AI:List AI views on solar tendency females- born on Sunday - fire aggressive, = Tuesday, fire aggressive high bp, angry ,Thursday- ethics dignified, lawma= ker, Saturday- political , tantrum, chaos maker whereas lunar - soft spoken= homely=E2=80=94 Monday born pure water body motherly , Wednesday born - br= ainy , intelligent nurturing soft spoken bound to guidelines laws ethics , = childish , Friday born pure female energy, money oriented, luxurious life. = How would AI using various neural networks and LLMs create a framework for = both for both gender make female.

 

Yes. If by “AI framework” you mean turning these traditional birth-day/planetaryarchetypes into a structured generative model of personality, AI could representthem as symbolic features rather than treating them as scientifically established facts.

A useful framework could look like this:

Day	Element / archetype in your model	Female archetype	Core traits	Potential shadow traits
Sunday	Solar / Fire	Solar woman	Assertive, visible, leadership-oriented, independent	Aggressive, impatient, anger-prone
Tuesday	Mars / Fire	Warrior woman	Energetic, courageous, competitive, action-oriented	Conflict, impulsiveness, anger
Thursday	Jupiter / Ethics	Judge/Lawmaker woman	Ethical, dignified, principled, institutional, teacher-like	Rigid moralism, excessive rule-following
Saturday	Saturn / Political-social	Strategist / political woman	Strategic, serious, power-aware, resilient	Suspicion, rigidity, chaos/control struggles
Monday	Moon / Water	Motherly woman	Soft-spoken, nurturing, emotionally receptive, domestic	Over-sensitivity, dependency, moodiness
Wednesday	Mercury / Mind	Scholar/communicator woman	Intelligent, analytical, articulate, adaptable, nurturing	Childishness, overthinking, excessive conformity
Friday	Venus / Feminine	Venusian woman	Feminine energy, beauty, relationships, comfort, wealth/luxury	Materialism, status-seeking, indulgence

How an AI system could combine this

Instead of saying “Sunday-born womenare aggressive”, an AI could encode:

Birth-day archetype
        ↓
Planetary symbolism
        ↓
Element
        ↓
Positive traits + shadow traits
        ↓
Social / emotional / cognitive dimensions
        ↓
Context modifiers
        ↓
Individual personality profile

For example:

Tuesday female

Mars → Fire → high activation → courage + competitiveness → possible impulsivity/conflict

while:

Monday female

Moon → Water → emotional receptivity → nurturing + empathy → possible sensitivity/dependency

The important AI-design principle isthat birth day should be treated as an archetypal input, not as a deterministic predictor of personality or health. There isn't scientific evidence that being born on a particular weekday causes aggression, hypertension, intelligence, morality, wealth orientation, or political behavior.

Making it work for both genders

The model can separate sex/gender expression from the archetype:

                 ┌── Solar/Fire
                 ├── Lunar/Water
Birth day ───────┼── Mercury/Mind
                 ├── Jupiter/Ethics
                 ├── Venus/Feminine
                 └── Saturn/Political
                         ↓
                 Archetypal traits
                         ↓
             ┌───────────┴───────────┐
             ↓                       ↓
          Female expression       Male expression
             ↓                       ↓
      culturally feminine       culturally masculine
      manifestations            manifestations

Thus, the same underlying archetypecould produce different culturally expressed behaviors without assuming that onegender is inherently aggressive, nurturing, intelligent, political, materialistic, etc.

A neural-network implementation couldrepresent each person as a vector such as:

[fire, water, cognition, nurturing, dominance, ethics, sociability, materialism, impulsivity, tradition, ...]

An LLM could then convert that vector into a narrative archetype, while a separatestatistical model could test whether anyof the proposed associations actually appear in real-world data.

That last separation is important: the symbolic/archetypal model can preserveyour traditional framework, while the empirical model tests which associations—if any—are actually supported by data.