thinking
colleges was created, and we are part of it.
Last year we continued with the second edition of the
Specialization Course in Artificial Intelligence.
We have introduced 3D printing, which is also a marvel how they work
with 3D printing at the end of computational thinking, sequencing
the instructions within the network that I mentioned of educational
centers, a working network has been generated for special
educational needs and yes we launch many.
We have been providing training to other teachers in the
community, we have had the opportunity to participate in
dissemination days and well, this year that we are already
immersed in the third edition of the course, in a month we
will have the AI ​​week that has been created.
We have received the approval of the Department of Education
to give us the option of designing it to our liking.
And well, and as a new development, this year we finished in partnership with the
Government of Navarre.
We have started a new project called Inclusiva, which, like our specialization
course,
will allow our students to do internships in special employment and
occupational centers that have this technological focus.
Our commitment through this project, which is a cross-border cooperation
project with France, is to enable our students to take the leap to
real company internships, with support and more.
But hey, that's the big challenge we have.
But aside from all these milestones, from this whole path we've
been developing in educational innovation, with computational
thinking and AI, does it really have all this?
It is successful, it has value when you see that there are two people like Ana
and there are not two students who have had the specialization course, who
have already signed their contracts and are already working in a new
technologies unit in a special employment center and with which it has had
an impact on their life, on their life project and the improvement of their
future.
And before I say goodbye, as I'm not sure how I'm doing with time, I think I'm
doing alright, I'll leave you with these recommendations that were very useful
to me back in the day, which you'll probably already know, but take a
look at the INTEF website of the Ministry, it's fantastic.
There are so many things.
The Educational Technology Observatory program we created is wonderful.
It opened up a whole new world for us when we had no idea what to do, and you
also
have a fantastic course on artificial intelligence and education, full
inclusion which is also essential.
As of today.
They have many digital education and digital skills manuals
for people with intellectual disabilities.
The ICT educational resources you see there are directly from the CREA
website that I mentioned earlier, which focuses on diversity in
Navarre, and there are many applications and recommendations.
The board games that I think we are going to turn the AI ​​week of our center
into board games, which allows you to work on all the computational thinking
skills.
And it is true that there is a new ICAS, which is the digital
section of the department, and they are putting a lot of
effort into taking away some of the technological aspect.
Then you have there, and in the end, there are international
teaching entities and other organizations that also have
a lot of material there, from directly structured AI programs,
super simple ones, from ethics, and the truth is, many very interesting things.
So that's it, I'm done with this and that's all.
I hope it has motivated you.
Inspired to at least encourage you to keep doing things in the classroom.
So, thank you very much.
Very good.
After Natalia, it's Chema's turn.
Then she keeps going up. Then.
I don't know if it's José María or José Manuel. I've never known.
José María González Morón, from the CEIP Gonzalo
Fernández de Córdoba, will tell us about his experience
with artificial intelligence and with boys and girls.
Very well, thank you for being here.
Well, that's it. First of all, thanks to Gregorio, Estefanía,
and María for the invitation, another year here at this event.
Thank you for also giving a voice to the elementary school students.
It seems that artificial intelligence is only for older children and even in
primary school.
It's been a while since we've been doing some interesting things, and especially
because I
say that we have to educate those children of the future who are of the
present, they
are not of the future, they are of the present and we have to bring that.
So I'm going to tell you a little bit about how we started working a little
bit with artificial entities at our school, how we got started, and I'll
tell you that we started working exactly in 1920 through the Computational
Thinking and Artificial Intelligence School organized by INTEF.
It was an online course that teachers from all over Spain signed
up for, and I was lucky enough to be a tutor. It changed because the
previous year it was a school of thought related to mathematics.
Then it was super interesting.
In fact, you can see that even the regulations today allow us to
work precisely computationally within mathematics or
environmental knowledge according to the law.
Well, in Madrid, since we have other areas, things change, but anyway, it was
there and precisely at this moment, that I met, had my first contact with
artificial intelligence, and just like my colleague said, that artificial
intelligence brings everything together, even though when it was me on that
day, and I wasn't a visionary, I'm sure many people thought it, I realized that
artificial intelligence was going to devour computational thinking, because
of course, it obviously gathers everything and takes it and encompasses
it, right?
And I thought it was going to take longer, but I was
wrong about that, it was much faster than I thought.
He's already eaten it, hasn't he?
It was precisely in that course that we started working and we started working
with Machine Learning for Kids.
From the famous Doyle and we started working
with her and she had a number of problems.
The first thing we had to do was use registration to enter it, which is
already
causing us to run into problems with the Data Protection Act.
And well, at first what we did was create a teacher account
and let the students work on your account.
They created their models within your account,
but in the end there was a data problem.
In that case, we did intend to do it only with
textual data, without images.
But of course, if the two images are of a cloud that belonged to
this gentleman, then it was even more complicated.
So, with a series of data, we started to create the
first models, always understanding from the
beginning how those artificial learning machines
of the scissor worked and how to reach them.
To think that in the 19th and 20th centuries, almost all elementary school
children didn't know what was artificial as they do today.
So, well, we tried to get inside the artificial entity to see how.
How it works, not how the artificial scissors learn, how, how they create
those models.
Well, like new textures.
And then the first exercise we had, which if you know it, the
typical one of being able to work with machine learning, connected perfectly
with Scratch.
You could create models with Scratch that instead of working with the tedious
Scratch lists, which sometimes, because of something that
failed, that is, didn't work, however, it did work.
Then we can create the typical action of turning a light on or
off, or turning the fan on or off, even if you had a typo or if there
was a fan with a V, it worked perfectly because the artificial
scissors were behind it and allowed it to work.
A list of classes wouldn't have worked then. Well, we learned those
first models and well, that, that was working and then in my school
we have many projects at a somewhat different level, as I was saying,
by the way I have to talk to him and my namesake, my son Manuel, No,
those projects interest me, they are going to university because
we do a lot for this society with artificial scissors.
I'm talking about this project that was related to the
topics of sports, physics, and the girls, boys, and children
of this team created a project to encourage people to do
sports using artificial intelligence.
Okay, I'm going to show the short video they created themselves explaining their
own project,
already using artificial intelligence and using machine learning.
for Kids.
We did some research and discovered that the residents of our neighborhood are
exercising less and less.
Therefore, we have been talking to doctors and they have told
us that the main sedentary diseases are osteoarthritis,
obesity, high blood pressure, and diabetes.
We want to end diseases so that adults can
look healthy, and for this we have created a
mobile application called Escape de Body
which will be based on an escape room.
Speaking with elite athletes, they told us that
the appropriate amount of daily exercise
time is between 30 and 45 minutes.
First, to design our skateboard, we looked at
our neighborhood and chose a series of areas to
create our script. But there's one special area
that you don't need to go anywhere to do.
And this is a Scratch program.
We did this mainly on a website called Machine Learning for Kids.
From there we taught our computers so that if you sent it a photo, it
could distinguish which steps it most resembled.
Then we put all this into a Scratch toy and what you
have to do is make sure it can see from head to toe and
there's a little figure and you have to follow the same steps as it.
And it depends on how fast you do it and how safe it is.
You're referring to that step, that's the score it will give you.
We have done 11 tests, each lasting
approximately three minutes.
For our tests.
We have used street furniture.
For example, one of the tests is an elliptical trainer, located on Cine
Street; we have installed an elliptical trainer there.
Another test is Escaleras, which is located on Avenida
de los Poblados and they will have to go up and down the stairs for three minutes.
We spent some time investigating and found the TNN project of six Network.
Juan Félix Mateos taught us this.
We had thought that we could use it for our application
because thanks to a button that we will put in all
the tests, we will be able to locate the users.
We will use a network called Lora.
This network has very good coverage in our neighborhood and in our
school, which we use for a project called Birds in the Cloud with
a sensor nest to see if there are any birds in it.
We have created a prototype of our application in an application called App
Pop.
You will be able to see this design at the end of our presentation.
Each time a user takes tests, points will be added,
and those points will go towards a ranking.
What will we do to win a prize for our application?
We will create a monthly ranking for the first
prize, which will be a free voucher that could
be used for skating in the south on occasion, etc.
Well, this is the project they came up with.
Did you know they explored the topic of artificial intelligence through sports?
There is a small part, the other part is also social.
It's a project that we end up working on throughout the whole course, and in the
end there are more things besides the artificial scissors, but in the end it's
part of that project and that's where she's starting to use it.
And you see that we obviously started in those courses because of the pandemic and
the mask.
It is clear that we started working on it in that course.
Well, right after the pandemic, just like the colleague
who mentioned Code, the first artificial intelligence
courses started to appear at Code.
Here you can see some coding through the artificial
license, the impact of artificial, generative energy and so on.
But how does AI work specifically for me?
Everything is a little bit more.
The one I really liked was the theme of and for the oceans.
I think the S medium for primary school is fundamental for them to understand
how, how artificial intelligence learns from models, how, how it makes
mistakes, because it doesn't have, hasn't trained well or doesn't have enough
models, right?
So, through that program that I recommend, reading
across the oceans, they learn how it works from the inside.
AI is like adding little machine things and then
we see this code stuff, come on, let's try it.
So above all we're getting ahead of ourselves with sounds.
And having seen this, well, with this it is possible, in 5th grade,
in 4th grade they can start working and they begin to understand how AI works
from the inside.
I don't know if you've seen Code yet, but when
Code.org started with the famous "Jesus"
from Obama, we're going to learn by 2025.
I think it was 2000, so I don't know how many programmers in the world have
changed completely.
And now, as you can see, AI is in the world, and now the goal is no longer for
children to learn
to program, but for children to learn artificial intelligence.
