Hi, Frederik. Nobody received your posts intended for peirce-l, because your 
email address differs from how it appears at the peirce-l server.  I just 
approved the emails for distribution and took the liberty of changing the 
server's record of your old email address to your current one.
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co-manager and moderator, PEIRCE-L.

On 12/18/2024 5:10 AM, Frederik Stjernfelt wrote:
Dear Gary R –
Did you receive my below message? I was informed that deliverance to peirce-l  was 
belated and my posting was returned by 
[email protected]<mailto:[email protected]>
Best
F


Dear Mike, Gary, Tuezuen, list  –

This is a great idea. This would also explain why LLMs “hallucinate” so much as 
they do, as abduction is neither necessary (like deduction) nor probable (like 
induction). Peirce, of course, stresses that abduction is indeed the source of 
new ideas but that it offers no assurance of their truth which has to be 
established by ensuing investigation using de- and inductions.

I have only experimented with the free versions of ChatGPT and they are, 
indeed, highly error-prone.
I tend to prefer the program Perplexity which is connected to a search engine 
which it utilizes to provide references to where it scraped its information.

Best
Frederik

Frederik Stjernfelt: Sheets, Diagrams, and Realism in Peirce – De Gruyter 2022

   *                         “Peirce as a Philosopher of AI”, in Olteanu et 
al.: Philosophy of AI, forthcoming


Fra: <[email protected]> på vegne af Tuezuen Alican 
<[email protected]>
Svar til: Tuezuen Alican <[email protected]>
Dato: onsdag den 18. december 2024 kl. 08.43
Til: Mike Bergman <[email protected]>, Gary Richmond <[email protected]>, 
Peirce-L <[email protected]>
Emne: RE: [PEIRCE-L] AI and abduction

Dear Mike and Gary,

If I’m not mistaken, John Sowa already utilizes LLMs this way. He argues that 
LLMs are great for abductive conclusions, and later, with an Ontology, he 
checks whether that “hypothesis” is true or not. At least, that’s my 
interpretation of his work.
@Mike Bergman<mailto:[email protected]>, sorry for the duplication; I pressed 
reply instead of replying to everyone.
Best Regards,
Dipl.-Ing. Alican Tüzün, BSc
PhD Candidate

University of Applied Sciences Upper Austria
Josef Ressel Centre for Data-Driven Business Model Innovation
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Web: https://coe-sp.fh-ooe.at/
[cid:[email protected]]




From: [email protected] <[email protected]> On 
Behalf Of Mike Bergman
Sent: Wednesday, 18 December 2024 02:52
To: Gary Richmond <[email protected]>; Peirce-L <[email protected]>
Subject: Re: [PEIRCE-L] AI and abduction

You don't often get email from [email protected]<mailto:[email protected]>. Learn 
why this is important<https://aka.ms/LearnAboutSenderIdentification>

Hi Gary,

This is a topic near and dear to me, and one I am very actively investigating 
(and using) personally (mostly with ChatGPT 4-o1, but also the latest version 
of Grok). My first observation, granted based on my sample of one, is that 
abductive reasoning in a Peircean sense is lacking with current LLMs (large 
language models), as is true for all general ML or AI approaches. Machine 
learning and deep learning have been mostly an inductive process IMO. A major 
gap I have seen for quite some time has been the lack of abductive reasoning in 
most ML and AI activities of recent vintage.

This assertion is most evident in the lack of "new" hypothesis generation by 
these systems, the critical discriminator that you correctly point out from Peirce. One 
can prompt these new chat AIs with new hypotheses, and in that form, they are very 
helpful and useful. It is for these reasons that I tend to treat current chat AIs as 
dedicated research assistants: able to provide very useful background legwork, including 
some answers that stimulate further questions and thoughts, often in a rapid fire 
give-and-take manner, but ones that are not creative in and of themselves aside from 
making some non-evident connections.

I believe that better matching of current chat AIs with Peirce's thinking (esp abductive reasoning as he 
defined) is a particularly rich vein for next generation stuff. Lastly, my own personal view is that the 
current state of the art is not "dangerous", but we are also seeing very rapid increases of what Ilya 
Sutskever<https://en.wikipedia.org/wiki/Ilya_Sutskever> calls "superintelligence", the speed of 
which is pretty breathtaking. We may be close to tapping out on this current phase with most Internet content 
already captured for training, but like with LLMs, there are certainly new innovations not yet foreseen that 
may continue to maintain this Moore's law<https://en.wikipedia.org/wiki/Moore%27s_law>-like pace of 
improvements.

Best, Mike
On 12/17/2024 6:00 PM, Gary Richmond wrote:

List,

In a brief article, "How Does A.I. Think? Here’s One Theory" in the New York Times today, 
Peter Coy, after noting that "Computer scientists are continually surprised by the creativity 
displayed by new generations of A.I.," comments  on one hypothesis that might help explain 
that 'creativity', namely, that AI is using abduction in its machine reasoning.  He writes:

One hypothesis for how large language models such as o1 think is that they use 
what logicians call abduction, or abductive reasoning. Deduction is reasoning 
from general laws to specific conclusions. Induction is the opposite, reasoning 
from the specific to the general.

Abduction isn’t as well known, but it’s common in daily life, not to mention 
possibly inside A.I. It’s inferring the most likely explanation for a given 
observation. Unlike deduction, which is a straightforward procedure, and 
induction, which can be purely statistical, abduction requires creativity.

The planet Neptune was discovered through abductive reasoning, when two 
astronomers independently hypothesized that its existence was the most likely 
explanation for perturbations in the orbit of its inner neighbor, Uranus. 
Abduction is also the thought process jurors often use when they decide if a 
defendant is guilty beyond a reasonable doubt.

Yet Peirce argues in the 1903 Lectures on Pragmatism that only abduction "introduces 
any new idea" into a scientific inquiry:

" Abduction is the process of forming an explanatory hypothesis. It is the only 
logical operation which introduces any new idea; for induction does nothing but determine 
a value, and deduction merely evolves the necessary consequences of a pure 
hypothesis."

I had always thought of abduction as the unique domain of the individual 
scientist, the creative genius (say, Newton or Einstein) who, fully versed in 
the most important relevant findings in his field, retroductively connects 
those pieces of scientific information to posit a testable hypothesis 
concerning an unresolved question in science.

But it makes sense that an AI program employing large data bases might indeed 
be able to 'scan' those huge, multitudinous bases, connect the salient 
information, and posit an hypothesis (or some other abductive idea).

Any thoughts on this? For example: Is it potentially a valuable feature and 
power of AI and, thus, for us (the use of AI in medical research would tend to 
support this view)? Is it a potential danger to us (some AI programs have been 
seen to lie, to 'hide' some findings, etc.; might this get out of control)? If 
AI can create testable hypotheses, is the role of the 'creative' scientist 
jeopardized?

Best,'

Gary R




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