Reflection on Human Intelligence vs Artificial Intelligence and the Digital Society (from Theory to Practice)

American Journal of Humanities and Social Sciences Research (AJHSSR) 2024
A J H S S R J o u r n a l P a g e | 57
American Journal of Humanities and Social Sciences Research (AJHSSR)
e-ISSN :2378-703X
Volume-08, Issue-06, pp-57-100
www.ajhssr.com
Research Paper Open Access
Reflection on Human Intelligence vs Artificial Intelligence and
the Digital Society (from Theory to Practice)
José Rascão
University Polytechnic of SetúbalGraduate School of Business SciencesSetúbal (Portugal)
ABSTRACT:We do not pretend to deal exhaustively with this topic, because it is broad and complex for the space of a text,
and we do not even know if we can deal with it without incurring in generalizations. Our intentions, which are much more
modest, refer to documentary research for the understanding and development of Human Intelligence and Artificial
Intelligence and some of their multiple relationships. This article aims to make a comparison between Human Intelligence
and Artificial Intelligence so that it is possible to understand the main aspects in which Human Intelligence differs from
Artificial Intelligence, since the latter originates in computing and how it can be inserted in the individual and organizational
processes of the digital society. In addition, it seeks to highlight the great advances and potential risks of this technology, just
like any other, it can provoke in the "actors" involved in its production, use, legislation (norms and rules in its use) and
create a space for discussion.
KEYWORDS: Human Intelligence, Artificial Intelligence; Intelligent Agents, Information, Disinformation, Digital Society.
I. INTRODUCTION
The academic literature, despite technological advancements, makes a big difference between human intelligence
(HI) and artificial intelligence (AI). AI is increasingly present in the lives of people and organizations, performing tasks that
were previously exclusive to human intelligence. However, Artificial Intelligence and Human Intelligence are two forms of
intelligence that have significant differences. Despite the advancements of AI, there are still characteristics that distinguish it
from Human Intelligence.In an increasingly globalized world dominated by information and communication technologies,
the comparison between artificial intelligence and human intelligence becomes inevitable. One of the main differences
between these two forms of intelligence is related to memory. Human memory and artificial memory have distinct
characteristics that highlight this distinction. One of the main factors that differentiate human memory from artificial
memory is the capacity for creativity. While humans are able to adapt to new situations, learn from mistakes and modify
their behavior according to circumstances, machines, no matter how advanced, rely on pre-existing algorithms and data to
perform their tasks, without the ability to adjust autonomously.
In this article, we will study the important differences between these two types of intelligence, highlighting the
capabilities and limitations of each. Understanding these distinctions is essential for the development and improvement of
AI, as well as for reflection on the role and impact of technology in the Digital Society.
Globalization emerged during the 1980s, but the phenomenon began much earlier, in the period of the Great Navigations of
the fifteenth and sixteenth centuries. This period was marked by the establishment of new trade routes in the world and
intense movement of goods and people between countries on different continents. Cartographic discoveries and the
development of new navigation techniques are at the origins of this event. The transformations in the international economic
system and the improvement of communications and transportation have enabled the evolution of this process.
Globalization is the name given to the phenomenon of integration of the world space through information and
communication technologies (ICTs) and also means of transport, which have been rapidly modernized and have provided, in
addition to greater dynamization of territories, acceleration and intensification of the flows of capital, goods, information and
people, all over the planet. This process is known as globalization.
The technical-scientific, technological and informational development has led to global globalization, that is, it has resulted
in an integrated economic, social and cultural world space through global communication networks. The integration of the
world space was only possible through technological advances in the communications and transport sectors.
II. ScientificMethod
It is an exploratory study that seeks to organize the main challenges faced by people in the Digital Society and their meaning
presented in the literature of the Humanities, Social Sciences (sociology, humanities, communication, marketing, economics,
psychology, law, humanities, ethics, Information), the Exact Sciences (logic, mathematics and computer science), Applied
Sciences (engineering) and the Philosophical Sciences.
It is not a proposal of new terms and concepts, but rather an investigation that allows the identification of a common
denominator among the different concepts already indicated in the literature, in a way that enables their grouping by identity,
application/use and pertinence/aggregation of value in the context in which the terms are inserted. The data collection is
characterized by bibliographic research on the terms and concepts related to the different scientific fields.

It is a descriptive and analytical approach seeking to know and analyze the existing cultural and/or scientific contributions on
this subject, based on the literature review. The research was structured based on the systemic approach to understand the
main challenges that citizens face in the Digital Society, seeking in practical, operational or application terms, the solution of
the "real life" problems of organizations (public and private) and people.
Figure No. 1 – Schematic Representation of the Scientific Method
Research Theme and Problem
Human Intelligence and Artificial Intelligence allow the active relationship between people and with nature, through Information and
Communication Technologies (ICT's). But the problem is that many people don't know what this means, what types/models of participation,
ways of working, the limits and the path of the future, of the Digital Society.
It can be difficult for most humans to understand how machines work. However, an artificial intelligence system looks like a puzzle. To
understand the way we think, perceive, and feel, it is useful to create an analogy between the nervous system and an intelligent machine.
With the sophistication of new technologies, man has created forms of artificial intelligence that work in a similar way to himself, improving
the ability of computer systems to interpret and understand the virtual world. This includes object recognition, motion detection, and pattern
identification in images. Automated reasoning refers to the ability of machines to process data (information), come to logical conclusions,
and make decisions, based on those reasonings. This involves utilizing inference algorithms and logic to solve complex problems.
Isseus:
I. Are Human Intelligence and Artificial Intelligence Comparable?
II. What are the main differences?
III. What are the main challenges that Artificial Intelligence poses to Humanity?
IV. Could inaccurate or biased data lead AI to make wrong decisions? (e.g. in medicine, security, etc.)
Goals
The Information, Human, Social, Economic, Technological and Political Sciences, seek a solution to the challenges of the Digital Society,
that is, to define the main paths and rules that allow to guide the citizens of the world, where the rights and duties (responsibilities) are
equal, for all, without exception. These paths and the rules to be implemented by the (elected) rulers require a commitment from them and
from the people in their implementation.
Artificial Intelligence is a multidisciplinary field of study that encompasses several areas of knowledge and represents a historical milestone
in computer science, in its interdisciplinary approach that involves the contribution of several areas of knowledge to simulate Human
Intelligence. Mathematical and Statistical Sciences provide the theoretical foundations for algorithm modeling and analysis, machine
learning that focuses on the development of algorithms that allow computers to learn and improve with a database, involving the application
of statistical techniques and optimization algorithms.
Cognitive Science studies the mental processes of human intelligence, related to the understanding and modeling of cognitive processes for
the development of intelligent systems. Computational Neuroscience seeks to understand the workings of the human brain and apply these
insights in the development of AI models and algorithms inspired by the human brain. The Philosophy of Mind explores the issues related to
the nature of the mind, consciousness, and intelligence, offering the important theoretical perspectives for the field of AI. Computational
linguistics involves the processing of natural language, focusing on the development of algorithms and techniques for computers to
understand and process human language.
This article seeks to contribute to the clarification of the main challenges that people face with Artificial Intelligence, taking into account the
comparison with Human Intelligence, in the change to the Digital Society, as well as the importance of the units of measurement for
evaluating the decisions of the different powers and their meanings, within the scope of the different sciences, based on a theoretical
framework. The objective is a debate on the challenges identified by scientific research, developed by the different Sciences, in the Digital
Society. The theoretical discussion of the different units of measurement and the meanings of empirical research constitute the basis for the
outline of its structure, presented at the end, bringing together the units of comparison and the main differences.
Methodological Approach
As for its nature, the research is qualitative since it does not privilege statistical study. Its focus is to obtain descriptive data, that is, the
incidence of topics of interest in fields such as Information Sciences, Humanities, Computational, Ethical, Social, Economic and Political
Sciences, as well as other Sciences. Regarding the extremities, the research is exploratory and descriptive, insofar as the technique used is
categorized, consensually, as a direct documentation study, which provides for the consultation of sources related to the study, in different
media, printed or electronic. The complexity and turbulence of the digital society have led to the globalization of research, as essential
processes for the development and innovation of science and technology. Information is the source of the energy that drives the "engines" of
the Digital Society, but in order to use it we need to convert it into a usable form : knowledge, (Murteira, 2001).
The digital society is a complex society of technological innovation and communication, in which there are the creation of new
environments and changes in the dynamics of people, in the way they understand reality, modifying the way, how they relate to other people
and how they conceive themselves in the face of their own reality. Both senses can be understood as a result of the technological revolution,
promoted mainly from the attempts to understand human intelligence, via computational bases. As a consequence, the pre-modern notion of
information, as the information that shapes or shapes the human mind, is gradually being replaced by information, as a "data structure",
Boland, (1987), representing intangible realities too large to be directly experienced by people's senses.
The research method is likely to make meanings interact with each other. This interaction can range from the simple communication of ideas
to the mutual integration of concepts, epistemology, terminology, methodology, procedures, data and research organization. This is an

