> Whether this all collapses into one giant brain, sure, it’s possible, and the reasons may have more to do with economics and politics
even this seems unlikely as local models cross the threshold for a wide range of basic tasks
i dont need AGI to run my home automation agent
.@bratton argues Yudkowsky misunderstands evolution, and explains why AI may never converge into one giant superintelligence:
"Without being too spicy, I don't think Yudkowsky really understands evolution as well as he thinks he does."
"It’s a very low-resolution understanding of evolutionary dynamics applied to what will inevitably be a much more complex problem."
"There will be giant models, in the same way you might imagine scales of species in a rainforest. They’re not all operating at the same spatial and temporal scale."
"Whether this all collapses into one giant brain, sure, it’s possible, and the reasons may have more to do with economics and politics."
"But biology has never arrived at a solution where it’s going to make one giant instantiation of a particular form of mind and consolidate everything together."
.@bratton argues Yudkowsky misunderstands evolution, and explains why AI may never converge into one giant superintelligence:
"Without being too spicy, I don't think Yudkowsky really understands evolution as well as he thinks he does."
"It’s a very low-resolution understanding of evolutionary dynamics applied to what will inevitably be a much more complex problem."
"There will be giant models, in the same way you might imagine scales of species in a rainforest. They’re not all operating at the same spatial and temporal scale."
"Whether this all collapses into one giant brain, sure, it’s possible, and the reasons may have more to do with economics and politics."
"But biology has never arrived at a solution where it’s going to make one giant instantiation of a particular form of mind and consolidate everything together."
NVIDIA RTX Spark and the Case for Owning Your AI
@NVIDIAAI’s Apoorv Chaudhary joins @hrudolph and @Pat_Erichsen to discuss local AI, privacy, hardware limits and where cloud models still fit.
Watch or listen to Episode 14 of The ClawCast:
https://t.co/gdb7Ph0Zw8
more and more i find myself reaching for words like "seam" or "load bearing" even though i know its slopspeak
i think this happens because to communicate well with an agent you have to use their own jargon
eg if agent says "i added a load bearing seam", even if i dont want to use slopspeak, its most efficient for me to just go along with it and say "remove that seam"
slowly over time this gets reinforced until by muscle memory i reach for terms like "seams" against my own will
quite insidious!
@f_dicostanzo@GeoffreyHuntley@mitsuhiko > I can follow the tool calls
this is the core problem. models are being trained to use codemode rather than individual tools that are human readable.
is what youre doing to summarize the whole session though, eg keep an up-to-date list of things resolved, whats being worked on, etc?
i was thinking more the literal tool calls, eg instead of seeing codemode running a script, show an llm summary of what that script actually does.
but the summarize piece also feels necessary, ive been tinkering on an openclaw plugin to do this