Mathematics and AI

Referring to certain software and hardware as "AI" and to such alleged "AI" as an agent "solving" problems in mathematics is profoundly misleading. The programs in question are not "intelligent" and are not "solving" mathematical problems as agents in any philosophically meaningful sense. And these programs will certainly never "replace" human scientific endeavor in any conceivable way, rather they are, and will ever remain, mere tools.

Let us be clear. Human beings can only process, check and produce data within definite finite bounds based on symbol systems with definite finite bounds and bounded fragments of finitarily determined rules.

Thus it is a triviality and a truism that all human external symbolic activity and productions could in principle - given a massive enough set of data is made available - be mimicked and processed by brute-force. We can imagine a supercomputer in space with processing power and storage a billion times surpassing any of the human bounds of symbol processing and text processing, checking and production. We can also give our supercomputer some kind of super-luminal processing velocity. This supercomputer through brute-force and crude machine learning algorithms would beat and outperform every man-made "AI" in every possible domain in an instant. There is nothing surprising here and there is nothing here that has anything remotely to do with "intelligence".

Intelligence is rather reflected in doing much with little external, material, processing power. Chess programs are just cheating machines. Our hypothetical supercomputer would beat any current Go or Chess AI and that would not make it "intelligent" in any meaningful way.

As for mathematics, let us take a proof assistant such as Agda or Idris 2. The mathematician develops a theory (or formalizes a previous theory) by means of type definitions, records and type declarations for terms. The problem is to find explicit proof terms. This could be attempted by using brute-force, heuristics or any kind of machine learning method. And this search could fail. Or the search algorithm could be refined and altered. There is nothing unusual going on here. The algorithm is not "doing mathematics", it is not constructing theories, refining definitions, improving its own heuristics or being telologically oriented towards a certain architectural vision. It is the mathematician's legitimate tool.

Speaking of "AI" "solving" mathematical problems or "doing" mathematics or potentially "replacing" mathematicians is sheer and utter nonsense.

If even published journal papers can contain errors, I certainly would not trust any "mathematical" output produced by generative AI that was not formalized and checked in all its details by current proof assistants and proof checkers.

Note on Large Language Models

LLMs have a certain analogy to compression. From the training data D we obtain a LLM T(D) which is supposed to contain (or "extract") the essential "information" or "statistical patterns" present in D. T(D) is much smaller than D. It is speculated that Claude models are trained on D of the size of a petabyte and that the models themselves range from 150 to 500 GB. The response to a given prompt is analogous to decompression. Supposedly T(D) can "generate" an approximation of all the information originally contained in D. Some questions:

1. Is it not true that the passage D -> T(D) is not lossless, that important information present in D is lost in T(D) and cannot be recovered by it?
2. Is there any way to study T(D) as a mathematical object, detect its structure and geometry? And to study likewise the correspondence between D and T(D)? If there are limitations to doing this are they practical or theoretical?
3. There is an analogy between passing from D to T(D) and passing from general to countable models of ZF set theory (which exist by the downward Löwenheim-Skolem theorems)?
4. Is there not some analogy between forcing using countable models and generic sets and the process of training to generate T(D)? In both cases there is pattern generalization from fragmentary data.
5. Is there any structural correspondence between the structure of T(D) and structures found in the world (not counting neurological analogues of MLPs)?
6. Can we construct toy universes, toy languages and toy training data and study how D -> T(D) works in this simplified idealized scenario to gain more insight regarding real world LLMs?
7. Do LLMs express an essentially emergent phenomenon in which hardware capabilities are a crucial factor? Can we formalize rigorously such a concept of emergent phenomenon or capability?
8. But most importantly LLMs are linear statistical predictors (next token predictors) and they are trained as such. We need to formalize clearly what LLMs are supposed to do in the first place. Suppose we have a (first-order) model M that represents the world. We want our LLM T to be able to deal with a good degree of approximation with the theory of M, Th(M). We are given a finite large set L of first-order formulae with probabilities of their belonging to Th(M). A transformation is applied to L to obtain the object T which is able to include the reliable part of L in Th(M) and to extrapolate to other elements of Th(M). Is this to be understood as both logical and statistical inference?
9. A LLM is just a finite state automaton. But recursively axiomatizable theories are in general not recursive. Can we can construct a theory T such that for any finite subset L of T all LLMs trained on L will err to an arbitrarily with regards to infinitely many sentences of T. We define metrics on expressions, that's the key.
10. And most importantly: are LLMs analogous to syntactic (and algebraic) models used in logic and category theory?  Or the training data is like the a poset P with the dense topology and the LLM is like the topos of sheaves over this site?

11. Do LLMs function essentially by analogy, metaphor, induction and extrapolation? This is an old idea in AI. 

Yet more short considerations on AI

The philosophical significance of LLMs and a potentially powerful philosophically based critique of LLMs have not yet been developed and perhaps their importance has not even been recognized yet.

Could LLMs be tied to a certain philosophical view regarding the mind, language and the world? And if such a view is manifestly erroneous could not this furnish a sound basis for acknowledging - alongside numerous other reasons - the social and cultural harm of LLMs?

