With integration of LLMs (and other ML) into AR and personal assistant
tech riding around on early adopters "shoulders", I would expect these
percieve-reason-act structures to be "in training" essentially learning
how to emulate (and extrapolate) their user's/wearer's/familiar's
decision processes?
It would seem that this is where Pearl and Glymour's causal inferencing
models would be directly applicable?
I read somewhere that Tesla's data gathered from their Self Driving
features represents a somewhat unique data-set due to these
percieve/reason/act implications. Does (less than full) self-driving
car tech not represent a real-life training opportunity?
An AR enhanced ML personal assistant would seem to be an equally obvious
place to begin to bootstrap training an AI in "everyday activities"?
On 1/28/24 5:23 PM, Russ Abbott wrote:
Thanks, Jochen, I know about LangChain. I'm not claiming that LLMs
cannot be used as elements of larger computations, just that LLMs on
their own are quite limited. I'll make that point in the talk if the
abstract is accepted.
_
_
__-- Russ Abbott
Professor Emeritus, Computer Science
California State University, Los Angeles
On Sun, Jan 28, 2024 at 1:31 PM Jochen Fromm <j...@cas-group.net> wrote:
Langchain is an agent framework started by Harrison Chase. A
Langchain agent uses LLMs to reason in a perceive-reason-act
cycle. One could argue that Langchain agents are able to think,
and we are even able to watch them thinking
https://github.com/langchain-ai/langchain
deeplearning.ai <http://deeplearning.ai> has free courses about
Langchain
https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/
-J.
-------- Original message --------
From: Russ Abbott <russ.abb...@gmail.com>
Date: 1/28/24 9:58 PM (GMT+01:00)
To: The Friday Morning Applied Complexity Coffee Group
<friam@redfish.com>
Subject: Re: [FRIAM] Honeymoon over!
Sorry you couldn't get through. The abstract for the abstract had
to be submitted separately. Here it is.
LLMs are strikingly good at generating text: their output is
syntactically correct, coherent, and plausible. They seem capable
of following instructions and of carrying out meaningful
conversations. LLMs achieve these results by using transformers to
produce text based on complex patterns in their training data. But
powerful though they are, transformers have nothing to do with
reasoning. LLMs have no means to build or to reason from internal
models; they cannot backtrack or perform exploratory search; they
cannot perform after-the-fact analysis; and they cannot diagnose
and correct errors. More generally, LLMs cannot formulate, apply,
or correct strategies or heuristics. In short, LLMs are not a step
away from Artificial General Intelligence.
A pdf of the full abstract is attached.
_
_
__-- Russ
On Sun, Jan 28, 2024 at 10:12 AM Steve Smith <sasm...@swcp.com> wrote:
And if you're interested, my long abstract submission to
IACAP-2024
<https://pretalx.iacapconf.org/iacap-2024/me/submissions/N388VQ/>
has related thoughts. (Scroll down until you get to the link
for the actual paper.)
Russ -
I am interested in reading your abstract/paper..._
_
I signed up for an IACAP account but the link you provided
seems to be dead?
- Steve_
_
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