Most courses are now focused, still in contact, but almost all courses
are now focused on children learning artificially.
I also want to, I also want to launch the ESA because we always say
that children are not digital natives and so on, and I think that
children are current and so it is artificial to use it in the same
way that any new little doll comes out in the bazaar and the next
day it is in school and they teach you what that little doll is that
you don't know because you don't know what it is.
Well, in this there is an equal.
Ultimately, they turn to AI just like we turn to anything else.
Are they there, are they in the world, so they are boys and girls
of today and they arrive and they know him, right?
Well, at school, after this whole spiel I just gave, we met
Learning Ml and Juanda and this changed everything.
It changed us too. Why?
Because Machine Learning for Kids we had those problems with the male, all the
data, and the registration.
And of course, ml already offered us another way of seeing things,
because it was open, we could use it, there was no need to register.
You could even download it, have it on your desktop, and work from
your model without having to send the data anywhere.
Of course, that's ultimately beneficial for an educational institution, isn't it?
Yes, I imagine you already know about it, that you've seen the snow.
I don't have to explain anything about what this is.
You know him perfectly well. But well, it was a whole world.
In fact, it didn't look like that anymore.
It looks much nicer now.
This image is of the previous model; it looks much, much nicer now.
Then we started using Learning ml at school and
Well,
No, I haven't mentioned it, but we've been working on the topic
of technology, robotics, and so on for a long time.
So from third grade onwards, everything good that even from first
grade onwards, but right now, but from scratch, everything in the
class does a project that we then present at a science fair that will
be in 15 days at school, that in May we do it at the end of May in a science
fair and everyone presents their small project or their big project.
There are some that have to do with exercise, some that are useful to us.
One of the ones that came out was this one that
I know was for a kind of underwater robot
that recycled the seas, right?
If you look at the model, they differentiate between marine animals.
Because of course, if his robot accidentally picked up a marine animal,
it had to return it to the sea.
And then there was the waste that they saw was most
abundant in the sea: plastic, rubber, and so on.
We took photographs with children.
Well, they collected quite a bit of data, but they also took photographs
of small plastic models and things that came into the classroom to handle, right?
Here is the plastic part too, which is also very important.
They made their own seabed with
origami and then on top what they did
here with the Lego pieces was their own machine.
But all of that was directed with a machine from China that Jorge Lobo
will surely talk about a lot when he comes, and we programmed it as machine
learning because you can connect machine learning to China and work with it.
So, in order to identify a marine animal made
with origami or rubber plastic, the robot that
you will see here in the next image, you see that
it was here, will allow us to introduce waste
into its corresponding space and if not, the
marine animal was just on the left which has a
small hole and would return to the seabed.
But they tell it better than I can.
I'm going to show you the video and you can watch it.
We started this super challenge by
researching fish extinction.
And we decided that we wanted to end
ocean pollution, so we started our model.
We watched some tutorials for
making origami sea animals.
Next, we began to paint the seabed.
This is what our submarine will be doing, which
will take the garbage to our sorter; the only
thing we were missing to complete our project were the areas.
When we finished the crafting part, we started to
undertake Artificial Intelligence, the basis of our invention.
This is our finished project.
This is our finished submarine
and this is our waste sorting depot.
The submarine goes underwater and then the robot
collects the waste, which then goes to our sorter,
where we classify all the waste.
This is how the program that recognizes waste
with artificial intelligence works.
And now my classmate showed a bottle and recognized
that it is plastic, and now she showed a fish and recognized that too.
Then, using the Chinese board and
Scratch, we managed to get our robot's waste sorted.
As you can see, it's a marine animal and we're returning it.
the sea.
As you can see here, my colleague is showing a scratch wheel.
Do you recognize it?
We threw it in the right place.
And she runs with the bottle.
As you can see, the waste is perfectly
sorted and the sea is cleaner
and less polluted.
Well, as you can see, it's another project that was already in place, that
was done with machine learning.
And well, each project has its own theme and all that.
Well, right after that whole pandemic,
regulations governing AI also appear.
I don't know if you're familiar with it, but we in Madrid are
fortunate to have Decree 61 2022, which is the curriculum
for the Primary Education stage, where schools have
the option to include the technology area.
robotics.
Just like the second foreign language in all or one of them.
In primary education courses, or in any course we want, we can really include
this area
from first grade, 6th grade, or in all courses, or in any course we want.
This area can be included in this, it says that it responds to a series
of societal needs and has three important areas: electricity,
the mechanics of computational experimentation, and other
disciplines, one of which is artificial intelligence, which is
also addressed, in addition to the Y, O, and others.
Well, look at 2022, we already have something in Madrid that talks about
artificial
intelligence in primary education, about how to start working on it, what that
decree
asks of us or what it says we have to work on. Well, look at knowing and
valuing the
possibilities that artificial intelligence has to act with machines or
systems in a way that facilitates work and gradually improves it.
What he's really asking us to do is allow children to learn about these virtual
social intelligence machines from the inside and how they work, right?
Apart from that, it's about seeing if you can see the evolution of people, knowing
about computer
applications or emerging digital technologies or artificial antigens,
integrating programs
into machines or systems that allow their autonomy in a responsible way.
So you're asking us to use machine learning and
then, for concerts and skills, to
learn the principles of using AI.
There he addressed the issue of ethics, biases, all the
things that I think children can perhaps understand in a
simple way, that there are people behind AI and I think
they should know this because they use it freely and without
anyone watching them and they need to know that there is something behind it.
No, things aren't always free, are they?
Then the strategy of the first automatic.
How they learn, how they learn, how they learn from their data.
And that's why I also think it's important that we learn from
these systems so that they input data and see how the data
they don't provide is generated, right?
And then, well, that's it.
The series of applications for Introduction to Artificial Agency,
or putting something that we do, was meant for more complicated topics, right?
They don't have to learn basic things about how AI works.
If you notice, it doesn't talk about generative AI either.
We are talking more about learning models than generative learning, obviously.
We'll eventually have to work on it, but that's not the end of it, is it?
And what for?
What else do we use AI for in school?
Well, in the end, so did we.
In 5th and 6th grade, with these projects that we mentioned at the end, we give it
an approach
that is why I said that I was very interested in what José Manuel said about
the social issues of working, therefore he works a lot on this.
We focus heavily on the SDGs and ultimately we want all the
5th and 6th grade students to do a team project that
has something to do with one of the SDGs.
In 5th grade we don't force them to do a technology project, but in 6th grade we
force them to do a
technology project, even though sometimes in 5th grade some students do a
technology project and projects come out that are so shady, I think, like this
one.
Thinking about disability, you can check out this really cool project.
Sometimes we don't realize that there are people who have more difficulty moving
around and encounter many obstacles.
This worries us a lot and we want to help you.
We have been investigating and have concluded that the biggest problems these
people face are related to mobility and communication.
That's why we've created a mobile app called Disability that will help these
people with everything they need.
The application is designed to be highly accessible for all types of disabilities.
In our main menu, each person will select their disability in each of them.
Receive the necessary help.
For example, people, as a reduced measure, will have a kind of Google Maps.
It will warn them about wider streets with fewer pedestrians, and even alert
them to possible architectural barriers, construction work, or similar issues that
they should be careful of.
The app will send alerts just like car navigation apps do and
will organize the best routes to get to the desired destination.
Now let's see how our app works with a person with reduced mobility.
I need to get to the bakery by the fastest route, please.
Straight ahead.
Turn left in 50 meters.
You'll see.
Now let's see how the degrees work with a blind person.
Where can I go from here?
Turn 90 degrees to the left.
Careful, there's a small curb.
Advance.
Six meters.
Careful!
There's another small curb.
It has a ramp.
Advance two meters.
Turn right.
Now he has a wide, empty street where he can walk.
Don't worry.
We have also seen that people are influenced by trends.
They have a lot of communication problems when it comes to greeting people.
I can't help you.
I don't understand you.
I don't understand what you're trying to tell me.
Our app will translate and interpret your
signs and allow us to show you the answers.
The message of signs.
We also spoke with funding organizations within our
app to raise money for the manufacture of prosthetics.
We will donate that money to associations like Ayúdame 3D with which we
collaborate,
which are dedicated to manufacturing prostheses with 3D companies
and sending them to countries with few resources in Africa.
Our app is now available on the Play Store.
Help us spread the word, because with it we will make
life a little easier for the people around us.
Furthermore, by using the mobility data from our
phones, we need many downloads to make
our system a little more reliable each day.
Download disability information and
collaborate with us to help people in need.
Once every time it becomes available.
The app for the app was done.
That's how they had done it, 1/1 of Google's manual, which is very
complicated, but the language part, if they had done it, they
knew, they had done how to translate all the images into text, the
part that they had done, that when they made the video it still
didn't work, but it did convert the symbols to text.
We did manage to do that.
And then, with these crazy things about projects, I would
like to finish by showing this project that I find very
interesting, because sometimes when we do projects at
school there are themes that make you say, "I don't know what will come of
this."
This group thought about suicide and I said I don't know what project we're
going to come up with here.
So, well, I'm going to show you the video because something quite interesting came
out of it.
We are from [unclear] and we've come to talk about a topic
that's been discussed a lot lately. This topic is suicide,
and there aren't many solutions to prevent it.
One of the solutions is the telephone of hope.
If a suicidal person calls this number, they will receive help from a
psychologist, but with these calls we try to prevent suicide in a friendly way.
We have realized that in Spain there were 4.3 suicides.
At least.
Spain is at level 59 on the suicide list of all
countries, with Greenland in first place.
