Yeah, the Markov blankets idea is a mechanistic explanation of a boundary. So
it's good. But Marcus' mention(s) of different/interesting distributions is
also a kind of boundary. Cody's recent post sent me (yet again, and again
[sigh]) down a rabbit hole trying to understand WTF p-values actually are. And
that reminds me of a great rant by Angela Collier about violin plots:
https://youtu.be/_0QMKFzW9fw?si=vbQ35tC47js1v31d
Anyway, I'm sure it's just because I'm not smart enough. But data fusion is
hard. A centralized function that transforms, say, 8000 tokens into, say, 2000
tokens, even if it's fairly well characterized by some math and an xAI model,
is already beyond me. But conflate lots of (universal) functions like human
brains (with some parts genetic memory - if not centralized, then shared
training - and some parts finely tuned by experience) and you get what looks to
me like a combinatorial explosion.
The only way I can see to *fit* the tails of the vast variety of distributions ineffibly
encapsulated in various writers, movie makers, artists, and scientists is through that
combinatorial explosion. Anecdotally, when a music nerd says tune X is good and tune Y is
bad, that classification is fundamentally different from when a music producer says tune
P is good and tune Q is bad. In the LLMs, we get them to do that sort of thing with the
prompt, basically saying "take this huge data table approximately encoded inside you
and hone in on the ontology a music producer would use rather than the ontology a music
nerd would use". But that we can do that means, I think, the *space* is convex. And
I doubt the space of AGI (whatever that actually means) is convex.
There is no prompt I can give the music nerd such that she will generate the
output of the music producer ... or worse, there is no prompt I can give the
crypto-bro such that he'll produce the output of a Buddhist monk.
On 3/20/25 8:48 AM, steve smith wrote:
Glen -
very insightful observation with interesting bookends, esp Corlin's internet
detox journal
The implications are sort of ringing in the echo chamber of my mind with
thoughts about power-law distributed process-structures. I can't really
render/distill this down to anything vaguely coherent, but I am hoping there
will be more discussion here.
My latest conceptual darling is "Markov Blankets" so true to form I'm
force-fitting the ideas onto what you say here about the value/necessity of some enforced
partitioning to maintain or elaborate compelexity?
- Steve
On 3/20/25 8:26 AM, glen wrote:
https://www.aaronrosspowell.com/p/why-tech-bros-overestimate-ai-s-creative-abilities
So, if we accept the assumption that the stuff way out in the tails (good
writing, good cinematography, good science, etc.) is somehow a function of the
stuff in the middle of the distribution, what happens when we replace the
generators of the mediocre stuff with AI? What happens to the generators of the
stuff in the tails? What *is* the functional relationship between the
generators in the middle and the generators in the tail(s)? (Note I'm talking
about processes more than artifacts.)
I think the relationship is diversity. And a critical part of the assignation
to the categories (incl. mediocre and great) depends on applying lenses (or
baffles) to the diverse, percolating stew. And that includes those lenses being
held by the components inside the stew. So not merely a diversity of
generators, but a diversity of lens sizes and types.
What I don't yet see in LLMs is that diversity of diverse generators. And, yes, it's more
than "multimodal". It's born of scope limiting. If we can limit the scopes
(experiences, fine-tunings, biasings) of a diverse population of otherwise expansively
trained (universal) LLMs, then we might be able to fit the tails as well as the middles.
This implies that while, yes, LLMs demonstrate a universality ratchet we
haven't seen before, in order to fit the tails, we need autonomy, agency,
embodiment, etc. And that implies moving them *off* the cloud/net, out of the
data centers. Similar to these diary entries of one of my favorite monks:
https://world.hey.com/corlin/burnout-an-internet-fast-5517ccaa
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