MÓDULO 5 - Vídeo 1: Principales áreas de la Inteligencia Artificial

Welcome to this video

for module 5, artificial intelligence,

a discipline that is transforming the

way we live and work.

In this video we will review in a

clear and simple way the main areas

that make up artificial intelligence

and how they appear in

everyday, academic and work activities.

Before continuing, we must consider

that the most important thing is to understand that

artificial intelligence is not a

single tool, but a set of

technologies that help solve

different types of problems. Remember to

take notes and pause the video when you

need to.

Now let's imagine the following situation.

A person starts their day by using their

cell phone to dictate a message.

Unlock your device with your face,

receive music recommendations, and

find the best route to your

job. All these actions seem

simple and are part of

everyday life, but

different areas of

artificial intelligence are involved behind them. However, it is

possible that we may know what type of

artificial intelligence is involved in

each of these activities. That's why

we made this video. Let's continue

exploring. Artificial intelligence

is a broad field comprised of

different areas of study and

application. Each of these areas

allows machines to perform

specific tasks that previously required

only human intervention. For

example, some areas of

artificial intelligence focus on

understanding human language. Others

learn from historical data to make

predictions. Some identify

hidden groups or patterns. Others process

complex information using

neural networks, and still others allow a

computer to interpret images or

videos. This means that

artificial intelligence does not work in only one

way. Its usefulness depends on the

type of problem you want to solve,

the data available, and the purpose

for which it is used.

An application that

translates text is not the same as a system that

detects bank fraud. This

information will help us better understand

artificial intelligence. Okay, let's take an

introductory look at five

main areas of

artificial intelligence. natural language processing

, supervised machine learning

, unsupervised machine learning

, deep learning, and

computer vision. Let's explore how these

technologies support the solution of

real-world problems and

decision-making. Before continuing, let's take

a very important pause to remember

that artificial intelligence must

be used responsibly. Although it

can facilitate many tasks, its

results must be reviewed,

contextualized, and evaluated with

human judgment, especially when they

relate to personal data,

important decisions, or

sensitive information. The first area

we will review is

natural language processing, also known as

NLP. This area allows

computers to work with

human language, whether written or spoken. Its

purpose is to enable systems to

analyze, interpret, generate, or

respond to messages in a language that

people use on a daily basis.

Thanks to natural language processing

. There are tools such as

virtual assistants,

automatic translators, voice dictation systems

, spell checkers,

customer service chatbots, and

text summarization applications. A

clear example is

voice recognition. When a person dictates a message

into their cell phone and the device converts

the voice into text,

natural language processing is involved. Another

example is machine translation, which

allows for the quick understanding of texts in other

languages. This area is

especially important because

language is one of the main ways in which

people communicate ideas, needs,

emotions, and instructions. Therefore,

NLP allows for a more natural interaction

between human beings and

technological systems.

The second area is

supervised machine learning. It is called that

because the system learns from

previously classified or

labeled examples. In other words, it is

provided with input data along with

the correct answer so that the model

can learn the relationship between the two. An

example of this can be seen in the

classification of emails

as spam or not spam. To train the

system, many emails that have already been identified are used [music]

. With these examples, the

model learns what characteristics

usually appear in unwanted messages

and can subsequently

classify new emails. Within

supervised learning there are

classification and regression techniques.

Classification helps to place a case

within a category. Regression

helps to estimate a numerical value such as

a quantity, a probability, or a

trend. [music]

Supervised learning relies on historical data

where the answer is already known. The

quality of the result depends on the

quality of the data used.

If the data is incomplete, biased,

or misclassified, the model may

produce incorrect or

unfair results.

Therefore, this area requires

human review and ethical criteria.

The third area is

unsupervised machine learning. Unlike

supervised learning, here the

data does not have a

predetermined response. The system analyzes

the information and looks for patterns,

similarities, differences, or groupings

on its own. A common example is

customer segmentation. A company

may have information about purchases,

frequency of consumption, or preferences,

but may not yet know what groups

exist among its customers. An unsupervised model

can identify groups with

similar behaviors, for example,

frequent customers, occasional [music]

customers, or customers

interested in a certain type of product.

There are clustering techniques such as ke

or hierarchical clustering that help to

sort data by similarities. [music]

However, at this introductory level,

the essential thing is to understand the

general idea. The system does not receive a

correct answer from the start,

[music] but explores the data to

find relationships that are not always

obvious at first glance. This area is

useful for initial data analysis,

pattern detection, and generating

questions that can guide

subsequent decisions.

The fourth area is

deep learning, also known as Deep

Learning. This area uses

multi-layered artificial neural networks

to process

complex information. Its name is related

precisely to that depth. The

system analyzes the data through

several layers that identify

increasingly sophisticated characteristics.

For example, to recognize an image,

the first layers of a network can

detect edges or simple shapes. Other

layers can identify more

complex parts, and the final layers can

recognize that it is a face, a

car, an animal, or a

specific object. Deep learning has

driven significant advances in

artificial intelligence with applications

in speech recognition,

image generation, machine translation, and

recommendation systems, to

name just a few. The important thing is to

recognize that deep learning is

a specialized form of

machine learning, especially useful when

problems are complex and data

has many features.

The fifth area is machine vision.

This area allows

artificial intelligence systems to analyze and

interpret images or videos. Its

purpose is to enable a computer to

identify objects, people, shapes,

movements, colors, patterns, or

visual situations.

Computer vision is present in many

current applications, for example, in

facial recognition to

unlock a phone, in

security cameras that detect movement, in

systems that read vehicle license plates, in

medical applications that support the

analysis of x-rays or tomography scans.

and in industrial processes where

product quality is checked using

images. [music] Some techniques

allow you to recognize faces or detect and

classify objects in an image.

However, computer vision does not

mean that the computer sees the same way

as a person. What it does is process

visual information using data,

models, and patterns. Based on that

analysis, you can identify elements or

support a decision.

When working with images of

people, faces, private spaces or

sensitive data, it is necessary to take care of

privacy, security and

consent.

As we can see, each area of

artificial intelligence has a

distinct function.

Natural language processing works with text and

speech. Supervised learning learns

from examples with known answers.

Unsupervised learning looks for

patterns in unlabeled data.

Deep learning processes

complex information using

multi-layered neural networks, and

computer vision interprets images and

videos. In practice, these areas

can be combined. A

modern application can use natural language to

understand a question,

deep learning to process it,

computer vision to analyze an image, and

predictive models [music] to

suggest a decision. That's why

many current tools

integrate several areas of

artificial intelligence at the same time. Understanding

these differences allows for a

more critical use of artificial intelligence

. It is not enough to know that a

tool uses artificial intelligence. It is

also worth asking what type of

problem it solves, what data it uses,

how reliable its result is, and what

responsibility the person

using it has. Do you remember the situation

presented at the beginning? It's time to

pick it up again. When the person dictated a

message, they were using

natural language processing. When I received a

music recommendation, machine

learning models were probably involved

.

When the cell phone was unlocked with the

face, artificial vision was involved

. And when an application

suggested a route or anticipated

journey conditions,

data, predictions, and

learned patterns could be combined.

In this video we review that each area

has concrete applications in

daily life. Work, education,

health, safety, communication,

entertainment, agriculture, and

decision-making.

Artificial intelligence can make our activities more efficient

, but it does not replace

human judgment, critical review, or the

ethical handling of information.

We hope that the information has been

useful for the development of this

module. Remember that you can watch

this video again whenever you need to.