Tokenization of the work “The Kiss” by the artist Gustav Klimt exhibited at the Belvedere Museum
THE IMPACT OF ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY IN CULTURE AND ART.
María Raquel Burgueño
BRIEF INTRODUCTION:
The “MALINOWSKI” effect:
Many will wonder how I got into the world of technologies. When I talk to other people who are immersed in different fields outside the notarial, the obligatory question they ask me is, What does a notary do talking about these topics? And that is when I reply that everything is thanks to the “Malinowski effect”.
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Many will have heard of the famous ethnographer and master of Anthropology, Bronislaw Malinowski, who was the founder of British Social Anthropology at the beginning of the 20th century and who revolutionized the ethnographic studies of the time with the development of a renewed methodology: Fieldwork through “PARTICIPANT OBSERVATION”.
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Many social science enthusiasts will know that in 1914 Malinowski traveled to the Trobriand Islands, and due to the declaration of World War I, he ended up living there without being able to immediately return to England. This allowed him to practice his profession from a different perspective, and he decided to spend some time among the native population of these islands, learning their customs, their way of organizing, their food, and even their own language. Upon his return to England in 1922, Malinowski published his masterpiece “The Argonauts of the Western Pacific”, where he shared these experiences and made recommendations that laid the foundations for modern anthropology: his method of participant observation.
Bronislav Malinowski with the inhabitants of the Trobriand Islands.
I share this colorful anecdote because when the pandemic arrived and lockdown became inevitable, like Malinowski, I went to live on the digital islands of the internet. While I was already a skilled netizen by nature and inheritance, it was in this way that I stayed “living” with many digital and IT tribes and began to learn their customs, their own logic of these groups, and above all, I understood, as a good student of Malinowski, that it was essential to also learn their language. And so my story began in the realm of technologies, as a participant observer, a curious mind exploring other worlds to solve the riddles of digital environments and my profession. Later, we gathered with other curious minds and thus formed the Innovation Commission of our college, where we all went out to explore other worlds for innovations and newness to share them with our friends and colleagues and together, in the best Walt Disney style, “Walk toward the future”.
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In this fast-moving and changing world, we are in the stage of validating new paradigms such as digitization, virtuality, immersive web 3.0 experiences, etc., which, like a Caribbean storm to palm trees, shake our principles, beliefs, values, and premises under which we were raised in life in general and in our profession, in particular. Thomas Kuhn foresaw that these events are not linear and that culturally we are traversed by constant movements of advance and retreat. The important thing is to be aware of this and know how to swing in that ebb and flow, taking everything as an experience and seeking to find answers that allow us to keep moving forward.
And thus, we come to understand how technology and culture are intimately connected, performing the twists of a dance where one pushes the other to the next movement in our socio-cultural evolution.
In this journey, we review the concepts of Culture to understand the phenomena that it goes through and that, in turn, “crosses” us humans every time Culture and Technology perform their fused dance.
PREVIOUS NOTIONS OF CULTURE
Culture is the element that distinguishes the human species from all other species. Throughout human history, culture begins when elements such as language, tools, social institutions, and a value system with aesthetic, moral, and religious content are present in the collective structure. The appearance of “rules” or “norms” clearly defines the division between what we understand as culture and nature.
Culture encompasses symbols, meanings, values, institutions, behaviors, and all their derivatives, which characterize a human population, identifying and distinguishing it from others. The word culture carries with it its own weight of associations in different languages and traditions. Cultures possess:
• A significant value system (that gives meaning to the total existence) and normative (that provide behavior rules, a worldview of life).
• A shared base (common territory, history, language, race, or ancestors) that identifies people as members of a group; and
• The will or decision to be primarily identified as members of that community.
Esteban Krotz 1 states that while human culture is many thousands of years old, the scientific analysis of culture –that is, its systematic study, undertaken by a community of specialists using methods, concepts, and theories created for this purpose, is barely a century old.
Krotz enunciates five fallacies about the notion of culture which we will state affirmatively here.
1) All human beings, by definition, have culture. As stated at the beginning: having culture, belonging to a culture, is the characteristic feature of human life compared to all other forms of life on this planet.
2) There exists a CULTURAL MULTIPLICITY, this is a huge number of past and present cultures. This cultural multiplicity lacks HIERARCHY; there is no superior culture over another, we can only say that they are similar or different.
3) Just as there are biological and ethnic mixtures, we can also talk about mixed cultures. It is a fallacy to think of pure cultures against the processes of deculturation and acculturation.
• 1 Full-time Professor-Investigator at the Social Sciences Unit of the "Dr. Hideyo Noguchi" Regional Research Center at the Autonomous University of Yucatan.
• Professor by Assignation at the Faculty of Anthropological Sciences of the Autonomous University of Yucatan.
• Member of the Mexican Academy of Sciences.
