[FRIAM] Honeymoon over!

[Steve Smith]

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 <jofr at 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.abbott at gmail.com>
>     Date: 1/28/24 9:58 PM (GMT+01:00)
>     To: The Friday Morning Applied Complexity Coffee Group
>     <friam at 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 <sasmyth at 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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