For instance, could we not explore the relationship between LLMs and Quinean extensionalism and meaning-as-use theories? Are not LLMs based on the rejection of the irreducible intensionality of human symbolic activity? Behind every symbol there is an intension. And a formal theory of intensionality must itself acknowledge the intensions of its meta-symbols.  But there is no intensionality in LLMs beyond that of the humans involved in their creation. Searle's Chinese Room ignores the deeper philosophical meaning of computation - or a potential associated geometric theory of meaning - but may be of interest for a critique of LLMs.

Do LLMs compute? Computation is a primordial intensional human activity (see our paper Analyticity, Computability and the A Priori). 

Perhaps there are other machine learning models of language of a more geometric or even combinatorial-algebraic nature which would have interested Riemann who wanted to relate meaning to a kind of cognitive geometry.   Perhaps statistical regularities should be traced back to geometry.

What are LLMs really, formally? Can we formalize the critical values wherein they become 'adequate' for their proposed task? What exactly in 'large'? How can this be formalized rigorously? Can LLM techniques be used for formal axiomatic-deductive systems and automated theorem proving? Or can we prove certain fundamental limiting theorems about the powers of LLMs akin to the unsolvability of the halting problem and Gödel's incompleteness theorems?

LLMs only exist because of the Internet. The Internet and LLMs are part of the same historical-cultural-technical process.  This ontological process might be described as the datafication of humanity. Language ceases to be a tool of human thought, communication and culture-building but rather a tool for the reduction, degrading, emptying, perversion and commodification of humanity itself. The Internet and LLMs are the anti-Gutenberg. Man has become text, sound, image, data, statistics. LLMs are a counterfeit reality, a monstrosity, the world becomes one big corporate controlled screen.

 If we compare the training data and the resulting LLMs is there or not a loss of information? Would it not be more worthwhile to develop sophisticated search algorithms and querying language to access the training data directly?

 LLM culture is the culture superficiality, atomization, banality, cosmetics and deception (a LLM is almost a trained deceiver in the biological sense - it detects and mimics human patterns). There is a loss of the multiple layers of meaning behind every symbol which cannot be reduced to statistical correlations with other symbols. Meaning is replaced with arbitrary social-statistical emergent correlative patterns.

The realm of pure mathematics - and that of pure logic, combinatorics and computability - is a pure realm which LLMs cannot touch or corrupt.  So the formal mathematics and formal philosophy projects, contrary to popular misconception likely resulting from deliberate propaganda and deception - are the antithesis of and antidote for LLM culture. There is a pure universal computational-mathematics-akin language (far beyond the natural language or the audio-visual data  that can be perverted and imitated by LLMs) and a pure logic and a pure thought and mankind may indeed hope to attain them. 

LLMs are not intelligent and statistics cannot solve formal computational problems nor can they encompass the pure a priori synthetic principles of formal computational systems (i.e. the cognitive certainly of the foundational principles for metatheoretic knowledge) as detailed in our paper mentioned above.

No statistical pattern analysis of the shadows are sufficient to lead to knowledge of the object. Pixel injection in image recognizers demonstrates this fact, and similarly for formal reasoning.  Besides lacking intensionality, LLMs lack reference and context (despite the misleading terminology of context windows). 

And most important of all since the true intellect is inseparable from morality, empathy and compassion, completely beyond the reach of LLMs. LLMs do not have the bondage to an illusion of a self.

A major task of philosophy is to destroy the evil empire of LLMs and a good starting point is deconstructing and refuting the worldviews (extensionalism, meaning-as-use) which LLMs embody. 

Is a lawyer someone trained in a specific system of laws or someone who has developed the skill to study, interpret and apply any given system of laws or perhaps be able to cope with significance changes in present laws?  Such a metalawyer is the analogue of a universal Turing Machine. A LLM could never be a judge or a metalawyer - it could not grasp the spirit of legal institutions or the deeper meaning of a given legal context.

Billions use light-bulbs without understanding the underlying physics. Billions could use LLMs thinking they are conscious.

LLMs will become more interesting in the measure in which the multilayer perceptrons models are replaced by geometrically and mathematically more sophisticated models (KANs are a step in the right direction).

Short philosophical considerations on AI

Hegel and Heidegger were thinkers about their own time, thinkers about historical events and happenings. Few have the insight and courage to fathom the full depth of the meaning of an historical event, the coming to be (coming of age?) of an historical process. Tragically,  it is only some time after the event (the time of monsters?) has hit humanity with full force that Hegel's famous owl can spread her wings. Is it not true that some of what the prophetic author of Sein und Zeit wrote about technology only makes full sense at the present?

This seems to us particularly true of the emergence of the age of the Internet and the age of generative AI which is its logical development.

And yet nothing could be further from our own philosophizing than any form of historicism or historical philosophy. As such both Hegel and Heidegger, for all their interest and insight, must be considered as having crafted systems based on an incorrigible error. 

