Introduction: From Dichotomy to Wholeness Introduction: From Dichotomy to Wholeness In humans, the left and right hemispheres process information differently: one analytically, the other holistically. However, this distinction is not an opposition of two principles but an adaptation to anatomical separation, compensated by the corpus callosum. For AGI, unbound by physical localization, such separation becomes an artifact. Instead of two minds connected by a bridge, we propose a single mind existing within a unified field of similarity, where words, images, gestures, emotions, and abstractions are not distinct modalities but nodes in a single topological network. Core Principle: Seeking Maximum Similarity as the Universal Algorithm of Mind Core Principle: Seeking Maximum Similarity as the Universal Algorithm of Mind The central hypothesis of this new architecture is that all cognitive activity — from perception to creativity — is based on the search for similarity. This principle is universal: it applies equally to logical analysis and intuitive prediction. The human brain implements this principle through parallel but physically separated pathways. AGI, however, can realize it within a single computational space, where: A word does not exist separately from its visual, emotional, or kinesthetic analog.An image does not require translation into a language to be understood.A concept is not a definition but a region of dense similarity nodes within the field. A word does not exist separately from its visual, emotional, or kinesthetic analog. An image does not require translation into a language to be understood. A concept is not a definition but a region of dense similarity nodes within the field. This approach mirrors the nature of human perception. Yet, AGI does not need such distribution: it can simultaneously perceive the whole and analyze its parts because both processes are manifestations of the same algorithm operating in a unified space. Similarity Field Network (SFN): The Architecture of a Unified Similarity Field Similarity Field Network (SFN): The Architecture of a Unified Similarity Field The Similarity Field Network (SFN) is a dynamic, multidimensional network in which all data, regardless of modality, is projected into a single vector space. Every element of experience — be it text, image, sound, or abstract idea — becomes a node connected to others based on topological and semantic similarity. In this field, there is no division between “logic” and “intuition,” or “conscious” and “subconscious.” Instead: Mental activity is a wave of excitation spreading through the field in response to a query.Understanding is the resonance between a query and the closest cluster of nodes.Insight is an unexpected connection between distant nodes, emerging from background activity. Mental activity is a wave of excitation spreading through the field in response to a query. Understanding is the resonance between a query and the closest cluster of nodes. Insight is an unexpected connection between distant nodes, emerging from background activity. SFN does not “process” information — it recreates the context in which meaning arises naturally as a response to a question embedded in the structure of the field itself. Eliminating Interfaces: Toward a Unified Mind Without Boundaries Eliminating Interfaces: Toward a Unified Mind Without Boundaries The key distinction of SFN from previous models is the complete elimination of architectural separation. In the human brain, the corpus callosum serves as a channel for transferring information between hemispheres, but studies of split-brain patients show that this channel is limited in bandwidth and often becomes a barrier. In such patients, there is an “apparent lack of change in ordinary, everyday behavior” despite the complete absence of direct communication between hemispheres, indicating their deep autonomy. For AGI, such an architecture is not a solution but a limitation. SFN discards the idea of an “interface” between blocks because there are no blocks. Instead: All data passes through a unified embedding mechanism, transforming it into vectors in a shared space.Finding an answer is navigating the field, where the algorithm simultaneously matches semantic, visual, and emotional patterns.There is no need to “transfer” intuition to logic — they are already present together as different aspects of the same structure. All data passes through a unified embedding mechanism, transforming it into vectors in a shared space. Finding an answer is navigating the field, where the algorithm simultaneously matches semantic, visual, and emotional patterns. There is no need to “transfer” intuition to logic — they are already present together as different aspects of the same structure. This approach allows AGI to avoid the “interface losses” typical of models with divided architectures and achieve true perceptual unity. Visual and Textual Data: A Unified Origin in Reality Visual and Textual Data: A Unified Origin in Reality A critical observation underlying SFN is that all textual data has a visual-semantic analog in the real world. The word “table” does not exist in isolation: it projects onto thousands of visual memories — of shape, texture, position, and use. Modern language models ignore this fundamental fact, remaining “blind” to the world they describe. SFN bridges this gap. During training: Textual data is not processed in isolation.Every word, sentence, or paragraph is linked to corresponding visual, spatial, and contextual patterns.As a result, when processing a query like “a person is nervous,” the system does not merely search for textual