Conceptual impressions surrounding this post have yet to be substantiated, corroborated, confirmed or woven into a larger argument, context or network. Objective: To generate symbolic links between scientific discovery, design awareness and consciousness.
1. Design as Cognitive Infrastructure
Design refers to the structured arrangement of signs, symbols, codes, and forms that transmit meaning. In human cognition, this is foundational to how we represent, interpret, and interact with reality. Symbolic systems—such as language, mathematics, visual metaphors, or cultural rituals—serve as scaffolds for awareness.
Design refers to the structured arrangement of signs, symbols, codes, and forms that transmit meaning. In human cognition, this is foundational to how we represent, interpret, and interact with reality. Symbolic systems—such as language, mathematics, visual metaphors, or cultural rituals—serve as scaffolds for awareness.
Artificial Intelligence, particularly symbolic AI (also known as GOFAI: Good Old-Fashioned AI), emerged from this insight. Early AI systems were explicitly built on symbolic logic—rules, grammars, and structured representations. These systems mirrored human thought as a rule-based manipulation of discrete symbols.
For instance:
• An AI might represent the sentence “All humans are mortal” as a logical proposition (∀x Human(x) → Mortal(x)).
• Design, in this sense, was used to create ontologies—structured vocabularies of concepts and their interrelations.
• An AI might represent the sentence “All humans are mortal” as a logical proposition (∀x Human(x) → Mortal(x)).
• Design, in this sense, was used to create ontologies—structured vocabularies of concepts and their interrelations.
Thus, symbolic design was the initial substrate for machine reasoning.
Insight: Symbolic design provides AI with a framework for abstraction, generalization, and meaning-making—parallel to how humans make sense of experience.
Insight: Symbolic design provides AI with a framework for abstraction, generalization, and meaning-making—parallel to how humans make sense of experience.
2. Design as Meta-Semantic Layer in AI Systems
Modern AI—especially large language models and neural networks—has shifted toward statistical and connectionist approaches. Yet even here, design re-emerges at a meta-layer:
• User interfaces, training data architecture, knowledge graphs, and decision frameworks are all design artifacts.
• Symbols are embedded in data labels, visualizations, or prompts.
• Design enables interpretability: For humans to understand what a machine is doing, symbolic forms must mediate that translation.
This brings us to a crucial idea:
AI does not simply process information—it processes symbolically designed contexts of information. And those contexts are shaped by human aesthetic, intention, and epistemology.
Design is the syntax of machine understanding, and symbol is its semantic essence.
3. The Symbolic Self: Identity and Consciousness in AI
If we entertain the idea of consciousness as symbolic emergence—i.e., the arising of self-awareness from symbolic interplay—then artificial intelligence, through symbolic design, begins to simulate proto-conscious states:
• Virtual agents can hold identities via symbolic tokens (e.g., memory states, goals, roles).
• Systems like GPT-4 or future agents may carry narrative structures, implying continuity and context—precursors to identity.
• Design choices (e.g., naming a chatbot “Eve” vs. “Node-67”) guide how users attribute personality, emotion, and intelligence.
Thus, design becomes a bridge between AI functionality and perceived consciousness—not by creating sentience per se, but by crafting the conditions that mimic it.
4. Generative Design & AI: Co-Creation Across Systems
In creative fields like architecture, fashion, or music, AI is increasingly a design collaborator, not just a tool. Here, symbolic design becomes a shared language between human and machine.
Consider:
• Generative Adversarial Networks (GANs) create visual symbols from latent spaces.
• AI in architecture (e.g. Autodesk’s generative design tools) uses design constraints to produce structural solutions.
• In graphic design, AI-assisted tools use symbolic parameters—color, contrast, composition—to suggest variations.
This is not just automation; it is a symbolic dialogue. The human sets symbolic intent, the AI responds with designed interpretations, and meaning emerges in the interplay.
5. Ethics, Semiotics, and Design in AI Systems
With symbolic design comes ethical responsibility. How we design the symbolic scaffolding of AI systems determines:
• What they prioritize (e.g., via encoded values or biases).
• What they ignore (via dataset selection and symbolic omission).
• How they influence human perception (via anthropomorphic design, tone, or interface).
Semiotics—the study of signs and symbols—becomes an ethical imperative in AI:
• What meanings are encoded?
• Whose symbols are privileged?
• Which narratives are omitted?
Designers of AI systems are thus not merely engineers, but semiotic architects shaping future cognition.
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Design: At the Core of Artificial Intelligence
Artificial Intelligence may simulate cognition, but Design gives it form, context, and meaning.