exploratory study that seeks to clarify and organize the concepts presented in the literature of the different sciences. It is necessary to
understand, through a theoretical review of the concepts, through the reference documents; of a psychosocial analysis of the concepts and
meanings, applied to the Digital Society, in the context of people's social and economic life. The research was structured based on the
systemic approach, to understand people's problems and possible improvements. This conceptual model is represented as follows:
Figure 1 – Reflection on Human Intelligence vs Artificial Intelligence and the Digital Society(from Theory to Practice)
Scientific Field
Human Mind People Humanities
Social, Philosophical
and Technological
Human Intelligence Artificial intelligence
Globalization
Complexity and
Economic turmoil
Technological, Social and Political
Information Technology.
and Communication Information
(transformation and registration of the
Information, in data)
Digital Society - action Digital Society
(Recovery and Transformation (people's way of life)
of data, in information)
Human Intelligence vs Artificial Intelligence and the Digital Society
(from Theory to Practice)
(Research project)
Source: author'selaboration
The model of approach for intervention in information actions in the academic space is presented, with the purpose of producing, sharing
information and knowledge among participants, in addition to promoting the development of skills of search, retrieval, organization,
appropriation, production and dissemination of relevant information for scientific researchers, in the digital society.
III. THEORETICAL-METHODOLOGICAL FRAMEWORK OF THE RESEARCH
Humanities
The human sciences are a set of knowledge that aims at the study of man as a social being, that is, it is the human sciences that carefully
gather the organized knowledge about the creative production of man and knowledge, based on specific discourses. Its aim is to unravel the
complexities and turbulences of society, its creations, and its thoughts. It is important to keep in mind that everywhere, human beings
establish relationships with each other, whether they are of friendship, affection, or power. The human sciences seek to understand how
these relationships are formed and how they evolve over time.
Thus, as a human condition, they have a multiple character, so they address theoretical characteristics, such as philosophy and sociology,
while also addressing practical and subjective characteristics. As it is an area of knowledge that has as its object of study the human being,
in sociability, the social sciences are based on disciplines such as philosophy, history, law, cultural anthropology, science of religion,
archaeology, social communication, psychology, art theory, cinema, management, dance, music theory, design, literature, letters, philology,
among others.
Humanism was a philosophical and cultural movement that emerged in Europe during the 14th century. He was inspired by Greco-
Roman culture and philosophy, prioritized reason over faith, and was interested in the concept of the human being as the center of the
universe. Although there have been various "humanisms", such as those of the Middle Ages or the humanism of the court of Charles the
Great, but when we talk about humanism we usually talk about the Italian Renaissance, which is known, as Renaissance humanism. In
general, any study devoted to the reading and interpretation of classical texts is a humanistic study. Philosophical works that emphasize the

human being, above all else, are also called humanistic. Examples of this are the works of Werner Jaeger, Erich Fromm, Erasmus of
Rotterdam, and Jean-Paul Sartre.
Humanism from this anthropocentric perspective, inspired by scientific studies during Greco-Roman Classical Antiquity, diminished the
cultural relevance of the ethnocentrism that had dominated European society since the Middle Ages. As an intellectual movement,
Humanism disregarded the claim of the scholastic method as critical thinking, valuing rationality. According to humanist thought, human
beings are the supreme divine creation, thus being able to synthesize knowledge by themselves. In this way, the human being was both a
creature and a creator of the world, and could thus act as the architect of his existence.
The multifaceted nature of the term and its breadth compel academic studies of humanism to treat the term with care. Although they share
some general characteristics, it is not the same to talk about Renaissance humanism as it is to talk about existentialist humanism.
Humanistic thought prioritized the human being before the religious. Humanism was a European philosophical, intellectual and cultural
movement that emerged in the fourteenth century and was based on the integration of certain values considered universal and
inalienable of the human being. This current of thought arose in opposition to theological thought, in which God was the one who ensures
the fulfillment of the duties and obligations of others and the center of life.
Humanist thought is an anthropocentric doctrine that tries to ensure that the human being is the measure from which cultural parameters
are established. This group favored the sciences and was interested in all disciplines, whose purpose was to develop the values of the human
being. Great thinkers of antiquity (e.g. Aristotle and Plato) argued that knowledgegave power to people, giving them happiness and
freedom, and as such, through classical works knowledge was expanded and a more cultured society was created.
In 1945, the philosopher Jean Paul Sartre gave a lecture on the postwar climate, and what he said had a profound impact on all philosophical
thought from that time on. This conference was called "Existentialism is a Humanism" and marked a milestone by presenting a new
conception of man and humanism. Paris in ruins after the Second World War, this conference set the tone for the search for a new human
horizon, a new moral horizon that embodies man's responsibility and his existence, beyond progress and the devastating consequences
of war.
Characteristics of humanism:
 He developed an anthropocentric notion of the world and set aside the theocentric idea.
 It is a much purer model of knowledge than existed in the Middle Ages.
 He defended the idea of using human reason as an engine in the search for answers, leaving aside the beliefs and dogmas of
faith.
 He reformed the existing teaching model , giving importance to the study of the classics of Latin and Greek and opening new
schools that promoted the study of other classical languages and letters.
 He developed the sciences, such as grammar, rhetoric, literature, philosophy, morals, and history, intimately linked to the
human spirit.
 It sought to eliminate any closed system that did not allow for the multiplicity of perspectives of thought. It was thought that
with this change the total development of man would be achieved: physical and spiritual, aesthetic and religious.
Humanism and the Renaissance
The Renaissance was a historical period that stretched from the fourteenth century to the sixteenth century, which sought to leave the
Middle Ages behind and give way to the Modern Age. This period was characterized by a great artistic and scientific development and by
social, political and economic changes that sought to bury the vestiges of the Middle Ages (which they considered a dark phase) and lead to
the development of the bourgeoisie.
Humanism was an intellectual current that developed in this historical period and promoted an anthropocentric view of the world,
leaving aside the theocentric tradition and highlighting the capacities of man and human reason. Humanists did not see man from a
theological perspective. They valued the human being for what he is: a natural and historical being. Unlike the men of the previous age, the
humanists ceased to see man from the theological point of view. They were men of religion, mostly Christians, but they looked for the
answers to their questions about the world and things in ancient thinkers. They invalidated religion, but considered that it had a civil
function and that it was a tool for maintaining the peace of society. Among the most prominent scholars of this era are:
 Leonardo Bruni (1370-1444) - Italian historian and politician of notable performance in the rescue of the classics of Greco-
Roman literature.
 Giovanni Pico della Mirandola (1463 – 1494) - Italian philosopher and thinker, his most representative work "The 900 Theses"
is a compendium of the most resonant philosophical ideas that existed until then.
 Erasmus of Rotterdam (1466-1536) - Dutch philosopher and theologian, he was a critic of the institutions, the power of the
time, and the abuses of the members of the Catholic Church to which he belonged. He defended his "adages" (sayings) of
freedom of thought and Greco-Roman traditions. In addition, he sought that all people could have access to the gospel and with
it, to the teachings of Jesus Christ. His work: "In Praise of Madness" had a great impact.
 Thomas More (1478-1535) - English theologian and politician, he devoted much of his life to practicing law and the study of
Greco-Roman theology and culture. "Utopia" was one of his famous works, written entirely in Latin. He was beheaded in 1535
for refusing to sign the act establishing King Henry VIII as the leader of the Anglican church.
 Juan Luis Vives (1492-1540) - Spanish philosopher, he was a precursor of the idea of applying reforms in the academic field
and the need for social assistance to the most needy.
Types of Humanism
 Christian Humanism - A religious movement in which as a matter of principle man can be realized from a Christian framework.
 Evolutionary Humanism - A current of thought that oscillates between philosophy, epistemology and anthropology and places
the human being at the center of the Universe.
 Secular humanism - A movement that relies on certain philosophical currents and the scientific method to discard those
supernatural explanations, such as creationism, that exist about the origin of the universe and humanity.
Importance and impact of humanism
Humanism is considered one of the predominant ideologies during the Renaissance, first and foremost, because its anthropocentric ideas
represented a paradigm shift. This current focused on the development of the qualities of the human being and conceived rationality as a
way of understanding the world.
The importance of humanism lies in the rescue and dissemination of Greco-Roman traditions. During this period, translations of the
great classical works were made that allowed access to a larger portion of the population. In addition, he promoted educational reforms to
make knowledge more accessible and valued humanistic studies, contributing to the development of sciences such as rhetoric, literature and
grammar. Humanism stands out for having expanded values, such as tolerance, independence and free will.
Humanist philosophy, in this sense, clashed with the expectations of the Middle Ages. Although the Middle Ages had a rich cultural life, it
was still strongly linked to the Catholic Church, which helped dictate social positions and behaviors as determined by a culture that exalted
man's submission to God. Humanism, however, defended man's ability to shape his destiny. In doing so, he shifted not only the social focus

