6 Jul
2024
6 Jul
'24
12:27 a.m.
I received the following reply in an offline note:
Anonymous: ChatGPT is BS. It says what is most likely to come next in our use of
language without regard to its truth or falsity. That seems to me to be its primary threat
to us. It can BS so much better than we can, more precisely and more effectively using
statistics with a massive amount of "test data," than we can ever do with our
intuition regarding a relatively meager amount of learning.
That is partly true. LLMs generate a text that is derived by using probabilities derived
from a massive amount of miscellaneous texts of any kind: books, articles, notes,
messages, etc. They have access to a massive amount of true information -- more than any
human could learn in a thousand years. But they also have a massive amount of false,
misleading, or just irrelevant data.
Even worse, they have no methods for determining what is true, false, or irrelevant.
Furthermore, they don't keep track of where the data comes from. That means they
can't use information about the source(s) as a basis for determining reliability.
As I have said repeatedly, whatever LLMs generate is a hypothesis -- I would call it a
guess, but the term BS is just as good, Hypotheses (guesses or BS) can be valuable as
starting points for new ways of thinking. But they need to be tested and evaluated before
they can be trusted.
The idea that LLM-based methods can become more intelligent by using massive amounts of
computation is false. They can generate more kinds of BS, but at an enormous cost in
hardware and in the electricity to run that massive hardware. But without methods of
evaluation, the probability that random mixtures of data are true or useful or worth the
cost of generating them becomes less and less likely.
Conclusion: Without testing and evaluation, the massive amounts of computer hardware and
the electricity to run it is a massive waste of money and resources.
John
6 Jul
6 Jul
12:43 a.m.
New subject: CG list does more checking than Ontolog
I sent the previous message to both Ontolog Forum and CG list.
I received a copy from both, but CG list inserted a warning in the Subject line. They
probably keep a list of dubious words.
My mistake: When I type the P in GPT, my finger went too high, and I hit ")".
Sorry.
John
----------------------------------------
From: "John F Sowa" <sowa(a)bestweb.net>
Sent: 7/5/24 6:28 PM
To: "ontolog-forum(a)googlegroups.com" <ontolog-forum(a)googlegroups.com>om>,
"CG" <cg(a)lists.iccs-conference.org>
Subject: [CG] {Suspected Spam?} ChatG)T is BS
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14 Jul
14 Jul
6:25 p.m.
https://xkcd.com/1838/
On Fri, 5 Jul 2024 at 15:27, John F Sowa <sowa(a)bestweb.net> wrote:
I received the following reply in an offline note:
Anonymous: ChatGPT is BS. It says what is most likely to come next in
our use of language without regard to its truth or falsity. That seems to
me to be its primary threat to us. It can BS so much better than we can,
more precisely and more effectively using statistics with a massive amount
of "test data," than we can ever do with our intuition regarding a
relatively meager amount of learning.
That is partly true. LLMs generate a text that is derived by using
probabilities derived from a massive amount of miscellaneous texts of any
kind: books, articles, notes, messages, etc. They have access to a
massive amount of true information -- more than any human could learn in a
thousand years. But they also have a massive amount of false, misleading,
or just irrelevant data.
Even worse, they have no methods for determining what is true, false, or
irrelevant. Furthermore, they don't keep track of where the data comes
from. That means they can't use information about the source(s) as a basis
for determining reliability.
As I have said repeatedly, whatever LLMs generate is a hypothesis -- I
would call it a guess, but the term BS is just as good, Hypotheses
(guesses or BS) can be valuable as starting points for new ways of
thinking. But they need to be tested and evaluated before they can be
trusted.
The idea that LLM-based methods can become more intelligent by using
massive amounts of computation is false. They can generate more kinds of
BS, but at an enormous cost in hardware and in the electricity to run that
massive hardware. But without methods of evaluation, the probability that
random mixtures of data are true or useful or worth the cost of generating
them becomes less and less likely.
Conclusion: Without testing and evaluation, the massive amounts of
computer hardware and the electricity to run it is a massive waste of money
and resources.
John
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8:40 p.m.
New subject: Machine learning (was ChatGPT is ...
Peter,
Thanks for that link. That cartoon is a precise characterization of how LLMs process
data. It was drawn in the 1990s when linear algebra usually meant something computed with
matrices. LLMs go one step farther by using tensors, but the results are in the same
ballpark (or sewer).
Fundamental principle: any machine learning system must be used with a system for
evaluating or checking the answers. For simple factual questions, a database can be used.
For more complex questions, logical deduction is necessary. For any kind of system,
ontology can detect obvious hallucinations, but ontology by itself is insufficient to
detect incorrect details that happen to be in the correct category.
John
_____________________________________________
From: "Peter Ludemann" <peter.ludemann(a)gmail.com>
Sent: 7/14/24 12:26 PM
https://xkcd.com/1838/
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