Social progress is not a law of nature but a legitimate hope - even if at present it seems a distant one - and it is our moral duty to work towards it in the midst of uncertain outcomes.

The advent of the internet was the advent of connection between people. This connection carried rhetorically moral undertones and echoed enlightenment ideals about the desirability of sharing and making knowledge available.  In the present age of the generative AI based Internet powered and controlled by corporations and governments aligned to anti-enlightenment ideals, it may be that it is morally called upon us to practice instead the process of disconnection and the purification and preservation of knowledge(not obviously in the sense of the 'great simplification' of the Canticle of Leibowitz).   

The most basic step is ensuring locality of core information. That is, to be in possession of machines onto which have been downloaded significant portions of Internet Encyclopedias (despite their serious shortcomings) as well as some decently performing LLM. To this we add, it needs not be said, massive of digital preservation of human cultural artifacts, notably libraries.

One can use Kiwix and download for offline viewing the most recent English Wikipedia (50GB text-only 150GB with pictures).  With a AMD Ryzen 7 5825U processor with 16GB RAM and 2GB Radeon Graphics one can use Ollama and download and use some decently performing LLMs (gemma4 comes in E2B, E4B, 31B and 26B A4B). 

Most living beings alternative between states of being awake and of sleep. Can would we design a dynamic LLM which similarly alternates between states of user interaction and re-training based on this interaction? The most important being the correction and/or updating of knowledge or perhaps the removal of harmful and biased content and "thought patterns". If LLMs can improve then it is not only  a question of having the number of parameters equal to the number of neurons of the human brain.

Computers can enhance and aid human cognition as well as hinder and destroy it (there is a growing body of evidence concerning the disastrous effect of excessive or inappropriate generative AI use for individual mental health and cognitive development, not to mention for society as a whole). 

But the harms of generative AI have little to do with lesser-known extremely powerful and beneficial aspects of the computer for human cognition. We cannot go into this in detail here. Let it just be said that it involves using adequate software for the rigorous formalization of human scientific theories and concepts (specially logic and mathematics and formal methods in the sciences) and the vital feedback-loop between human thought and the software interface (IDE) which results in the simultaneous enhancement of human understanding and production and the quality of the software-based formalization and implementation. 

The software in question includes not only Rocq (formerly Coq), Agda and functional programming language but such languages as Python, Javascript and C/C++. Python is a multi-paradigm and highly versatile language with an elegant syntax. Python comes close to achieving the ideal of a universal language in the Leibnizian sense and is a wonderful tool for formalization, implementation,  verification and exploration in a variety of areas in mathematical logic and finite mathematics. 

We note also the importance of minimalism (using as few dependencies as possible) and building things from the ground up - this goes for scientific and philosophical projects, not of course for commercial and industrial ones.  We will address in the future the question of the possible role of machine learning in this process.

On generative AI

Is generative AI corrupting human knowledge and language and by extension human thinking and human culture themselves?

A wikipedia dump is around 100 GB. Wikipedia could be improved and be semantically formatted to be computer readable and advanced query systems could be developed. Would not this be better for the acquisition of knowledge and the advancement of science? Are AI generated summaries of books or papers valid replacements for human ones? What justifies our trust in generative AI as compared to a search engine?

Generative AI is corrupting the internet. Maybe it is a zombie or Frankenstein of human language and knowledge. Or a bland blend of stolen and adulterated intellectual property. By adulterating human language and knowledge it adulterates thought and culture. In the old internet one could generally become aware of the source and context of bad material. But in generative AI the poison is injected and dissolved into the whole body in an often subtle, not immediately detectable way. The 'neutral' sounding language and fake 'objectivity' are misleading. The term 'subjective' is used ad nauseam. Due to the nature of the training data, in generative AI the truth of a belief-system is a function of the power of the people upholding or promoting it.

The real danger of AI has to do with the advent of systems which no single person can fully understand or control. This is the case for standard operating systems which due to their size and hardware and firmware-linked complexities, have passed beyond being able to be understood by a single person. And generative AI is a black box.

And yet there is no reason why a slim, efficient OS with readable kernel code could not be running on most devices. Would such a kernel, understandable by a single person, be more secure than current bloated constantly updated ones? And is there a reason to abandon the semantic web project? Would the semantic web be better than both the ordinary internet and LLMs?

But we must acknowledge that philosophically the advent of LLMs is something profoundly uncanny and thought-provoking. We hold that 90% of valid criticism consists in just criticism of the poor quality, the fatal presence of previous AI-generated 'slop' and biased nature of the training data, while only 10% is criticism of LLMs as AI.

Are LLMs an emergent phenomenon caused by the size of linguistic data and hardware power capable of processing it? An emergent phenomenon for massive linguistic data in which it becomes possible to talk to data? An uncanny situation wherein a uniquely human trait (linguistic communication) is convincingly mimicked by a machine as it spontaneously emerges, in a way still little understood, statistically from massive linguistic data. As if the unique prerogative of the logos had been stolen from humanity. Maybe a human super-logos needs to be developed to prevail against the AI-logos which offers the illusion of a divine oracle, of having a god as a friend.