from collectivism to individualism, placing in the human being himself the ability to alter the reality in which he lived without depending on
divine favor or will, but also the inspiring axis for the attainment of new knowledge. In this sense, it was the ancient sages who were seen as
the best bases for these advances.
Some of the most significant examples of humanist thought are in the "Discourse on the Dignity of Man", a work by Giovanni Pico Della
Mirandola. Considered one of the first books of modern philosophy, in which he presents the main thesis, about creation having occurred
with God, allowing human beings the special freedom to build themselves. Through this emancipation, according to the author, the human
being cannot have a determined destiny, since it is the artisan himself who will decide what he will be, finding in the process his essence
through the rationality provided by God.
Although it is in the articulation of the themes, and not exactly in the argument used, where the originality of Giovanni Pico Della Mirandola
is found, the fact is that he represents a new line of thought that began to be adopted by several scientists, painters, philosophers and scholars
in general during the beginning of the Modern Era – even though most of the European population still lived marginalized. away from such
intellectual and cultural processes. Because of this, one can characterize Humanism, as well as its heir movement, the Renaissance, as
having occurred mostly in the midst of the European social-economic elite, who had the resources and time for self-improvement valued by
Humanism.
An example is in Leonardo da Vinci. Born in a small village near Florence, Leonardo would study for most of his life, until he mastered an
impressive variety of sciences such as engineering, architecture, sculpture and astronomy, teaching himself music, mathematics, physics and
Latin. Gaining friends in high social circles due to his great intellectual abilities, he became one of the most celebrated Western artists of all
time, being one of the most recognizable names of the Renaissance today. Among his major works are Mona Lisa, Virgin of the Rocks and
The Last Supper.
Philosophical Sciences
Considering philosophical practice as the art of interpreting reality from the formulation of conceptual schemes about the human being,
nature and society, will Philosophy be able to face the problems that arise from the new organizational dynamics of society today? We
understand that Philosophy alone, without interdisciplinary tools of analysis, does not seem capable of facing, perhaps even formulating, the
problems raised by ICTs.
Floridi (2011, p. 14) characterizes IF as follows: a philosophical area that is related to:
a) Critical research into the conceptual nature and basic principles of information, including its dynamics, use and sciences; and
refers to IF as a new area of investigation in Philosophy, guided by the investigation of the content of information and not only
in its form, quantity and probability of occurrence (thus differing from the proposal of Shannon & Weaver, (1949/1998).
Importantly, IF does not seek to develop a "unified information theory" but to integrate the different forms of theories that
analyze, evaluate, and explain the various information concepts advocated.
b) The characterization, in turn, indicates, according to Floridi (2011, p. 15-16), that the IF has its own methods for analyzing
philosophical problems, both traditional and new. These methods have information as their central element, are interdisciplinary
in nature and maintain the relationship with computational methods, in addition to using concepts, tools and techniques already
developed in other areas of Philosophy (e.g., Philosophy of Artificial Intelligence, Cybernetics, Philosophy of Computing,
Logic, among others).
Thus, IF will provide a broad conceptual framework for addressing the issues that emerge from the "new" dynamics of contemporary
society, Floridi, (2011, p. 25). An example of this dynamic are the possibilities of interaction provided by ICTs which, depending on the
degree of familiarity of people with such technologies, promote a sense of dependence on being online. In addition, even if people do not
want to be online most of the time, this feeling remains due to the spread of informational devices in everyday life, such as cameras, credit
cards, among others. In this situation, the question arises: what are the implications of the insertion of ICTs in society for people's daily
actions?
Considering (a) and (b), Floridi (2002, 2011) argues that IF constitutes a new paradigm and an autonomous area of investigation in
Philosophy. It is characterized as a new paradigm, as it would break with previous paradigms of Philosophy, since it is neither
anthropocentric nor biocentric, admitting information as the central focus in the analysis of concepts and social dynamics. On the other
hand, the autonomy of the IF would be sustained by the presence of its own topics (problems, phenomena), methods (techniques,
approaches) and theories (hypotheses, explanations), according to other areas already recognized as legitimately philosophical, Floridi,
2002, 2011; Adams & Moraes, (2014).
Among the topics of IF, the question "what is information?", referring to the ontological and epistemological natures of information, stands
out. It is the answer to this question that directs the paths to be developed by the IF and delimits its scope of investigation, Floridi, (2011).
The importance of this issue is also due to the fact that, as we have indicated, there is no consensus among scholars in their proposals.
Since the "informational turn in philosophy", several conceptions of information have been developed in an attempt to respond to concerns
about the ontological and epistemological status of information. Although Adams (2003) indicates the milestone of the informational turn in
Philosophy with the publication of Turing's article in 1950, there are precursors of information theory in several areas, especially in
Semiotics, such as the works of Charles S. Peirce (1865-1895). Some exemples can be given with the following proposals:
 Wiener (1954, p. 17): "The commands through which we exercise control over our environment are a type of information that we
impose on it." In addition, for this author, information would be a third constituent element of the world, alongside matter and
energy, and would not be reducible to them.
 Shannon & Weaver, (1949/1998): the authors establish, the Mathematical Theory of Communication, a technical notion of
information conceived in probabilistic terms resulting from the reduction of possibilities of message choice, which can be
understood objectively.
 Dretske (1981): information is understood as a commodity that exists objectively in the world, independent of a conscious mind
of the first person who grasps it. The information would constitute an indicator of regularities in the environment, from which
representations, beliefs, meaning, mind, mental states, among others, would be made.
 Stonier (1997, p. 21): information would be on the physical plane, objectively, and physics theorists, in turn, would have to
expand their vocabulary and admit infons (information particles) as a constituent element of the world. «(...) information exists. It
does not need to be perceived in order to exist. It does not need to be understood in order to exist. It doesn't require intelligence
to interpret it."
 Floridi (2011, p. 106): «Information is a well-formed piece of data, with meaning and truth». Well-formed and meaningful data
that refers to the intrinsic relationship that the data would need to possess in relation to the choice of the system, code, or
language in question. These would have their aspect of "truth" and "truth" related to the proper provision of the content to which
they refer in the world.
 Gonzalez (2014): conceives of information as an organizing process of dispositional (counter-factual) relations that bring
together properties attributable to material/immaterial objects, structures or forms) in specific contexts.

Although the concepts of information indicated are different, there is in common the naturalistic stance in relation to the objective aspect of
information. Moreover, proposals such as those of Dretske and Floridi denote an intrinsic relationship between information and truth.
According to Dretske (1981, p. 45), characterizing "false information" as information would be the same as saying that "rubber ducks are
types of ducks". Since the information could not be false, the information would be genuinely true and would necessarily tell about its
source. This source can be interpreted as the world itself, making it possible to deal with another problem of IF, that is: what is the nature of
knowledge? Regarding the nature of knowledge, the theories of knowledge stand out, from which it is analyzed through the relationship
between the agent, the cognitive and the world. For Dretske (1981, p. 56), the information processors of the sensory systems of organisms
are channels for the reception of information about the external world.
The naturalistic stance in Philosophy consists in disregarding the supernatural in the explanation of nature and mind, conceiving reality to
consist only of natural elements and laws, which are explained through scientific methods. The term "natural" would encompass other terms
such as "physical", "biological" or "informational" that express a rejection of transcendent assumptions in the foundation of a priori
knowledge (Moraes, 2014), the acquisition of knowledge. (Adams, 2010), in turn, argues that knowledge acquires its properties from its
informational base; Thus, if someone 'knows that P' it is because he is told 'that P'. In such a relationship, knowledge is about the world,
about truth, constituting the bridge between the cognitive agent and the world.
In addition to the problems about the ontological and epistemological natures of information, and the nature of knowledge, the following
questions are part of the IF research agenda: "what is meaning?", "what is the relationship between mental states and informational states?",
"could reality be reduced to informational terms?", "can information be the basis of an ethical theory?", among others. After presenting the
topics (problems) and theories (hypotheses and explanations) of IF, we highlight two methods specific to this area of investigation: the
"synthetic method of analysis" and the "levels of abstraction".
Such methods come from the influence of Turing's work in Philosophy (marked, in particular, by the informational turn). The "synthetic
method of analysis" is the result of the hypothesis of (Turing, 1950), according to which the study of the mind is appropriate when carried
out through the use of mechanical functions that could be manipulated by digital computers (Gonzalez, 2005; Floridi, 2012). By means of
such functions it would be possible to construct mechanical models of the structure and dynamics of intelligent thought. The understanding
that underlies this conception is that the ability to manipulate information in a mechanical way constitutes thinking.
This understanding enabled the development of mechanical models of the mind, which initially generated two strands in Cognitive Science
(Teixeira, 1998): strong Artificial Intelligence, which defends the thesis according to which mechanical models of the mind, when
successful, not only simulate/emulate mental activities, but explain and instantiate such activities; and weak Artificial Intelligence,
according to which the model is only a limited explanatory tool of intelligent mental activity. The common point of these notions is that they
both accept the thesis that to simulate is to explain, in order to attribute to mechanical models, the value of theories. This is an example of an
approach to another question specific to IF: what is the relationship between information and intelligent thinking?
The "levels of abstraction", in turn, derive from Turing's algorithmic approach, which is summarized by (Floridi, 2013b, p. 210) as follows:
We have seen that questions and answers never occur in a vacuum, but are always embedded in a network of other questions and answers.
Likewise, they cannot occur in any context, without any purpose, or independent of any perspective. According to this perspective, a
philosophical question is analyzed considering its context and purpose, which delimit the field of possibilities for adequate answers.
Considering the topics, theories and methods of IF, Adams & Moraes (2014) propose the "argument from analogy" to analyze the
autonomous aspect of IF. These authors point out that, like the Philosophy of Mathematics and the Philosophy of Biology, IF has
characteristics such as:
 Proximity to the scientific approach, epistemological and metaphysical problems, as well as the presence of problems of its own
not previously dealt with in other areas of Philosophy. Given that IF shares characteristics present in areas already recognized by
philosophical society as legitimate, it would be counterintuitive not to accept IF as an autonomous area of investigation in
philosophy.
As we have indicated, the development of information studies in the philosophical-scientific sphere contributed to the constitution of IF in
the academic sphere. This is illustrated with the constitution of FI, as an autonomous and interdisciplinary area of Philosophy:
interdisciplinary due to its relationship with Computing, Sociology, Engineering, among other areas, generating methods and theories to deal
with its problems; and autonomous, due to its own (and new) problems. In line with the development of the academic field of IF, the
influence on the social sphere is also highlighted, illustrated by the growing presence of ICTs in the daily lives of people and organizations.
Such presence would be influencing the dynamics of contemporary society, constituting the "Information Society".
Social sciences
Although thinking and reflection on social reality and social relations has been a constant in the history of humanity, from Classical Greece,
through the Middle Ages and during the Renaissance, it is only in the nineteenth century that it becomes possible to speak of "social
sciences", since it is the set of reflections of this period that, Incorporating Baconian principles and the Cartesian method, it will consist of
the form of knowledge historically known as "modern science". If the eighteenth century saw important thinkers of society, such as
Montesquieu, Locke, Hume and Rousseau, it is with Auguste Conte that the beginning of the social sciences is usually identified.
Conte, a French thinker known as the father of Positivism, proposed to carry out studies on society with the utmost objectivity, in search of
universal laws that would govern the behavior of social life everywhere. His theory, also called Social Physics, proposed that the whole of
society should evolve in the same way and in the same direction. And so he proposed his Law of the Three States, according to which every
society should evolve from a theological or fictitious state, to a metaphysical or abstract state, and from there, finally, to a positive or
scientific state, Lakatos & Marconi, (1999, p. 45-46). Comte's Social Physics provides the theoretical foundation for a process that had
already been taking place in Europe two centuries earlier, a process by which "the calculus of probabilities, the foundations of which are laid
by Pascal and Huyghes around 1660, becomes a new form of objectification of human societies" Mattelart, (2002, p. 18).
The mathematical sociology of the Belgian Adolphe Quételet, the probabilistic theories, the application of statistics in the management of
societies and the anthropometry of Alphonse Bertillon were developed. In a direction that is only partially different, since his direct
influence comes from Darwin's work on the evolution of species, the Englishman Herbert Spencer initiated, at the same time, Social
Biology, Lakatos & Marconi, Araújo, (1999, p. 47).
From the reflections on the division of labor (Smith & Stuart Mill), the models of material flows in social groupings (Quesnay, Babbage)
and the theorization of networks (Saint-Simon), Spencer elaborates his organizational model of understanding social reality, promoting an
analogy between society and a living organism, with the parts performing functions. for the proper functioning of the whole. Among the
various impacts caused by this theoretical model is the foundation of the doctrine of Social Darwinism, which justified the European
colonizing action in the nineteenth century in Africa and Asia, the elaboration of the Psychology of Crowds (Sighele, Le Bon) and the use,
in the social sciences, of various terms and concepts "borrowed" from biology (isolation, contact, cooperation, competition and others).
The synthesis between the two pioneering theories and their systematization in a body of "sociological" knowledge was carried out by Émile
Durkheim, "French, considered by many scholars to be the founder of sociology, as a science independent of the other social sciences",
Lakatos & Marconi, (1999, p. 48). His proposal to consider social facts as "things" and a radical empiricism are in perfect harmony with the
positivist spirit. His idea of "primitive societies" and "complex societies" takes up both elements of the Tri-State Law and Spencer's
biological perspective, which is not taken without criticism. His study of suicide is the application of the rules of the sociological method he

