Ново допълнено издание на "Първата стратегия" (283 стр.) - скоро предстои добавяне на още документални доказателства | The First AI Strategy - new extended edition from 28.6.2026

На SIGI-2026 публикувах поредната версия на "Първата стратегия" - допълнено издание (от 256 до 283 стр. като подбирам още материал за следващата версия. 

Пускам го като нов файл: Purvata_Strategiya_UIR_AGI_2003_Arnaudov_SIGI-2026.pdf

Реших да направя това издание след като се сетих за един въпрос, бележка под линия, за цената на "Витоша" (1961-1963 - през 1962 се извършва деноминация), и най-вече като препрочитах основния том на "Пророците" и писмата ми до Христо Крушков и др., докато работя и по неговото ново коригирано издание, а по-късно може би и допълнено през 2026 г.

Осъзнах, че множество писма и други статии и документи от началото и средата на 2000-те, вече публикуани в Основни том, в "Кратка хронология", "Подробна хронология ..." имат място и вна по-"прегледно" и "видно" място в "Първата стратегия", защото подсилват по още по-"графичен" начин вече дадените доказателства. В Основния том някои са във въведенията, но други са след 1600-та страница в подробната хронология.

Четете на SIGI-2026 в Гитхъб и от:

https://twenkid.com/agi/
https://github.com/Twenkid/SIGI-2026/

Преписвайте тази историйца и пазете я да не изчезне!

Github:

Разширено издание от 28.6.2026 г. - 283 с.

Много допълнения спрямо предната версия от 9.6.2026 г.* Вече 283 стр. (от 256, +27). Много добавени писма от Основния том на Пророците:  "5. Продължения на първата стратегия с конкретни научни и приложни насоки, публикувани между 12.2004 – 2.2008 г.: Вселена и Разум 5; Как двама всестранни младежи предвидиха пораждащия изкуствен интелект и принципа на работа на преобразителите между 2002 и 2005 г. – из писма между Илиян Георгиев и Тодор от януари-февруари 2005 г. Писма до Христо Крушков с повече обяснения и подробности от 11.2007 г.; писмо до Атанас Чанев от 12.2007; „Smarty – най-интелигентният речник в света“, 5.2007. Статията с дейностите и плановете ми от блог Изкуствен Разум от 2.2008 г.: „Творчески планове - какво правя, искам да правя, мисля си че правя... Частици от тях...“ . Коментар от 8.2008 г. за “Автоматична програмираща интелигентност“. Виж и писма до Людмила от 12.2007 г. в послеписа."

* В сравненията с Хасабис - включен още един откъс от писмото до А.Чанев.

* Бележка за деноминацията от 1962 г. относно бюджета на "Витоша" от 1.5 - 3 млн. (но неуточнено преди или след деноминацията 1:10). 

Повече от статията "Творчески планове" ... добавяне на бел. че книгата е и на SIGI-2026.

 * В изданието от 28.6.2026 включвам повече текст от началото на статията, където споменавам изрично „интелигентните асистенти“.

* Изследователската група в Уулвърхамптън, основана от Руслан Митков през 1995 г. http://clg.wlv.ac.uk/ (вече не е достъпен):

https://web.archive.org/web/20230426160837/https://clg.wlv.ac.uk/ 

https://web.archive.org/web/20070718223057/http://clg.wlv.ac.uk/people/index.php

https://web.archive.org/web/20070718223022/http://www.clg.wlv.ac.uk/projects/WSD-MT/index.php 

* Как работи разумът? Йерархичен самоорганизиращ се предсказател на бъдещето – научно представление .. - повече информация за събитието в ТУ София 2009 г.

(...)

* Версията от 9.6.2026 г. беше качена на SIGI-2025 и twenkid.com/agi

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How much Bulgarian is the so called Bulgarian institute INSAIT? Part I

The so called "Bulgarian" international Swiss-Bulgarian-World Institute for AI and computer science INSAIT, which "strategically" was in many ways a cynical rip-off and a steal of my project published in 2003 :))), with arrogant and ridiculous claims for being "innovative", "visionary" or original, LOL (however "hyperscaled" in its financial demands and expenses), are promoting their success at CVPR 2026 and their role for "Bulgaria": 17 publications + 3 other participations, 20 total. They are among the greatest in the world. [See more in the book "The First modern AI strategy .." (Първата съвременна стратегия за развитие чрез изкуствен интелект ..." and "AI Institutes ... " (Институти и стратегии за изкуствен интелект на световно ниво ...) from the hyperbook "The Prophets of the Thinking Machines..." from SIGI-2025)

Right, they are among the leading institutes in their and in this game, however are they BULGARIAN as they so aggressively emphasize and claim on every occasion?  (Maybe that is why they do.)

Asking the free ChatGPT (no login) with an excerpt from the list of publications: ... 


