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Groups > comp.lang.prolog > #15940

The recursive AI bottom line (Was: What will microsoft say, will they buy it?)

From Mild Shock <janburse@fastmail.fm>
Newsgroups comp.lang.prolog
Subject The recursive AI bottom line (Was: What will microsoft say, will they buy it?)
Date 2026-09-24 15:38 +0200
Message-ID <119393s$a6b6$2@solani.org> (permalink)
References <107cdg8$3ok7g$1@solani.org> <10p44s1$16ps$1@solani.org> <1191b63$6fpr$1@solani.org> <119358v$a3jo$1@solani.org> <11938ti$a6b6$1@solani.org>

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Hi,

So every non AGI development is still an AGI development.

Bye

Mild Shock schrieb:
> Hi,
> 
> Disclaimer: While clairvoyant valuation has a heavy
> math based literature, that can be based on a couple of
> approaches from rational reasoning, the LLM literature
> might be a little messy and less known,
> 
> so the above is what an LLM gave me. I didn't select and
> only did cherry picking, to show the drift. EyeProlog is
> also a little disappointing, more postulating some human
> driven science loop, not a semi-automated experimenter?
> 
> The rigid bento forms logical english that requires
> million mouse clicks, might also hit a wall, since LLMs
> are smoother. Since Excel and VB already solved the "business-
> user-readable rule" problem decades ago with localized
> 
> function names and cell references. Their solutions to
> the textit feature itselfs, are probably more mature.
> But given that Microsoft has its foot directly into AI
> laptops and does their own model development,
> 
> there are only a few chances that they would be interested.
> Maybe there are some take aways from a Prolog implementation
> that they then will happily patent and reimplement on their
> own, just like they didn't adopt Java but developed C#.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Its really amazing how advanced the 80’s were concerning
>> the early expert system wave. You already found expert systems
>> or fault diagnosis systems, that sorted their abduction questions
>>
>> via a clairvoyant value computation. It refers to a theoretical
>> benchmark in decision analysis, maintenance optimization,
>> and information economics. I didn’t find some highly visible
>>
>> Prolog implementation yet, but I might be mistaken. s(CASP)
>> doesn’t have it yet right? Neither does Web Prolog mention
>> the concept yet? LLMs use clairvoyant value as well, obviously,
>>
>> when they stir the dialog. Maybe as an emergent phaenomenon?
>>
>>                    ┌─────────────────────────────────────────┐
>>                    │ HOW LLMS VALUE PERFECT INFORMATION      │
>>                    └─────────────────────────────────────────┘
>>                                         │
>>           ┌─────────────────────────────┴──────────────────────────┐
>>           ▼                                                        ▼
>> ┌─────────────────────────────────┐ ┌─────────────────────────────────┐
>> │       EXPLICIT SCAFFOLDING      │         │       EMERGENT 
>> PHENOMENON       │
>> ├─────────────────────────────────┤ ├─────────────────────────────────┤
>> │ The architecture forces a VPI   │         │ Autoregressive 
>> next-token       │
>> │ calculation (e.g., using        │         │ prediction mirrors human 
>>       │
>> │ information entropy metrics).   │         │ cost-benefit dialogue. 
>>      │
>> └─────────────────────────────────┘ └─────────────────────────────────┘
>> One might spin the idea further and just say the valuation
>> doesn’t predict some physical system behaviour, but rather
>> reflects user preferences. See also:
>>
>> Evaluating User-Agent Collaboration
>> https://arxiv.org/html/2608.27818v1
>>
>> LLMs can be also bad clairvoyants:
>>
>> LLMs Fail to Let Go
>> https://arxiv.org/pdf/2609.25337
>>
>> Bye
>>
>> Disclaimer: While clairvoyant valuation has a heavy math
>> based literature, that can be based on a couple of approaches
>> from rational reasoning, the LLM literature might be a little
>>
>> messy and less known, so the above is what an LLM gave me.
>> I didn’t select and only did cherry picking, to show the drift…
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Creating a compare/3 on cyclic terms is a fascinating
>>> topic. Obviously the following implementation would work,
>>> namely transform a cyclic term into a non-cyclic representation,
