Machines

Machines#

Business | Feeding the machine#

What are the threats to the $1tn artificial-intelligence boom?#

A fast-growing supply chain is at risk of over-extending#

                1. f(t)
                       \
            2. S(t) -> 4. y:h'f(t)=0;t(X'X).X'Y -> 5. b -> 6. SV'
                       /
                       3. h(t)

Character arc#

ii. Departure from path of forebears by the fledgling#

  • \(f(t)\) The harmonic series implied when a single root note is sounded

  • \(S(t)\) Unifying ii-V7-I arc (variations by mode, qualities, relatives is seperate issue)

  • \(h(t)\) Landscape of travelled & less travelled paths 31

V7. Struggles breed a form of collective idealism#

  • \((X'X)^T \cdot X'Y\) Collective Unconscious that allows for for Dhatemwa Kulala, Dhayawukana Emibala

I. Returning home to the divinity of self#

  • \(\beta\) Conceptualized as a hierarchy or rank-order of “weights” and “tensions”:

    • qualities: melody < dyad < triad

    • extensions: < 7th < 9th < 11th < 13th

    • alterations: b, #

  • \(SV'\)

    • individual: how many of the “barriers have been encountered and overcome?

    • change of mode, quality, relative?

    • diatonic: min7b5, dom7(#9, b9, b13), dim, etc

Personalized service#

The “quality” of AI is linked to the number of parameters needed to accurately predict the needs of individual users. With 10 billion humans, 10,000 genetic loci, each with 2-100 variants, and with functional interactions amongst them and environement, you can do the math to show that there’s no upper limit to the quality of AI.

What is required for the very best-in-class is a self-updating, self-improving, self-learning system that gets better and better, with better customization for the end-user.

Quick deployment and transportation do not seem to be limiting factors in this landscape of affairs. Its the sheer amount of “compute” and the energy it will demand. We are less inclined to bothere ourselves with the quality of the data on which it is trained, since simulation is a viable alternative, with increased quality of simulations over time

This framework captures concepts like average-joe (base-case), national-character (group), stereotype (subgroup), and individualized (accurate). Trumps inaccuracies about Kamala are are failure to see that “accurate representation” is the sum of all subgroups one belongs to. Not surprising from an average Joe

  • Base-case

    • \(f(t)\)

    • \(S(t)\)

    • \(h(t)\)

  • Group \((X'X)^T \cdot X'Y\)

  • Individual

    • Subgroup \(\beta\)

    • Personalized \(SV'\)

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