And let's hope these high numbers come down.
Okay, okay, lots of talk about suicides, but what about our candle project?
We want suicidal people to chat with people who share their interests and
become friends.
But how will we achieve that?
First, we investigated and found that many suicidal people post that they
want to take their own lives on social networks like Instagram. We would
detect certain posts and send a message to the suicidal person who posted
that message on social networks. That message would lead them
to a questionnaire with questions about their tastes.
If we see that another person who has also taken the questionnaire has similar
tastes to the other person, we would put them both in a private chat so they
can talk to each other and get to know each other that way.
They knew, by word of mouth and with luck, through vicarious means, about the
two suicides.
We hope this video has been informative.
You see, in the end it seems that from something like this an
interesting project emerged because they saw that there was
a bot that could discover comments within social
networks and it was pretty cool, let's say.
And now that we're working on this, I'm going to give you a scoop.
We will be presenting this soon at the Federation and at the fair we have this
June.
But look, now we're working on a recycler, but this year, this
time with a conveyor belt that then has that container on the
right that rotates and things fall into it. It was fully automated.
The one in the fish tank was manual.
We're going to do everything this way. We're still working on it.
Today we changed the recycling bin because it had gotten quite heavy.
I think we were talking about the cardboard final, but well, we're getting
there and they're working on it.
Same with China, Ml with and with the Chinese plate.
We are working with her to be able to connect to all of this.
And then we're also working on a really cool process, which you'll
know as Microbit AI Create, which is for working with the Microbit
board, especially in movement projects like the one we saw at the
beginning. Now you can work on them because they put the Microbit
on your wrist or leg and you can control a series of movements your
body makes and see if they do them well or not, or do more or less exercise.
And there's a really cool thing we're working on, which is an anti-gender
violence button.
So, based on a woman's movements, she can have the
bracelet, the microbit, and how does
he know which pattern made the bracelet?
Well, she could be calling an emergency number to report that there is
gender-based violence.
So I say that really cool projects come out of them, things that seem like
nothing, but really cool little things come out of them.
And nothing.
For my part, I thanked everyone for listening and I hope you liked the
projects.
We have time for.
Very good.
Thank you very much, Chema.
We have time for a few questions.
Look here in the aisle to my right, in the 5th row.
Maria.
I teach elementary school, and sometimes I find it a bit overwhelming how to reach
elementary school students, and I've loved all the projects you've done.
Congratulations!
The thing is, it makes me a little doubtful,
with all the limitations that the Community
of Madrid is putting on us regarding
the use of computers and screens.
How do you do it?
Because what it makes me feel is that what they're doing is slowing down
all these projects, meaning we can only use it once a week.
What they're doing to me is telling me to just leave it, because we
don't give you the opportunity to do all these things.
So, despite all these limitations, you manage to carry
out these projects. The robotics technology area,
because it's outside of that, allows you to have an
additional session to work on this within the area.
We are outside of that regulation, the one that allows us to work in session,
apart from what is done in class, and well, then regulate the rest.
But hey, with that I now work as a team, sharing devices and so on.
In the end they do come out eventually, but basically from the
area of ​​robotics technology, basically from there.
How many hours do you have in a session per week? In the session
per week itself, we have two, because of the same project, but
well, one session per week is... but we start from first grade,
we imprison them from first grade until 6th grade.
We have everything there, including many emotional prevention programs;
it's all a bit lower, and what we do is gradually creating.
We were teaching about artificial intelligence, 3D design, and other
things.
By the time they get to this point, we'll show you the artificial project,
but in the virtual project, 3D printing projects appear, projects from
each person appear, it comes from the theme, from what they have been working
on, we appear there.
I am a big supporter of using technology, but of creating things with technology,
because I believe that they should not be so much users but creators with
technology.
So, that's the idea that they end up having a
project that ultimately changes things.
You're going to talk to him here, but when we do the science fair at school,
you'll see him on the table.
When children talk about one of their
projects, they speak in a different way.
They bring it to you, they take it there to the project and they make you fall in
love with it.
He knows what's good, but with the area, I'm telling you,
the area is fundamental for this, especially because,
look, you already include it in the resume and you have a guaranteed time.
You work on it during the week, but when it's not within an area, it's
because the math teacher, whoever wants to work on it, or doesn't want
to work on it, so it can also happen that there are some courses that are
worked on, others that are not, so because the teacher likes it more,
maybe less, because of the robotics technology. I go back to my
school, but we make sure that everyone goes through it.
I think it's something, something good for that reason.
Well, welcome back.
After the break, I have Jorge Lobo here who is going to speak to us.
It's also primary school and she's going to talk to us now, we're going
to go off-topic a bit because we're going to introduce robotics and circuit boards,
which is this board.
It's enchanted, I think they keep it in the community, it's another one.
The important thing is to grasp the idea and then transfer
that knowledge to the plates they give you in the Community
of Madrid, which are very similar in reality.
That's clear.
Jorge got it from China because he
collaborated on the design of that plate.
So, robotics and artificial intelligence with e-learning like us, well, there's
Jorge Lobo.
Thank you, thank you.
Well, good afternoon and first of all,
thank you for being here again.
And indeed I'm going to talk a little bit about
another layer of programming and artificial
intelligence, and that's adding that robotics layer.
What I'm going to do is talk first about tools,
the ones I normally use at school.
Then I'll show some projects we've carried out.
And finally, I'll answer a question that often
comes up: how difficult is this?
Normally, as Gregorio says, I use the boards
from China because, among other things, we
designed them together: three colleagues,
José Pujol, who is from secondary school;
Xavier Rosas, who is from vocational training;
and myself, who is from primary school. We put
everything we thought was interesting for
working on robotics and programming into this board.
And as for software we have it, which is a
clone of Scratch in which you can also
work with machine learning thanks to Learning Ml.
What does the plate have?
Well, a lot of sensors and actuators,
which allows us to work from
primary to secondary.
I can tell you that my colleagues have used it in secondary
school and in vocational training courses.
So we have, from a different LED diodes, RGB
LEDs, we have a light sensor, pushbuttons,
an accelerometer that allows us to know what
position the board is in so that by
moving it, we can also work with it.
Microphone, that is, a lot of devices, with
the advantage that they are already integrated
into a board, which is one of the problems we
saw mainly when we worked, which I started quite a while ago.
I think it was the year 2013 or 2014.
I worked with Arduino and protoboards, which are
boards with holes where you put the wires and build
the circuit there, which is very interesting
because it allows you to focus on doing that
precision work, requiring a lot of concentration
to know where you have to connect.
But it's true that assembling the circuit took
quite a bit of time, and with this board, which was
initially a shield for Arduino—that is, it was
mounted on top of the Arduino board—what we wanted
was to focus on programming and leave the assembly aside a bit.
the circuit.
Regarding the software issue.
I always like to start like this.
When I talk about artificial intelligence in the classroom,
two questions before we start working: Is it relevant to
work with artificial intelligence in the classroom? And
is it appropriate when we are going to use a tool?
I think it's important to be clear that this tool
has a justification, a justification that helps
us to work on the content we want to develop and to
work on those skills we want our students to acquire.
This is what I mean by the question of whether it is relevant.
And then, on the other hand, if it is appropriate.
Although there are some tools that can be
very interesting for working in the classroom,
we also have to keep in mind the ethical
guidelines on the use of artificial
intelligence because a tool may be very
interesting but not comply with, for example, the data protection we need.
So it always seems strange that he comes up with
this in a talk about artificial intelligence,
which is counterintuitive.
It's not good, but you're going to talk to us about artificial intelligence, not
about not using it.
Nothing could be further from my intention.
I am very much in favor of using it, but also taking
into account these two aspects, not using it just for
the sake of using it, because it is true that in recent
years there has been an expansion and perhaps we
sometimes let ourselves be blinded by the glitter
and the sparkles that these applications give us.
And sometimes there are things we can achieve to work on those
skills that don't require artificial intelligence.
And sometimes there are tools that perhaps we shouldn't use because they
don't meet the conditions.
Since you'll be doing the presentations later,
I'm going to skip this a bit quickly to get to
the projects, but we need to keep in mind all
these features of the applications we're going to use.
And one of the first documents that
discussed the appropriateness of using
these applications in the classroom is
the Beijing Consensus, which I also have linked here.
A brief summary in case it's of interest, but basically what
it tells us is that AI in the teaching and learning process
should promote a fair and inclusive use of artificial intelligence.
Ensure that artificial intelligence promotes gender equity and contributes to
gender equality.
To ensure the ethical, transparent, and verifiable application of AI in the
educational context.
This got cut off when I made the PDF, but I imagine
it will come out fine later and I can do ongoing
monitoring, evaluation, and research on the matter.
And well, when it comes to discussing
the suitability of a tool, I find a
document from INTEF in 2024 very
interesting, which you can also link
to here, that tells us how appropriate
it is to use a tool or not depending on
the perspective of data protection.
Here's a screenshot so you can see a little bit about how he organizes it.
The tool's name is included, along with a brief description,
whether it's free or proprietary software, if registration
is required, a series of notes, and, well, a classification
that, as you can see, is very intuitive.
If it's green, then it can be used with confidence;
if it's red, then its use is not recommended.
If it's yellow, then you need to take
certain precautions or consider some factors.
Having said that, I'm not going to talk much about
Learning ml because it has already been mentioned
quite a bit and you know it perfectly well, but it
is the tool that I have chosen for many years to work
with artificial intelligence in the classroom.
Just like I mentioned before.
Chema: Well, I started with Machine Learning
for Kids. We had certain problems that he has
already mentioned, and at one point Juanda
Rodríguez said, "Hey, I can create a tool that
can solve these problems," and the
reasons for working with this tool.
Learning Ml is the first thing that is based on free
software, which is a choice that interests me a lot.
In fact, now when trying to put up the presentation, it
took me longer than anyone else, because since it was
a Windows computer I wasn't familiar with it.
I usually use free software for everything, so
that's a very important personal choice.
It can be used without registration, it does not track or use user data,
and it has a desktop version here in the Community of Madrid.
It is integrated within Max.
Is the operating system that is distributed
free of charge and is open source software,
and has a Scratch-based environment, an
advancement or just another step forward
when we have worked with programming in Scratch?
It's simply a matter of adding a few more blocks.
The students somehow feel
in a safe, familiar place.
He already knows how the whole workflow works and
it's simply a matter of taking one more step to
integrate these machine learning blocks.
And we are fortunate that there is software available
for these homemade boards that already integrates
Learning ml, so we have a set that allows us to work on
programming, robotics and artificial intelligence.
The two legs it has are, on the one hand, Hechiza
Blocks, which would be all the hardware control
part of these boards, and Learning ml, as you
know it, is totally integrated within the software.
So the Learning ml part is exactly the
same, but when we click on the y Blocks
it takes us to this Scratch clone just
like the Learning ml one, but with
specific blocks that allow us to work
with LED diodes, with the RGB LED,
with the temperature sensor, the MK
input light sensor, MK, which has the
same functionality as the Mackey boards.
In short, we have a fairly comprehensive system
on a single board and with a fairly integrated
ecosystem where we can work on these three things:
robotics programming and artificial intelligence.
After this, I'm going to show you some of
the projects developed at school.
I haven't said it, right now I'm in the Code
School 4.0 program, but my school is Vega in
Carabanchel and these are some projects we've done there.
This is a
A slightly older version of something
Chema has already shown you.
Let's see if
This is over.
The announcement.
This was the first version of a garbage
sorter like the one Chema showed, but with
a much older version and with a bucket made
from recycled materials, with a simple
cardboard bread box that has a servomotor
that moves from side to side to be able to
sort different types of waste.
Remove it.
This is one of the projects I have repeated
the most, because it has something that
interests me a lot, and that is the
connection of the area of ​​Plastic Arts with technology.
Many times, and Chema has mentioned it before,
it's very interesting to have the technology
area because you have a time dedicated to
programming, robotics, very specific
things, but I also find it very interesting.
When you apply that robotics, that artificial
intelligence, or that programming within other
areas, and the area of ​​plastic arts is fantastic for
this, because you can work on many projects that you then animate in some
way.
In this case, what we did was create models
of some rooms, and if you remember the room
they showed us before, in which it said
"turn on the fan" or "I'm hot" or "I can't
see," and through a text model, it would
turn the light on in Scratch or turn it off or whatever.
So we bring that into the physical world
through this platform where we have an
assistant that says, "What can I do for you?"
I'm hot, okay?
I turn on the fan and we have the students' creations.
As you can see, its different rooms, each
with its own style, in which a servomotor
becomes the fan and an LED diode becomes
the little light that will illuminate the room.
This allows each person to express themselves
artistically by creating the room as they wish.
On one hand, we have the drawing phase, so to
speak, in which he decorates his room.
We have another phase in which, as you can see, he creates
3D furniture with cardboard or cardstock.
I'm going to stop this for a moment because I also find it interesting.
If you notice, we have a double room with two floors here.
Because?
Well, because this particular student—this was a
student—found it interesting that her house had two floors.
In other words, we also have the possibility of
expanding, of personalizing, so that each
person can do it exactly as they like.
We're going to look at another example where
instead of having two floors, it has two rooms
but they are next to each other, I think it's the following one.
Well, here you can see that what also
allows us to work with these technologies
is that the expansion adaptations
are very simple.
As you saw earlier, we had a project with Solo
el gatito, who was our assistant, and this
team consists of two people.
Since it ended sooner, she decided she could put a background
in the room, change some things, and decorate it differently.
The expansion comes about in a super simple way.
Have you already finished the Minimum Viable Project?
Well, now you can improve it by adding backgrounds,
adding sounds, adding whatever you want.
It looks pretty good here.
These are some small supports we
made with the 3D printer to hold the
servomotors, and this is where the LED went.
All of this was an improvement.
This was the second year we did it because in the
first year they were inserted into the room
itself and then with practice you realize that
you can make it simpler, in this case with the
supports to simply fit them into the room.
You save time, you also save them
from having to make cuts and from
having to somehow break into their rooms.
And well, these are small improvements that are
also made with the different iterations.
Here you can see what I was telling you about.
Another of the most representative rooms, which in
this case were two, one next to the other and which had a small door to connect
them.
Anyway, what I was saying are
things that are quite customizable.
This is another project.
This was taken to the Science
Fair last year and what we have
is a model image in which again the.
Art education, the visual arts, has a lot to do with it.
Here we have two little animals, a dog and a cat.
This is a practice they did in pairs.
Each pair agreed on who would play the dog,
who would play the cat, and then a model was
trained with food that was either for dogs
or cats, showing the food in question to the camera.
The dog was happy wagging its tail, or the cat was wagging its tail.
And here we have it.
Presenting their work at the Science Fair.
And the interesting thing about this, as
you can imagine, is not only doing it, but
having these meeting points where students
present their work and expose it to
people, in this case adults.
And it's fantastic because it's such a personal
project, and it exudes such confidence; it's
something they've worked hard on, and it's definitely worth seeing.
Another thing we've been doing at school for a while
now—I mention this because it might be useful as an
idea—is putting on small exhibitions for families
with all these things, not just science and technology
stuff, but also content they've worked on during the
course, instead of doing the typical
festival we've done for many years.
What we do is exhibitions of their classwork,
and I think it's really worthwhile because
they are exhibiting work they have
invested a lot of time in.
Furthermore, in our case, the students choose what they want to present.
In fact, there are some years when they keep technology
projects that interest me more and that I think are really
cool, and they put in others like showing their notebooks.
I think it's very interesting to give them
these exhibition spaces because it also tells
us what their interests are and, as I said
before, what their own work is like, the work
they've invested time in. They're very happy
to exhibit it, and we realize that they've
understood things and have been able to
transmit that understanding to others, which
leads us to know that the teaching and learning
process has been very fruitful.
Another project, a parking barrier
that recognizes license plates, in
this case we had to change it and instead
of numbers we changed it to geometric shapes,
because the
The model with numbers wasn't working quite right.
And we arrived at this compromise solution
which also served us quite well.
And one of the latest projects
in which we are also using
Lego pieces is...
Look, it's not going full screen for me, but I think you can see it pretty well.
It's a box made with Lego pieces that opens when
it detects the face it has been trained
on, and closes if it doesn't.
And now,
As I was saying, how difficult is this?
Well, it's as difficult as we want it to be.
On the one hand, what's important is what's behind these practices.
In other words, it's very interesting
that when we do a project, these artifices
or artifacts that we create are like the
culmination. But there is prior work
where the learning really connects and
where that process is really taking place.
We've seen things related to robotics
because that's what the presentation asked
for, but if we look at other examples, for
instance, one of the jobs we usually do is
artificial intelligence for the environment.
So that?
For whatever you want.
They have a research phase to determine how we can use
artificial intelligence to help us improve the environment.
Something that comes up often because it's recurring is the
topic of recycling, but other topics have also come up, such as
vehicles, which are the most polluting and which are the least polluting?
How can we help reduce the ozone layer,
i.e., through free projects?
Through these investigations, they create their
learning process and integration with robotics,
programming, or artificial intelligence.
It's like the final period.
I want to link this because earlier we were asking, or I was
asking, a colleague how to combine all this technology with the new regulations?
It's true that it doesn't work well because we do have
fewer hours of screen time and fewer hours where we can use technology.
We're going to have less effective time, but we also have to
think that there's an important part that doesn't have to be with screens.
When we are doing this type of work there are some
preliminary steps of research, of content creation
that can be done without screens, even when we are
working with Learning ml and we are going to create
a text model, an image model in primary school.
I don't make numerical models, but that could also be the case.
We can even write and search for those
photographs or make those drawings beforehand,
for which we don't need screens, and that type
of screen is the effective time in which we
finish shaping that project.
So,
We can get into all that preliminary work as much
as we want, and the part about creating a model and
robotizing it is actually quite short,
which is what I wanted to show you.
This will happen at some point.
I've done it live with Programming the
board and seeing how it's done in five minutes.
But since we have the problem here of what these
computers and such have to be, well, I'll take advantage of it
a video of a
an event in which I also did it through videoconference,
which I'm going to leave for you so we can see that
in just five minutes it detects when there is a
bird so that we can have it, look how it can scare it away.
So we're going to add a class of images
that will be bird, I'm going to put
see and another one that will be
not to see.
And now, directly with the camera.
Let's see if it opens for me, and well, I don't know. If you're connecting
from your camera, disconnect the camera and it will definitely work.
Okay?
That's what I was going to say.
I mean, maybe he's fighting with...
Exactly.
Okay,
So here we're going to take a few
pictures without a bird in this case.
Well, it'll be me. Let's see, 21 people came up.
That's very good.
And now we're going to take a few
pictures for when there is a
bird, which in this case will also be me.
But with this mask.
So we're going to take a similar number of photographs as well.
And we're going to give him the opportunity to learn.
Since it's an image model, it takes a little longer than if
it were a text model, but not too much, as you can see.
Then the next one is for the class camera, because you might have
trouble with the camera. Oh, yes, yes, yes, thank you.
The next step will be to test our model.
