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2026

2025

GAIM Gen-1: Reproducing and improving existing human art (Mona Lisa)
GAIM Gen-2: De novo content creation (Mother Mona Lisa)
(V.1) GAIM Gen-3: Autonomous Creative Science and Arts SOCR GAIM, ca. ~2150
(V.2) GAIM Gen-3: Autonomous Creative Science and Arts SOCR GAIM, ca. ~2150
(V.3) GAIM Gen-3: Autonomous Creative Science and Arts Chromo-Sculpture Anime, ca. ~2150
  • September 2025: SOCR example visually demonstrating the abilities of contemporary Generative AI Models (GAIM)/Augmented Intelligence Models (AIA) with progressively multi-generational improvements:
    • Gen-1: Reproducing and improving existing human art: Identify the real (by Leonardo Da Vinci) vs. the AI-generated Mona Lisa paintings.
    • Gen-2: De novo content creation: completely AI-generated Renaissance atelier scene, which depicts a noble lady (mother) with an expressive and directive gesture, actively overseeing the commissioned painting of her daughter in the background by an artist. In both AI-generated scenes, the lady's face and posture reflect her authority and noble status, while her daughter is shown seated, calmly posing for the portrait, with her presence reinforcing the mother’s directive role. A Leonardo DaVinci-like painter is at work with an easel, palette, and sketches around him, inside a warm and atmospheric Renaissance atelier with wooden beams and muted light. The entire composition uses warm Renaissance tones, soft sfumato shading, and layered detail to capture the cultural essence of the 15th–16th century while emphasizing action and interaction, not just passive posing like in real (by Leonardo Da Vinci) vs. the AI-generated scene.
    • Gen-3: Autonomous Creative Science and Arts: GAIMs/AIAa can generate realistic autonomously creative art of new prospective, ubiquitous form of human visual art in the year 2150. This image shows realistic (high-precision and detailed) painting of a futuristic Chrono-Sculpture, a form of AI-predicted future 4D work of art anchored to a specific physical location, composed of light, data, and algorithmically generated form, which evolves over time based on a complex set of inputs. This is an AI-speculative forecast of a future Primary Art Form, Chrono-Sculpting (Temporal Kinetic Augmentation).
By 2150, the distinction between digital and physical reality may be expected to become functionally meaningless for most of the population, who experience the world through ambient, non-invasive neural or optical interfaces. This mixed-reality environment is the canvas for a new form of a dominant visual art form Chrono-Sculpting, which does not have a physical composition. Its substance is a localized, computationally intensive projection of phased photons and contextual data streams, rendered in real-time for each viewer.
Visually, a Chrono-Sculpture appears as a polychromatic, semi-translucent, and kinetically fluid form. Its shape is never entirely static, often resembling a slow-motion capture of smoke, a living fractal, or a liquid crystal formation. Its texture can range from smooth and ethereal to sharp and crystalline, and its opacity might shift, allowing the physical world behind it to be an integral part of the composition. The experience is multi-sensory, often accompanied by localized spatial audio or even subtle haptic feedback for those with compatible interfaces.
The color palette is not fixed but is a dynamic function of input data. For example, a sculpture in a public park might translate the local atmospheric pressure, pollen count, and ambient noise level into a constantly shifting color field. An artist might map the emotional sentiment of local network traffic to a spectrum from deep violets (negative) to brilliant golds (positive). The "value" in the artistic sense is derived from the harmony and poignancy of these data-driven transformations.
The shape of a Chrono-Sculpture is a probability cloud of form. The artist does not define a single, static shape. Instead, they design a set of rules and a constrained set of aesthetic possibilities within which the sculpture exists and evolves.
Ontological Kernel: The core of the sculpture is its ontological kernel - the foundational algorithm and aesthetic constraints designed by the human artist. This is the artist's unique signature and vision. It defines the sculpture's behavioral tendencies, its potential geometric vocabulary (e.g., biomorphic curves, sharp crystalline structures), and its response logic to data inputs.
Temporal Dimension: This is the crucial fourth dimension. A sculpture might be programmed to grow over time, e.g., reflecting the changing seasons. It might relive a key historical event associated with its location every day at noon, with the data from each new day subtly altering the re-enactment. The artwork is a performance unfolding over decades.
The creation of a Chrono-Sculpture is a process of systems architecture and aesthetic curation, not direct manipulation. The artist acts more like a choreographer or a gardener than a traditional painter or sculptor.
Site Anchoring & Historical Curation: The artist first selects a physical location (the anchor). They then curate vast datasets relevant to that site—geological surveys, historical archives, demographic shifts, ecological data, etc. This is a deeply humanistic and research-intensive phase.
Algorithmic Weaving: Using highly advanced AI as a collaborative tool (often referred to as a latent space brush), the artist designs the ontological kernel. They do not code in a traditional sense but rather guide the AI through aesthetic choices, defining the relationships between data inputs and visual/auditory outputs. The goal is to imbue the system with a specific character or soul. The process can be described by a simplified function: \(A(t) = f_{\theta}(L, D(t), V(t))\), where \(A(t)\) is the state of the artwork at time \(t\); \(f_{\theta}\) is the kernel function designed by the artist with parameters \(\theta\); \(L\) is the static, curated data set for the location; \(D(t)\) is the set of real-time dynamic data streams (e.g., weather, network traffic); and \(V(t)\) is the data from viewers interacting with the piece (e.g., gaze duration, proximity, biometric feedback).
Neuro-Kinetic Tuning: The artist rehearses with the sculpture in a simulated environment, often using a direct brain-computer interface (BCI). They experience the sculpture's evolution and refine its responses, not by rewriting code, but by providing direct neural feedback of approval or dissonance, tuning the parameters \(\theta\) until the sculpture's behavior aligns with their artistic intent.The finalized kernel is uploaded to the global mixed-reality mesh and permanently anchored to its physical coordinates, where it begins its long, evolving existence.
Value and Appeal: The ubiquity and appreciation for Chrono-Sculpting stem from several key factors that address a post-industrial, data-saturated society's needs: (1) Re-enchantment of Place, in a globally connected world, Chrono-Sculpting re-invests physical locations with deep, unique, and evolving meaning. A simple street corner can become a profound historical and aesthetic experience; (2) Living Art, it is fundamentally anti-static. The desire to see "what the sculpture is doing today" drives repeat engagement. Communities form around observing and interpreting the long-term behavior of their local public sculptures; (3) Authenticity of Intent, while AI is a core part of the toolset, the value is placed entirely on the human artist's vision. The genius lies not in crafting an object, but in designing a beautiful, meaning-making system. The ontological kernel is the revered artifact, and its elegance and depth are what critics assess; and (4) Participatory Experience, viewers subtly influence the art through their presence and attention, creating a gentle, subconscious dialogue between the artist, the art, the public, and the place itself. This resolves the modernist tension between the artwork and the viewer, making the viewer a part of the artwork's environment. This art form represents a synthesis of land art, generative art, performance art, and data visualization. It is an art not of static objects, but of dynamic, living systems, reflecting a future where the boundary between information and reality has beautifully and irrevocably blurred.

2024

This paper Statistical foundations of invariance and equivariance in deep artificial neural network learning was recognized as being of remarkable quality by the AmStat/SMI review committee.

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