>>>
>>> and compare its representation:
>>>
>>> compare_rep(C, X, Y) :-
>>>      rep(X, A),
>>>      rep(Y, B),
>>>      compare(C, A, B).
>>>
>>> Provided rep is injective, if the non-cyclic representation can be
>>> completely ordered, the original cyclic terms will be also
>>> completely ordered. One might add further requirements to
>>>
>>> rep, namely that it is conservative, ordering acyclic terms in
>>> the standard order as require by the ISO core standard. This
>>> was a discussion on SWI discourse a few months ago. But it
>>>
>>> never adressed the issue how to efficiently sort/2 or keysort/2,
>>> when the involved lists or pair lists contain cyclic terms. Now
>>> since AI has become so omniscent, it easily handled me a tip,
>>>
>>> both Gemini(*) and Deepseek(**) did that, and pointed me to the
>>> Schwartzian Transform. Possibly even more ideal when one
>>> has ultra fast minimization. The idea is very simple, sketch:
>>>
>>> 1. List' = [ (rep(x),x)  | x e List ]
>>> 2. List'' = sort_on(π1, List')             %% sort on 1st argument
>>> 3. Result = [ π2(x) | x e List'']          %% project to 2nd argument
>>>
>>> The benefit when measured against predsort/3, that would use
>>> compare_rep/2, is that predsort might call O(N log(N)) or more
>>> comparisons. While the above only does a collation key computation
>>>
>>> once per element, making it O(N). AI being quite a buddy here!
>>>
>>> Bye
>>>
>>> See also:
>>>
>>> Schwartzian transform
>>> https://en.wikipedia.org/wiki/Schwartzian_transform
>>>
>>> (*)
>>> https://gemini.google.com/
>>> (**)
>>> https://www.deepseek.com/
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> How would we do a reverse sorted map?
>>>>
>>>> I find in Java:
>>>>
>>>> TreeMap(Comparator<? super K> comparator)
>>>> Constructs a new, empty tree map, ordered
>>>> according to the given comparator.
>>>> https://docs.oracle.com/javase/8/docs/api/java/util/TreeMap.html
>>>>
>>>> Or in Dogelog Player:
>>>>
>>>> tree_new(T):
>>>> tree_new(T, F):
>>>> The predicate succeeds in R with a new red-black tree.
>>>> The binary predicate allows specifying a term compare F.
>>>> https://www.dogelog.ch/typtab/doclet/book/12_lang/05_libraries/03_util/06_tree.html 
>>>>
>>>>
>>>> Here is an example, using the destructive API. But
>>>> the same constructor works also for the non-destructive API.
>>>>
>>>> ?- tree_new(_T), tree_add(_T, 0rInf, foo),
>>>>     tree_add(_T, 0rNaN, bar), tree_pairs(_T, L).
>>>> L = [0rNaN-bar, 0rInf-foo].
>>>>
>>>> And now using a comparator modifier, aggregate with a comparator,
>>>> as a closure. Some Joy of Higher Order logic programming:
>>>>
>>>> reverse(C, R, X, Y) :- call(C, R, Y, X).
>>>>
>>>> ?- tree_new(_T,reverse(compare)), tree_add(_T, 0rInf, foo),
>>>>     tree_add(_T, 0rNaN, bar), tree_pairs(_T, L).
>>>> L = [0rInf-foo, 0rNaN-bar].
>>>>
>>>> ?- tree_new(_T,reverse(reverse(compare))), tree_add(_T, 0rInf, foo),
>>>>     tree_add(_T, 0rNaN, bar), tree_pairs(_T, L).
>>>> L = [0rNaN-bar, 0rInf-foo].
>>>>
>>>> Just toying around with my new NaNs.
>>>>
>>>> Have Fun!
>>>>
>>>> Bye
>>>>
>>>> Mild Shock schrieb:
>>>>> Hi,
>>>>>
>>>>> Functional requirement:
>>>>>
>>>>> ?- Y = g(_,_), X = f(Y,C,D,Y), term_singletons(X, L),
>>>>>     L == [C,D].
>>>>>
>>>>> ?- Y = g(A,X,B), X = f(Y,C,D), term_singletons(X, L),
>>>>>     L == [A,B,C,D].
>>>>>
>>>>> Non-Functional requirement:
>>>>>
>>>>> ?- member(N,[5,10,15]), time(singletons(N)), fail; true.
>>>>> % Zeit 1 ms, GC 0 ms, Lips 4046000, Uhr 11.08.2025 01:36
>>>>> % Zeit 3 ms, GC 0 ms, Lips 1352000, Uhr 11.08.2025 01:36
>>>>> % Zeit 3 ms, GC 0 ms, Lips 1355333, Uhr 11.08.2025 01:36
>>>>> true.
>>>>>
>>>>> Can your Prolog system do that?
>>>>>
>>>>> P.S.: Benchmark was:
>>>>>
>>>>> singletons(N) :-
>>>>>     hydra2(N,Y),
>>>>>     between(1,1000,_), term_singletons(Y,_), fail; true.
>>>>>
>>>>> hydra2(0, _) :- !.
>>>>> hydra2(N, s(X,X)) :-
>>>>>     M is N-1,
>>>>>     hydra2(M, X).
>>>>>
>>>>> Bye
>>>>
>>>
>>
> 

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        What will microsoft say, will they buy it? (Was: Food for thought: Bayesian Experimental Designer) Mild Shock <janburse@fastmail.fm> - 2026-09-24 15:34 +0200
          The recursive AI bottom line (Was: What will microsoft say, will they buy it?) Mild Shock <janburse@fastmail.fm> - 2026-09-24 15:38 +0200

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