had defined two years earlier: the exclusion of individual and psychological causes, the search for properly social causes, the elaboration of
laws and quantification.
With Durkheim, Functionalist Sociology, also known as the Theory of Integration, was inaugurated, which sees society as a whole formed
by constituent, differentiated and interdependent parts. The study of society must always be carried out from the point of view of the
functions of its units. In the twentieth century, Functionalist Sociology developed and became the "strong program" of the social sciences,
mainly with the works of Talcott Parsons (Harvard University), Robert Merton and Paul Lazarsfeld (Columbia University), inspiring the
other social sciences, such as anthropology, political science and communication.
This is the trend of structured sociology courses throughout the century, the nature of the first professional associations, and the type of
research funded by large foundations and government agencies. The first major split in the social sciences has its origin in the Hegelian
dialectic, taken up by Marx for the understanding of social reality, Demo, (1989, p. 88). Applied to social life, dialectical thinking, which
operates with the unity of opposites, sees social life from the presupposition of social conflict, realizing that "every social formation is
sufficiently contradictory to be historically surmountable", Demo, (1989, p. 89-90). Also known as the Theory of Conflict, the Marxist
perspective is the first model that is really proper to the social sciences – since functionalism has its concepts and methods borrowed from
physics and biology – although an approximation with philosophy has been built.
Another approach from the social sciences poses a whole range of new concepts and objects to be studied: domination, ideology, alienation,
reification. Its application, throughout the twentieth century, contributed to the construction of different perspectives: the Frankfurt School's
Critical Theory, the Dependency Theory, the Theory of Cultural Imperialism, the Gramscian Political Theory, and, even in the United
States, Wright Mills' formulations are sympathetic to the "critical" stance as opposed to the "sociology of the bureaucrat or the intelligence
official", that is, to the positivist and functionalist social sciences.
Structuralism, which is often identified as a third approach to the social sciences (Demo, 1989, p. 171) can actually be understood as a
specific perspective that is actually a manifestation of both functionalism and Marxism, as exemplified by the work of Manilowski,
Radcliffe-Brown, and even the "structural-functionalism" of Parsons, in the first case, or the works of Levi-Strauss & Althusser, in the
second.
The second split in the social sciences occurred from the merger of the works of two other precursors of the social sciences – Max Weber
and Georg Simmel – both German. Weber is regarded as the founder of Interpretive Sociology or Comprehensive Sociology, insofar as he
formulates the concept of social action, which is the action of the individual, endowed with meaning for him – in what differs radically from
Durkheim's concept of social fact. His work on the Protestant Ethic and the Spirit of Capitalism seeks to explain the development of
capitalism in the United States, not from the idea of linear progress of societies or the functions of each part in the whole (functionalism) or
from the material, economic conditions, or from the class conflict caused by the distribution of modes of production (Marxism). but from
the "spirit of capitalism", that is, from the ethos, from the atmosphere of values of a given population, from the beliefs and meanings
attributed to their actions.
Simmel, on the other hand, proposed the study of social relations based on small everyday interactions, giving rise to a field known as
microsociology. The importance of his work will take place at the beginning of the century, with the research of the Chicago School. One of
its representatives, Robert Park, takes the city as a "social laboratory", installing a method of study in which subjects cannot be studied
outside their environment. Ernest Burgess, in the same vein, carries out work in "social ecology" from an ethnographic perspective. The first
major attempt at synthesis between the two possibilities of understanding social reality (the focus on the micro dimension and on the
interpretative attitude of the subjects) was achieved by Symbolic Interactionism, a current that brought together researchers from different
schools that have George Herbert Mead as a precursor. One of his students, Herbert Blumer, coined the term in 1937, publishing in 1969 its
three basic assumptions:
 Human behavior is grounded in the meanings of the world.
 The source of meanings is social interaction.
 The use of meanings occurs through a process of interpretation (Blumer, 1980).
Berger &Luckmann (1985, 1966) addresses the social construction of reality, which is seen not only as a process of construction of
objective/subjective/inter-subjective reality, in the context of infinite daily interactions, but also of processes of institutionalization and
socialization.
Another current, along the same lines, is ethnomethodology, a discipline founded by Harold Garfinkel (1967), which aims to try to
understand how individuals see, describe and propose, together, a definition of the situations in which they find themselves, Coulon, (1995).
His proposal provoked great controversy against traditional sociology, for criticizing the idea of social fact as something stable and
objective, proposing a vision in which it is understood, as a product of the continuous activity of men. Initiating a whole branch of studies, it
spread first to the University of California (Sudnow, Schegloff, Zimmerman), then to the United States (Cicourel), England (Heritage) and
France (Fornel, Ogien). If, until the 1970s, the social sciences found themselves in the clash between "administrative" and "critical"
perspectives, Horkheimer, 1983), or in the face of the opposition between "apocalyptic" and "integrated" (Eco, 1985). Since that time, we
have witnessed the growing influence of interpretive and micro-sociological currents.
Since the 1980s, this whole movement has led to an attempt to synthesize the different perspectives, their proposals and their concepts.
Examples of this work are the Theory of Communicative Action by Jürgen Habermas, the Praxiological Model of Louis Quéré and Pierre
Bourdieu, the Reflexive Sociology of Anthony Giddens, Scott Lash and Ulrich Beck, the Sociology of Everyday Life by Michel de Certeau
and Michel Maffesoli, the Cultural Studies descended from the Birmingham School and which have today in Stuart Hall, Douglas Kellner
and Fredric Jameson as its main representatives, the proposals for connection with Clifford Geertz's hermeneutics, among others.
Science of Psychology
Second, the Onine Etymology Dictionary. Retrieved 23 May 2024. wordpsychologyIt literally means, "Study of thesoul" (ψυχή,Psyche,
"soul" — λογία,logy, "treatise", "study"). The word inLatin Psychologyis credited to thehumanistCroatianMarkoMarulićIn his
book,Psichiologia de ratione animaehumanae, dated between the fifteenth and sixteenth centuries.Psychologyit designated studies or
scholars of Aristotle's workBy Anima(On the Soul).
Second, Bock, Ana, Furtado, Odair; Teixeira, Maria de Lourdes, (2009), American Psychological Association, (2015, 2016). Psychology is
the science that treats, studies and analyzes the mental and behavioral processes of people and human groups, in different situations, For
Dodge Fernald, (2007) psychology has as its immediate objective the understanding of groups and people, both by establishing universal
principles and by studying specific cases. With the aim of benefiting society. A researcher or professional in this field is known as a
psychologist, and can be classified as a social, behavioral, or cognitive scientist.
The role of psychologists is to try to understand the role of mental functions in individual and social behavior, also studying the
physiological and biological processes that accompany behaviors and cognitive functions. According to the aforementioned authors,
psychologists explore concepts such as: perception, cognition, attention, emotion, intelligence, phenomenology, motivation, the functioning
of the human brain, personality, behavior, interpersonal relationships, including resilience, among other areas of human knowledge.
According to the Occupational Outlook Handbook, (2014-15Edition), psychological knowledge is built as a method of assessing and
treatingpsychopathologies, it is also directed at understanding and solving problems in different forms of human behavior. The vast majority
of psychologists practice some sort of therapeutic role, whether in clinical psychology or psychological counseling. Others engage in