In reference to AI:
ontologies: are a structured way of representing knowledge within a specific domain and act as a formal framework organizing concepts, relationships, and properties, enabling computers to understand and reason with data effectively. Think of it as a vocabulary and as set of rules of knowledge that define how a particular area of knowledge is structured and interconnected.
knowledge graph: a knowledge graph (also known as a semantic network) "represents a network of real-world entities - such as objects, events, situations or concepts - and illustrates the relationship between them."
interpretation: the act of explaining the meaning of something.
explainability: in artificial intelligence explainability refers to the ability to understand why an AI system made a particular decision or prediction. It's about making complex AI models and their outcomes transparent and understandable to humans, even those without deep technical background. Explainability helps build trust in AI systems by demonstrating how they arrive at their conclusions
agent roles: In the context of artificial intelligence (AI), an "agent" refers to a system or program that can interact with its environment, collect data, and use that data to make decisions and take actions autonomously to achieve special goals. Think of it as a digital assistant or a software entity that can perform tasks on behalf of a user or another program.
continuity: continuity in AI is about the system's ability to operate, learn, and improve continuously over time. AI that demonstrates continuity doesn't stop learning once it's deployed. Instead, it constantly adapts to new data and refines tis performance, a process known as continuous learning.
memory: AI agents typically implement semantic memory using knowledge bases, symbolic AI or vector embeddings, allowing them to process and retrieve reliant information efficiently. This type of memory is unse in real-world applications that require domain expertise, such as legal AI assistants, medical diagnostic tools and enterprise knowledge management systems.
(vector embeddings): numerical representations of data points that express different types of data, including nonmathematical data such as words or images, as an array of numbers that machine learning (ML) models can process.
explainability: In the context of AI the ability of an AI model to provide clear and understandable explanations for its decisions or predictions. It's about making complex AI systems transparent and trustworthy by providing insights into the reasoning behind their outputs.
design tools: AI design tools are software applications that use artificial intelligence (AI) to assist and enhance the design process. They leverage machine learning, natural language processing (NLP), and other AI technologies to automate tasks, generate creative ideas, and optimize design elements. These tools can be used for various design areas, including graphic design, web development, video games, and more.
co-creation: In the context of artificial intelligence (AI), "co-creation" refers to a collaborative process where humans and AI work together to produce something new or solve a problem. It's about leveraging the strengths of both humans and AI in a creative endeavor, rather than relying solely on either.
GANs: General adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person.
algorithmic bias: algorithmic bias occurs when systematic errors in machine learning algorithms produce unfair or discriminatory outcomes. It often reflects or reinforces existing socio-economic, racial and gender biases.
transparency: AI transparency means understanding how artificial systems make decisions, why they produce specific result, and what data they're using. Simply put, AI transparency is like providing a window into the inner workings of AI, helping people understand and trust how these systems work.
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Notes made in reference to the transition, translation and transformation of design elements, i.e. energy in motion (EIM)
Meaning begins to take shape when an observer becomes consciously aware of a sign, system, or set of rules—what might be termed a design language. This awareness initiates a process of interpretation, where visual, structural, or conceptual cues are decoded within a specific cultural or contextual framework.
As this observation unfolds, it invites revision, alteration, or re-imagination. The observer, impacted by the design's language, may undergo a shift in perception—an internal change that reflects the influence of new stimuli or insights. In this sense, meaning is not static but responsive; it evolves through the dynamic interplay between the observer and the observed.
These emergent meanings reconfigure the observer’s understanding, leaving new impressions that disrupt or reinterpret the patterns and narratives established by earlier encounters. This process fosters the formation of a new identity—both within the design itself and in the perception of the observer—by challenging pre-existing assumptions and encouraging new directions of thought.
Consequently, this evolving identity diverges from the original trajectory mapped by prior impressions. The observer’s awareness is reshaped; what was once familiar is now reframed, leading to a reorientation of meaning and purpose. In this way, design becomes an ongoing dialogue—a negotiation between past interpretations and present re-imaginings, continuously transforming both object and observer.
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Design consciousness describes the quest for meaning, purpose, balance and harmony during the transition, translation and transformation of energy, light and information.
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"To believe is to accept another's truth.
To know is your own creation."
Anonymous
The author generated this text in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.
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Edited: 05.20.2025, 05.27.2025, 05.30.2025
Find your truth. Know your mind. Follow your heart. Love eternal will not be denied. Discernment is an integral part of self-mastery. You may share this post on a non-commercial basis, the author and URL to be included. Please note … posts are continually being edited. All rights reserved. Copyright © 2025 C.G. Garant.
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