We're going to do it through the camera and
in this case it says there are no birds.
What if I had the mask?
Well, it detects that yes, there is a bird.
Once we have the model, I'm going to directly
transfer the camera to the camera.
Okay, thanks.
The controls appear here and it confuses me because I can't easily switch between
them.
Okay, so I'm going to create a very simple
project that will be triggered by pressing the green flag.
forever.
If it detects that there is a bird,
we will do that with the Learning ml blocks.
In this case, when classifying an
image that we will obtain from the
video, we will tell it if it classifies it.
It is within the category of the bird category see has.
It is very important, then, that we use the same nomenclature,
the same name that we have given to the class.
We are going to propose that it be moved.
Where are you? Here, the servo
should go to 35 degrees.
For example
for a little while
0.2 seconds
and then.
Go to, for example, 135.
Okay, so what I'm going to do now is add
the camera.
Let's put it on.
Make it look good.
And now, yes.
We can also see
the scarecrow.
What if a little bird appears?
Our scarecrow waves his broom to chase it away.
Well, as you can see, all the
programming of both the model and
the Scratch program, actually took five minutes.
Is it true that with students it's a little more, but what is important in
this work?
That preliminary art work in which they created
the scarecrow, which is what ultimately takes
the most time in a single session, we can schedule
it, but the art sessions, well, they have been
two sessions invested in work that involves
cutting and pasting, that involves planning
the scarecrow and that involves that design,
so, well, the effective screen time is reduced,
but it is true that there is preliminary work
that we can work on in other classes and in
primary school, which is where I teach.
We are fortunate that, since we can integrate
everything in a more free way than in
secondary school, we can make our adjustments
and work in a more global way.
And that's all I wanted to tell you.
I'm also leaving you a couple of links to the China website and the Learning Ml
website, although you've probably already seen them quite a bit.
And well, if you have any questions.
Happy to answer.
Hello!
hello, good afternoon.
I loved the idea of ​​integrating robotics, programming,
and artificial intelligence, but I'm getting the feeling
that it depends on using this specific type of board
from China and in Code School.
What we have received in the institutes has been Arduinos and Microbit.
So.
I don't know, I'm somewhat related to what my colleague said earlier, that I'm
seeing difficulties.
When it comes to data protection, you have to be careful and limit sessions
with screens.
We need to use a specific type of license plate that I
don't know if we were sent with the school code.
So, well, this isn't a complaint, it's just a comment.
But I'd like to ask if there's a way to easily integrate
the kits we have as a school code with what you're proposing?
So, the robotics component wouldn't be integrated?
How could we do it?
Obviously,
The best and most optimized solution
would be to have the boards, but the
technology behind the boards is flawed.
Let's see, can they be programmed the same way as an Arduino?
In other words, you can use an Arduino board,
you have to install the standard signed firmware
that comes directly in the Arduino ID, you can find it.
And the problem? The problem, the
difficulty, is that the software is
designed to work with the sensors and
actuators already integrated into the
board, but you do have the option of using
some of the Arduino pins, the inputs and outputs.
With the sensors you want and with the actuators you want.
In other words, you'll have limited use,
but you can use it with Arduino boards.
Okay, thanks.
And one more question, while we're on the topic of evaluation,
could you tell us a little bit about how you evaluate this?
Yes, this
At my school we don't have a technology
and robotics subject, so I don't have
these activities here to show you, but
I usually use an evaluation rubric that
is combined, it comes with the content
parts and with the parts, so to speak,
of programming, robotics or
artificial intelligence.
They are very simple rubrics; what I do is ask questions.
I say ask because apart from doing them myself,
each person self-evaluates with the same rubric, slightly simplified.
So, for example, in the one where we
saw that I really like the rooms, the
questions, which I also adapt a lot,
are things like, "The room has turned
out very well, a little so-so, it
could be better," that is, with super
plain and simple language so that
each student can identify the
state of their room.
Of its creation.
And I related it to a word that there
are some of, that are very everyday
and each one has a punctuation.
Yes, it was the room, it turned out very well, it's
three points and it turned out okay, two points, if
it can be improved one point, then another thing that is.
Regarding the Scratch program with Learning
ml, have you included improvements such as
audio? I'm speaking from memory and I don't
remember exactly, but have you included
improvements such as audio, other characters, a series like that?
Whether quite a few, some, or not, I haven't done it.
And then, according to that rubric, they are
evaluated one by one in a fairly simple way.
Thank you very much. You're welcome.
All this taking into account that the evaluation
is not only of the practice, but of everything
integrated, and that these grades, for example, go towards art education.
Then.
Longer.
We have time for one more question, if there is one.
Thank you.
Thinking skills?
Can you repeat the question so it's recorded? Yes.
Good afternoon, If the rubric you propose
also assesses higher-order thinking
skills, the rubrics are very simple.
The short answer is no,
because of the rubrics.
What interests me is that the students
also evaluate themselves.
So
There are some where they do teamwork,
for example, they do ask questions about
how they have been in that team, the level
of participation, but they are all
questions very focused on rating or
evaluating their work from the object they have created.
Thank you so much.
Thank you very much, José.
Well, that's it.
We see the thanks to Jorge again.
Thank you so much.
Very good.
And we're going to move on from...
We've had two presentations for elementary school and now we're going to
move on to secondary school.
Well, first of all, thank you very much for calling me to present the project.
I come from a high school, El Burgo Ignacio,
which is in Las Rozas and we have students who are.
We have this precedent center.
TGD is also a center for Vocational Training, Training Cycles,
Electricity, Electronics, Computing and Communications.
And then it also has classrooms of excellence inside.
So we have there a mix that is very much a mix of students, but
at the same time also for doing technology projects.
It's very interesting because we have very good students, we have
many students with ASD who may not seem like it, but when it comes to
programming, artificial intelligence and such, it turns out they are especially
good at it.
And then, with the issue of vocational training cycles, we have
teachers who I think are very technical, not just in technology,
since I'm from a tech background, but there are many teachers of
electronics, electricity, people who can help you with many things
related to technology, so it's very interesting.
I really like this center, and in fact I haven't been here long, but I like it a
lot for that reason.
So, at the beginning of the school year I took over
4th year of ESO (Compulsory Secondary Education)
and well, I love active methodologies and, well,
that children do real projects that they can then
apply to solving problems in society.
Then it occurred to us that they participate in retouching, which I'm sure
you know about.
The Endesa Foundation's ES competition
is a technology competition whose final
format is a fair, similar to the Science
Fair mentioned earlier, where students
present their projects. Teachers from
different schools and institutes visit,
and the students have to "sell" their
product and convince the visiting
teachers, who will ultimately vote in
the final round. Different prizes are
awarded depending on the category, so
the students have to convince the teachers,
present their project, create brochures
and posters, and explain it well.
So, we decided to participate in the
Endesa competition and...
The idea is to introduce service learning, which is to do
technology projects that serve to solve a social problem.
Then in.
So we started thinking, okay, what are we going to do? The
current topic is artificial intelligence, the humanoids
that are coming very soon, well, they're already here, but
it seems they're going to fill society in a few years, and
then also, but how could we connect the humanoid and artificial
intelligence with what it can contribute to us and to them?
Well, in the end they'll tell you because I've been compiling different videos, but
I'll give a brief summary.
So, how could they help?
How could a humanoid help in a high school?
Then the topic of bullying came up, the topic of school harassment and...
And not only that, but also the emotions that a teenager can experience in a high
school.
And then the idea that we had, a little bit
among the whole class, was to make a humanoid
that would serve to accompany the
secondary school student.
Well, it could also be useful for schools,
so that
The students could ask him related questions.
I'm nervous.
I don't know why I'm feeling this, and the
humanoid will answer you as best as I can.
Why are you nervous?
Because you're not handling this well as
responses related to their emotions.
So.
Well, from there on the topic, first send
a video of the team and so on, and then
halfway through they ask you to send a
video of what was done in the end.
Well, this is the 16th, the fair is on June 16th, so we still have some work to
do.
But anyway, this is what we've sent
for now, I hope you like it.
So, a short video of the idea, right?
We know that one in three children have suffered bullying at some
point in their lives, and one in ten continues to suffer it.
Cyberbullying today.
That's why, of course.
Etxeberria, a public school in Las Rozas de Madrid.
We are very aware of this issue.
We wanted to combine an artificial intelligence that is capable of
helping all students with their psychological problems.
Therefore, our project can be directly linked to a number of sustainable
development goals, including the 2030 Agenda.
Well, this project can be applied to public, semi-private, and private
schools, because it is a help for all types of students who need it.
But it can also be extrapolated to a lot of companies,
such as those that need help for their employees.
Competitions, challenges.
We want to participate and it's Ignacio Echeverría, I'm sure he'll stand out with
my technological project.
We want to show that we will overcome any challenge we face.
Mate Rap, for the latter, is the math teacher
who, on top of everything else, makes math ugly.
Then he put it in there. Okay, then.
Okay, this is the mini video you submit.
This is the product we are going to create.
So
Then we made it a little longer so that we haven't delivered this one yet.
But, to put it simply, more than three children have suffered bullying at some
point in their lives, and one in ten places are currently experiencing
cyberbullying.
That's why Echeverría, a public school in Las Rozas de Madrid, was born.
Paint them on the mask.
Excuse me, I've picked the wrong video.
This one was good.
Let me give you a brief summary first. Basically, it's the idea behind the
project.
That was it, to generate not only the guide that spoke, which we do
with Scratch, or Scratch Stretch, which is a kind of Scratch but modified.
But for that, we wanted to put it into something physical, into a body.
Okay?
