LLMs are universal interpolators. Pretty much the same thing as the 
interconnected breadth of knowledge, it seems to me. 

From: Friam <friam-boun...@redfish.com> on behalf of Prof David West 
<profw...@fastmail.fm>
Date: Thursday, December 12, 2024 at 11:43 AM
To: friam@redfish.com <friam@redfish.com>
Subject: Re: [FRIAM] Ramsification and Semantic Indeterminacy 


1) Humans can’t easily travel to new planets due to radiation and hostile 
environments. ["Conan The Bacterium" might make this more possible.] 

2) Our appetite for energy is vast [Microsoft is recommissioning Three Mile 
Island to feed an AI server farm, both Google and Amazon are exploring "pocket 
nukes" for same purpose. Power demand is projected to be pretty linear until 
you factor in AI and then the curve becomes exponential.] and our decadence 
unbounded. A rising standard of living for all humans [Never Happen—only the 1% 
will survive and they are not numerous enough to consume all that they own.] 
will accelerate this due to increased demands for fossil fuels. 


3) At least in the United States, our education system is not serving the whole 
of the population effectively, leading to the election of people that make our 
problems worse. [Personally, I think the education system is intentionally 
designed to create tractable proles easily distracted by promises of 'free 
stuff' from the Guvmnt] 



4) We can’t cooperate to solve or even identify real problems. [As Neil 
DeGrasse Tyson says, "Science can only solve the simple problems, not complex 
ones," so it is unlikely that AIs as a product of science will be any more 
capable.] 

5) AI seems to be successfully harvesting human knowledge and extending it, 
e.g. AlphaFold. [Extending it in depth within narrow context, yes. "More and 
more about less and less." but it is interconnected breadth of knowledge that 
would be really useful.] 



davew 








On Thu, Dec 12, 2024, at 12:24 PM, Marcus Daniels wrote: 

Seems there are good reasons to replace humans.

1) Humans can’t easily travel to new planets due to radiation and hostile 
environments.
2) Our appetite for energy is vast and our decadence unbounded. A rising 
standard of living for all humans will accelerate this due to increased demands 
for fossil fuels.
3) At least in the United States, our education system is not serving the whole 
of the population effectively, leading to the election of people that make our 
problems worse. 
4) We can’t cooperate to solve or even identify real problems.
5) AI seems to be successfully harvesting human knowledge and extending it, 
e.g. AlphaFold. 

From: Friam <friam-boun...@redfish.com> on behalf of Prof David West 
<profw...@fastmail.fm>
Date: Thursday, December 12, 2024 at 10:16 AM
To: friam@redfish.com <friam@redfish.com>
Subject: Re: [FRIAM] Ramsification and Semantic Indeterminacy 

Thank you Roger. Fascinating read. Moravec has evolved considerably from his 
Mind Children (1990) days when he predicted we would all be "uploaded" to robot 
bodies by now. 



The University where I started my teaching career, St. Thomas in St. Paul MN, 
recently announced a new center https://www.stthomas.edu/e 
<https://www.stthomas.edu/e> AI for the Common Good. I just wrote them a 20 
page missive that strangely paralleled the Moravec article as a caution and 
with suggestions for where they might find success. 



My very first professional publication was a two part article in AI Magazine 
(then the journal of record for AI research). I did a lot of work with neural 
nets and was heavily involved, academically/researching and 
professionally/building, Expert Systems—the previous explosion of irrational 
exuberance about AI. My Ph.D. dissertation included a model of cognition 
derived from the topographic metaphor explaining neural nets and incorporating 
culture as a force helping shape the topography of the net. vTAO, virtual 
Topographic Adaptive Organism. 



Moravec notes, that within the AI community, Winograd was a leader in 
suggesting that AI should be used to augment humans and not replace them. It 
should be noted that others have long advocated computing/computers should have 
the same goal: Vannevar Bush (1945), Douglas Englebart (1962), Alan Kay (the 
Dynabook 1972), and Steve Jobs (computer as "bicycle for the mind") are some 
examples. 



One piece of advice I gave to St. Thomas was to focus on where the the 
'intelligence' in current AI systems really is—training set tutors, prompt 
engineers, and interpretation of generative outputs. 



davew 









On Thu, Dec 12, 2024, at 10:24 AM, Roger Critchlow wrote: 

Hans Moravec kicks off a forum, 
https://www.bostonreview.net/forum/the-ai-we-deserve/ 
<https://www.bostonreview.net/forum/the-ai-we-deserve/>, about why the 
instrumentalist computer science and AI we inherited from DARPA grants isn't 
the only possible version or the only version we need. Life is not entirely 
composed of self aiming gun turrets and supply chains. 



-- rec -- 




On Thu, Dec 12, 2024 at 7:19 AM glen <geprope...@gmail.com 
<mailto:geprope...@gmail.com>> wrote: 

Interesting. What was your prompt? 



