⏰🐔🥚⏰🥚🐔TICK: chicken lays egg, TOCK: egg grows to chicken

[hyunjimoon]

Definition of manufacture is make (something) on a large scale using machinery. Using ML to generate business hypothesis especially for startups can benefit from manufacturing know-hows.

the reason I'm more inclined to do unsupervised learning at this point is, after supervising gpt from last summer, I think it's time to set it free (Dobby is free gif).
With gpt as a process and business hypothesis as a product, iterating supervised and unsupervised learning is my tick-tock strategy i.e. gibbs sampling learning of process and product (quality of design and conformity) in manufacturing. Initial product that trained the current gpt was your and Scott's hypothesis on NSS and compass.

With gpt as a process and business hypothesis as a product, iterating supervised and unsupervised learning is my tick-tock strategy. Initial product that trained the current gpt was your and Scott's hypothesis on NSS and compass.
supervised learning (update process given product via method with flesh/representation/approximation)

• input: (hypothesis_t, process_t) -> method: comparing angie's classification trained with hypothesis with that of gpt's classification and update prompts train gpt -> output:(hypothesis_t, process_ $t+1$ )
  unsupervised learning (update product given process via method with flesh/representation/approximation)
• input: (hypothesis_t, process_t+1) -> method: based on the statistics from trained gpt (controlled process), angie choosing causally interesting hypothesis (e.g. dynamic weight across NSS, dependence of sequence on strategy, effect of industry atomness) -> output: (hypothesis_t+1, process_t+1)

[hyunjimoon]

Aspect	Apollonian	Dionysian
Representation in Greek mythology	Apollo: god of light, reason, and the arts	Dionysus: god of wine, ecstasy, and wild abandon
Nature	Order, reason, form, individuation	Chaos, emotion, formlessness, unity
In your ML approach	Supervised learning	Unsupervised learning
Role in hypothesis generation	Quality control, conformity to existing knowledge	Creative generation, novel ideas
Manufacturing analogy	Precise machinery, controlled processes	Innovation, unexplored possibilities
Benefit to startups	Structured approach, reliability	Adaptability, breakthrough insights
In your tick-tock strategy	"Tick" phase: refining and structuring	"Tock" phase: exploring and expanding
Relation to GPT training	Fine-tuning GPT on specific hypotheses	Allowing GPT to generate freely
Goal in your research	Ensure hypotheses conform to established principles	Discover unexpected patterns or relationships
Challenge	May limit innovation if overemphasized	Could lead to irrelevant results if unchecked
Balance in your approach	Provides foundation and validation	Introduces novelty and potential breakthroughs

This table illustrates how the Apollonian and Dionysian concepts from Nietzsche's work can be mapped onto various aspects of your research methodology, highlighting the balance you're striving for in your approach to generating business hypotheses using machine learning.