Experts believe that the intelligence of machines will match that of humans by 2050, thanks to artificial intelligence. The immediate consequence of the progress of artificial intelligence is the advancement of unemployment that is inevitable because it results from economic forces that are out of control. The artificial intelligence is positive for the capitalist who makes use of it because it would face its competitors in a more competitive way and would also be extremely negative for the capitalist because it tends to reduce the income available to the mass of the excluded workers of the production contributing in this way, to the fall in demand for products and services. The great threat of artificial intelligence is that it could lead to the extinction of the human race, according to scientist Stephen Hawking, who will become unmanageable to the point of putting humanity at risk.

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THE PROGRESS OF ARTIFICIAL INTELLIGENCE AND ITS
CONSEQUENCES
Fernando Alcoforado *
A reading of the books Artificial Intelligence by Jerry Kaplan (New York: Oxford
University Press, 2016), Thinking Machines by Luke Dormehl (New York: Tarcher
Perigee Book, 2017), Rise of the Robots by Martin Ford (New York: Basic Books,
2016) and Le mythe de la Singularité by Jean-Gabriel Ganascia (Paris: Éditions du
Seuil, 2017) allowed us to understand the extent of the advance of artificial intelligence
and its consequences for mankind that are exposed in subsequent paragraphs.
There are many definitions of artificial intelligence, but many of them are strongly
aligned with the concept of creating computer programs or machines capable of
behaving intelligently like humans. Artificial Intelligence (AI) is the ability of a digital
computer or a robot controlled by computer to perform tasks commonly associated with
intelligent beings. The term is often applied to the project of developing systems
endowed with the intellectual processes characteristic of humans, such as the ability to
reason, to discover meaning, to generalize or to learn from past experience.
What is Intelligence? Psychologists generally do not characterize human intelligence by
only one characteristic, but by the combination of many different abilities. AI research
focused primarily on the following intelligence components: learning, reasoning,
problem solving, perception and use of language. As for learning, there are several
different forms applied to artificial intelligence. The simplest is to learn by trial and
error. For example, a simple computer program to solve chess game problems. The
program can store the solutions with the position of one of the pieces of chess, so that
the next time the computer finds the same position of the same piece, it would
remember the solutions adopted. This simple memorization of individual items and
procedures - known as rote learning - is relatively easy to implement on a computer.
More challenging is the problem of implementing what is called generalization.
Generalization involves the application of past experience to analogous new situations.
Reasoning is the ability to draw inferences appropriate to the situation. Inferences are
classified as deductive or inductive. An example of deductive inference is the case of
previous accidents that were caused by failure in a component from which it is deduced
that the accident was caused by the failure of this component. In deductive inference,
the truth of the premises asserts the truth of the conclusion, whereas in the inductive
case the truth of the premise supports the conclusion without giving an absolute
guarantee. Inductive reasoning is common in science, where data are collected and
tentative models are developed to describe and predict future behavior until the
appearance of anomalous data forces the model to be revised. Deductive reasoning is
common in mathematics and logic, where elaborate structures of irrefutable theorems
are constructed from a small set of axioms and basic rules.
Problem solving, particularly in artificial intelligence, can be characterized as a
systematic search through a series of possible actions to achieve some goal or
predefined solution. The methods of problem solving are divided into special purpose
and general purposes. A special purpose method is tailored to a specific problem and
often exploits very specific characteristics of the situation in which the problem is
embedded. In contrast, a general purpose method is applicable to a wide variety of
problems. A general-purpose technique used in AI is step-by-step or incremental

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analysis of the difference between the current state and the ultimate goal. The program
selects actions from a media list - in the case of a simple robot until it reaches the goal.
In perception, the environment is scanned through various sensory organs, real or
artificial, and the scene is broken down into separate objects in various spatial
relationships. Perception is complicated by the fact that an object may look different
depending on the angle from which it is viewed, the direction and intensity of the
illumination in the scene, and the object contrasts with the surrounding field. Currently,
artificial perception is advanced enough to allow optical sensors to identify individuals,
autonomous vehicles drive at moderate speeds on the open road and robots visit
buildings collecting empty soda cans. One of the first systems to integrate perception
and action was FREDDY, a stationary robot with a moving television eye and a
tweezers hand, built at the University of Edinburgh in Scotland during the period 1966-
73 under the direction of Donald Michie. FREDDY was able to recognize a variety of
objects and could be instructed to assemble simple artifacts, such as a toy car, from a
random pile of components.
Regarding the use of language, it is important to note that a language is a sign system
with meaning by convention. In this sense, language need not be confined to the spoken
word. Traffic signs, for example, form a minilanguage, and it is a convention issue that
{hazard symbol} means "danger ahead" in some countries. An important feature of
human languages with traffic signs is their productivity. A productive language can
formulate an unlimited variety of phrases. It is relatively easy to write computer
programs that seem capable, in severely restricted contexts, to respond fluently in
human language to questions and statements. While none of these programs really
understand language, they can, in principle, reach the point where their mastery of a
language is indistinguishable from that of a normal human being.
Since the development of the digital computer in the 1940s, it has been shown that
computers can be programmed to perform very complex tasks - such as finding
evidence for mathematical theorems or playing chess - with great proficiency. Yet
despite continued progress in speed and memory capacity of computer processing, there
are still no programs that can combine human flexibility into larger domains or tasks
that require a lot of everyday knowledge. On the other hand, some programs have
achieved the performance levels of human experts and professionals in performing
certain specific tasks, so that artificial intelligence in this limited sense is found in
applications as diverse as medical diagnosis and voice recognition.
Machine learning is a field of computer science that gives computers the ability to learn
without being explicitly programmed. Arthur Samuel, an American pioneer in the field
of computer games and artificial intelligence, coined the term "Machine Learning" in
1959 while working at IBM. Evolved from the study of pattern recognition and
computational learning theory in artificial intelligence, machine learning explores the
study and construction of algorithms that can learn and predict data. These algorithms
overcome by following strictly static program instructions by making predictions or
decisions based on data, by constructing a model from sample inputs. Machine learning
is employed in a variety of computing tasks such as email filtering, network intrusion
detection, or malicious beginners who work for a data breach, optical character
recognition by computer classification and computer vision.

