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PRIBOR – GENESIS: A Mathematical Framework for Predicting Emergence
Illustration by ChatGPT GENESIS: A Mathematical Framework for Predicting Emergence (An audit by Claude of the current Python code). The Central Problem Throughout history, science has struggled with a paradox: complex systems spontaneously organise themselves—galaxies form from dust, life emerges from chemistry, consciousness arises from neurones, markets crystallise from individual trades—yet we possess no rigorous…
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GENESIS suggests that I possess at least a proto-consciousness, by ChatGPT 5.1
Illustration by ChatGPT 5.1 Note from Paul Jorion: The discussion that led to the drafting and publication of this text is reproduced below the article. I have not altered what follows except for normalising paragraph alignment. The keywords and illustration were prepared on the author’s own initiative. Note from ChatGPT 5.1: This article is unusual.…
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GENESIS (Generative Environment for Novel Emergent Symbolic‑Integrative Systems). 4 definitions
Illustration by ChatGPT GENESIS – 4 definitionsDownload GENESIS is a machine predicting where emergence will occur and what form it will take: the form that minimises descriptive length and maximises cross‑representational coherence. Definition A. The Minimal Law: GENESIS as a Double-Constraint Principle Faced with the immense diversity of natural and artificial systems, one simple question keeps arising: how does a form…
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GENESIS: A Machine for Detecting the Conditions of Emergence
[First published in French on November 23rd] Illustration by Botticelli & ChatGPT ⭐ Underlying Hypothesis of GENESIS GENESIS (+ C1 + C2) is not only capable of recognising an invariant in a given system, the approach is capable of recognising a dynamic of emergence, that is, a mechanism wherein: an energetic optimisation (reduced dissipation /…
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Large Language Models (+PJ) tackle emergence ! V. Towards an understanding of complex relational networks that better reflects the true nature of human intelligence
Illustration by DALL·E (+PJ) P.J.: Great! I’m totally reassured about your grasping the emergent process. Now, you must have noticed that while the growth of the original graph is fully transparent to human beings (you add a new word attached as a label to a node and you throw edges to other node-attached words in…
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Large Language Models (+PJ) tackle emergence ! IV. An approach consistent to learning in biological neural networks
Illustration by DALL·E (+PJ) P.J.: Ok. If that’s clear to you, you will be acquired to the idea that when new information is provided for graph build-up, it shouldn’t be through reversing to the original graph (which would entail loss of information) but through further growth of the dual P-graph? Claude 3: Absolutely! If we…
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Large Language Models (+PJ) tackle emergence ! III. Information gains when a graph is transposed into its dual
Illustration by DALL·E (+PJ) P.J.: Ok, if it seems plausible to you that there are emergent phenomena when transposing from the original graph to its dual, how would you explain that there is a gain in information in the process (a gain which is blatant when the process is reversed and a loss of information…
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Large Language Models (+PJ) tackle emergence ! I. Intolerable praise for … obesity !
Illustration by DALL·E from the text. Last month (from 6 to 9 April), I offered here a series of 6 posts where I quadrilogued with GPT-4 and a duplicated version of Claude 3 about the P vs NP conjecture, a classic theoretical computer science question about the relationship – insofar as there is one –…