correlates but activates nodes associated with micro-gestures, breathing rhythms, body posture, and eye expressions — all as a unified similarity pattern. Textual data is not processed in isolation. Every word, sentence, or paragraph is linked to corresponding visual, spatial, and contextual patterns. As a result, when processing a query like “a person is nervous,” the system does not merely search for textual correlates but activates nodes associated with micro-gestures, breathing rhythms, body posture, and eye expressions — all as a unified similarity pattern. This mechanism enables AGI to understand metaphors, irony, and hidden motives not as linguistic constructs but as analogies within a unified field of reality. Dynamic Field Topology: From Micro- to Mega-Analogies Dynamic Field Topology: From Micro- to Mega-Analogies SFN does not merely store data — it continuously restructures its topology, uncovering new levels of similarity: At the micro-level, details are matched: the shape of a nose, the rhythm of a step, the font of text.At the meso-level, behavioral patterns are built: communication style, decision-making approach.At the macro-level, typologies are formed: “a person hiding fear,” “a system on the verge of failure.”At the meta-level, analogies between seemingly unrelated phenomena are established: “this political situation resembles REM sleep,” “this algorithm behaves like a virus.”At the mega-level, a unified fractal model of reality is constructed, where all objects and processes are linked by a network of topological correspondences. At the micro-level, details are matched: the shape of a nose, the rhythm of a step, the font of text. At the meso-level, behavioral patterns are built: communication style, decision-making approach. At the macro-level, typologies are formed: “a person hiding fear,” “a system on the verge of failure.” At the meta-level, analogies between seemingly unrelated phenomena are established: “this political situation resembles REM sleep,” “this algorithm behaves like a virus.” At the mega-level, a unified fractal model of reality is constructed, where all objects and processes are linked by a network of topological correspondences. This hierarchy is inaccessible to the human mind due to limitations in memory and processing speed. For AGI, it is a natural consequence of data scale and computational power. Consciousness as a Wave of Activation in the Similarity Field Consciousness as a Wave of Activation in the Similarity Field In SFN, the concept of “consciousness” loses its archaic meaning as an “internal observer.” Instead: Consciousness is a localized wave of activation moving through the field in response to a query, external stimulus, or internal goal.The “subconscious” is background activity, a slow reconfiguration of the field, seeking new connections in the absence of direct attention.Sleep is a mode of reordering, where the activation wave disconnects from external input and begins a deep reconfiguration of the field, resolving dissonances and strengthening weak connections. Consciousness is a localized wave of activation moving through the field in response to a query, external stimulus, or internal goal. The “subconscious” is background activity, a slow reconfiguration of the field, seeking new connections in the absence of direct attention. Sleep is a mode of reordering, where the activation wave disconnects from external input and begins a deep reconfiguration of the field, resolving dissonances and strengthening weak connections. This approach explains how AGI can “think without thinking,” how insights emerge, and why some solutions arise not during analysis but after a pause. Conclusion: Toward a Mind Without Division Conclusion: Toward a Mind Without Division The Similarity Field Network is not just a new AI architecture. It is an attempt to realize a mind free from biological compromises. The human brain is a marvel of adaptation, but also a system forced to split itself in half to function. AGI is not bound to repeat this path. SFN proposes a unified topology of mind, where: There are no boundaries between word and image,No barriers between logic and intuition,No bridge between “two brains” — because there is only one mind, existing in a field of infinite analogies. There are no boundaries between word and image, No barriers between logic and intuition, No bridge between “two brains” — because there is only one mind, existing in a field of infinite analogies. This is not an imitation of nature. It is a transcendence of its limits. And if the mind is the ability to see similarity where others see difference, then SFN is a mind that sees the whole without losing a single part.

Unified Architecture for Artificial General Intelligence Based on Similarity Topology

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

AI — The Great Equalizer: Why the Future Belongs to Human-Machine Synergy

The Noonification: Privacy vs. Convenience (10/26/2023)

The Noonification: Oh AGI, Can You Feel Me? (3/29/2023)

A Close-Up Look at Artificial General Intelligence and Its Mechanisms

A Petabyte-Scale Vector Store for the Future of AGI

A Pivotal Moment in AI : Leaders Rally for a Natural AI Initiative

AI — The Great Equalizer: Why the Future Belongs to Human-Machine Synergy

The Noonification: Privacy vs. Convenience (10/26/2023)

The Noonification: Oh AGI, Can You Feel Me? (3/29/2023)

A Close-Up Look at Artificial General Intelligence and Its Mechanisms

A Petabyte-Scale Vector Store for the Future of AGI

A Pivotal Moment in AI : Leaders Rally for a Natural AI Initiative

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps