This popular-press article came through my Google News feed recently
which I thought might be useful to the Journalists/English-Majors on the
list to help understand how LLMs work, etc. When I read it in detail
(forwarded from my TS (TinyScreenPhone) on my LS (Large Screen Laptop))
I found it a bit more detailed and technical than I'd expected, but
nevertheless rewarding and possibly offering some traction to
Journalism/English majors as well as those with a larger investment in
the CS/Math implied.
Decomposing Language Models into Understandable Components
<https://www.anthropic.com/index/decomposing-language-models-into-understandable-components>
and the (more) technical paper behind the article
https://transformer-circuits.pub/2023/monosemantic-features/index.html
<https://transformer-circuits.pub/2023/monosemantic-features/index.html>
Despite having sent a few dogs into vaguely similar scuffles in my
careen(r):
Faceted Ontologies for Pre Incident Indicator Analysis
<https://apps.dtic.mil/sti/tr/pdf/ADA588086.pdf>
SpindleViz <https://www.ehu.eus/ccwintco/uploads/c/c6/HAIS2010_925.pdf>
...
... I admit to finding this both intriguing and well over my head on
casual inspection... the (metaphorical?) keywords that drew me in most
strongly included /Superposition/ and /Thought Vectors/, though they are
(nod to Glen) probably riddled (heaped, overflowing, bursting, bloated
... ) with excess meaning.
https://gabgoh.github.io/ThoughtVectors/
This leads me (surprise!) to an open ended discursive series of thoughts
probably better left for a separate posting (probably rendered in a
semasiographic language like Heptapod B
<https://en.wikipedia.org/wiki/Heptapod_languages#Orthography>).
<must... stop... now... >
- Steve
-. --- - / ...- .- .-.. .. -.. / -- --- .-. ... . / -.-. --- -.. .
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