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Machine learning is closely related to computational (and often overlapping) statistics,
which also focuses on forecasting through the use of computers. It has strong links with
mathematical optimization, which provides methods, theory and fields of application to
the field. In data analysis, machine learning is a method used to design complex models
and algorithms that lend themselves to prediction. In commercial use, this is known as
predictive analytics. These analytical models allow researchers, data scientists,
engineers and analysts to "produce reliable, repeatable decisions and results" and
discover "hidden insights" by learning historical relationships and trends in data.
In 1950, British computer scientist Alan Turing was already speculating on the
emergence of thinking machines in his work "Computing Machinery and Intelligence",
and the term "artificial intelligence" was coined in 1956 by scientist John McCarthy.
After some significant advances in the 1950s and 1960s, when artificial intelligence labs
were set up in Stanford and the Massachusetts Institute of Technology (MIT), it was
clear that the task of creating such a machine would be more difficult than previously
thought. Then came the so-called "winter of artificial intelligence", a period without
major breakthroughs in this area and with a sharp reduction in the funding of its
research.
In the 1990s, the artificial intelligence community set aside a logic-based approach that
involved creating rules to guide a computer how to act, to a statistical approach, using
databases and asking the machine to analyze and solve them problems on their own.
Experts believe that the intelligence of machines will match that of humans by 2050,
thanks to a new era in their ability to learn. Computers are already beginning to
assimilate information from collected data, just as children learn from the world around
them. That means we are creating machines that can teach themselves to play computer
games - and be very good at them - and also to communicate by simulating human
speech, as with smartphones and their virtual assistant systems.
The immediate consequence of the progress of artificial intelligence is the advance of
unemployment. This negative social effect is inevitable because it results from
economic forces that are out of control. Artificial intelligence is positive for the
capitalist who makes use of it because it would face its competitors in a more
competitive way since it would, among other advantages, increase its productivity and
reduce its costs. However, it would also be extremely negative for the capitalist because
it tends to reduce the income available to the mass of workers excluded from
production, thus contributing to the fall in the demand for products and services. The
great threat of artificial intelligence is that it could lead to the extinction of the human
race, according to scientist Stephen Hawking who published an article addressing this
issue on May 1, 2014 in The Independent. Hawking asserts that technologies evolve at
such a dizzying pace that they will become unmanageable to the point of putting
humanity at risk. Hawking concludes: today, there would be time to stop; tomorrow
would be too late.
*Fernando Alcoforado, 78, membro da Academia Baiana de Educação e da Academia Brasileira Rotária
de Letras – Seção da Bahia, engenheiro e doutor em Planejamento Territorial e Desenvolvimento
Regional pela Universidade de Barcelona, professor universitário e consultor nas áreas de planejamento
estratégico, planejamento empresarial, planejamento regional e planejamento de sistemas energéticos, é
autor dos livros Globalização (Editora Nobel, São Paulo, 1997), De Collor a FHC- O Brasil e a Nova
(Des)ordem Mundial (Editora Nobel, São Paulo, 1998), Um Projeto para o Brasil (Editora Nobel, São
Paulo, 2000), Os condicionantes do desenvolvimento do Estado da Bahia (Tese de doutorado.
Universidade de Barcelona,http://www.tesisenred.net/handle/10803/1944, 2003), Globalização e

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Desenvolvimento (Editora Nobel, São Paulo, 2006), Bahia- Desenvolvimento do Século XVI ao Século XX
e Objetivos Estratégicos na Era Contemporânea (EGBA, Salvador, 2008), The Necessary Conditions of
the Economic and Social Development- The Case of the State of Bahia (VDM Verlag Dr. Müller
Aktiengesellschaft & Co. KG, Saarbrücken, Germany, 2010), Aquecimento Global e Catástrofe
Planetária (Viena- Editora e Gráfica, Santa Cruz do Rio Pardo, São Paulo, 2010), Amazônia Sustentável-
Para o progresso do Brasil e combate ao aquecimento global (Viena- Editora e Gráfica, Santa Cruz do
Rio Pardo, São Paulo, 2011), Os Fatores Condicionantes do Desenvolvimento Econômico e Social
(Editora CRV, Curitiba, 2012), Energia no Mundo e no Brasil- Energia e Mudança Climática
Catastrófica no Século XXI (Editora CRV, Curitiba, 2015), As Grandes Revoluções Científicas,
Econômicas e Sociais que Mudaram o Mundo (Editora CRV, Curitiba, 2016) e A Invenção de um novo
Brasil (Editora CRV, Curitiba, 2017).