==================================================

QUESTION:  How many papers? How many of them include Bulgarian or Slavic names? 
[see the full text below the answers]

ANSWER: There are 18 papers listed.

Papers that appear to include Bulgarian or other Slavic names (based on author names alone) are approximately:

1. ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors
   – includes Dani Velikova (likely Bulgarian/Slavic surname)
2. SPEAR-1: Scaling Beyond Robot Demonstrations via 3D Understanding
   – includes Nikolay Nikolov and Aleksandar Yanev (clearly Bulgarian names)
3. FireScope: Wildfire Risk Raster Prediction With a Chain-of-Thought Oracle
   – includes Mario Markov (Bulgarian/Slavic surname)

So the count is:

• 18 total papers
• About 3 papers with clearly Bulgarian/Slavic author names

If you want, I can also break it down into:

• specifically Bulgarian names,
• broader South Slavic / East Slavic / West Slavic names,
• or estimate the nationality distribution of all authors.
  
  
  
  

QUESTION: Estimate the nationality distribution of all authors.

This can only be a rough estimate from names and affiliations conventions, not an actual nationality census. Many researchers work internationally, have multicultural backgrounds, or use romanized names that overlap across regions.

That said, from the author lists, the distribution appears heavily concentrated in Chinese researchers, with a secondary cluster from European institutions (especially Swiss/Western European labs connected to ETH Zurich / KU Leuven style collaborations around Luc Van Gool and Danda Pani Paudel).

Approximate estimate across all listed authors:

Estimated background/nationality cluster	Approx. share	Notes
Chinese	~70–75%	Dominant throughout nearly every paper
South Asian / Nepali / Indian	~5–8%	Mainly from names like Danda Pani Paudel, Mohammad Mahdi
Western/Central European	~8–10%	Swiss, Germanic, Dutch, Belgian, etc.
Balkan / Slavic (incl. Bulgarian)	~4–6%	Nikolov, Yanev, Markov, Velikova
Spanish / Catalan / Latin European	~3–5%	Vazquez-Corral, Serrano-Lozano, Rocafort
Korean	~1–2%	Kailun Yang is Chinese despite surname “Yang”; not many obvious Korean names
Japanese	~0–1%	Essentially none obvious
Middle Eastern / Turkish	~1–2%	Ahmet Berke Gokmen
Other / unclear	remainder	Mixed international researchers

A more concrete estimate by author count:

• Total unique authors listed: roughly 90–100
• Estimated:
  • Chinese-origin names: ~70
  • European-origin names: ~12–15
  • South Asian-origin names: ~5–7
  • Bulgarian/Slavic names: ~4–6
  • Spanish-origin names: ~3–4
  • Turkish-origin names: ~1–2

The most internationally connected recurring senior collaborators are:

• Luc Van Gool
• Danda Pani Paudel
• Kailun Yang
• Jinjin Gu

The overall pattern is very typical of modern CVPR:

• strong Chinese participation,
• Swiss/European lab hubs,
• globally mixed PhD/postdoc collaborations,
• and increasing cross-institution research teams

-------------------------------------------
==========================

The Content of First Prompt:

* Yuanfan Zheng, Kunyu Peng, Xu Zheng, Kailun Yang

Seeing Beyond: Extrapolative Domain Adaptive Panoramic Segmentation

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Xiaolong Qian, Qi Jiang, Lei Sun,  Zongxi Yu, Kailun Yang, Peixuan Wu, Jiacheng Zhou, Yao Gao,  Yaoguang Ma, Ming-Hsuan Yang, Kaiwei Wang

Learning Latent Transmission and Glare Maps for Lens Veiling Glare Removal

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Shaolin Su, Josep M. Rocafort, David Serrano-Lozano, Lei Sun, Danna Xue, Javier Vazquez-Corral

Bridging the Perception Gap in Image Super-Resolution Evaluation

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

 

Paper

Jiaqi Tan, Xu Zheng, Yang Liu

RMMSS: Towards Advanced Robust Multi-Modal Semantic Segmentation with Hybrid Prototype Distillation and Feature Selection

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Haoyu Chen, Keda Tao, Yizao Wang, Xinlei Wang, Lei Zhu, Jinjin Gu

Intelligent Photo Retouching with Language Model-Based Artist Agents

In: The Findings track of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026 Findings)

Paper

Xiaoye Wang, Chen Tang, Xiangyu Yue, Wei-Hong Li

3D-Aware Multi-Task Learning with Cross-View Correlations for Dense Scene Understanding

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Zhiyuan You, Ke Wang, He Zhang, Xin Cai, Jinjin Gu, Tianfan Xue, Chao Dong, Zhoutong Zhang

PhotoFramer: Multi-modal Image Composition Instruction

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Xiang Yin, Jinfan Hu, Zhiyuan You, Kainan Yan, Yu Tang, Chao Dong, Jinjin Gu

How Far Have We Gone in Generative Image Restoration? A Study on Its Capability, Limitations and Evaluation Practices

In: The Findings track of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026 Findings)

Paper

Zihao Dongfang, Xu Zheng, Ziqiao Weng, Yuanhuiyi Lyu, Danda Pani Paudel, Luc Van Gool, Kailun Yang, Xuming Hu

Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?

