but the artificial intelligence is already present in the daily lives of all people.

For example, in the development of video games that

use this type of study to create more complex games.

In football games, each player has very specific and

similar characteristics to those of a real competitor.

That is, one is better to pass but runs less than the other.

The Intelligent Systems techniques are applied to simulate this action.

Another example is the cameras that make the autofocus

on people's faces or shooting to find a smile.

Even in spell checkers of computer word processors,

an intelligent system is necessary to detect that there is a

syntax problem in the sentence and provide a possible fix.

Many people complain that the broker always errs.

But we must remember that, as intelligent systems

simulate the human, they make mistakes like us.

This is designed for you to have many questions about a subject so controversial

that arouses an enormous curiosity about the world, but also frightened.

Artificial Intelligence - History

The term "artificial intelligence" was created in 1956

upon the occasion of the important meeting at Dartmouth,

where they met minds like Allen Newell, Herbert Simon,

Marvin Minsky, Oliver Selfridge and John McCarthy.

At the end of the 50 born the symbolic processing with a result of efforts

Newell, Simon, and J. C. Shaw that instead of building systems based on numbers,

engineered systems capable of manipulating symbols.

Thus, the different currents of thought in Artificial Intelligence, now called

"Distributed Intelligence" have studied ways to establish, on the machines,

"intelligent" behavior, which could be easily expressed by Minsky in the

book Semantic Information Processing, "How do computers understand things?"

The term "Artificial Intelligence" can refer to an entire universe of

programming techniques used to try to solve problems more efficiently

than algorithmic solutions and the closest to the intelligent human behavior."

Main Technical

They stand out in the large family of techniques of

artificial intelligence, the following research fields:

A) natural language, which addresses a set of techniques aimed at the

recognition and generation of natural language, written and spoken.

The main applications are in the field of universal translators,

editors and the mining of texts and controls voice devices;

B) automation and robotics, i.e., the set of technological resources

that aim to create autonomous robots, able to learn and make decisions;

many of these systems are already in operation

and have been reported here in Hypescience.

C) perceptual systems, which aim to create

visual pattern recognition systems, sound, and

textures, to simulate and enhance the perception, whether optical, hearing or touch.

Its main applications are in the medical

diagnostic field and industrial quality control;

D) expert systems, which capture the knowledge in defined areas

of knowledge and human experience, using it in decision-making.

Observe currently important applications in medical diagnostics,

identification of chemical compounds and in decision-making

processes of business managers and brokers in the stock market.

A special feature of expert systems is the occurrence of

systems that support the decision in case-based reasoning;

E) genetic algorithms, which consist of several troubleshooting techniques based

on the principles of Darwinian evolution, or mutation breeding and selection.

They are used to solve problems involving a large number of variables and calculations,

such as the Fairings Aerodynamic projects

developed by aerospace and automotive industry.

F) intelligent agents that characterize the set

of stand-alone software that works in networks,

or in parallel to a primary software created to

achieve predictable, precise and repetitive tasks.

For example, operating systems, software that manage

e-mail and network tools are hosts for intelligent agents.

Noteworthy is its primary applications in the management

of large volumes of information, such as, for example,

in the stock market in search engines and the internet

in the monitoring and management of e-commerce;

G) neural networks, which are simulations of the human brain

processing patterns, such as plasticity and learning.

Has its architecture based on an approximation of the animal brain and instead

of being present, the neural network "learns" a particular training environment?

Its construction is based on Perceptron, a discrete component

that tends to simulate the physical behavior of a neuron.

The association of thousands of perceptrons obtained enough

plastic networks, which can recognize complex patterns,

such as cracks in metal welds in pipelines or quality

of an apple, diagnosed by color patterns of its bark.

In this book, we will talk about artificial intelligence from

the time that we would never think that such a concept existed.

The artificial intelligence is older than you think

The area is divided into two parts:

AI symbolic, which is connected to the psychology;

and the connectionism, or artificial neural networks, which comes from neurophysiology.

The latter, for reference, was that Google mentioned when he

explained how the AlphaGo, AI Robot DeepMind trained to play Go.

Computer science is a new field of study, but not artificial intelligence.

It is beyond the Greek philosophers, of Plato, of Aristotle.

It is very interesting to observe these scholars of the past.

They will reassemble and building what we understand today as a scientific model.

Not today we address tools of artificial intelligence in Tecnoblog.

We see Google leveraging technology to beat world champions

complex board games and even combat Aedes aegypti.

Microsoft has signaled several investments in the area, either

with "human capacity" or application of Microsoft Research.

What few people know is that it is one of the ...

old news, being idealized in time to before Christ.

Artificial intelligence, although much more palpable today, comes to the

Greek philosophers and had a breakthrough also in the twentieth century.

To understand what exactly this term, we need to

turn to the origin of artificial intelligence.

It is an area of computer science in which researchers

sought different realities of an only programmatic model.

Not to solve simple problems, such as adding two numbers,

intending to create a kind of thinking in computing.