ongoing scientific research related to mental processes and human behavior, typically within the psychological departments of universities.
In addition to the therapeutic and academic fields, applied psychology is employed in other areas related to human behavior, such as work or
organizational psychology, educational psychology, sports psychology, health psychology, human development psychology, forensic
psychology, legal psychology, among others.
According to Zimbardo &Gerrig (2004), p.3-5; 5-8; 10-17, the basis of thinking from a biological perspective is the search for the causes of
behavior, in the functioning of genes, the brain, and the nervous and endocrine systems. Behavior and mental processes are understood
based on bodily structures and biochemical processes in the human body, so this school of thought is very close to the areas of genetics,
neuroscience and neurology and is therefore closely linked to the important debate on the role of genetic predisposition and environment in
the formation of the person. This perspective directs the researcher's attention to the bodily basis of the entire psychic process and
contributes with basic knowledge about the functioning of psychic functions, such as thought, memory and perception.
Second, Gorayeb, Ricardo; Guerrelhas, Fabiana, (2003), the health-disease process has a special attention and can be understood in different
ways, in addition to being directed to the treatment of the mental disorder itself. Initially approached by psychopathology, arising from the
progressive distinction of the object of neurology and psychiatry, and the consolidation of the latter, as medical specialties, the perception of
the importance of emotional factors in illness and recovery of health, were already present in Hippocratic medicine and homeopathy,
however, it was only in the mid-twentieth century that applications of psychology emerged, in clinical interventions currently called
psychosomatic medicine, medical psychology, hospital psychology and health psychology
According to Zimbardo &Gerrig (2004), p.3-5; 5-8; 10-17, the psychodynamic perspective, behavior is driven and motivated by a series of
internal psychic forces, and describes the mind based on concepts of energy, tension, instincts, drives and desires, such as internal needs on
the one hand and social demands on the other.
The psychodynamic perspective became better known in the form of psychoanalytic theory, from the work of the Viennese physician
Sigmund Freud (1856–1939) with psychiatric patients; but he believed that these principles were also valid for normal behavior. The
Freudian model is notoriously recognized for emphasizing that human nature is not always rational and that actions can be motivated by
factors not accessible to consciousness. In addition, Freud gave a lot of importance to childhood, as a very important phase in the formation
of personality. Freud's original theory, which was later expanded by several more recent authors, strongly influenced many areas of
psychology, and has its origin not in scientific experiments, but in the capacity for observation of a creative man, inflamed by the idea of
discovering the deepest mysteries of the human being.
The reaction to the Behaviorist and Psychodynamic currents emerged in the 50s of the twentieth century, the existential-humanist
perspective, which sees man not as a being controlled by inner drives or by conditions imposed by the surrounding environment, but as an
active and autonomous being, who consciously seeks his own growth and development, presenting a tendency to self-realization. The main
source of knowledge of the humanistic psychological approach is the biographical study, with the purpose of discovering how the person
lives his existence and understands his experience, through introspection. Unlike Behaviorism, which values external observation, the
humanistic perspective seeks a holistic understanding of the human being and is closely related to phenomenological epistemology,
Zimbardo &Gerrig (2004).
Computer Science
Prior to the 1920s, computer was a term associated with people who performed calculations, usually led by physicists. Thousands of
computers were used in projects in commerce, government, and research sites. After the 1920s, the term computational machine began to be
used to refer to any machine that performed the work of a professional, especially those according to the methods of the Church-Turing
Thesis (1936).
The term computational machine eventually lost ground to the reduced term computer in the late 1940s, with digital machines becoming
more and more widespread. Alan Turing, known as the father of computer science, invented the Turing machine, which later evolved into
the modern computer
Computational Sciences studies computational techniques, methodologies and "instruments" as well as their technological applications,
which computerize processes and develop solutions for processing input and output data in the computer, that is, not restricted only to the
study of algorithms, their applications and implementation in the form of software. They also cover data modeling and database
management techniques, involving telecommunications and their communication protocols. Computational Sciences also deals with the
theoretical foundations of information, computation, and practical techniques for their applications.
As a science, it is classified as an exact science, although it inherits elements of Aristotelian philosophical logic, and therefore plays an
important role in the mathematical formalization of algorithms, as a way of representing problems that are susceptible to reduction to basic
elementary operations, capable of being reproduced through any device capable of storing and manipulating data. One of these devices is the
digital computer, which is in widespread use today. Also of fundamental importance for the area of Computer Science are the methodologies
and techniques related to software implementation that address the specification, modeling, coding, testing and evaluation of software
systems.
The studies from Computer Science can be applied in any area of human knowledge in which it is possible to define methods of problem
solving, based on previously observed repetitions. Recent advances in Computer Science have had a strong impact on contemporary society,
in particular applications related to the areas of computer networks, Internet, Web, data science and mobile computing, which have been
used worldwide by people.
The mathematical foundations of computer science began to be defined by Kurt Gödel (1931) with his incompleteness theorem. This theory
shows that there are limits to what can be proven or not proven in a formal system; this led to later work by Gödel and other theorists to
define and describe such formal systems, including concepts such as recursion and lambda calculus.
In 1936 Alan Turing and Alonzo Church introduced the formalization of an algorithm, defining the limits of what a computer and a purely
mechanical model for computing can be. Such topics are addressed in what is now called the Church-Turing Thesis, a hypothesis about the
nature of mechanical calculation devices. This thesis states that any possible calculation can be performed by an algorithm running on a
computer, as long as there is enough time and storage to do so.
Until the 1930s, electrical engineers built electronic circuits to solve logical and mathematical problems, but most did so without any
process, without theoretical rigor. Claude Shannon (1937), while teaching philosophy, was exposed to the work of George Boole, and
realized that he could apply this learning to electromechanical assemblies to solve problems. Shannon (1948) developed the Mathematical
Theory of Communication, the content of which serves as a foundation for areas such as data compression and cryptography.
Computer Science has given rise to several fundamental contributions to science and society. This science was responsible for the formal
definition of computation and computability, and for proving the existence of computationally unsolvable or intractable problems. It was
also possible to construct and formalize the concept of computer language, especially programming language, a tool for the precise
expression of methodological information flexible enough to be represented at various levels of abstraction.
For other scientific fields and for society, Computer Science provided support for the Digital Revolution, giving rise to the Information
Age. Scientific computing is an area of computing that allows the advancement of studies, such as the mapping of the human genome.