• Coordinator of the South-Southeast Region of the Mexican Council of Social Sciences (Comecso, A.C.)
• Director of the Revista Sur de Mexico.
• National Research System: Level III.
• Doctor of Philosophy [Philosophy of Social Sciences] Hochschule für Philosophie, Munich.
4) Museums, theaters, and libraries are only some of the many homes of culture. In reality, they are places where the idea of culture is perpetuated. Most cultural life is carried out, preserved, reproduced, and transformed outside of them.
5) The field of cultural creation and reproduction is much broader than the field of state institutions. States have always been interested in intervening in cultural creation and the preservation of cultural heritage because in this way they control and sometimes even create an important factor of social cohesion.
As previously said, in all cultures and at all times, influences from one culture to another have been documented. The problem does not lie in the existence of such influences, but whether the humans belonging to a culture can freely decide whether they want to accept these influences and, if so, which ones and how. The ability to choose between alternatives presupposes, of course, knowing alternatives and recognizing a particular influence as an alternative among others.
TECHNOLOGY AS A CULTURAL PHENOMENON:
Darcy Ribeiro2 defines SOCIOCULTURAL EVOLUTION as the historical movement of change in the ways of being and living of human groups triggered by the impact of successive technological revolutions on concrete societies, tending to lead them to the transition from one evolutionary stage to another, or from one to another sociocultural formation.
2 Darcy Ribeiro (Montes Claros, Minas Gerais 10/26/1922. Brasília, 02/17/1997) was a Brazilian intellectual and politician known for his works in education, sociology, and anthropology.
One of the central problems of understanding technology as culture and as a form of it is that it must be recognized as having a nexus with the society that produces it, since it is the creation of the historical process that generated it and at the same time, it produces new transformations in the world that begins to develop with its influence.
From a religious perspective, technology is inscribed within the human "doing" or action. As such, it should be subordinated to “being”. It should be at the service of the development of being.
Technological culture is a means that has reached new social cultures where people relate technology to their attitudes, values, thoughts, beliefs, and behaviors, which are reflected in their everyday actions and cultural aspects.
TENSIONS GENERATED BY TECHNOLOGICAL CULTURE:
Within these new interactions, new situations of tension can also be appreciated in individuals' behavior, and this can be reflected in social coexistence.
In terms of the art world, these tensions exist, and following the thoughts of several philosophers like the German Markus Gabriel, the Conicet researcher and Doctor in Social Sciences Hernán Borisonik observes some of these, which we summarize below, allowing us to link their thinking to the role of technologies:
• The blurring of boundaries between art and design. The break with the “academic art” paradigm.
• The concept of an “original” piece has become virtually elusive in the face of the immediate and infinite possibility of copying and circulating any image (The NFT – Non-Fungible Token phenomenon through Blockchain technology).
• The boundary between artists and the public has been blurred. We all produce content and become both subjects and objects of aesthetic contemplation in technological culture.
• The concept of a work loses its notion of materiality and a new stage begins: The concept of work from immateriality. The work represented through software code and algorithms.
• Information and Communication Technologies (ICTs), the Internet of Things (IoT), and emerging technologies like AI and Blockchain have altered the gap between cultural producer and consumer.
• Everything that circulates on the Internet, including artistic content, becomes data that gets processed and transformed into information, driving the advance of digital analytical marketing due to the use of Big Data and Data Lake.
• In this regard, the so-called artificial intelligence assumes humans as huge series of data that can be captured, understood, and even manipulated.
• As a response, in contemporary today, the representation of humanity is being shaped in another sense: we will no longer speak of what we are in general, but algorithms will tell us what each one of us is specifically.3
Let's briefly see how some of these technologies function to better appreciate their impact on the world of art:
BRIEF DESCRIPTION OF ARTIFICIAL INTELLIGENCE.
Artificial Intelligence is considered an excellent combined technology tool that allows automating tasks from the mental process domain of humans at an extraordinary speed compared to human intelligence. This is due to the ability to successfully handle raw data processing, which, after treatment, forms a “Dataset” used to train the algorithm – INPUT – so it can find the answer to human needs – TRAINING – facilitating solution scenarios with minimal human assistance – OUTPUT.
For this, the algorithm requires the following elements:
- Access to the data (DATASET) which will be the source of its training (INPUT)
- The learning and selection process (TRAINING)
- The validation process
- Implementation in the selected domain field.
Artificial Intelligence has great computational capacity for data management. It would be extremely important to develop a program that has an automated, traceable, and transparent learning process, classified as a “White Box”, meaning it has explainability and interpretability regarding the learning process.
When working with Artificial Intelligence, we must consider:
• a) The need or problem to solve;
• b) Who to apply IA use for;
• c) Thinking about the key user who should be involved from the start;
• d) The formation of a multidisciplinary team;
• e) That the applied IA results in a situation that adds Value and therefore improves people's lives.
