Showing posts with label generative AI. Show all posts
Showing posts with label generative AI. Show all posts

Saturday, July 4, 2026

A thesis on generative AI

We present to following thesis on generative AI.  That to understand fully the essence of generative AI we must first understand the socio-cultural and philosophical essence of the advent of the internet.  Generative AI is only possible not merely because of raw hardware capabilities and certain neural net architectures and training processes, but on a more fundamental level because of the nature of the large data sets employed. Generative AI is only possible because of the previous availability of a very special kind of massive data set - precisely the kind of data sets provided by the internet. Generative AI is simply the natural further development of the same factors at work in the genesis of the internet.  So our main task is to describe the nature of internet data as well as its social, psychological, linguistic and cultural correlates.  This is a difficult task. One should consider and evaluate the following aspects: the democratization of knowledge and expertise (what certain authors once called "the cult of amateurism"), the lessening of the both the physical and semantic volume of content - flattening of layers of semantic depth, connotation, subtext and extra-textual reference, a  massive atomization and repetition with small variants of certain templates, memes and "virality", propagation and reproduction of persistent psycho-linguistic patterns throughout the whole body of the data - all which structurally contribute to forming the kind of atomized, grid-like, decentralized, self-entangled and self-connected  massive data set which is precisely what allows the generative AI technology to produce LLMs which perform as they do. While traditional cultural-linguistic data involved relatively few authors with massive linguistic content, the internet data involves a massive amount of authors with relatively scarce linguistic content each.  Humanity is caught itself in the textual universe of the internet.  The internet data set can be described as an enmeshed chaos of micro cognitive-linguistic units - which we can compare to the set P of partial information used in forcing. If we wish to combat generative AI we need to get at the root of the whole historical-cultural process involved in the genesis of the internet. We need to instigate a revolution regarding authorship, epistemic authority and the nature of the written text. The semantic web project should be revived. Curiously enough the Pali canon is an very early example of large textual data which exhibits some of the characteristics of internet data.

If the internet era was the era of communication, personal development through sharing and community, but now not only has the "internet" as a medium become content itself but it has become dead content, i.e., generative AI. Thus we are in the heroic age in which the human person must again find development and peace in isolation again - so that again there is something to share. The highest form of human knowledge must be global and synthetic, partial analysis has as its ultimate aim furnishing the support for such a knowledge. Global and synthetic knowledge is the only knowledge that frees one from Plato's Cave. 

Can we reverse engineering generative AI using generative AI? Could we train an AI model M on the data D = (T,L) consisting of training data T and a LLM L trained on T, to solve the following problem:

Given a prompt P fed into L and response R, identify the portions of T which can be considered as having (in some mode or another) the largest weight in the generation of R from P.

Can we define the concept of essential dependence of response R to P via L on a portion S of T? Could we train M to return, for a given P, portions of T which have a certain structural similarity to R?  

Monday, April 27, 2026

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.

Wednesday, February 11, 2026

Another view of TPC

Perhaps it to attempt to attain passadhi (samatha) through vipassana is putting the cart before the horse. Or rather a different kind of insight is called for as a foundation. Yoga citta vrtti nirodha. Understanding the relationship between consciousness and the body - and the existence of a middle subtle body-consciousness field which carries the feedback interaction between both (the neuro-muscular aspect is important). The unified field has some analogy the solutions of Klein-Gordon and Dirac equations (Dirac was perhaps the greatest physicist of the 20th-century).The models of René Thom come very close to the idea of a field of harmonic oscillators over space-time.  The goal of the fundamental stage of TPP is to attain the pax profunda, possibly using psycho-somatic feedback as a support (to dampen or muffle the spectrum of mutually exciting harmonic oscillators). What we ordinarily call 'body' and ordinarily call 'mind' are two complementary modes of the same underlying consciousness-field. It is only in the deep clarity and stillness of the mind-body field that authentic TPC can blossom 孰能濁以靜之徐清. Thus the initial TPC involves perceiving consciousness as an excitation and self-interaction (producing the illusory perception of the individual self) of an underlying psycho-somatic field, and understanding the effective dynamics (and functional stratifications ) and feedback mechanisms to attain the desired goal. This initial TPC is indeed the pure impersonal perceiving of the flux of consciousness as thus, but also the inner first-person experience of the body (i.e. we have nama-rupa): it is also the perception of how the inner body generates a kind of frame of reference or proto-space (proto-topology) for the total sphere of consciousness. Temporality is on one hand transcendent and a condition for thought and consciousness rather than being generated by consciousness, on the other hand it is generated as an illusory excitation. We must find the lost original deeper meaning of Pyrrhonism, regarding belief, thought and agitation - the same underlying TPC insight.