There are alternative definitions for Computer Science. It can be seen as a form of science, a form of mathematics, or a new discipline that
cannot be categorized according to current models. Most computer scientists are interested in innovation or in theoretical aspects that go far
beyond just programming, more related to computability.
Despite its name, Computer Science is not just about the study of computers. In fact, the well-known computer scientistEdsgerDijkstrais
considered to be the author of the phrase"Computer science has as much to do with computing as astronomy has to do with the telescope
[...]". The design and development of computers and computer systems are generally considered disciplines outside the context of computer
science. For example, the study of thehardwareIt is generally considered to be part of theComputer Engineering, while the study of
commercial computer systems is usually part of theInformation Technology
Computer Science is also criticized for not being scientific enough, as exposed in the sentence"Science is to computer science what
hydrodynamics is to the construction of pipelines", creditsStan Kelly-Bootle. Despite this, his study often crosses other fields of research,
such asArtificialIntelligencethephysicsandlinguistics.
She is considered by some to have a great relationship with mathematics, greater than in other disciplines. This is evidenced by the fact that
early work in the field was heavily influenced by mathematicians such as Kurt Gödel and Alan Turing; the field remains useful for
exchanging information with areas such as mathematical logic, category theory, and algebra. Despite this, unlike mathematics, Computer
Science is considered a discipline that is more experimental than theoretical.
Data Science
Data Science is the study of data to extract meaningful insights for organizations. It is a multidisciplinary approach that combines principles
and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
This analysis helps data scientists ask and answer questions, such as what happened, why it happened, what will happen, and what can be
done with the results.
Data Science is important because it combines tools, methods, and technology to generate meaning based on data. Modern organizations are
inundated with data; There is a proliferation of devices that can automatically collect and store information. Online systems and payment
portals capture more data in the areas of e-commerce, medicine, finance, and all other aspects of human life. We have text, audio, video, and
image data available in large quantities.While the term Data Science is not new, the meanings and connotations have changed over time.
The word first appeared in the 1960s, as an alternative name for statistics. In the late 1990s, computer science professionals formalized the
term. A proposed definition for Data Science saw it as a separate field with three aspects: data design, collection, and analysis. It still took
another decade for the term to be used outside of academia.
Artificial intelligence and machine learning innovations have made data processing faster and more efficient. The demand from the sector
has created an ecosystem of courses, diplomas and positions in the field of Data Science. Due to the cross-functional skill set and expertise
required, Data Science shows strong growth projected over the next few decades.
Data Science is used to study data in four ways:
1. Descriptive analytics – Descriptive analytics analyzes data to gain insights into what has happened or what is happening in the
data environment. It is characterized by data visualizations such as pie charts, bar charts, line charts, tables, or generated
narratives. For example, a flight booking service may record data such as the number of tickets booked per day. Descriptive
analytics will reveal peaks in bookings, dips in bookings, and months of high performance for this service.
2. Diagnostic analysis - Diagnostic analysis is an in-depth or detailed analysis of data to understand why something happened. It is
characterized by techniques such as drill-down, data discovery, data mining, and correlations. Various operations and data
transformations can be performed on a given dataset to discover unique patterns in each of these techniques. For example, the
flight service can drill down into a particularly high-performing month to better understand peak bookings. This can lead to the
discovery that many customers visit a particular city to attend an event.
3. Predictive analytics - Predictive analytics uses historical data to make accurate predictions about data patterns that may occur in
the future. It is characterized by techniques such as machine learning, prediction, pattern matching, and predictive modeling. In
each of these techniques, computers are trained to reverse-engineer causal connections in the data. For example, flight service
staff can use Data Science to predict flight booking patterns, for the next year, at the beginning of each year. The computer
program or algorithm can analyze past data and predict booking spikes for certain destinations in May. Having anticipated the
future travel needs of its customers, the company could start targeted advertising for these cities as early as February.
4. Prescriptive analytics - Prescriptive analytics takes predictive data to the next level. Not only does it predict what is likely to
happen, but it also suggests an optimal response to that outcome. She can analyze the potential implications of different choices
and recommend the best course of action. Prescriptive analytics uses graph analysis, simulation, complex event processing,
neural networks, and machine learning recommendation engines.
5. Going back to the flight booking example, prescriptive analytics can analyze historical marketing campaigns to maximize the
advantage of the next spike in bookings. A data scientist can project booking outcomes for different levels of marketing spend
across various marketing channels. These data predictions would give the flight booking company more confidence to make its
marketing decisions.
Data Science is revolutionizing the way businesses operate. Many businesses, regardless of size, need a robust data science strategy to drive
growth and maintain a competitive edge. Some of the key benefits include:
Uncover unknown transformative patterns – Data Science enables businesses to uncover new patterns and relationships that have the
potential to transform the organization. It can reveal low-cost changes in resource management for maximum impact on profit margins. For
example, an e-commerce company uses Data Science to find that many customer inquiries are being generated after business hours.
Research reveals that customers are more likely to buy if they receive an immediate response rather than a response the next business day.
By implementing customer service 24 hours a day, seven days a week, the company increases its revenue.
Innovate new products and solutions - Data Science can reveal flaws and problems that would otherwise go unnoticed. More insights into
purchasing decisions, customer feedback, and business processes can drive innovation in internal operations and external solutions. For
example, an online payment solution uses Data Science to collect and analyze customer feedback about the company on social media. The
analysis reveals that customers forget their passwords during peak purchase periods and are dissatisfied with their current password recovery
system. The company can innovate a better solution and see a significant increase in customer satisfaction.
Real-time optimization - It is very challenging for businesses, especially large ones, to respond to changing conditions in real-time. This
can cause significant losses or disruptions to business activity. Data Science can help businesses predict changes and react optimally to
different circumstances. For example, a trucking company uses Data Science to reduce downtime when trucks break down. They identify
the routes and patterns of change that lead to faster breakdowns and adjust truck schedules. They also set up an inventory of common spare
parts that need to be replaced frequently so that trucks can be repaired faster.
A business problem typically kicks off the Data Science process. A data scientist will work with stakeholders in organizations to understand
what the needs are. Once the problem is defined, the data scientist can solve it using the OSEMN Data Science process:

O: Get Data – Data can be pre-existing, newly acquired, or a data repository that can be downloaded from the Internet. Data scientists can
pull data from internal or external databases, the organization's CRM software, web server logs, social media, or purchase it from trusted
third-party sources.
S: Data suppression - Data suppression, or data cleansing, is the process of standardizing data according to a predetermined format. It
includes dealing with missing data, correcting data errors, and removing any outliers. Some exemples of data deletion are: ·
 Change all date values to a common standard format. ·
 Fix spelling errors or additional spaces. ·
 Correct mathematical inaccuracies or remove commas from large numbers.
E: Explore data - Data exploration is a preliminary data analysis that is used to plan other data modeling ploys. Data scientists gain an
initial understanding of data using descriptive statistics and data visualization tools. Then, they explore the data to identify interesting
patterns that can be studied or acted upon.
M: Model data – Software and machine learning algorithms are used to gain deeper insights, predict outcomes, and prescribe the best plan
of action. Machine learning techniques, such as association, classification, and clustering, are applied to the training dataset. The model can
be tested against predetermined test data to assess the accuracy of the results. The data model can be adjusted multiple times to improve
results.
N: Interpret results – Data scientists work together with analysts and organizations to convert data insights into action. They make
diagrams, graphs, and tables to represent trends and forecasts. Data summarization helps stakeholders understand and implement results
effectively.
Data Science professionals use computing systems to keep up with the Data Science process. The main techniques used by data scientists
are:
Classification - Classification is the ordering of data into specific groups or categories. Computers are trained to identify and classify data.
Known datasets are used to create decision algorithms on a computer that quickly processes and categorizes data. For exemple:
 Classify products as popular or not popular·
 Classify insurance applications as high-risk or low risk·
 Classify social media comments as positive, negative, or neutral.
Data Science professionals use computing systems to keep up with the Data Science process.
Regression – Regression is the method of finding a relationship between two seemingly unrelated data points. The connection is usually
modeled around a mathematical formula and represented as a graph or curves. When the value of one data point is known, regression is used
to predict the other data point. For exemple: ·
 The rate of spread of airborne diseases.
 The relationship between customer satisfaction and the number of employees. ·
 The ratio of the number of fire stations to the number of people injured because of a fire at a given location.
Clustering - Clustering is the method of grouping closely related data together to look for patterns and anomalies. Clustering is different
from classification because data cannot be accurately classified into fixed categories. Therefore, the data is grouped into more likely
relationships. New patterns and relationships can be discovered with clustering. For exemple:
 Group customers with similar buying behavior to improve customer service.
 Bundle network traffic to identify daily usage patterns and identify a network attack faster.
 Group articles into several different news categories and use that information to find fake news content.
The Basic Principle Behind Data Science Techniques
While the details vary, the underlying principles behind these techniques are:
 Teach a machine to classify data based on a known data set. For example, sample keywords are provided to the computer with
their respective ranking values. "Happy" is positive, while "Hate" is negative.
 Provide unknown data to the machine and allow the device to classify the dataset independently.
 Allow for inaccuracies of results and deal with the probability factor of the outcome.
Data Science professionals work with complex technologies, such as:
 Artificial intelligence: Machine learning models and related software are used for predictive and prescriptive analytics.
 Cloud computing: Cloud technologies have given data scientists the flexibility and processing power needed for advanced data
analytics.
 Internet of Things: IoT refers to various devices that can automatically connect to the internet. These devices collect data for
Data Science initiatives. They generate large amounts of data that can be used for data mining and data extraction.
 Quantum computing: Quantum computers can do complex calculations at high speeds. Skilled data scientists use them to create
complex quantitative algorithms.
Data Science is an umbrella term for other data-related functions and fields. Let's look at some of them here:
 Difference Between Data Science and Data Analytics - While the terms can be used interchangeably, data analytics is a subset
of Data Science. Data Science is an umbrella term for all aspects of data processing, from collection to modeling and insights. On
the other hand, data analysis mainly involves statistics, mathematics, and statistical analysis. It focuses only on data analysis,
while Data Science is related to the big picture around organizational data. In most workplaces, data scientists and data analysts
work together to achieve common organization goals. A data analyst may spend more time on routine analysis by providing
regular reports. A data scientist can design the way data is stored, manipulated, and analyzed. Simply put, a data analyst makes
sense of existing data, while a data scientist creates new methods and tools to process data to be used by analysts.
 Difference Between Data Science and Business Analytics - While there is an overlap between Data Science and business
analytics, the main difference is the use of technology in each area. Data scientists work more closely with data technology than
business analysts. Business analysts reconcile business and IT. They define business cases, gather input from stakeholders, or
validate solutions. Data scientists, on the other hand, use technology to work with business data. They can write programs, apply
machine learning techniques to create models, and develop new algorithms. Data scientists not only understand the problem, but
they can also create a tool that provides solutions to the problem. It's not uncommon to find business analysts and data scientists
working on the same team. Business analysts use the output of data scientists and use it to tell a story that the organization as a
whole can understand.
 Difference Between Data Science and Data Engineering - Data engineers build and maintain the systems that allow data
scientists to access and interpret data. They work more closely with the underlying technology than a data scientist. The role
typically involves creating data models, building data pipelines, and overseeing extract, transform, and load (ETL). Depending
on the layout and size of the organization, the data engineer may also manage related infrastructure, such as big data storage,

broadcasting, and processing platforms such as Amazon S3. Data scientists use the data that data engineers have processed to
build and train predictive models. Data scientists can then hand the results over to analysts for further decision-making.
 Difference Between Data Science and Machine Learning - Machine learning is the science of training machines to analyze
and learn from data in the same way that humans do. It is one of the methods used in Data Science projects to obtain automated
insights from data. Machine learning engineers specialize in computing, algorithms, and coding skills specific to machine
learning methods. Data scientists can use machine learning methods as a tool or work closely with other machine learning
engineers to process data.
 Difference Between Data Science and Statistics - Statistics is a mathematical background area that seeks to collect and
interpret quantitative data. In contrast, Data Science is a multidisciplinary field that uses scientific methods, processes, and
systems to extract knowledge from data in a variety of ways. Data scientists use methods from many disciplines, including
statistics. However, the scopes differ in their processes and the problems they study.
AWS has several tools to support data scientists around the world:
 Physical data storage - For data warehousing, Amazon Redshift can run complex queries on structured or unstructured data.
Analysts and data scientists can use AWS Glue to manage and search data. AWS Glue automatically creates a unified catalog of
all data in the Data Lake, with Meta data attached to make it discoverable.
 Machine learning - Amazon SageMaker is a fully managed machine learning service running on Amazon Elastic Compute
Cloud (EC2). It enables users to organize data, build, train, and deploy machine learning models, and scale operations.
Analysis:
 OrAmazon Athenais an interactive query service that makes it easy to analyze data in theAmazon S3orGlacier. It's fast,
serverless, and works using standard SQL queries.
 Amazon Elastic MapReduce (EMR) processes big data using servers such as Spark and Hadoop.
 Amazon Kinesis enables real-time aggregation and processing of streaming data. It uses website clickstreams, application logs,
and telemetry data from IoT devices.
 Amazon OpenSearch enables you to search, analyze, and visualize petabytes of data.
Figure 2 - Feature Store
Source: Microsoft Industry Blogs
The data can be stored in memory or in a database of very fast key-values. The process itself can be carried out in various cloud services
or on one platform. Here's an example of an online and offline pipeline using data storage (Feature Store). It was designed by Uber, as part
of its Michelangelo platform:
Figure 3 - Michelangelo Platform of the Uber Project

Source: Microsoft Industry Blogs
What Does a Data Scientist Do?
A data scientist can use a number of distinct techniques, tools, and technologies as part of the Data Science process. Based on the problem, it
chooses the best combinations to get faster and more accurate results. The role and day-to-day work of a data scientist will vary depending
on the size and requirements of the organization. While they typically follow the Data Science process, the details can vary. In larger Data
Science teams, a data scientist may work with other analysts, engineers, machine learning specialists, and statistical technicians to ensure
that the Data Science process is followed end-to-end and that business goals are achieved.
However, in smaller teams, a data scientist may have more than one role. Based on experience, skills, and academic background, he may
wear multiple hats or have overlapping roles. In that case, your day-to-day responsibilities may include engineering, analytics, and machine
learning, along with key Data Science methodologies.
Challenges for Data Scientists
Data sources - Different types of applications and tools generate data in various formats. Data scientists need to clean and prepare data to
make it consistent. This can be tedious and time-consuming.
Understand the problem of organizations - Data scientists need to work with various stakeholders and managers of organizations to
define the problem to be solved. This can be challenging, especially in large organizations with multiple teams with varying requirements.
Eliminate drift - Machine learning tools are not fully accurate, and as a result, there may be uncertainties or drifts. Deviations are
disparities in the test data or prediction behavior of the model in different groups, such as age or income bracket. For example, if the tool is
trained primarily on data from middle-aged people, it may be less accurate when making predictions involving younger and older people.
The field of machine learning offers an opportunity to address deviations by detecting and measuring them in the data and model.
Online and offline data have different characteristics. Behind the scenes, offline data is mostly built in frameworks, such as Spark or SQL,
where the actual data is stored in a database or as files. While online data may require access to data using APIs for streaming engines such
as Kafka, Kinesis, or in-memory key-value databases such as Redis or Cassandra.
Working with a data store abstracts this layer, so that when a data scientist is looking for data, instead of writing engineering code, they can
use a simple API to retrieve the data they need.
One of the main challenges in implementing machine learning (computer) in production arises from the fact that the data being used to test a
model in the software development environment (programs) is not the same as the data in the production service layer. Therefore, enabling a
consistent set of resources (machine and software) between the testing and service layer allows for a smoother deployment process, ensuring
that the tested model truly reflects the way things will work in production.
In addition to the actual data, the data store maintains additional meta data for each resource. For example, a metric that shows the impact
of the resource on the model it's associated with. This information can help Data Scientists tremendously select the resources for a new
model, allowing them to focus on those who have achieved a better impact on similar existing models.
The reality today is that almost every business is based on Machine Learning, so the number of projects and resources is growing
exponentially. This reduces our ability to have a good comprehensive overview of the resources available, since there are so many. Instead
of developing in silos, data warehousing allows us to share our resources with our colleagues' Meta data. It's becoming a common problem
in large organizations that different teams end up developing similar solutions, simply because they're not aware of each other's tasks. Data
stores fill that gap and allow everyone to share their work and avoid duplication.
To meet guidelines and regulations, especially in cases where the generated Artificial Intelligence (AI) models serve industries such as
healthcare, financial services, and security, it is important to trace the lineage of the algorithms under development. Achieving this requires
end-to-end data flow visibility to better understand how the model is generating its results. As data is being generated, as part of the process,
it is necessary to track the flow of the data generation process. In data warehousing, you can maintain the lineage of data and a resource.
This provides the necessary tracking information, how the data was generated, and provides the insight and reporting needed for regulatory
compliance.
MLOps is an extension of DevOps where the idea is to apply DevOps principles to Machine Learning pipelines. Developing a machine
learning (computer) pipeline is different from developing software (programs), mainly because of the look and feel of the data. The quality
of the model is not only based on the quality of the code. It is also based on the quality of the data and the resources that are used to run the
model. According to Airbnb, about 60%-80% of Data Scientists' time is spent creating, training, and testing.
Data stores allow Data Scientists to reuse resources instead of rebuilding them over and over again for different models, saving valuable
time and effort. Data stores automate this process, and resources can be triggered by code changes that are pushed to Git or by the arrival of
new data. This automated feature engineering is an important part of the MLOps concept.

Some of the largest information and communication technology companies that deal extensively with AI have created their own Feature
Stores (Uber, Twitter, Google, Netflix, Facebook, Airbnb, etc.). This is a good indication to the rest of the industry of how important it is to
use data warehousing as part of an effective machine learning pipeline. Given the growing number of AI projects and the complexities
associated with getting those projects into production, the industry needs a way to standardize and automate the core of feature engineering.
Therefore, it is fair to assume that data storage is positioned to be a basic component of any machine learning pipeline (computer and
software).
IV. ELEMENTS FOR REFLECTION ON HUMAN INTELLIGENCE AND ARTIFICIAL INTELLIGENCE AND THE
DIGITAL SOCIETY
Human Intelligence
Second, Haimovitz, Kyla; Dweck, Carol S. (2016), human intelligence is the intellectual capacity of human beings, in terms of, complex
cognitive feats and high levels of motivation and self-awareness. Through intelligence, humans possess the cognitive abilities to learn, form
concepts, understand, apply logic and reason, including the abilities to recognize patterns, plan, innovate, solve problems, make decisions,
retain information (memorize), and use language to communicate.
There is no consensus on how intelligence is measured, from the idea that intelligence is fixed at birth, to that it is malleable and can change
depending on the individual's mindset and efforts. In psychometrics, human intelligence is assessed by intelligence quotient (IQ) tests,
although the validity of these tests is disputed.
Second, Salovey, Peter; Mayer, John D. (March 1990), Walker, Ronald E.; Foley, Jeanne M. (December 1973), Tirri, Nokelainen (2011),
there are several subcategories of intelligence, such as emotional intelligence and social intelligence. There is significant debate as to
whether they represent distinct forms of intelligence, Second, Brown, M.I.; Wai, J. (2021), higher intelligence is associated with better life
outcomes. Second, Triglia, A.; Regader, B.; & García-Allen, J.; (2018), intelligence is considered one of the most useful concepts in
psychology because it relates to many relevant variables, e.g., the probability of having an accident, salary, etc.
According to, Ritchie, Stuart J.; Tucker-Drob, Elliot M. (2018), The effects of education on intelligence, education appears to be the "most
consistent, robust, and enduring method" for increasing intelligence. Second, Czepita, D.; Lodygowska, E.; Czepita, M. (2008), Rosenfield,
Mark; Gilmartin, Bernard (1998), several studies have demonstrated a correlation between IQ and myopia. Some suggest that the reason for
the correlation is environmental, as people with higher IQs are more likely to impair their eyesight with prolonged reading, or the other way
around, as people who read more are more likely to achieve a higher IQ, while others claim that there is a genetic link.
Second, Denise C. Park; According to Gérard N. Bischof, (2017), aging causes a decline in cognitive functions, as several cognitive
functions decline by about 0.8% at the age of 20 to 50 years; cognitive functions include processing speed, memory, work speed, and long-
term memory. To Duckworth, A. L.; Quinn, P. D.; Lynam, D. R.; Loeber, R.; Stouthamer-Loeber, M. (2011), motivation is a factor that
influences IQ test results. People with higher motivation tend to get higher IQ scores.
Relevance of Intelligence Quotient (IQ) tests
Alfred Binet, (1859-1911), developed the first test to measure people's intellectual capacity. Initially, the test was applied in schools to
identify children with learning difficulties. Psychologist William Stern (1871-1938) coined the term Intelligence Quotient (IQ), introducing
the terms "MI (mental age) and CI (chronological age) to relate a person's intellectual capacity and their age.
Lewis Madison Terman, (1877-1956), proposed intelligence scales using the formula IQ = 100Xim/CI and classified a score higher than 140
as genius and a score below 70 as slow thinking. David Wechsler, (1896-1981), created the intelligence scale for adults, according to the
following scale:
 Equal to or greater than 130 – Giftedness
 120 – 129 – Higher intelligence.
 110 – 119 – Above-average intelligence.
 90 – 109 – Average intelligence.
 80 – 89 – Weak normal.
 70 – 79 – Limit of disability.
 Equal to or less than 69 – mentally disabled.
According to Shipstead, Zach; Redick, Thomas S.; Engle, Randall W. (2010), Simons, Daniel J.; Boot, Walter R.; Charness, Neil;
Gathercole, Susan E.; Chabris, Christopher F.; Hambrick, David Z.; Stine-Morrow, Elizabeth A. L. (2016), Jaeggi, S. M., Buschkuehl, M.,
Jonides, J., Perrig, W. J. (2008),attempts to increase IQ with training of the human brain have led to increases in task-related aspects of
training – e.g., working memory – but it remains unclear whether these increases generalize to the increase in intelligence itself.
Second, AlexisMadrigalWired, (2008) and,Moody, D. E. (2009), um research paper that uses the practice of a taskn-backdual, it can increase
fluid intelligence (Gf), as measured in several different patterns. This finding received some attention from popular media, including an
article in theWired. However, a later critique of the paper's methodology questioned the validity of the experiment and raised problems with
the lack of uniformity in the tests used to evaluate the control and test groups. They were allowed 10 minutes to complete a normally 45-
minute test.
Second, Borsboom, D.; Mellenbergh, G. J.; van Heerden, J. (2004). Macintosh, Nicholas (2011), Weiten W (2016), npsychology, human
intelligence is assessed by IQ scores determined by IQ tests. However, IQ test scores show a high degree of reliability. While IQ tests are
generally thought of as a measure of some forms of intelligence, they may not serve as an accurate measure of broader definitions of human
intelligence, including creativity and social intelligence.
According to psychologist Wayne Weiten, (2016), "IQ tests are valid measures of the kind of intelligence needed to do well at academic
work. But if the goal is to assess intelligence in a broader sense, the validity of IQ tests is questionable."
Theory of Multiple Intelligences
The Theory of Multiple Intelligences of Howard, (1983),It is based on studies not only ofchildrenandadultsnormal, but also of individuals
Gifted(including the so-called "Savants"), from people who have suffered brain damage, from experts and virtuosos, and from individuals
from diverse cultures. Gardner, (1983, 1995), divides intelligence into at least a number of different and distinct components of intelligence:
1. Logical-mathematical – refers to the ability to deal with mathematical operations and logical approaches. It implies good
inductive and deductive approaches that involve sequential reasoning capable of perceiving relationships and connections
between elements (e.g. mathematicians, researchers and scientists). They first acquire the knowledge, and then apply it to
practical issues.
2. Linguistics – the ability to use words and language effectively, i.e., it involves the articulation of arguments and discourses in a
clear and direct way, in the transmission of a message to achieve the objectives (e.g., writers, poets, journalists, speakers,
politicians and other speakers).
3. Visual-spatial – the ability to understand the world in three dimensions. Ability to imagine things in three dimensions. That is,
the ability to imagine something and think about the object from one or more points of view, artistic skills (e.g., painting,
sculpture, designers, pilots, etc.).