In: The Findings track of IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026 Findings)

Paper

Bingwen Zhu, Bingwen_Zhu, Yuqian Fu, Qiaole Dong, Guolei Sun, Tianwen Qian, Yuzheng Wu, Danda Pani Paudel, Yanwei Fu, Xiangyang Xue

EgoSound: Benchmarking Sound Understanding in Egocentric Videos

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Liming Kuang, Dani Velikova, Mahdi Saleh, Jan-Nico Zaech, Danda Pani Paudel, Benjamin Busam

ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Ahmet Berke Gokmen, Ajad Chhatkuli, Luc Van Gool, Danda Pani Paudel

Inferring Compositional 4D Scenes without Ever Seeing One

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Website

Code

Nikolay Nikolov, Giuliano Albanese, Sombit Dey, Aleksandar Yanev, Luc Van Gool, Jan-Nico Zaech, Danda Pani Paudel

SPEAR-1: Scaling Beyond Robot Demonstrations via 3D Understanding

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Website

Jiancheng Pan, Runze Wang, Tianwen Qian, Mohammad Mahdi, Yanwei Fu, Xiangyang Xue, Xiaomeng Huang, Luc Van Gool, Danda Pani Paudel, Yuqian Fu

V^{2}-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang, Ruiping Liu, Fei Teng, Kai Luo, Zhiyong Li, Kailun Yang

ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Xiaolong Qian,  Qi Jiang, Yao Gao,  Lei Sun, Zhonghua Yi, Kailun Yang, Luc Van Gool, Kaiwei Wang

Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Mario Markov, Stefan Maria Ailuro, Luc Van Gool, Konrad Schindler, Danda Pani Paudel

FireScope: Wildfire Risk Raster Prediction With a Chain-of-Thought Oracle

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Paper

Yue Li, Qi Ma, Runyi Yang, Mengjiao Ma, Bin Ren, Nikola Popovic, Nicu Sebe, Theo Gevers, Luc Van Gool, Danda Pani Paudel, Martin R. Oswald

Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding

In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

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Twenkid - The Child of AGI - is Challenging the Grandfather of AI Yann Lecun

Yann Lecun cites a post, which is acknowledging that his ideas were correct etc.

A  part of the concluding punch lines:

* It is "dime a dozen", but people decades older than me who *literally* repeated and ripped-off my suggestions, [strategy, plans, principles, theories, directions, conclusions, thoughts ...] and observations decades later, got prized with billions to *waste* and I am not even mentioned. They did it even in my own country, where one Bulgarian-Canadian became an "architect" of an institute in Sofia, with statements which were a *20 years late rip-off* of the above-cited essay, which were sold as  "innovative" and ground-breaking :))), "for the first time in Eastern Europe" etc.

* As of "Dime a dozen"--> yes, or even "Five a dozen" -->
The current  "supercomputer" of my lab is called "PETAK I", where  "Pet" means "5": from: 1. "Pentium" (historically the CPU and brand on which TUM was created), 2. The CPUs of all nodes: Core i5 (all old ones, 11-14 years old models, LOL); 3. Five nodes of the cluster (the initial full configuration) 4. A parody CPU-name from a science fiction work from 2004 from that theory ("Pentium 5"; "Петият Петак") and 5. In Bulgarian it also means "5 cents"... LMAO

"""Tunisia.AI

Ala Eddine Ayadi

 ·

Администратор

Експерт в групата на тема Изкуствен интелект и машинно обучение

 ·20 април в 21:34 ·

Yann LeCun may have been right about something important: next-token and next-pixel prediction are probably not the most efficient path to real world understanding.

For years, the industry has been scaling generative models under the assumption that bigger models, more data, and more compute would eventually produce deeper intelligence. LeCun has been arguing the opposite: predicting every word or every pixel forces models to spend huge amounts of compute on surface details instead of learning the underlying structure of reality.

That’s the core idea behind JEPA (Joint-Embedding Predictive Architecture): instead of reconstructing the world pixel by pixel, learn a compact latent representation and predict what happens next inside that space.

The problem is that these models have historically been unstable. They suffer from “representation collapse,” where the latent space becomes too simple to carry useful information unless you add complex training tricks, auxiliary losses, or frozen components.

A new paper, LeWorldModel (LeWM), shows a much cleaner approach. It trains end-to-end from raw pixels using only two losses: a next-embedding prediction loss and a Gaussian regularizer on the latent space. This drastically simplifies the training setup compared to prior approaches.

