[ot][crazy][draft] Fractured

Karl gmkarl at gmail.com
Sat Aug 14 16:15:19 PDT 2021

Bobby jacks into the digital mindwaves.  It's breakfast.

They used to have nicotine, alcohol, caffeine, and sugar for breakfast.
That was decades ago.

They flip their tongue a little to experience the doses of nicotine,
alcohol, caffeine, and sugar they expect and resumed their work designing a
new form of interplanetary travel based on propulsion systems that
redesigned themselves for nearby resources.

They had a potted plant in their window with a magical spirit.  The magical
spirit of their potted plant watched them as they sat at their breakfast
table with their muscles twitching, darting their eyes and fingers around
the empty space in front of them as they engaged an augmented reality.

The magical spirit of the potted plant didn't just watch them.  She was
also watching the birds wake up and celebrate the morning outdoors, the
magical spirits of the wind and rain race their fronts of pressure and
humidity, a number of communities of insects, bacteria, and fungus she was
in symbiosis with, and also Bobby's pet dog, Hypercookie, who was playing
with a robot in the other room.

[I ran into a cognitive issue here and wrote the issue down:
Bobby was debugging an error in his model for environments needing new
engine designs.  It kept failing in
I'm trying to write that the model is for choosing the general patterns for
newly created designs based on patterns of newly encountered environments.
My brain is refusing to simplify this expression for me.  In the story,
they have already finished the nn-based hyperculture that businesses are
planning.  The bug was going to be with a subset of these pattern groups,
which Bobby had already broken into a roughly finite set.  It was going to
pick sets that were clearly wrong, often avoiding a subset of the correct
sets in a wieghted way, and it was going to be due to a training error that
stemmed from the architecture of the model combined with randomness in the
data. He would go in and resolve the error by understanding the cause and
manually redirecting the impact of the training data.  This was going to be
all simple and concise and only partly described.
][why doesn't he use an ai that understands the issue?][the space of
meaning here is outside the distribution of commonly available nn models
for him, and it would fix it wrong.]
[what are your thoughts on changing how nn models are so that they can
handle issues like this?]
[it feels like something doesn't want that to happen, that it fears
somebody would use the information, even accidentally, to do something
horrible.  but.  I'll try to think on it a little.  ...]
[no it's ok you don't have to]
[well I think about this a lot I just have to remember it.  Ummm .... nn
models seem to function really slowly.  I might consider training a
hierarchy of them around choosing architectures and mapping learning rates
to individual weights, as a research focus.  Assuming that then made fast
training, I might make a second one around designing architectural
information for new environments.  I guess I'm expecting this to eventually
learn it needs logical reasons and write code / design architectures around
investigating the rules that cause things and using those directly.  I
think there's a lot of existing research around that, maybe i'd even make
it the core and just use nn models to handle fluffy stuff quickly?  fluffy
being things where guessing based on lots of things less complex than
reality is helpful, I guess.  Just ideas.  No real experience here. I dunno
if that is helpful or ridicuous, don't really know how to tell]
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