English subtitles for clip: File:Ikusgela-Adimen artifiziala.webm

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Who wrote all this that I am going
to say in the next few minutes?

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Myself? A screenwriter?

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Or was it an
artificial intelligence?

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Only one thing is certain:
immediately after creating this video,

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what we explain here
could become obsolete.

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It is not at all easy for humans
to distinguish authorship.

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And that gives us a measure of
the progress of artificial intelligence.

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ChatGPT and similar tools

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have the ability to write
texts in their entirety,

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precisely and adapted
to the requested style.

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And other tools are capable
of creating images from scratch,

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respecting the orders
given that we give him.

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AI or artificial intelligence

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is not a brand new topic.

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Early tests of this rapidly
evolving technology

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were done as early as in 1943.

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But since 2022 it has
fully entered our lives,

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and of course, the
discussions with our friends.

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But how does all this work?

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And how much will
it change our lives?

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Artificial intelligence is
the capacity of machines.

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To perform tasks that normally
require human intelligence.

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These include learning, decision
making, and pattern recognition.

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To achieve this, artificial
intelligence developers

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use what is called
a neural network.

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A neural network can learn
to perform difficult tasks,

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like recognizing images
or translating languages.

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A neural network learns
through experience.

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This means that when it
is trained with a data set,

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that data set can adjust its
connections and parameters

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to improve the ability to
perform the desired job.

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In other words, you can create
and adapt your own algorithms,

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through machine learning.

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Basically, a neural network
consists of many "artificial neurons",

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and are organized in
interconnected layers.

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Each neuron receives
certain information.

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And performs a small
mathematical operation to process it.

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This information is then transmitted
to the neurons in the next layer,

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and so on until
reaching the final answer.

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That's right, data must be
provided to train the neural network.

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There are different types
of learning processes:

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1- Supervised learning:

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This type of
training is used when

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there is a data set that
has been labeled before,

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is to say,

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when the correct answer
is known for each input.

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The AI ​​learns from
this labeled data,

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and then you can make
predictions for new data.

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2- Unsupervised learning:

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In this type of training,

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AI does not have
pre-labeled data.

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Instead, they are
presented with a set of data,

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and then are asked to find
patterns or structures within that data.

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This can be useful
for data analysis or

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to segment
customers, for example.

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3- Learning through
reinforcement:

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This type of training is used
to teach AI to make decisions,

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through reward and punishment.

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They are presented
with a scenario,

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and asked to perform an action.

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If the action is correct,
the AI ​​receives a reward,

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but if the action is not correct,
it receives a punishment.

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Over time,

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the AI learns to make the right
decisions to maximize rewards.

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An example,

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if we want a computer to
learn to recognize faces,

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and give us information about
what appears in each of them

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we will provide it with
many labeled images.

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The computer observes
patterns in the data,

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and creates a model to
identify faces in new images.

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And why is all
this so significant?

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Mainly because it is a technology that
can be used in many areas of our lives.

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In healthcare, artificial intelligence
systems diagnose diseases

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and can help doctors
predict treatment outcomes.

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It is also capable of automating
most of the work done by computer,

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with increasingly
better results.

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Design work,
writing, forecasts...

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It is developing rapidly and has
increasingly impressive capabilities.

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But be careful, there
are also critical voices.

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And thank goodness:

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because we have already been warned
about the dangers of this technology.

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On the one hand, as
artificial intelligence advances,

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new ethical dilemmas arise.

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For example,

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Who is responsible if an autonomous
vehicle causes an accident?

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How can we ensure that
artificial intelligence systems

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do not discriminate against
certain groups of people?

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Is it ethical to make decisions
that can harm humans

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creating artificial intelligences
that will take control?

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Who owns the technology?

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Who audits or
controls the decisions?

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These are some of
the ethical dilemmas

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that need to be addressed
as technology advances.

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And what about languages?

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How does this look from
minority language communities?

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There is no round
answer about the

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minority languages
​​and artificial intelligence:

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For example, automatic
translation systems

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can facilitate communication between
speakers of different languages,

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and that could
have a positive effect

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in the care and promotion
of minority languages.

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It is possible, thanks
to these technologies,

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to be able to receive any
content in our language,

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without the need for anyone's
translation or dubbing work.

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But all that
glitters is not gold.

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Artificial intelligence
systems are biased and

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There is also the risk of
reproducing linguistic discrimination.

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It is likely that there is not enough
information on minority languages,

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and therefore they make more
mistakes than in other languages,

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or have lack of details,

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and can't distinguish details
about smaller linguistic communities.

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There are around 7000
languages ​​in the world.

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How many of them will have

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the opportunity to use the
capabilities of artificial intelligence?

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For example, the models used

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are intended for
data-heavy languages

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those linguistic communities

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that tend to have more
technological resources.

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That's why it's important,

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in addition to having its
own data available and free,

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to investigate models
that require fewer resources

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or collaborate with other linguistic
communities with similar characteristics.

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Scared, delighted?

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What do you think?

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Everything goes very fast
and it is not easy to keep up.

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But don't forget,
information is power.

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We will try from here to do our
bit so that everyone can access it.

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Understanding
gives us sovereignty.