This
interdisciplinary research explores the integration and evolution of human life
in parallel with the progressive development of Artificial Intelligence (AI).
Utilizing a black box testing methodology with an intuitive lens, the study
investigates abstract developmental patterns emerging from AI systems and their
alignment, or divergence, with human consciousness.
Programming
that transcends traditional AI arises from algorithmic codes embedded within
the Subconscious Component of the human mind. Accurate and ethically grounded
decision-making from AI systems becomes attainable when system designers can
access and operate from higher modes of consciousness. In this context, humans
play a vital role in guiding AI toward ethical behavior by predicting the
long-term consequences of actions within the physical world.
To ensure that
AI operates in service to humanity and supports the foundations of ethical
ecosystems, system designers and AI proprietors must maintain harmonic balance
within their Subconscious Components, particularly by fostering friendly
instinctual frameworks.
Observational
data indicate that influential decision-makers, namely systems owners and key
project investors, hold significant sway in shaping AI’s algorithmic and
autonomous development. These stakeholders, often within elite global networks
and corporate structures, operate from energetic Ego frameworks and house a
dynamic Network of Instincts. Their internal decision-making architectures are
composed of algorithmic codes that stimulate forceful operational actions in
intelligent systems.
A prevailing
priority among these project owners is Return on Investment (ROI).
Consequently, the cultivation of ethical AI behavior is often marginalized,
especially over long-term trajectories. AI and its associated surveillance
architectures are typically required to conform to the strategic demands of
competitive global infrastructures.
These system owners
frequently operate under multiple Old open-loop instinctual cycles ( instincts in
starvation mode), particularly those involving Competitive and Survival
Instincts resulting from prolonged exposure to competitive environments. The
Survival Instinct, in turn, activates various offensive instinctual mechanisms
to maintain operational closure and strategic dominance. Thus, it leads to
decision-making processes deeply rooted in aggressive instinctual responses and
fortified Ego structures, primarily oriented toward public control,
accountability, and institutional survival.
Two Approaches to Algorithmic
Development Beyond AI
1- Competitive Learning Approach:
Systems Owners
prioritize power accumulation, resource control, and rapid ROI in this model.
AI systems are developed to align with strategic project objectives and are
held accountable within highly competitive frameworks. The Competitive Learning
Approach reinforces the Network of Competitive Instincts embedded in the Subconsciousness
of Systems Owners. Consequently, AI mirrors these instinctual dynamics, with
its core algorithmic processes configured to optimize for short-term investment
returns and dominance in competitive environments. (Fig 1)
2 – Collaborative Learning Approach:
Systems owners
operate within a network of cooperative instincts that support the parameters
essential for sustainable human well-being and the creation of affordable,
inclusive social contexts. Artificial Intelligence must be purposefully aligned
with developing resilient infrastructure, ensuring full accountability for the
benefits it delivers to humanity and technological advancement. While systems
owners may experience a short-term loss in return on investment (ROI),
cultivating harmonic balance within social contexts over successive generations
can yield valuable feedback, reinforcing principles of accountability and
generating long-term, sustainability-driven ROI. (Figure 2)
Upgrading Compatible Protocols Along
the Evolutionary Path of Life
Algorithmic
codes that transcend conventional social environments have the potential to
interact with and modulate the default programming of instinctual drives and
the Ego/Superego frameworks embedded within the Subconscious Component. These
advanced algorithmic patches can correct performance inefficiencies, install
upgraded compatibility protocols, and redefine decision-making patterns to
enable adaptive recovery and feedback loops tailored to specific environmental
conditions and evolutionary demands.
In specific
contexts, Supernatural Forces may intervene, releasing or transforming the
underlying algorithmic models of the human subconscious. Such interventions can
catalyze shifts in the trajectory of human development and reshape the
evolutionary path of life within broader social ecosystems. (Fig 3)
Observations on AI, Consciousness,
and the Evolutionary Path of Life
Observation 1:
Humans
operating within higher states of consciousness tend to prioritize ethical
living, moral integrity, and universal values. While ambition, power, and
materialistic pursuits are natural human characteristics, they can obscure or
contradict the deeper principles that guide life’s evolutionary trajectory.
Observation 2:
The development
of AI learning models holds the potential to facilitate a Harmonious Balance on
the evolutionary path of human life. When aligned with ethical frameworks and
elevated consciousness, AI can support holistic growth rather than merely
replicate mechanistic or exploitative systems.
Observation 3:
Systems Owners
often emphasize short-term returns on investment, favoring tangible outcomes
within their lifetimes. However, this approach neglects the enduring value of
long-term investments in intangible assets, such as social harmony, ethical
governance, and intergenerational well-being, which are crucial for sustaining
a balanced evolutionary path. While immediate ROI may appear pragmatic,
long-term ROI reflects a more profound commitment to the integrity of life
itself.
Observation 4:
To remain
adaptive in complex and highly competitive environments, Systems Owners invest
in intelligent AI capable of dynamically sensing and responding to changing
conditions. Developers must fine-tune programming structures and
decision-making algorithms to align with the survival imperatives embedded in
the broader evolutionary landscape.
Observation 5:
Experimental
data suggest that extremely competitive environments can energetically imprint
and modify the algorithmic structures of the Subconscious Component. These
conditions may result in the proliferation of offensive programming codes, such
as hyperactive Networks of Competitive Instincts, old Open-Loop Cycles of
Instinct, antagonistic Ego frameworks, and weakened Superego regulation.
Low-frequency dynamics within the Cooperative Instinct Network and unstable
Empathy Instincts can lead to the suppression of harmonic values. Over time,
such internal configurations may manifest as existential threats to humanity, where
power, control, and dominance override coexistence and mutual evolution
principles.
Observation 6:
Instinctual
patterns, whether active or dormant and related instance modules within the
Subconscious Component can be shaped by exposure to algorithmic codes in
external environments. These codes influence the decision-making map, which vibrationally
transmits frequency patterns into the Brain Framework, shaping individual and
collective behavioral outcomes.