In turn, the project process must respect the following stages:
We will use as an example the application of a I.A. system to identify hate speech in human rights protection:
• Training: The learning process will be done through data input (INPUT). 80% of the total available data (DATASET) will be used, reserving the remaining 20% for the validation stage. For this case, both natural language processing algorithms4 that recognize words used in hate, segregationist, xenophobic, ethnocide, etc. speeches and algorithms that work with image and video detection making identical references will be used. Considering that we are dealing with issues closely related to the perception of feelings, all of them from the intrapsychic world, this initial learning must necessarily be supervised by a team of specialists. In our field: representatives from Psychology, Sociology, Anthropology, Law, Human Rights Activists; Doctors, Members of International Governmental and Non-Governmental Organizations, etc.
• 2) Validation: At this stage, the tool's generalization power is measured. Here, the 20% of the data available reserved for this stage is used, and it is studied whether the learning process needs any adjustment.
• 3) Test: At this stage, the process aims to evaluate the tool's diagnosis power for situations related to hate speech.
• 4) Evaluation: At this stage, the goal is to establish effectiveness in the human rights environment with 4 See natural language algorithm GPT3, one that has shown optimal results in natural language handling.
direct application for verifying early detection prediction of hate speech.
• 5) Implementation: Here, the goal is for the application to function and produce the effects it was designed for.
The European Union in its “White Paper on AI”5 defined it as:
Systems that display intelligent behavior by analyzing their environment and taking actions, with some degree of autonomy, to achieve set goals. AI-based systems can consist solely of software, acting in the virtual world (voice assistants, image analysis software, search engines, voice, and face recognition systems), or AI can be integrated into hardware devices (advanced robots, autonomous cars, or Internet of Things (IoT) applications).
It’s also important to understand that algorithms explicitly define the process through which a decision is made and know their answer in advance because they were given it with programming instructions, while an “AI Model” learns or infers how to make that decision. The learning in this latter case is because thousands of possible solutions to the final result were previously shown to the model, and based on that large amount of input data, it can infer the most adequate solution. In other words, there is no prior programming of the solution for the model to follow the instruction, but it must reach the best possible answer on its own due to the learning of situations it was given beforehand. The program learns autonomously to make the best decisions. That’s why systems like Spotify or Netflix seem to “know” our preferences, but what they are really doing is learning from our own decisions about which song or series they can recommend to us based on their learning of our tastes.
AI and Ethics:
A very important factor to consider when talking about AI is its ethical use. This implies that the very purpose of its use must be to apply value and improve people's lives, as already mentioned. Therefore, the responsible use and application of AI must safeguard humans and their personal rights from biases in terms of gender, age, ethnicities, data in general that make up the dataset, as well as the security of these (infosec). There is a saying among those who work with data: “Garbage in, Garbage out”.
Consequently, in data matters, caution must be exercised in labeling it, doing so through multidisciplinary professional groups, and spending as much time as possible on the dataset assembly process, the cornerstone in the success of Machine Learning.
Equally important is the anonymization of this data, preserving the identity of those who have provided it and preventing those data from being attributed to them, giving full compliance with international and local regulations regarding Personal Data Protection.
AI Ethical Principles.
Learning Types.
All this machine training development is known in Data Science as "Machine Learning" or "Data-based IA" and is characterized by always having human supervision in the training process. The distinction between Machine Learning and an algorithm is this capability the AI model has to make the best decision by itself drawing on its training without previously having the answer to its goal. In addition, in terms of learning, it is also useful to distinguish the expressions “White Boxes” and “Black Boxes”.
Both are closely related to the system's "Explainability" factor. When it is said that a certain system, whether algorithm or IA model, is a White Box system, it means that all and each of the processes that the system does can be understood, interpreted by the human mind. Allowing myself the freedom of a domestic example, I will refer to a culinary recipe. Think of the steps in a cooking recipe. If we see how a chef prepares a certain recipe, we understand step by step how he gets to the result, and that result is explicitly pointed out. We can always understand how the machine arrived at that conclusion, and it can show the path it took to get there. If I show a photo to the machine saying, “This is a Cat”, the labeling I’m showing implies that every time it sees a cat, it must recognize it as such. That is supervised learning, and we can also simultaneously refer to it as a White Box.
When we talk about Machine Learning, the machine does not have labeling but a massive dataset with photos, for instance, of “cats” and learns through the analysis of each pixel in the photo that it “is” a cat by finding the “pattern”, making its own classification (clustering), so when it confronts similar data that wasn’t shown in training, it resorts to its own pattern and consequently can recognize the photo of a cat. As long as we can know the clustering it performed, meaning the parameters it based itself on to group the data in one way or another, we can also say we are in the presence of a White Box.
When, in addition to performing these Machine Learning processes, the machine uses what is called an Artificial Neural Network (ANN), we refer to a specific way of performing learning.
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