This essay aims to present the scientific and technological advances of artificial intelligence, their uses in productive systems and their impacts in the world of work.

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ARTIFICIAL INTELLIGENCE - USES IN PRODUCTIVE SYSTEMS AND
IMPACTS ON THE WORLD OF WORK
Fernando Alcoforado*
Abstract- This essay aims to present the scientific and technological advances of artificial
intelligence, their uses in productive systems and their impacts in the world of work.
Keywords - Artificial intelligence. Artificial intelligence and productive systems. Artificial
intelligence and the world of work.
1. Introduction
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 computer controlled robot 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
solely by a characteristic, but by the combination of diverse abilities. AI research
focused mainly on the following intelligence components: learning, reasoning, problem
solving, perception and language use. 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 perform on a computer. More challenging is the problem
of performing what is called generalization that is being faced. 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 of a machine from which
it is deduced that the accident was caused by the failure of this component. In deductive
inference, the truth of the premises assures 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 is 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 and
general purpose 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

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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
analysis of the difference between the current state and the ultimate goal. The program
selects actions from a list of means - 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 decomposed into separate objects in various spatial
relationships. Perception is complicated because the object may look different
depending on the angle from which it is seen, 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, that is, without a driver, drive at moderate speeds on the open
road, and robots roam buildings collecting empty soda cans. One of the earliest systems
to integrate perception and action was FREDDY, a stationary robot with a moving
television eye and a tweezer hand, built at the University of Edinburgh in Scotland
during the period 1966-73. 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 perception is complicated by the fact that an object may
look different depending on the angle. 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. Although 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 daily knowledge. On the other hand, some programs have achieved
the performance levels of experts and human 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
follow strictly static program instructions by making predictions or decisions based on
data, by constructing a model from sample entries. Machine learning is employed in a

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variety of computing tasks such as email filtering, network intrusion detection, or
malicious beginners working for a data breach, optical character recognition by
computer classification and computer vision.
Machine learning is closely related to computational (and often overlapping) statistics,
which also focuses on forecasting through the use of computers. It has strong ties to
mathematical optimization, which provides methods, theory and domains 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 enable 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 created at Stanford and the Massachusetts Institute of Technology (MIT), it was
clear that the task of creating such a machine would be more difficult than it was
thought. Then came the so-called "winter of artificial intelligence", a period without
great discoveries 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, with the adoption of 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're creating machines that can teach themselves to
play computer games - and be very good at them - and also communicate by simulating
human speech, as with smartphones and their virtual assistant systems.
2. The use of artificial intelligence in productive systems
Artificial intelligence is already being widely used in productive systems. Artificial
Intelligence can replace human beings in production activities and can also maximize
their productivity. According to data presented by Accenture in one of its surveys, by
2035, the AI will contribute to an increase of up to 40% of the productivity of the
industrial sector, reducing costs and increasing the production of manufactures around
the globe. The current landscape of AI systems enables them to understand the entire
production and business process and automatically identify which are the major issues
that need to be addressed. A neural network of an AI system is capable of analyzing
more than a billion data in a few seconds, being an incredible tool to support a decision
maker within a company, thus guaranteeing the best option among the possible ones. As
the data collected are constantly updated, AI systems always update their results,
making it possible for managers to have access to recent information on changes in the
company's market. Among the benefits of AI for production systems are:
Reliability of decisions: automation and availability of data allow conscious decision
making, with confidence that they will be the best alternatives at all times.

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Insights: In addition to searching for information at certain times, analyzing the data
presented by the AI can also generate business opportunities that would otherwise not
have been viewed.
Security: Keeping IA systems within the company helps prevent errors and data leaks
by employees, as their contact with information is considerably reduced and various
processes are carried out by the system.
There are several examples of IA application in the business sector with the goal of
helping and improving productivity. Here are some examples of how Artificial
Intelligence is being used:
Virtual Assistants
We have several examples of virtual assistants who use technologies such as voice
recognition to capture and understand the demands of their users and process
commands. Even though this technology has not yet secured a captive place inside the
offices, it is already possible to use the virtual assistants for various activities, which
makes the employees' lives easier and improves their productivity. For example, these
systems are already capable of scheduling meetings, managing schedules, conducting
surveys, among other activities, all coordinated by voice commands, making the life of
a company's employees easier. The main players on the market today are Amazon, with
its Alexa solution, Microsoft with Cortana assistant, Apple with the famous Siri and
Google Assistant, from the search giant.
Demand Forecasting
AI and machine learning allow solutions to anticipate market demands through complex
mathematical models, which is a great way to stay one step ahead of the competition.
Once AI systems identify a future business opportunity, they can pass this information
on to the decision maker, who decides what attitude they will take in anticipation.
Identifying Threats
Information security within a company is critical to ensuring business continuity and
hence your productivity. The AI can be used to carry out constant surveys of the
company's infrastructure and ensure its protection. In addition, artificial intelligence
solutions can perform preventive maintenance on multiple systems, thus preventing
them from being out of date or slow, which can ultimately affect the company's
processes.
Check employee satisfaction
The high turnover of employees can end up becoming a problem for productivity,
because you lose time with training, and the employee loses interest or seeks other
opportunities soon after. AI solutions can accompany employees by analyzing their data
and identifying dissatisfactions, thus helping HR to take the necessary steps to keep the
professional satisfied and productive.