Then we came up with the idea of ​​projecting it with a projector onto a
semi-translucent surface.
Okay.
So, as a result of that.
We had to generate the face that they had already used to
generate the face to do the programming in Scratch, which
meant that they had to generate each expression of the face
with it, but then the programming of the mouth and what it
says had to be connected with the web services, these, the
AWS, the Amazon Web Services, so that it would be converted
to speech text and then synchronized with the mouth and
those images of the mouth that are actually the vowels that
are associated with each syllable, well, they also had to
create them and then program them using lists in Scratch.
Try associating those syllables you are saying with the image of the word they.
He explains it very well there, okay, now, I'll put it to you later then, okay.
So they've put AI into the part of the face,
that face that it then projects.
Then they have to introduce something physical,
optical, because they put in a projector and from the
projector they have to use an angled lens and from there
a mirror so that the distance is not so long and then the.
Then the AI ​​in the generation of the face, the programming of the
face, and then it also has to understand what you ask it.
So, to understand what you're asking, we use Google Teachable Machine.
As the saying goes, "machine."
They generate a list of questions that a teenager can
ask a kind of psychologist, and then a battery of
answers that programs with faces and text from Amazon Web Services.
Furthermore, the idea was also like this concept in psychology
that one of them came up with, in which the patient's gestures
are imitated to make them feel better when you are talking to them.
Then they came up with the idea of ​​introducing
the servos that would nod and shake their
heads, copying the person who was talking to him.
Then they introduced AI with Arduino, and for
that I had to give them a kind of recipe for how to implement it.
Yes, if you go to Google, the Machine explains how to integrate
that Google Matching link, which you can easily integrate with
Scratch to connect it to the Arduino board; you just have to add
some JavaScript in between. I made them a sort of guide, but then
they expand on it a lot because you teach them a few things and you think, "Wow,
they learn so much!"
And we were doing it, and then they did it much more.
So, well, I'll show you.
I'm
That's it. Well, that's it, the Amazon Web Services, the machine, the
topic of physical optics, which I've already mentioned, Scratch, the
topic of optics to be able to do that video mapping of the face on a surface
and then the creation of the surface was done with 3D printing, the edge
of the frame of the face and then we were working on the plastics syllabus,
which is also seen in third, in 4th year plastics are seen in technology.
So they made it with polypropylene, they tried to undo the molds, then in the end
they left it flat.
Well, in the end we worked on many more technological things, not
just artificial intelligence, the neck motors to feel and
to deny and then the AI ​​that goes into the face.
And those are the basic concepts.
Then they made this brochure and everything else too.
Nothing works with AI.
Okay.
So, compiled from the work we've been doing in class, including
homemade videos of what we've been doing with them, I'm showing them
to you so you can see how it works, and at the end I'm including two
more short parts that show a little bit of how it works.
We still have a little while until the 16th, but that's it.
Almost.
We know that one in three children have suffered bullying at some
point in their lives, and one in ten continues to suffer it.
Cyberbullying today.
That's why Echeverría, a public school in Las Rozas, Madrid, was born from Google.
We are very aware of this issue.
We wanted to combine an artificial intelligence that is capable of
helping all students with their psychological problems.
The project we've created is called therapy, designed to help
people who have any difficulties or problems at school. It
combines humanoids with artificial intelligence in a sensitive way.
Companies like Tesla's Sophia or Optimus are
demonstrating that this is the present; the
main goal of this project is to develop a humanoid
robotic psychologist to help high school
students with their problems, mainly bullying,
thus promoting awareness about bullying.
Use different branches of technology in a real
project and apply the knowledge from the third
and fourth years of ESO in a real project.
Also
Currently the. The.
Robots and humanoids are developing a lot, and even
more so with artificial intelligence.
Examples of this include Tesla robots, Hanson
Robotics robots, and figure-e.
On the other hand, bullying also continues and is a serious
problem for educational institutions around the world.
According to UNESCO, a 2024 report
showed that roughly one in three
students have suffered bullying,
and this is quite worrying.
It's not much anymore.
for the creation of the projection plate.
We have gone through several processes and in this video we are going to show
some of them.
Well, all of them.
In the first one we used a translucent, matte,
flesh-colored polypropylene plate that we tried
to melt with a heat gun on a ceramic mold.
This is here.
However, this did not yield results, as the
heat was not distributed evenly and it did not remove well.
The second step we went through was very similar
with another piece of polypropylene.
This one is white, translucent, and matte, which we wanted
to heat in a home oven so that we could achieve the same result in an oven.
The heat was distributed evenly this time, but we
encountered some problems, since the plastic,
due to its local composition, completely lost its translucency.
The matte finish wears off and also appears in bubbles.
The third and final step consisted of a flexible copper
mesh that we placed over the ceramic mold
and then applying epoxy resin on top.
If we can make it harden and adapt to nobody's shape.
However, it was no longer translucent and shone too brightly.
It's a bit far away for us, but hey, we're using
that side for skeletal polypropylene.
Are you seeing if Enrique can get out?
No no.
As a final result, we decided to opt for this flat
polypropylene plate with a Verge plastic edge made
from recycled bottles, designed in the Blender
program and printed on our 3D printer.
In the end, we decided, using the methods available to us, to leave
the face flat using a pink polypropylene plate framed on a 3D base.
of PC.
This way we have guarantees that the image will be sharp and clear, since
polypropylene is a material that is quite translucent.
Let's see, this is the way to.
Average.
Well then, above all, we can take it with us.
This plate, of course, this one needs to be washed more often.
To allow an optimal image of the humanoid on the mask.
Our project had to be at a distance of between 0.8 meters
and 2.5 meters, which was an abysmal distance, so
we decided to shorten it using lenses.
We started using diverging lenses, but these
didn't work because they stopped the light and
left us with a much more distant image.
So we decided to use converging lenses, which allow
us to enlarge the image and therefore bring it closer to the mask.
But we still had a very large distance left,
so we decided to use a flat mirror that allowed for projection.
I'm rambling. Never mind.
We have the head, cut off the neck.
How's it going? So, planning
well, but not just because.
What you have to do is form a right angle with
your eyes closed, using some movements.
It had to be the same face.
With the eyes in the same position, because otherwise it would look like the
eye was closing lower. That would be very strange.
And for example, if we go to everything related to the mouth, there it requires a
much
greater complexity, because it votes for the same thing, teeth, etc., etc. And we
also had to have costumes for smiling, closed mouth and open mouth, naturally.
So, of course, for the conceptualized AI, as it
is a naturally open mouth, we have to give it a
rundown through Google Mouse, cyberbullying,
cyberbullying, differences, types, because
it is responsible for passing the series.
Change your disguise from a serious mouth to a smiling mouth to make
it more dynamic, and then try to think a little bit and be flexible.
And we've also added Vale.
And in the end, well, I'm finishing up, sorry, it comes out
there a little bit, it starts to work when a harassment situation
appears, as they are in the trial phase they still have a month left.
What is more common are insults, mockery,
pushing or social exclusion and movement,
which they also achieve.
It's in the trial period.
It's a moment that if we leave something wrong.
So, if you want to see the whole thing...
He decided to come to the Tec challenge on June 16th.
Close.
Sorry.
And here we have Antonio Prieto Sánchez making a
fuss, and you see a project that is not project-based
learning for the first year of ESO, Spanish
Language and Literature, so it's going to be interesting.
Thank you so much.
Well, good afternoon everyone.
First of all, thank you to the entire course organization for inviting me to
give this presentation.
Secondly, taking into account the time it is.
I will try to be as brief as possible.
Furthermore, all the data is in the course database, and somewhat
using the analogy that Gregorio made at the beginning.
Well, I think that after having seen everything we've seen
here about plates, humanoids, robotics, etc., etc., I
think this dish is going to be much lighter and I also hope to
get a few laughs, at least within the auditorium, right?
The most important thing about this project is that it is a learning
situation, because it places the first-year ESO students of the
school where I teach the Language and Literature subject in a real
context such as Carnival and links it to the lyric genre and to the
prior knowledge that we have been giving throughout the second evaluation.
But precisely to foster a critical spirit regarding the advantages
and disadvantages of artificial intelligence, that is basically
the foundation of this learning situation.
This is the index that I will follow in my presentation of the teaching proposal.
At times I will go much faster, but I repeat because
all the detailed information is in the activity
sheet and all the evidence, photographs, images,
etc. are linked precisely in that sheet.
Regarding the context, it is important to note that the
class, or rather the center where I teach, is art, and Gasset is a public
center.
Specifically, I teach this subject at the M87 campus.
It is a venue that specializes in high-performance
athletes, specifically and especially in aquatic
disciplines, sports, especially water sports.
There are two other locations, one is the headquarters at the Higher
Sports Council and the main one is at the Santa Fe headquarters in Puente de los
Franceses.
And precisely for this reason it has some rather particular characteristics that I
will describe.
In principle, well, the proposal was designed for about
ten or 11 hours, approximately ten, 11 sessions.
Finally there were 12 and we have them
in this classroom specifically, because
here the ratios are in a completely opposite way.
Here the funnel widens as the courses
progress, and we only have 15 students.
Basically, within the programming, we are in the second quarter.
The subject of Spanish Language and Literature, which
connects everything that is the lyric genre and all its
elements rhyme, types of verses, stanzas, rhetorical
resources, etc. And the project is designed above all
thinking about the development of the students' competence.
The educational objectives are all of these.
Basically, we want to put into practice all the knowledge
of the lyric genre, but linking it to the Carnival festival.
We also value all technological advances, especially those
related to the development of artificial intelligence, while
at the same time practicing and learning about generative AI
tools, both for creating texts, images and sound.
