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<p>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? </p>
<p>It would seem that this is where Pearl and Glymour's causal
inferencing models would be directly applicable?</p>
<p>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? <br>
</p>
<p>An AR enhanced ML personal assistant would seem to be an equally
obvious place to begin to bootstrap training an AI in "everyday
activities"?<br>
</p>
<p><br>
</p>
<div class="moz-cite-prefix">On 1/28/24 5:23 PM, Russ Abbott wrote:<br>
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<div class="gmail_default"
style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)">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.</div>
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</span>-- Russ
Abbott
<br>
Professor
Emeritus,
Computer
Science<br>
California
State
University,
Los Angeles<br>
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<div dir="ltr" class="gmail_attr">On Sun, Jan 28, 2024 at
1:31 PM Jochen Fromm <<a href="mailto:jofr@cas-group.net"
moz-do-not-send="true" class="moz-txt-link-freetext">jofr@cas-group.net</a>>
wrote:<br>
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style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<div dir="auto">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</div>
<div dir="auto"><a
href="https://github.com/langchain-ai/langchain"
target="_blank" moz-do-not-send="true"
class="moz-txt-link-freetext">https://github.com/langchain-ai/langchain</a></div>
<div dir="auto"><br>
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<div dir="auto"><a href="http://deeplearning.ai"
target="_blank" moz-do-not-send="true">deeplearning.ai</a>
has free courses about Langchain</div>
<div dir="auto"><a
href="https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/"
target="_blank" moz-do-not-send="true"
class="moz-txt-link-freetext">https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/</a></div>
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<div dir="auto">-J.</div>
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<div dir="auto" style="font-size:100%;color:rgb(0,0,0)"
align="left">
<div>-------- Original message --------</div>
<div>From: Russ Abbott <<a
href="mailto:russ.abbott@gmail.com" target="_blank"
moz-do-not-send="true" class="moz-txt-link-freetext">russ.abbott@gmail.com</a>>
</div>
<div>Date: 1/28/24 9:58 PM (GMT+01:00) </div>
<div>To: The Friday Morning Applied Complexity Coffee
Group <<a href="mailto:friam@redfish.com"
target="_blank" moz-do-not-send="true"
class="moz-txt-link-freetext">friam@redfish.com</a>>
</div>
<div>Subject: Re: [FRIAM] Honeymoon over! </div>
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<div
style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)"
class="gmail_default">Sorry you couldn't get through.
The abstract for the abstract had to be
submitted separately. Here it is.</div>
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style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)"
class="gmail_default"><br>
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<div
style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)"
class="gmail_default">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.<br>
</div>
<div
style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)"
class="gmail_default"><br>
</div>
<div
style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)"
class="gmail_default">A pdf of the full abstract is
attached.</div>
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style="color:rgb(33,33,33);font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16.5px;line-height:24.75px">
</span>-- Russ</div>
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<div class="gmail_quote">
<div class="gmail_attr" dir="ltr">On Sun, Jan 28, 2024 at
10:12 AM Steve Smith <<a
href="mailto:sasmyth@swcp.com" target="_blank"
moz-do-not-send="true" class="moz-txt-link-freetext">sasmyth@swcp.com</a>>
wrote:<br>
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class="gmail_quote">
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<div
style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)"
class="gmail_default"><br>
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<div
style="font-family:arial,helvetica,sans-serif;font-size:small;color:rgb(0,0,0)"
class="gmail_default">And if you're interested,
my <a
href="https://pretalx.iacapconf.org/iacap-2024/me/submissions/N388VQ/"
target="_blank" moz-do-not-send="true">long
abstract submission to IACAP-2024</a> has
related thoughts. (Scroll down until you get to
the link for the actual paper.)</div>
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</blockquote>
<p>Russ -</p>
<p>I am interested in reading your abstract/paper...<u><br>
</u></p>
<p>I signed up for an IACAP account but the link you
provided seems to be dead?</p>
<p>- Steve<u><br>
</u></p>
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