[Avodah] LLM and AI

[Micha Berger]

Dear R Dr Bryode (Cc: Avodah),

Over Shabbos, I read your article about getting ChatGPT to discuss
whether a kohein in a same-sex marriage may go up to duchen.

(Avodah crowd: see
<https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4554572_code5079269.pdf>.)

I don't know the strength of your background in Large Language Models
in general or GPTs in particular. I am now researching their use at my
job. Here is my impressions as a programmer who has put in a couple of
months writing clients for a GPT.

Hopefully, much of it is material you already know, and hopefull my
attempt to fill in information I didn't see in your essay won't come
across as talking down to you.


First, a large language model isn't an AI in the sense of having a good
representation of what it is talking about. Words are assigned strings of
numbers, vectors, that do correlate to the word's usage. So that king minus
male will bring you to a similar vector as queen minus female. And GPT
has a system for using context to dintiguish the uses of the word "flies"
in
    Time flies like an arrow, but
    fruit flies like bananas.

Still, it doesn't so much reason as copy patterns, meta-patterns, meta-
meta-patterns, etc... that match the texts it was trained on. Kind of
like a mega-advanced version of Google's search window's ability to
guess the rest of your sentence.

It is incredible that it can simulate intelligence by modeling language.
There is something here about Unqelus's "ruach memalela", or all
the rishonim who classigy the human soul as "medaber" (or the Greek
Philosophers who also did so), but I am not sure what it is.

In any case, I would say that a LLM simulates intelligence, and calling
it SI would be more honest. (If harder to fund.) The kind of AI where
you would have to wonder if you should say "that" or "whom" is now
re-branded Artificial General Intelligence (AGI). Me, I think "SI" vs
"AI" would be more honest.

In that a LLM isn't modeling concepts, it is doing something else which
results in something we think of as intelligent. And it certainly cannot
stay up in the dorm room at night discussing how one knows whether its
mental image of red is my mental image of blue, we just use the same word
"red" because we are looking at the same thing. And we don't even know
homw much of SI may just be the same pareidolia tendency that turns two
headlights and a bumper that curves up at the ends into a face.

"Training" here means tuning the constants in billions of equations until
the output succeeds in "predicting" the training set. (Because GPTs are
self-training. Some LLMs are trained using a test that an output could
pass or fail.)

So that's the first input to a GPT -- the training data it came with. And
for ChatGPT, that is the Common Crawl data set, is a publicly available
collection of billions of web pages pre-chewed into an easy to use format
by an educational non-profit (a 501(c)3 charity).

The second possible data set is a database prepared like the training data,
but after it was trained. For example, at the hedge fund I am working for,
there are people trying to do this with broker reports and analyses so that 
a chatbot can answer questions about how various companies and industries
are doing.

For this you need the programmer interface, not the web dialog page most
people are using ChatGPT with.

This material isn't stored on the GPT itself. It's your database, that it
is searching repeatedly. Kind of like giving a human being a library, if
they could read really really quickly.

And it doesn't have a token limit.

Third is what you did, telling it things in prompts. This information
isn't stored at all. In fact, when you continue a conversation, the web
page is sending your new text after sending the entire discussion so
far. So that each response requires the entirety as its input; it isn't
even saved in the GPT for the length of the discussion!


In terms of making an AI poseiq...

First I would look at issues like the pro-forma requirements of a poseiq.
If a non-Jew cannot be a poseiq, can a LLM? Discussions of women as
posqos would be very related.

But what if it advises without being formal pesaq? Like R Henkin's model
for Yoatzot? (Assuming I understood him when he joined the discussion on
Avodah.) Such as requiring a poseiq for QA and it's they who are taking
responsibility for the decision.

Second, I would look at posqim who take Siyata diShmaya as a part of pesaq
seriously. Those who take theoretical questions as less authoritative
than lemaaseh ones. One could say a GPT doesn't get such Siyata diShmaya,
or one could say that given its indeterminism (if the "temperature"
setting is above 0.0) it could be an ideal vehicle for such siyata!

Third, I would look at the three ways of giving a GPT information:

Information given in the prompts or even in an external database won't
change how it puts one word after the other. You are just giving it
a "what". The "how" is only during training.

So, if we wanted to feed Bar Ilan into a GPT in a database, it would still
be emulating someone who doesn't think like a poseiq but does have access
to a database.

If we want to simulate someone who was meshameish talmidei chakhamim
the way the gemara expects, we would need to train a GPT from scratch
on teshuvos and some sefarim of lomdus and sevara. (PoseiqGPT would
have more Bar Ilan and Otzar, and less Wikipedia.)

But I still think one would need a Jewish adult, who has the requisite
knowing-how-to-think (and not just a simulation) to review and approve
the results for it to be a real "pesaq". And IMHO, a male one.

Tir'u baTov!
-Micha

-- 
Micha Berger                 When memories exceed dreams,
http://www.aishdas.org/asp   The end is near.
Author: Widen Your Tent                      - Rav Moshe Sherer
- https://amzn.to/2JRxnDF