Of course, we analyze the biases and impacts in the process, as we have
learned in the course, and also in the end, what we want, above all and
primarily, is to develop a critical spirit regarding the pros and cons of
this technology.
Why is it a competency-based project?
Well, linguistic competence, especially if we had to break it down or
divide it, is divided into four basic skills as you know: speaking, listening,
reading and writing.
In this case, especially placing special emphasis on critical evaluation,
since the various sources from which the students obtain information.
And in this specific case, in those that are
accessed through artificial intelligence.
Digital competence, above all, is based on the advantages
and disadvantages we find in the digital age, but also
here, in the creative use of these tools, as well as in
fostering a critical spirit towards them, and the social
or personal competence to learn and learn.
A little, as you will see in the workshop mechanics in which the
project has been developed, in the autonomy itself, which is
gradually fostering civic competence in the students.
Because?
Basically because we want to train, or I think, as a teacher.
This is a personal premise.
I always tell them that we have to train good people, good
citizens and good future professionals, and in this case,
this study of the advantages and disadvantages of AI
connects with the development of the goals of the 2030
Agenda and in terms of awareness and cultural expressions,
because, well, knowledge of the artistic and cultural
diversity of Spain, specifically of a festival as popular
as Carnival, has a special importance here in its
connection with the lyrical genre itself.
As we mentioned before.
Regarding the contents, they are all broken down in the sheet, but
basically it takes it from these three content blocks of the subject curriculum.
Regarding methodology, it is an important
aspect because several methodologies have
been used; learning through debate and Visual
Thinking have also been very important
throughout the implementation of the project,
but also the scientific method itself, which
is ultimately to observe, analyze, and formulate hypotheses.
These hypotheses become premises, and these premises then
lead to certain diagnostic approaches that have been implemented
throughout the entire collaborative project, because it is
not a collaborative project, and of course it has resulted in
a final product in terms of addressing diversity, because
here we have to keep in mind that there is a common denominator
and that is that all these students are high-performance athletes.
This means that many of them get up at 6:00, arrive at class at 10:30,
eat in the center's dining room, but then many go back to training again.
In other words, they have hardly any time and their
weekends are mainly focused on training or competing.
Therefore, most of them barely have any time.
Added to this is the exhaustion and fatigue and the great pressures
they have, not only those of themselves, their own self-demand, but
also those that in many cases are exerted on them by their own coaches,
clubs or federations, not always with a very humane treatment,
something that is not often said and also those of their own families.
And this is a very important element to consider because
these are students who have real difficulties in being able
to learn throughout their normal educational stage.
It's very complicated, of course.
Then there are also some students with special needs,
for whom, especially in this case, Visual Thinking was quite important.
Regarding the evaluation, different tools have been
used, since what is the process we have followed throughout
all the sessions, in which there has been continuous
interaction with all the students, both in the lectures,
but those lectures have also had a lot of feedback from the students.
Also, the absorption of the entire process, the use of digital
tools, but also the textbook notebook, the notes and of course,
the feedback that we have been giving not only me, but also a
student who was with me in the practice of the Master of Teaching
to guide precisely the work and the moments.
Well, everyone.
The entire project has been scrutinized, every single moment examined closely; the
whole process has been evaluated through everything they have been doing little by
little.
It has been very structured and organized in workshops and we have obviously also
valued its evolution.
Elements that we have all valued are in this rubric.
Basically, these have been the four elements, although others were introduced
later.
We introduced a change that I will explain later, which is teamwork, the
process
and organization, the adaptation to the content, that is, that they have
followed everything that has been passed on to them and the oral expression
which is the final presentation of their projects, both orally and in a
presentation that they had to design in the last session.
Regarding the schedule, from now on I present the presentation
I made, which is the backbone of the entire project throughout all the
sessions.
This is what they have been seeing; she tried to summarize it at times, but you
must
take into account, especially regarding the circumstances of these students and
also of the center.
Very important.
It's a public school, but we don't have a gym, we hardly have any
recess, we don't have a computer room, we don't have a library, the
teachers' room is tiny, and you have to use computers in this school
because there's no technology room by using a trolley.
When they are not disconnected, the operating system does not work or they
connect to another operating system that they do not know.
Or basically put it in the car, take the car down because it's on
the second floor, and go down to ours which is on the first floor.
It's also a quixotic task. Often, putting a computer in
the classroom is a completely risky undertaking because
you never know what's going to happen.
Therefore, I want to say that we have to forget about technology rooms here, and
obviously, after everything that has been seen here, it is a completely different
context.
What do we do in the first session?
There are basically six workshops.
The first is the presentation, in which we try above all
to introduce the students to the world of Carnival.
What is Carnival?
And in the end, this is the underlying principle and umbrella concept
of the project: that Carnival and poetry are exactly the same thing.
I am gradually introducing the students to the slang of carnival,
to the different terms they need to be aware of in order to optimally develop
this project.
We are, therefore, obviously talking about the history of Carnival.
Where does it come from?
What is a drop, what is a murga, what is a comparsa?
Why has Carnival become such a popular and
important festival in those anthems?
Well, it's not available here.
It was basically Celia Cruz's carnival, the song "Y ya
vez que intromos estas conceptos vamos a dar la murga".
Because?
Because what we really want is for our voice to be heard, and we're
going to try, even though we're talking about
disadvantages or advantages, to have a good time.
What do all these carnival groups have in common?
Well, they're aiming for a parody, a social satire of some current event.
And what is it?
What exactly are these groups made up of, which in
different parts of Spain hold competitions that are
more or less half-hour performances in which they
dedicate a lot of time and money and which are also
festivals of great tourist and cultural interest?
Well, we explain that all these parts are the ones in which these voices are
divided.
What are they going to do?
They're going to do a medley of choruses, basically selecting three
choruses from songs and adapting them to a chosen theme.
But we give many examples.
Here's the visual.
Look, I only want to reproduce this one because it seems representative to me,
especially because of its relation to the subject, you know.
I live on a planet that speaks our grandmother's language.
Languages ​​of Jaén, different languages ​​with their own identity.
They told me at school that these are
super important off-hours and that
nobody has the right to impose their
way of speaking, erasing the message of freedom.
Ladders.
Staircase in Cancun, in his shop in Galegos
to a seafaring countryside and the words
Sing la, la, la, la la freedom and they will
die and the language of many
generations of my planet.
People get lost,
the hearts of many.
This is an example, for instance, of that awareness and cultural expressions, not
only regarding the linguistic diversity of Spain, but also because they have to
promote those values ​​of respect for our linguistic diversity.
But at the same time, as you can see, the introduction also gradually
introduces them to the concept of type and the important thing, which is
not just the lyrics, because in fact we tell them that there are many people
in the López de Ayala theater or in the COAC of the Falla Theater of Cádiz,
who dedicate themselves all year simply to living precisely from these lyrics
that they compose.
For these groups, and throughout many examples we
give, there are different types, for example, in the
introduction of this chirigota we simply see how
important the staging is and of course also the non-verbal language.
I'm only including the beginning of it because we can't.
I live with a couple who are very
naughty in bed and often shout
"Antonio" at many nights.
David is making a child, Teresa, please don't
make any more children, they won't all fit on the table.
Mrs. Bueno, there are many examples collected from
different Carnival celebrations throughout Spain.
More recent ones are fine, less recent ones too.
We also play them some versions of well-known songs about this
hostel phenomenon, although they're talking about it all day
long, and then, once we've covered the specifics of Carnival
and these groups in this presentation, we'll explain what they need to do at
this point.
Put yourselves in context, they only have one.
Basically, their faces are a picture, and it's a picture because they're both
waiting to
find out if they're going to have to make a stop in this direction because they
don't have time.
And two, knowing with whom.
Since they know it's a team project, they need to know who they'll have to meet
with.
And that, of course, is basically his only concern at the moment.
So, well, it is explained to them what a project is, that
is, a collaborative project based on a final product
in which they have to relate and put into practice all
the knowledge about the lyrical genre that they will
work on in teams and that in the end the objective is that
they compose or adapt the songs of three choruses of
popular songs to a theme that they choose through a
workshop that we will be doing at the beginning, which
will help them decide on what the theme is
and of course they are shown the rubric.
And obviously the important thing here is basically that all they
have to do is choose the three, adapt and rewrite the lyrics, but
they have to relate it to a theme of how the development of artificial
intelligence affects the Sustainable Development Goals of 2030,
which they will then set to music with their own song, and then, in
addition to that lyric in which they also have to incorporate and
respect the meter and rhyme approximately of those choruses, they have to.
They have to edit that lyric audiovisually using another tool, like a
video,
and then they have to present it, they all have to present and explain it,
participating more or less at the same time and taking into account all the
requirements they have to meet.
They show them, for example, a video from one of
the Muga del Temple series, in this case the one
from Gran Canaria, in which, well, if I go to the
chorus, what I do with all the waste, the Luis
Fonsi song, everything in its place, I put, you
said, if cans in the yellow bin and the green
container only for glass, I put all the little
papers in the blue bin and if I see that you don't
do the same I'm going to force you to pick up little
pieces. I'm passing by so that you don't.
Okay, we formed the teams, we communicated it.
Here, angry faces relax, some even show joy, and it's explained to everyone in
the workshops.
So far we have been at the project presentation.
We now continue with the research workshop.
Then we have the Creative Writing workshop, followed by music
production, the audiovisual editing workshop, and finally
the performances related to the research workshop.
We'll start, and you'll all recognize this quickly, by talking about what AI
is.
Well, I'm following the process we saw in the course in the first place.