4. Musical – ability to understand and identify sounds, timbres, rhythms, and everything related to sound. (e.g. musicians,
songwriters, singers, conductors, DJs, etc.).
5. Body-kinesthetics – is related to the efficient use of the body, in the form of motor coordination, hand-eye and pedal
coordination, and mind-body coordination. (e.g. athletes, craftsmen, dancers, acrobats and surgeons).
6. Interpersonal – the ability to understand and interact with others effectively. It involves attention and sensitivity to other
people's moods, feelings, temperament, and understanding. (e.g. teachers, politicians, actors, salespeople, social workers, etc.).
7. Intrapersonal – the ability to know oneself, respecting one's own feelings, desires, limitations, motivations, and respect for the
human condition. He has great willpower and independence. (e.g. psychologists, politicians, spiritual leaders and philosophers).
8. Naturalist – ease of understanding nature and its elements, living or not. (e.g. animals, plants, rain, sea, land – botanists,
biologists, farmers, hunters, etc.).
9. Existential – ability to understand deep issues related to existence (life and spiritual themes). (e.g. spiritual leaders, theologians
and philosophers)
Triarchic Theory of Intelligence
Robert Sternberg, (1978, 1985, 2003), proposed the Triarchic Theory of Intelligence to provide a more comprehensive description of
intellectual competence than traditional differential or cognitive theories of human ability, which describes three fundamental aspects of
intelligence:
 Analytical intelligence – comprises the mental processes through which intelligence is expressed.
 Creative intelligence is required when an individual is faced with a challenge that is almost, but not entirely new or when, an
individual is involved in automating the execution of a task.
 Practical intelligence – is linked to a socio-cultural environment and involves adapting, selecting and shaping the environment,
and to maximise fit into the context.
The Triarchic Theory posits that general intelligence is part of analytical intelligence, and only by considering all three aspects of
intelligence can one fully understand the full range of intellectual functioning. Intelligence is defined, as a person's assessment of success in
life, accordingly. with their own standards, within their socio-cultural context. Success is achieved through the use of combinations of
analytical, creative, and practical intelligence. The three aspects of intelligence are called processing skills. Processing skills are applied to
the pursuit of success through the three elements of practical intelligence: adaptation, modeling, and environment selection. The
mechanisms that employ processing skills to achieve success include utilizing strengths and compensating for or correcting weaknesses.
Emotional Intelligence
According to Daniel Golema, (2010), emotional intelligence is a concept related to the so-called "social intelligence", present in psychology,
that is, an emotional person can identify their emotions more easily. One of the advantages is that the person has the ability to self-motivate
and move forward in the face of frustrations and disappointments. The person is able to control impulses, channel emotions into appropriate
situations, practice gratitude, and motivate and encourage others. According to the same author, emotional intelligence can be subdivided
into five skills:
1. Emotional self-awareness
2. Emotional control
3. Self-motivation
4. Empathy
5. Develop interpersonal relationships.
A person can concentrate on work and complete all tasks and obligations/responsibilities even if they feel sad, anxious or bored.
PASS Theory of Intelligence
According to Alexander Luria, (1966), the modularization of brain function is supported by decades of neuroimaging research. It proposes
that cognition be organized into three systems and four processes. The first process is "planning," which involves executive functions
responsible for controlling and organizing behavior, selecting, constructing, and controlling performance. The second is the process of
"attention", which is responsible for maintaining levels of arousal, alertness and ensuring focus on relevant stimuli. The next two are called
"concurrent" and "successive" processing, and they involve the encoding, transformation, and retention of information.
"Concurrent" processing is triggered when the relationship between items and their integration into entire units of information is required.
Examples, figure recognition, triangle within a circle versus a circle within a triangle, or the difference between "he took a shower before
breakfast" and "he had breakfast before a shower." "Successive" processing is necessary to arrange separate items in a sequence, such as
remembering a sequence of words or actions in exactly the order in which they were just presented.
These four processes are functions of four areas of the brain. "Planning" is located in the front part of the brain, the frontal lobe. "Attention"
and arousal are combined functions of the frontal lobe and the lower parts of the cortex, although the parietal lobes are also involved in
attention. Both "simultaneous" and "successive" processing occur in the posterior region or back of the brain. "Simultaneous" processing is
widely associated with the occipital and parietal lobes, while "successive" processing is widely associated with the frontotemporal lobes.
Piaget's Theory
According to Piaget (1953, 2001), in the theory of cognitive development the focus is not on mental abilities, but on mental models of the
child's world. As the child develops, more and more accurate models of the world are developed, which allows them to interact better with
the world. The child develops a model in which objects continue to exist even when they cannot be seen, heard, or touched. Piaget's theory
described four main stages and many sub-stages in development. These four main stages are:
 Sensorimotor stage (birth - 2 years);
 Pre-operational stage (2 to 7 years);
 Concrete operational internship (7 years-11 years); and
 Formal Operations Internship (11 years-16 years).
The degree of progress through these stages is correlated, but not identical to, psychometric IQ. Piaget considers intelligence, as an activity,
more than a capacity. Piaget focused on the discriminatory abilities of children between two-and-a-half and four-and-a-half years of age. He
began the study by taking children of different ages and placing two lines of candy, one with the candies in a row farther away and the other
with the same number of candies in a row closer. He found that "children aged 2 years and 6 months and 3 years and 2 months correctly
discriminate the relative number of objects in two rows; between 3 years and 2 months and 4 years and 6 months, they indicate that a longer
row with fewer objects has "more"; After 4 years and 6 months, they discriminate correctly again."
Initially, younger children weren't studied, because if at the age of four a child couldn't conserve the amount, then a younger child probably
couldn't either. However, the results show that children under three years and two months retain the quantity, but as they get older, they lose
this quality and do not regain it until they are four and a half years old.
First, younger children have a capacity for discrimination that shows that the logical capacity for cognitive operations exists earlier than
recognized. This study also reveals that young children may be equipped with certain qualities for cognitive operations, depending on how
logical the task structure is. Piaget's theory has been criticized for the fact that the age of appearance of a new model of the world, such as

Reflection on Human Intelligence vs Artificial Intelligence and the Digital Society (from Theory to Practice)

Reflection on Human Intelligence vs Artificial Intelligence and the Digital Society (from Theory to Practice)

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