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Sentient avant-gardism and the principles for geometric cognition models

Sentient avant-gardism and the principles for geometric cognition models

Sentient: able to perceive or feel things
Avant-garde: new and experimental ideas and methods

The increased computational power provided by the advancement of technology creates the opportunity to build models that accurately reflect, analyze and build upon their metadata.

Human interventions and development in a system are bound to be entangled with that of artificial intelligence. By deploying algorithms, machine learning, graph networks, etc, that are based on the architecture of human cognition, a new style of data visualization is created: sentient avantgardism.

The cognitive processes of acquiring and applying knowledge, the ability to learn and remember information, as well as to think, reason, and make decisions have been used in the development of Artificial intelligence (AI). This deployment of human traits created the ability of a computer to perform tasks that would normally require human intelligence, such as understanding natural language and recognizing objects. For example, one of the ways that human cognitive processes can be deployed in artificial neural networks is by using a technique called deep learning. Deep learning is a type of machine learning that is inspired by the way that the human brain learns. In deep learning, artificial neural networks are able to learn by example. They learn by looking at a large number of examples and then extracting the rules that they need to learn from those examples.

With many of the tools of data visualization that use AI becoming mainstream, the development of new visual languages and styles is inherent to the new technology and digital environment.

Since all neural network models are mimicking those of human cognition, the overlap of these frameworks is inevitable, leading to the creation of new and experimental ways of expressing ideas and methods by using both artificial and human intelligence. An avant-garde of the sentience of neural networks in becoming.

Developing visual environments with neural networks (with Midjourney) © Tib Roibu

In the context of graph networks, metadata can be used to describe the relationships between nodes in the network. By creating a graph network, researchers can gain a better understanding of the complex relationships between different pieces of data. A simple process of converting a graph network to data visualization is as follows:

  1. Convert the graph network to a list of nodes and edges.
  2. For each node in the list, create a corresponding data point.
  3. For each edge in the list, create a corresponding connection between data points.
  4. Visualize the data points and connections.
A simple graph network for geometric cognition that can generate metadata and variables to create visual environments, or new geometric models © Tib Roibu
A simple graph network for geometric cognition that can generate metadata and variables to create visual environments, or new geometric models (circular view) © Tib Roibu

Geometry has been used as a framework to build in many sciences and industries, from technology to academia, design, and innovation in order to discover mathematical beauty or to predict the success of a story. Since all frameworks (of data, behavior, etc.) are cognitive frameworks, the created systems will have a set of universal properties that can be translated into geometric models:


• Axioms, laws, and geometric postulates translate the metadata into experience and behavior.
• Human perception can be measured, therefor new systems (cognitive, technological) can be built for and based on sensorial mechanisms;
• Understanding the intricacies of perception (of these models) can lead to creating new systems that have universal values:

1/ Function: for an entire system or its sub-elements: to connect, explore, protect, escape, learn, and innovate.

2/ Relations: that develop the backbone of the system and create new shapes, spaces, and environments.

3/ Identity: unique values of individuality are part of a perceptual hyperplane that can be identified and used in different combinations; the principles of the self are immutable, and their applications are infinite.

4/ Meaning: the topological interactions between sub-systems (via relationships) may be used to directly deduce the collective attributes of the system and create a semiotic environment.

5/ The geist of elements: by replacing things with experiences, the perceptual borders of any sub-system are aligned with that of the entire model.

See Also

6/ Language: introducing conceptual or hidden variables into an environment, creates the opportunity for incipient communication and individual expression

7/ Perceptual navigation: building on cognitive processes creates experiences, that can be mapped and explored in cognitive spaces

8/ Style: any human intervention in a system is bound to be entangled with that of artificial intelligence: a sentient avant-gardism

9/ Sense of order for social development: defining the sub-systems of a model will create order in higher-dimensional systems

10/ Innovation: transforming concepts into cognitive processes, creates the opportunity to explore the un-explored

Exploration of embedding geometric axioms and postulates in conceptual environments. Pursuing a geometric model for cognition and ways to visualize its meta:data © 2022, Tib Roibu

© 2022 GEOMETRY MATTERS. ALL RIGHTS RESERVED.
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