What are the "commons", the "common goods", the "common heritage", the "common things"? These notions are strongly mobilized today in many social science disciplines and citizen actions. Their growth reflects an evolution in social practices: under the pressure of the ecological crisis and digital transformation in particular, goods are increasingly being shared. Free software, encyclopedia and participative housing, bicycles or cars in successive use, an undertaking that would be the "common good" of all stakeholders: notions of the common good are of interest in the fields of culture, environmental protection, urban planning, health, innovation, work, etc. Mobilization is intense because they allow us to think about social change on the basis of reinvestment by the collective, communities, use and sharing. They make it possible to propose reinterpretations of the founding values of contemporary societies such as the role of the State, property and forms of expression of democracy. They call for both theoretical reflection and political debate, and are translated into concrete citizen experiences. This dictionary, halfway between a vocabulary and an encyclopedia, is intended to be a tool for understanding all of these phenomena.
Dictionary of common goods
The common good is a notion first developed by theology and philosophy, then grasped by law and social sciences and invoked by political actors. It refers to the idea of a patrimonial good shared by the members of a community, in the spiritual and moral sense of the word "good", as well as in the material and practical sense (what one has or possesses).
Western philosophy has been questioning what constitutes a community since Plato and Aristotle. The concept of the common good appears in Christian theology from Thomas Aquinas in the 13th century, where it refers to the natural inclination of creation as a whole (including the human community) towards the Good that is God.
This notion is often used in questions of ownership of certain resources and refers to the relationship between access to equitably shared resources and interests that unite the members of a community and contribute to its existence. For the Italian political scientist and economist Riccardo Petrella, the common good is what sustains societies. According to the French economist Jean-Marie Harribey (2011), this notion, which also involves the notion of property, is notably linked to the progressive awareness of the existence of a common heritage of humanity.
The "good living" is a concept based on the Andean indigenous wisdom (Sumak Kawsay** in Quechua and Suma Qamaña*** in Aymara language) and the practice of more balanced relationships within ecosystems and between all beings, human and non-human, as a path to common well-being. For Plato, this is the vision that guides our practices and our missionary starting point. The Platohedro organization is based on research around this concept: "what we do is linked and adapts to this way of thinking, doing and being. And as such, we consider it a dynamic concept, which we translate and make our own with respect and attention, situating it in time, context, needs and desires".
Definition of Artificial Intelligence: Artificial Intelligence (AI) is the implementation of a number of techniques to enable machines to mimic a form of real intelligence. AI is being implemented in a growing number of application areas.
The concept was born in the 1950s thanks to the mathematician Alan Turing. In his book Computing Machinery and Intelligence, he raises the question of bringing a form of intelligence to machines. He described a test now known as the "Turing Test" in which a subject interacts blindly with another human and then with a machine programmed to formulate sensible answers. If the subject is not able to tell the difference, then the machine has passed the test and, according to the author, can truly be considered "intelligent".
Deep learning is a type of artificial intelligence derived from machine learning, where the machine is able to learn by itself, as opposed to programming, where it simply executes predetermined rules to the letter.
Deep Learning is based on a network of artificial neurons inspired by the human brain. This network is composed of tens or even hundreds of "layers" of neurons, each receiving and interpreting information from the previous layer. The system will, for example, learn to recognize letters before attacking words in a text, or determine if there is a face in a photo before finding out which person it is.
At each step, the "wrong" answers are eliminated and sent back to the upstream levels to adjust the mathematical model. As it does so, the program reorganizes the information into more complex blocks. When this model is subsequently applied to other cases, it is normally able to recognize a cat without anyone ever telling it that it has never learned the concept of a cat. The starting data is essential: the more different experiences the system accumulates, the better it will perform.
A GAN (Generative Antagonistic Network) is a Machine Learning technique based on the competition between two networks within a framework. The generator is a type of convolutional neural network whose role is to create new instances of an object. On the other hand, the discriminator is a "deconvolutional" neural network that determines the authenticity of the object or whether or not it is part of a data set.
During the training process, these two entities are in competition and this is what allows them to improve their respective behaviors. This is called retropropagation. The objective of the generator is to produce outputs without being able to determine if they are false, while the objective of the discriminator is to identify the false ones. Thus, as the process proceeds, the generator produces better quality outputs while the discriminator detects the false ones better. In fact, the illusion becomes more and more convincing over time.
CAN (Creative Adversarial Network) is a logarithm created by the Rutgers Art and Artificial Intelligence Laboratory. In art, this algorithm is programmed to create original works of art. One part of the algorithm analyzes the existing aesthetics, creates a work of art and the second part penalizes if what is created is similar to the analyzed styles. On the one hand, it tries to learn the aesthetics of existing artworks. On the other hand, he will be penalized if, when creating his own work, he imitates too closely an established style.
At the same time, AICAN adheres to the principle of "least effort", according to which too much novelty will discourage viewers.
This ensures that the art generated will be new but will not deviate too far from what is considered acceptable. Ideally, it will create something new that builds on what already exists.
The quantitative explosion of digital data has forced researchers to find new ways of seeing and analyzing the world. It's about discovering new orders of magnitude for capturing, searching, sharing, storing, analyzing, and presenting data. Thus was born the "Big Data". It is a concept for storing an inexpressible amount of information on a numerical basis. According to the Association for Computing Machinery (or ACM) Digital Library archives in scientific articles about the technological challenges of visualizing "big data sets", the term was coined in October 1997.
A cognitive bias is a distortion in the cognitive processing of information. The term bias refers to a systematic deviation of logical and rational thinking from reality. Cognitive biases lead the subject to give different importance to facts of the same nature and can be detected when paradoxes or errors appear in a reasoning or judgment.