Artificial intelligence in antiquity

Not that existed systems that did things for themselves, but the idea of a non-human

intelligence that thought itself was already conceived, according to the professor.

He says Aristotle, teacher of Alexander the Great, king of Macedonia (in

ancient Greece), thinking about how to free the slave of your business.

The slave was treated like nothing in the social aspect.

He imagined the following:

is an object like a broom, or an element that makes cleaning,

can have an own will and establish the storage system?

Thus, we would not need more than slave labor.

These guys saw that it was not cool to have this

domain and sacrifice of another human being.

He invented robotics in 300 B.C.

Philosophers were asking things like, "will be a slave in possession of

innate information (which come with the nature of man), could learn math?".

Of course yes.

The revolutionary is the line of thinking because they have

idealized cognitive science, which deals with human learning.

Development of Science

The 50s can be considered the time of the golden years of artificial intelligence.

There was a psychological chain called "behaviorists"

who treated science as only the act of human behavior:

his hand holding a doorknob turns because you want to open the door, for example.

According to the doctor, this is not a good explanation.

"I have to have information processing, which is what cognitive science does."

"It is not only an input box, input, and output.

I wonder what's in here [the box].

It is what artificial intelligence studies, know what's in this big box.

Artificial intelligence branches in many areas, from games to philosophy.

We can imagine this science as a significant

capillarity, which can be applied mainly at all."

The key, then, is to understand that artificial

intelligence is one, but its reach is huge.

Fernando works in this area for 40 years,

going from music and computing (in the 90s!)

to a homodinamic description of the functioning

of the heart with the lungs, in 1983.

This concept of artificial intelligence has been well established for several years.

What we know today as artificial intelligence was detailed in

a Congress by Professor John McCarthy of Stanford University.

He began using the term in the conference

made from Dartmouth College in New Hampshire.

At the time, there were already several theories of complexity,

language simulation, neural networks and learning machines.

He decided to give the name of artificial intelligence to

those of human imagination systems that use computer science.

Several engineers, mathematicians, psychologists

and neuroscientists participated.

The capacity and electronic functionality doubled every 18

months, an almost exponential growth that almost kept up.

Participants of the Congress left there believing that one

day computers would be able to be as smart as humans.

Well, it has not happened yet.

Still, McCarthy made great advances in his laboratory, one of the

first dedicated to the development of artificial intelligence.

He won the Turing Award in 1971, given the computer scientists

who have made outstanding contributions to the area.

Fernando points out that this award is basically the Nobel of computer science.

Turing test

The name comes from the mathematician Alan Turing,

portrayed in the movie The Imitation Game.

Turing is also one of the forerunners in the area of artificial intelligence,

according to Fernando, as the mathematician working with complexity.

Turing was responsible for accelerating the process of breaking the Enigma machine

code, to understand how the Germans communicated during the Second World War.

Another fascinating work, which also came from him, was the Turing test.

You stand in front of a teletype and do not know

what that tells you, and you are a man or a machine.

If you can not know how to identify whether it is man or machine

at the end of the conversation, the robot passed the Turing test.

In 2014, a group of Russian researchers created the Eugene

Goostman, a robot that is a boy of 13 who barely speaks English.

He tricked the judges and ended up winning Loebner

Prize, which is given to those who pass the Turing test.

However, many experts are skeptical about the achievements of Russian researchers.

Instead, they make a high-level Turing test, and they created the low-level test.

It is a Ukrainian boy who barely speaks

English, and the test is being done in English.

When he curls up on the process, he says he is only

a child of 13 years and can not respond very well.

In other words, it's easy to fool the judges when

you reduce the parameter of the robot can do.

You can not reduce the complexity of the problem.

I'll fool people there.

Will pass the Turing test, with a limitation.

But the limitation is the Russians, and not intelligence.

What we want is much higher.

The Turing test has used today.

According to the doctor, he must have served as inspiration

for Microsoft Make Bot Framework, for example.

Everything lies in the central core, the Turing test.

How does he do to achieve this, it could use technologies based

on psychological systems, or artificial neural networks.

Artificial neural networks are recognition machines,

which check if it passes through the machine learned.

It is estimated that only 20 years from a machine will be able to deceive man.

When asked if the Bot Framework Microsoft could

pass the Turing test, it shows disbelief.

Not necessarily.

Further learning this way, asking for a human when he does not know.

What exactly is artificial intelligence?

According to Elaine Rich, although most attempts to define

precisely terms complex and extensive use is exercise in futility,

it is necessary to outline at least a rough border around the concept

in order to have idea about the discussion will follow in chapter 4.

To do this, we propose the following definition,

although it is not universally accepted.

The Artificial Intelligence (AI) is the study of how to

make computers perform tasks that when people are better.

The term "artificial intelligence" was born in 1956 in the famous meeting in Dartmouth.

Among those present at this meeting included to Allen Newell,

Herbert Simon, Marvin Minsky, Oliver Selfridge and John McCarthy.

In the late 50's and early 60's, scientists Newell,

Simon, and J. C. Shaw introduced the symbolic processing.

Instead of building systems based on numbers, they