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The investment in technologies that use AI to increase business productivity tend to
increase gradually. Companies can use various strategies to remain competitive in the
market. However, there must already be an investment in digital transformation and
increasingly lean processes to take advantage of this evolution. Organizations that fail to
make this adaptation may be sealing their destiny to failure, since they will no longer be
able to keep pace with the hectic pace of technology evolution.
3. The impacts of artificial intelligence on the world of work
Brynjolfsson and McAfeee state in the book The Second Machine Age that the
combination of massive computing power with comprehensive networks, machine
learning, digital mapping, and the "Internet of Things" are producing a complete
industrial revolution , on the same scale as the transformations caused by steam and
electricity. Boston Consulting Group predicts that by 2025, up to a quarter of jobs will
be replaced by softwares or robots, while a study by the University of Oxford in the UK
points out that 35% of current jobs in the country run the risk of being automated in the
next two decades (BRYNJOLFSSON and McAFEEE, 2016).
Martin Ford states that in our economy and society, machines are gradually undergoing
a fundamental transition: they develop beyond their historical role as a tool and, in
many cases, become "autonomous workers" (Ford, 2015). If we accept the idea that it is
unrealistic to stop automation and that more investment in education and training is
unlikely to solve the problem of unemployment, Ford believes that the most effective
solution is to adopt an income guarantee policy for workers. This idea is not new.
Friedrich August von Hayek, Austrian economist and philosopher, later naturalized
British, considered one of the greatest representatives of the Austrian School of
economic thought, was the proponent of this idea when published between 1973 and
1979 his work Law, Legislation and Liberty (Routledge, 1988).
In addition to the need to provide basic net security, Ford (2015) states that there is a
powerful argument for the adoption of income guarantee policy because technological
advancement, in addition to promoting mass unemployment and vertiginous social
inequality, threatens capitalism itself with the prospect of a sharp drop in consumption.
As the labor market continues to erode and wages stagnate or fall, the mechanism that
ensures consumers' purchasing power begins to break down and the demand for
products and services decreases as a result. Given this fact, the income guarantee policy
would provide the conditions for the unemployed workers to consume. It would oblige
the governments of the countries of the world to levy taxes on high-tech companies to
ensure the adoption of the income guarantee policy for the unemployed population. The
Income Transfer Program through which the State would provide income to the
unemployed would be complemented by the adoption of the Creative Economy and the
Social and Solidarity Economy as a solution to combat mass unemployment resulting
from technological advancement.
The term "Creative Economy" refers to activities with socioeconomic potential that deal
with creativity, knowledge and information and would be less affected by the
advancement of artificial intelligence. To understand this, it must be borne in mind that
companies in this segment combine the creation, production and marketing of cultural
and innovative creative goods such as Fashion, Art, Digital Media, Advertising,

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Journalism, Photography and Architecture. In common, area businesses rely on talent
and creativity to effectively exist. They are distributed in 13 different areas: 1)
architecture; 2) advertising; 3) design; 4) arts and antiques; 5) crafts; 6) fashion; 7)
cinema and video; 8) television; 9) publishing and publications; 10) performing arts; 11)
radio; 12) leisure software; and, 13) music. It is important to say that by focusing on
creativity, imagination and innovation as its main characteristic, the creative economy is
not restricted to products, services or technologies. It also encompasses processes,
business models, management models, among others (DESCOLA, 2016).
The Social and Solidarity Economy is a new model of economic, social, political and
environmental development that has a different way of generating work and income, in
several sectors, be it community banks, credit cooperatives, family agriculture
cooperatives, the issue of fair trade, exchange clubs, etc. Social and Solidarity Economy
is a new way of organizing work and economic activities in general, emerging as an
important alternative for the inclusion of workers in the labor market, giving a new
opportunity to them, through self-management. On the basis of the Social and Solidarity
Economy, there is the possibility of recovering corporations with a failed bankruptcy,
and continue with them, with a new mode of production, in which profit maximization
ceases to be the main objective, leading to maximization of the quantity and quality of
work (LACROIX and SLITINE, 2016).
REFERENCES
AGRAWAL, Ajay; GANS, Joshua e GOLDFARB, Avi. Prediction Machines. Boston:
Harvard Business Review Press, 2018.
BRYNJOLFSSON, Erik e McAFEEE, Andrew. The second machine age. New York:
Norton paperback, 2016.
DESCOLA. A economia criativa no mundo moderno. Available on the website
<https://descola.org/drops/a-economia-criativa-no-mundo-moderno/>, 2016.
DORMEHL, Luke. Thinking Machines. New York: Tarcher Perigee Book, 2017.
FORD, Martin Rise of the Robots. New York: Basic Books, 2016.
GANASCIA, Jean-Gabriel. Le mythe de la Singularité. Paris: Éditions du Seuil, 2017.
VON HAYEK, Friedrich August. Law, Legislation and Liberty. Abingdon: Routledge,
1988.
KAPLAN, Jerry. Artificial Intelligence. New York: Oxford University Press, 2016.
LACROIX, Géraldine e SLITINE, Romain. L´économie sociale et solidaire. Paris:
Presses Universitaires de France, 2016.

Fernando Alcoforado, 79, awarded the medal of Engineering Merit of the CONFEA / CREA System,
member of the Bahia Academy of Education, engineer and doctor in Territorial Planning and Regional
Development by the University of Barcelona, university professor and consultant in the areas of
strategic planning, business planning, regional planning and planning of energy systems, is author of the
books 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

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Barcelona,http://www.tesisenred.net/handle/10803/1944, 2003), Globalização e 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), A Invenção de um novo
Brasil (Editora CRV, Curitiba, 2017), Esquerda x Direita e a sua convergência (Associação Baiana de
Imprensa, Salvador, 2018, em co-autoria) and Como inventar o futuro para mudar o mundo (Editora
CRV, Curitiba, 2019).