DaveW -
I really don't know much of/if anything really about these modern AIs,
beyond what pops up on the myriad popular science/tech feeds that are
part of *my* training set/source. I studied some AI in the 70s/80s and
then "Learning Classifier Systems" and (other) Machine Learning
techniques in the late 90s, and then worked with folks who did Neural
Nets during the early 00s, including trying to help them find patterns
*in* the NN structures to correlate with the function of their NNs and
training sets, etc.
The one thing I would say about what I hear you saying here is that I
don't think these modern learning models, by definition, have neither
syntax *nor* semantics built into them.. they are what I colloquially
(because I'm sure there is a very precise term of art by the same name)
think of or call "model-less" models. At most I think the only models of
language they have explicit in them might be the Alphabet and
conventions about white-space and perhaps punctuation? And very likely
they span *many* languages, not just English or maybe even "Indo European".
I wonder what others know about these things or if there are known good
references?
Perhaps we should just feed thesemaunderings into ChatGPT and it will
sort us out forthwith?!
- SteveS
On 2/7/23 2:57 PM, Prof David West wrote:
I am curious, but not enough to do some hard research to confirm or
deny, but ...
Surface appearances suggest, to me, that the large language model AIs
seem to focus on syntax and statistical word usage derived from those
large datasets.
I do not see any evidence in same of semantics (probably because I am
but a "bear of little brain.")
In contrast, the Cyc project (Douglas Lenat, 1984 - and still out
there as an expensive AI) was all about semantics. The last time I
was, briefly, at MCC, they were just switching from teaching Cyc how
to read newspapers and engage in meaningful conversation about the
news of the day, to teaching it how to read the National Enquirer,
etc. and differentiate between syntactically and literally 'true' news
and the false semantics behind same.
davew
On Tue, Feb 7, 2023, at 11:35 AM, Jochen Fromm wrote:
I was just wondering if our prefrontal cortex areas in the brain
contain a large language model too - but each of them trained on
slightly different datasets. Similar enough to understand each other,
but different enough so that everyone has a unique experience and
point of view o_O
-J.
-------- Original message --------
From: Marcus Daniels <mar...@snoutfarm.com>
Date: 2/6/23 9:39 PM (GMT+01:00)
To: The Friday Morning Applied Complexity Coffee Group
<friam@redfish.com>
Subject: Re: [FRIAM] Datasets as Experience
It depends if it is given boundaries between the datasets. Is it
learning one distribution or two?
*From:* Friam <friam-boun...@redfish.com> *On Behalf Of *Jochen Fromm
*Sent:* Sunday, February 5, 2023 4:38 AM
*To:* The Friday Morning Applied Complexity Coffee Group
<friam@redfish.com>
*Subject:* [FRIAM] Datasets as Experience
Would a CV of a large language model contain all the datasets it has
seen? As adaptive agents of our selfish genes we are all trained on
slightly different datasets. A Spanish speaker is a person trained on
a Spanish dataset. An Italian speaker is a trained on an Italian
dataset, etc. Speakers of different languages are trained on
different datasets, therefore the same sentence is easy for a native
speaker but impossible to understand for those who do not know the
language.
Do all large language models need to be trained on the same datasets?
Or could many large language models be combined to a society of mind
as Marvin Minsky describes it in his book "The society of mind"? Now
that they are able to understand language it seems to be possible
that one large language model replies to the questions from another.
And we would even be able to understand the conversations.
-J.
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