Well, first, I'll refer to its definition in the RAE (Royal
Spanish Academy) and clarify that we are talking about a computer language.
Later I talk to them about the main technological resources and technological
advances that have given rise to those foundational models like GPT, which are
the ones they know.
And we talk about those concepts like neural networks and algorithms
at the same time, because I also illustrate it with very simple things
that they understand.
I don't know if they look very good, but well, in the morphological
analysis guidelines we give in language, I do a bit of that neural
network in which we are faced with a variable word, yes or no, and that
variable word has a degree, if so, an adjective no, well, the rest.
I mean, so that you fully understand how it works.
But also, well, we apply machine learning, that is,
we do, you know, the task we've seen about cats, dogs, white, black.
We talked about the basic processes of artificial intelligence: training,
learning, and testing, which they, as athletes, understand very well.
We tried the Aquinate for a moment.
Later we talked about a good pron, and how important it is to
obtain the most reliable information possible.
Although we have biases and impacts, we relate these to the elements of
communication, which is important and something you've already seen since
the beginning of the course. And finally, we also discuss biases and their impacts.
So here we simply discuss the topic of professions.
As you know, they showed us this in the course,
and this really caught his attention.
The goal is basically to then establish a debate about these
2030 Agenda goals and see what the pros and cons are regarding
artificial intelligence in relation to them.
We write down everything they create on the board, we discuss it, and we resolve
any doubts at the end.
Well, we've come to this sort of thing.
We talked, I insist, about both the advantages and the disadvantages.
But what happened, and what was surprising about the project?
So basically, they finally decided to talk about the disadvantages in all their
groups, perhaps because they were the ones they understood the most, or perhaps
because in the professional sphere the advantages when they go months and they,
well, for them it is an advantage, is being able to copy through AI, but
they know that it doesn't bring them anything good, of course.
In this first creative writing workshop they don't have
any technological tools, we don't have computers and I
simply ask them to think about what theme they want to decide
on and they have to choose three choruses from popular songs.
I also insist that it's not that they shouldn't choose reggaeton
or any music at all, because normally these lyrics, well,
adapting them isn't so easy and they write them down.
It consists of three creative writing workshop sessions.
In the first one, the goal is basically for them to at least manage to adapt a
chorus.
Once they have adapted that refrain, we move forward and
through the Notebook LM tool with an account password
that we created directly with the intern, we create four
notebooks, each one has a notebook and in the second session
I offer each group a computer with the pro maestro that you
will remember that they offered us in the course, but adapted to this project.
Well, it's not here, I haven't included it, but of course, in the one
where they have to behave like carnival troupes, basically you have
to give them the context, what the role is, etc. All the important
elements of a pron and to my surprise, in the second session you start
to hear how when they interact with the tool and are critical
of it as most of the options that Notebook proposes.
LM because they do not conform to the syllabic scheme of the original refrains.
As for what they want, there are things I don't understand.
Start listening.
To my surprise, the AI ​​doesn't listen to me. The AI ​​is useless. It's much
more fun if we do it ourselves.
Why would we use AI if we can do it faster ourselves?
In short, except for one group out of the four I formed,
basically everyone adapted the lyrics in their own handwriting.
Later, once we had all those letters, because I controlled
this creative writing process a lot, then they had to leave
everything on a stick, on a pen, they couldn't take it home.
He also didn't want them to do anything at home; everything had to be done in the
classroom.
So we started with the tool once we had all the lyrics to set to music here.
The milestone was that they would ask for different versions of
different musical genres: electronic, pop, rock, Spanish music,
flamenco, that is, whatever they wanted, but they had to do tests with different
musical genres.
And then I'll tell you what the surprise of the elections was.
And finally, the audiovisual editing workshop.
Once they had their choruses set to music, I used the video tool.
Today I already knew that I wasn't going to get any performance,
obviously, like the one we saw at the Manuel de Falla or López
de Ayala theaters, it was impossible, but I wanted them to
realize that the artificial intelligence tool was obviously
always much more powerful if they had a paid version.
In fact, when they used their credits here in video there or checked
that they all had the same images in all the letters, well, they
said that this is a deception, this is a scam.
How can that be?
But that was the goal I intended with this.
And what happened in the end?
Well, I announced here that I was going to
give an extra point in the rubric to those
teams that chose that letter which they then adapted to music.
Act like a drop.
In other words, they didn't just have to talk while
the music played, or do some kind of crime or performance
to put them in context about what he had decided.
Because there was one student who didn't want us to
offer it, whose mother didn't want us to offer it.
I didn't ask the family for permission, but the
important thing at the end of the project was the results.
And what results have we had?
Well, from the four teams.
To give you an idea, I'm sometimes able to figure out the first team.
His carnival group or chirigota was called Los Copian and his theme song
was...
The main issue has been the misuse of AI for copying.
I'm not going to tell you what the choruses they used are, but
I will see if someone divinely revealed it later.
This is the result, okay?
Ti. I copied the answers
from my snitch,
I copied and Olivia got caught. I copied.
He caught me with the paper in
my hands, I copied it and I only
got a failing grade because.
And my mother covered me with sticks.
I copied and copied, and the conversation never left me.
I copied and copied, and I hadn't reminded her of the argument.
Another song.
I'll turn them to stone to pick up my train.
And even though the teachers are
good at this, I'm always the one who copies.
But replicating in order to keep
changing never works out well.
I will endure the punishments and I will never give up.
And even if the exam falls apart, I'll repeat it.
I will repeat.
I'm going to use this template with the cheat sheet.
Don't let them catch me off guard by trusting them.
I am like the trumpet that deceives me.
The same note and I want to try it.
I'm so sad about all this biology, I want a little
bit of ease, but look at me and tell me, teacher,
I can't see my grade and I can't pass.
I will remember the argument in front of everyone.
I'll turn them to stone.
Okay, you can listen to it in full within the virtual
classroom, but basically here they have used.
Se fue de Laura Pausini Resistiré del dúo anímico y Zapatilla del Canto del Loco.
This is actually the longer version because they decided to lengthen it.
The others are just over a minute long.
I say this because our humans have already risen up here. So in the
next chirigota, "The Soulless Friend," the chosen theme was basically
the dehumanization of AI, and from my point of view it was one of the
most interesting, especially because many understood
that in the end there was a human behind them.
And I am, well, obviously, a Spanish language and literature expert.
What I want is for them to go inside and put aside their
computers or mobile devices all day long.
And this is what they did at the end of
this song: they name the choruses they used.
Give your code a little energy,
because you are not human and you
don't have a soul, you don't have
life and you don't feel empathy.
Cold.
It has neither consciousness nor a
little bit of being, nor can it hurt you.
Tell me.
And both were, "What do you intend to do?"
deceiving.
You're welcome.
The con artist is going to use artificial
intelligence, an international deal.
A friend who isn't real is in the AI.
Everyone trusts him.
It's in AI or we haven't done it.
Beware of La Macarena by Los del Río and Yo de la Luna by Mecano.
Rosalía's pearl.
I'm putting on the third one.
We left the 4th one so that it looks good enough to be discovered in the database.
In this case, they were called the professional leagues, and the theme
they used was basically that the AI ​​is trained to agree with us. As you
can see here, since they have done all the presentations, they show us
both the adapted and original songs with their metric scheme, the
justification of the project, the problems they encountered, and how they
solved them.
And here we have the potpourri
of this.
I've been through life and I'm not going back.
And seven here, Piti, I'm not going back in.
I believed everything like a child and I'm not getting involved again.
They all agreed with me, I'm not getting involved again.
He tells me that everything is rosy.
I tell him not to say that to me.
He tells me he doesn't mind lying to me.
I'm always right, he won't sway me.
And if I approach the daughter and she
lies to me, if she agrees with me.
What can I do?
The world is catching me off guard.
If I create a public square and have
clouded ideas that are useless, that's it.
It's basically just guitar playing, so it's not important.
And well, the conclusions of the project, which are the most
important for me, are that I have had to give it more time, something
I already anticipated, but also because they have
been very involved throughout the entire project.
I was also surprised that they themselves, moreover, through all that
deductive reasoning, after the analysis in the research of what AI is,
have decided precisely to talk about the disadvantages.
They also noted that the use of prompts has caught their attention.
Is it something they didn't do because many of them have copied me, in fact in
class, and I've realized why?
Because the register in which they spoke was not at all appropriate
to what they are used to, and also because the biases in the
presentation that we have identified in the presentation of
all the projects have been clear, because when putting them all
together and all the musical registers that they had asked the
tool for, basically they have all chosen Latin rhythms, flamenco or reggaeton.
Because?
Because clearly, Castilian music, for these tools, is surely that.
It's not because it's not rock or other echoes like
not to Castilianize these GPTs, these GPTs.
So some of these things have also made them perfectly
aware of what these biases and impacts are, which was
also one of the objectives of this project.
So, well, that's all, and thanks for listening.
Thank you.
I'm not going to put you on.
That's it, that's it.
Very good.
Let's see, let's see, let's see.
I'll leave it here later, if you want.
I'm going to close quickly because we're already outside.
Out of time, and with this,
we finish the course.
I think it's already pending.
All that remains is for us to give the signature sheets to our colleagues
in the Community of Madrid. You don't need to sign them on the way out,
to confirm, you don't need to sign the signature sheet, and
they will send you the certificate at some point.
So I wait.
I hope you enjoyed this course.
As you know, we try to make it as diverse as possible in terms of ages and
subjects.
That's complicated, but at least you've
taken away a few key ideas and that will
make your classes as fun as Antonio's.
I wasn't that lucky.
Literature.
University was much more traditional, so that's how it is.