It's important to remember that Claude and GPT are prone to bullsh¡t. When 
asked to compare apples to oranges, they will happily and confidently make the 
comparison even if it's a category error. Leitgeb's footnote might be of use: 



"This motivation for Ramsifying classical semantics is orthogonal to 
instrumentalist or 

functionalist motivations: the point of Ramsey semantics is neither to show 
that talk of 

interpretation is merely instrumental nor to convey insights into the ‘nature’ 
of truth, but 

to deal with semantic indeterminacy. In contrast, e.g., Wright’s [85] paper on 
Ramsification 

and monism-vs.-pluralism-about-truth does not apply Ramsification for the sake 
of doing 

semantics and in fact presupposes semantic determinacy (see [85], p. 272)." 



where [85] is: 



Wright, C. (2010). Truth, Ramsification, and the pluralist’s revenge. 
Australasian Journal of Philosophy, 88(2), 265–283. 
https://philpapers.org/archive/writra.pdf <_blank> 



On 12/11/24 21:55, Pieter Steenekamp wrote: 

> Different strokes for different okes, indeed. In my realm of AI — and 
> previously in control systems — fuzzy logic has been the trusty spanner for 
> tackling vagueness. Seeking a fresh perspective, I turned to ChatGPT, which 
> delivered this thoughtful comparison: 

> 

> "Ramsey semantics and fuzzy logic both grapple with vagueness but chart 
> fundamentally different courses. Ramsey semantics clings to the rigorous 
> shores of classical logic and binary truth values (true/false), navigating 
> semantic indeterminacy by emphasizing the roles terms occupy rather than 
> insisting on their precision, making it a philosophical and theoretical 
> endeavor. Meanwhile, fuzzy logic boldly abandons binary constraints, 
> introducing gradations of truth (e.g., 0.3 or 0.7), rendering it an elegant 
> mathematical tool for practical domains like control systems and AI. Where 
> Ramsey semantics contemplates the hazy edges of meaning, fuzzy logic 
> quantifies vagueness as a smooth gradient between truth and falsehood." 

> 

> I must admit, ChatGPT's knack for juxtaposing the lofty with the practical 
> was a pleasant surprise—perhaps an unintended nod to my eclectic career path! 

> 

> On Thu, 12 Dec 2024 at 02:45, glen <geprope...@gmail.com <_blank> 
> <mailto:geprope...@gmail.com <_blank>>> wrote: 

> 

> 

> https://philpapers.org/rec/LEIRAS-3 <_blank> 
> <https://philpapers.org/rec/LEIRAS-3 <_blank>> 

> 

> via https://mastodon.social/@dailyn...@zirk.us <_blank> 
> <https://mastodon.social/@dailyn...@zirk.us <_blank>> 

> 

> I found this paper by Weinberg's post to Mastodon through the write up of 
> Leitbeg's projects here: 

> https://gepris.dfg.de/gepris/person/85399322?language=en <_blank> 
> <https://gepris.dfg.de/gepris/person/85399322?language=en <_blank>> 

> I didn't find any papers on scholar or philpapers talking directly about the 
> reference patterns of paradox through graph theory. But both his composition 
> of similarity and non-eliminative structuralism were graph heavy. Good stuff. 
> I have yet to read the ramsification paper. But, as usual, here's Claude's 
> summary of it: 

> 

> > - Classical semantics presupposes the existence of a unique factually 
> > determined intended interpretation of a language. However, there are 
> > reasons to doubt this presupposition holds in general, due to phenomena 
> > like vagueness, structuralism in mathematics, and theory change in science. 

> > 

> > - The author proposes Ramsey semantics as an alternative that avoids 
> > presupposing a unique intended interpretation. Instead, it merely 
> > postulates the existence of an admissible interpretation from which truth 
> > is defined classically. 

> > 

> > - Formally, Ramsey semantics replaces the intended interpretation I in 
> > classical semantics with an epsilon term εF(F∈Adm) that "picks" an 
> > interpretation from the class Adm of admissible interpretations. Truth is 
> > then defined relative to this interpretation. 

> > 

> > - Ramsey semantics preserves the key features of classical semantics - 
> > classical logic, a classical concept of truth, compositionality, bivalence, 
> > etc. But it allows the intended interpretation to be indeterminate when Adm 
> > contains more than one member. 

> > 

> > - The author argues Ramsey semantics is closer to classical semantics than 
> > supervaluationism while still allowing semantic indeterminacy. It provides 
> > reasonable treatments of the Sorites paradox, higher-order vagueness, and 
> > interpretational continuity between theories. 

> > 

> > - Overall, Ramsey semantics aims to capture the advantages of classical 
> > semantics while being less risky, by not presupposing semantic determinacy. 
> > It shows how semantic indeterminacy can be reconciled with an otherwise 
> > classical approach to meaning and truth. 

-- 

¡sıɹƎ ןıɐH ⊥ ɐןןǝdoɹ ǝ uǝןƃ 

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