Some things to explore: how can the role of symmetry in physics be transposed to understand the central role of symmetry in consciousness ? And in the symmetries in logic ? Philosophically, how can our theory of a priori computability relate to the obviously rich computational nature implicit in the study of solutions of PDEs ? Radiation, diffusion, harmonic equilibrium. Geometric optics. Singularities of the solutions of PDEs such as the Hamilton-Jacobi equation. Meaning is perhaps described as symmetry invariance in the dynamical system of consciousness.

What are sense and reference ? It seems that we can reconcile psychologism and objectivity by a theory analogous to that of the invariance under group actions used in physics. The conscious content for two different people can differ, but the contents must be related to each other through a well-defined law, a continuous symmetry group, a deformation. Galilean relativity has perhaps an immense overlooked philosophical significance.  It shows that objectivity is relational (all inertial frames of reference are equivalent, there is no absolute frame of rest), but not less objective for that. Once the idea of group action and group invariance had been brought to light - in physics as well as geometry - it is the most natural thing in the world to investigate hyperbolic geometry (Gauss, Lobachevsky) in this light. Why is the connection of Minkowski space (the pseudo-sphere) to hyperbolic geometry obscured (obtained by the analogue of the stereographic projection of a sphere)?

In order to develop these ideas we recall a previous note on computational linguistics. Large Language Models such as ChatGPT-3 use high dimensional ($dim V$ = 12,288) vector-space representations of meanings of certain textual units ('tokens'). These are generated from context in large data sets. The idea of having certain semantic 'atoms' (sememes) from which are combinatorically constructed possible meanings can be found for instance in Greimas (cf. Osgood's semantic differential for studying the variation of connotation across different cultures). Some (such as René Thom) have claimed that the idea that meaning should have a continuous, geometric aspect is found in Aristotle. Leibniz' characteristica used 'primitive terms' but it is not clear if they are combined in a simple algebraic, combinatorical or mereological way, or if complex logical expessions must be involved (or associated semantic networks). But in embedding matrices we have what would seem to be a quantification of meaning, each 'sememe' is given a 'weight' which determines its geometric relation to other meaning-vectors in a crucial way (the weights cannot be dismissed as probabilistic or 'fuzzy' aspects). To us this would correspond to the 'more-or-less' aspect of species in Aristotle. A very interesting aspect of embedding matrices is how they capture analogy through simple vector operations. This suggests another possible formalization of Aristotelian 'difference' , the same difference operating on two different genera. We get a notion of semantic distance and semantic relatedness. This also revindicates Thom's perception of geometry and dynamics in the spaces of genera.

Some questions to ask: are these token-meaning-vectors linearly independent ? If not can we work with a chosen basis ? If the token is ambiguous is the corresponding vector a kind of superposition of possible meanings, as in quantum theory ? How are we to understand the idea of the meaning of complex expressions being linear combinations of the meaning representations of the tokens occuring in the expression ? It would of course be interesting to analyze these questions relative to the other fundamental components of LLMs (attention in transformers, multi-layer perceptrons) - even if these are more practically oriented rather than reflecting actual linguistic and cognitive reality.

Suppose we are given a large text $T$ generated by a set of words $W$ and a context window $S$ of size $n$. Suppose we wished to represent the elements of $W$ as vectors of some vector space $V$ in such a way that given $v,w \in W$ the modulus of the inner product $|\langle v,w\rangle|$ gives the probability of the two words being co-occurrent in contexts S. Consider the situation: it is very rare for words $s_1$ and $s_2$ to co-occur but words $s_1$ and $s_3$ co-occur sometimes as do $s_2$ and $s_3$. But there is also a word $s_4$ which never co-occurs with $s_3$ but has the same co-occurrence frequencies with $s_1$ and $s_2$ as does $s_3$. Then it is easy to see that there is no way to represent $s_1$,$s_2$,$s_3$,$s_4$ in the same plane in such a way that these properties are expressed by the inner product. Thus the dimension must go up by one value. We can define the geometric $n$-co-occurence dimension as the minimal dimension of a vector space adequate to represent co-occurrence frequencies by an inner product. We can ask what happens as $n$ increases, does the geometric dimension also increase (and in what manner) or does it stabilize after a certain value ?

