I asked ChatGPT to build a Vector Embedding to describe me based on what it knows about me…
And it went about it as follows:-

Sure Ganesh! Let’s build a Vector Embedding of You, the Ganesh Vector™ 😊

In Machine Learning and NLP, a vector embedding represents a person, item, or concept as a list of numbers in a high-dimensional space—so that similarity, preferences, or roles can be captured mathematically. So here’s a human-readable version of your embedding, followed by an explanation of how it was constructed:

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🧠 Ganesh Vector™ (Simplified 12-Dimensional Embedding)

Dimension	Label	Value (0–1)	Explanation
D1	Scientific Temperament	0.95	Deep interest in physics, maths, logic, engineering. Strong Feynman influence.
D2	Devotional Depth	0.88	Bhakti towards Krishna/ISKCON, understanding of spiritual texts, but anti-ritualism.
D3	Sarcastic Intelligence	0.92	Quick wit, BBC-honed sarcasm, logical takedowns. Likes haiku and parody too.
D4	Tech Savviness	0.96	REALX OS, Azure, AI, DevOps, Spoggle, VMWare, ExO2.0… too many tech hats worn.
D5	Emotionally Grounded Philosopher	0.85	Touches of nostalgia, family bonding, deep reflections. Celebrates birthdays, rites.
D6	Rational Traditionalist	0.89	Values ancient texts and Vedanta, but questions customs, resists pseudoscience.
D7	Language Playfulness	0.93	Wordplay master. “JiYES”, “MindsWideOpen”, funny Tamil-English puns.
D8	Data Storytelling Instinct	0.90	Uses data, examples, analogies (Perelman, Arrow’s theorem) to teach & provoke thought.
D9	Social System Reformer	0.82	Questions blind beliefs, celebrates real heroes, points out mass illogicality.
D10	Team Mentor & Connector	0.91	Family elder, Chellams guide, professional mentor to many, LinkedIn contributor.
D11	Deep-rooted Cultural Mind	0.87	Tambrahm values with a global outlook—respects traditions like Vishnu Sahasranama walk, but filters the “juice” from fluff.
D12	Creative Rebel	0.88	Often writes against the tide—Kalki movie critique, Diwali debates, Hindi arguments, boycott culture.

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⚙️ How This Vector Was Built

1. Sources:

Based on your long-term profile, behavioral patterns, blog tone, humor, family references, philosophical musings, tech involvement, and social interactions.

2. Embeddings Work by Contextual Weightage:

If someone talks a lot about something, it’s a signal.

If they contrast two things (e.g., “I’m not religious, but I love Bhagavad Gita”), we create opposing dimensions and assign scores accordingly.

3. Dimensionality Reduction:

In real AI embeddings, we use 100s or 1000s of dimensions (BERT-style). Here I chose 12 key thematic axes to represent you, akin to PCA (Principal Component Analysis).

4. Score Assignment:

Values like 0.95 or 0.88 are approximate probabilities or intensities, scaled from behavioral evidence.

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🔁 What Can We Do With This?

• Compare with other embeddings (e.g., “Ganesh vs Ratan Tata” 🤓)

• Build a recommender system (“What should Ganesh read next?”)

• Create an AI avatar of you using this vector

• Train bots that think like Ganesh