Thus we can think of different people as having semantic vector spaces which must be related in a well-defined way and in such a way that the semantic information remains coherent. Thus the mental content of the term 'horse' for Alice and Bob may be quite different, but each is related to the other through a kind of continuous deformation related to some structure contrasting the background of Alice and Bob. Thus we need to define a kind of relation space for contrasting and comparing different subjects - and in such a way that we have a representation of the algebraic structure of this space in terms of continuous deformations of mental content.

René Thom proposed that concepts were analogous to living beings and that mathematical models of the regulation structures of living beings could be applied to concepts themselves. This is kind of obvious for natural kinds and not very clear for other kinds of concepts. We need a very different approach.  We need to understand representation, the subject's mental and yet objectified representation of the world. The question: what is a world ? Software engineering and the structure of Object Oriented software aiming at creating virtual worlds (such as Unreal Engine 5, Unity or in general RPG games - we are thinking here only of the classical ones such as the Ocarina of Time which were also works of art besides sophisticated puzzles) including automated agents are of some interest though with great limitations. Generative AI is likely to be followed by more sophisticated models which can train in real-time. The run-time process structure of operating systems is also important. The irony here is that these approaches become more interesting once we discard neuro-reductionism - once we abandon the pointless attempt to view the brain as the hardware of the mind. The central hardware of the mind is to be sought elsewhere, the brain itself is a kind of auxiliary cache.

There is much analogy between the structure of a computer program and that of a novel. 

To obtain a mathematical understanding of consciousness we must first bridge the gap between mathematical models of nature and computer systems.

Also we need to take into account altered or higher states and modes of conscious experience (once harmful and falsified approaches to the spiritual life have been discarded - those that hide the truth that a royal path to spiritual realization can consist in a pure love for a real person). 

Do these higher states of consciousness possess a geometry, a topology, a semantics ? It is curious, how many Henads are there is Proclus' system ? Or does cardinality itself not apply to them ? 

Also the entire discipline of lexicology needs to be reformed. Indeed what was the ancient project of the classification and division into genera and species but a lexicological program ? We need to greatly clarify the insight involved in defining a term by its context. It is not only that we need to know the meaning of words to understand a narrative but also narratives themselves give meaning to words.  Being multilingual and practicing translation offers unique insight into the pure semantic universe.

Maybe natural language is a kind of super-mathematics which contains ordinary mathematics as a special case. It is presumptuous to ridicule the concept of an 'ideal language'. Learning other languages and in particular ancient languages is surely on the path of wisdom. In natural language we cannot in general define lexemes in the way we define mathematical or scientific concepts (and the ancient theory of genera and species must have been derived from Euclidean mathematics, law and medicine).  This is polymorphism. Meaning is in an inseparable feedback loop with life and experience, depending on whether we are engaging in solitary discourse or on which person we are conversing with.

Naive dictionaries with obvious circularity in definitions should not be despised as non-scientific. Rather they  express something profound about polymorphism, the circulation, the flow, the dynamics of meaning. Instead of a oriented tree we have a directed graph with cycles. There is an analogy with commutativity and non-commutativity. Meaning circulates like a living current or flow through the whole web or tapestry of language. The name generates a story, the story a name.

Even for mathematical concepts we gain a deeper understanding or apprehension of them through practice, through exercises, through studying proofs in which they are applied. Are these degrees of apprehension - or degrees of meaning?  Formal logic acts as an ultimate arbiter which rarely needs to act, mathematicians with distinct intuitions and apprehensions of a given mathematical concept generally can agree that their concepts are 'the same'.

Thurston's On Proof and Progress in mathematics (1994)

Schopenhauer offers a strikingly alternative theory of consciousness, concepts, intuition and representation as well as super-consciousness. So does Hume. Even Sextus. The problems discussed above are not some kind of puzzle of which one needs to find a solution. Rather they are all the result of delusion and deception, consciousness pulling itself down as an illusion over its own eyes. Only vipassana, only TPC and TPP can break through this illusion. It is foolish to ask about meaning and language without first asking about consciousness and experience. Meaning and language are within consciousness. There is a higher form of non-linguistic cognition. Thus the so-called philosophy of language is not fundamental and does not represent a radical or critical approach to philosophy, rather a dogmatic one (and its arguments against psychologism, against empiricism, against the a priori vs. a posteriori distinction, against the analytic vs. synthetic distinction, all fail). And there is the fact that consciousness can calm itself.

Esoteric programming languages

"There is the truth." - Ludwig van Beethoven    https://en.wikipedia.org/wiki/Esoteric_programming_language Introduction to Piet; ...