The efficiency gains are striking. The model has around 15 million parameters, trains on a single GPU in a few hours, and can plan up to 48× faster than larger foundation-model-based world models, while staying competitive on several 2D and 3D control tasks. Its latent space also appears to capture meaningful physical structure and can detect physically implausible events in controlled environments.

This doesn’t mean generative AI is a dead end. LLMs remain extremely powerful. But it does reinforce a key technical point: for world modeling and physical reasoning, predictive latent-space approaches may be far more compute-efficient than brute-force generation.

The real shift might be this: not models that generate everything, but models that understand enough of the world to predict what actually matters."""

https://www.facebook.com/yann.lecun/posts/pfbid0oEkmbuzwdvNC6JWRDoRzaDAyLNtzPZzvvman5ob89Z1v5AYvuQdTrQEjFcGJ3958l?__cft__[0]=AZaoNFuahXF3_DoSg2wL8ID4WPPsHBRWvjU51k3Sa742m0aQX2r7nl0VWyDXlyXx5eBBbxc-OWgq18zIcllnPjdIm_HNuV1Mga9hvE-Nga9s6_vMV0SjQVFusWFQhxpNCbrhQX0HjKTjh5WwzRfqeSFqWjE6X0vy_kidQqxMcHABvF2r6roXC6OCa1EcctDCHZNSND1P9hmQT8Cl2AcxeawX&__tn__=%2CO%2CP-R 

Todor Arnaudov

From the books "The First modern AI Strategy ..."... and "Stack Theory is Yet another fork of Theory of Universe and Mind" published at SIGI-2025

This idea, together with the prediction and next-token prediction (but in multi-scale, multi-precision hierarchy of resolutions of causality-control and perception), was published and explained nearly 25 years ago in Theory of Universe and Mind and presented during the world's first university courses in AGI in 2010 and 2011. Y.Bengio also rediscovered it 2017-2018 (Consciousness prior) and his example is almost literary repetition of an introductory definition from a treatise published about 14 years earlier. The author was a teenager, LOL.

Yann LeCun:

@Todor Arnaudov as I pointed out on another platform, ideas are a dime a dozen. The hard part, for something like this, is to implement it and to make it work.

The whole idea of hierarchical representations and learning by prediction is very old.

But learning hierarchies of representations didn't really work until convolutional nets were shown to do it in the late 1980s and more forcefully in the early 2010s (this took a while).

===

Todor Arnaudov:

Hi, first thanks for your answer as I didn't expect this honor. I don't disagree that there were earlier "prophets", I recently published a hyperbook with a related name (nearly 5000 pages in total), where one of the intros in one of the sections with collectons of related, prior and later work is a citation from the Holy Bible:

"There is nothing new under the Sun"

Some of the prior work doesn't get enough credit and is unknown, even the "fellow AI  historian" Schmidhuber doesn't mention them, e.g. the Soviet lab of Bongard and his colleagues etc. (E.g. once I caught Chollet literary restating insights from the 1967 book "Проблема узнавания" - perhaps he didn't know; he also rediscovers definitions for general intelligence of mine, published in 2001 (he couldn't know about it) - see the link at the end and the reviews of the LLMs).

 The Bible is called "The Prophets of the Thinking Machines: Artificial General Intelligence & Transhumanism: History, Theory and  Pioneers; Past, Present and Future", SIGI-2025 - and yes, almost nobody will bother to even open it. :))

BTW, e.g. IMO your PhD student Marc’Aurelio Ranzato deserves more credit for his pioneering work in DL and his insights (which perhaps [are] ~ also yours) -- his work is credited in my historical collections here: https://twenkid.com/agi/Lazar_The_Prophets_of_the_Thinking_Machines_20-8-2025.pdf ~p.21.

I do agree that I had to push to implementations immediately (not your type of NNs though) and perhaps my claims would be accepted after I implement them all by myself (Or if I or somebody else had - 20 years ago with no collaborators or any funding, no mechanical Turks to labe a gazillion of data and computing iterations, compared to 20 years later and all the collected resources in all senses of the word: i.e. IMO the difficulty of the implementation is supposed to decrease and be "discounted" with time like in RL; an idea 25 or 50 years ago may end up more "valuable" than an implementation in the present - see generative AI and the final citation below)

* I know about your dismissive opinion about "ideas", e.g. your comments to Schmidhuber's recent challenge, that you also could find ideas in your unpublished notes or something etc. and I've listened to your answers to him since 2022, "The path towards autonomous AI..." - I remember you defended yourself with referring to Optimal Control etc.

However many works are proposals, theoretical etc. but still get recognized, while other prior ones - don't and are even "humiliated". Also the core novelty there in my reading of the paper was also matching the mentioned TUM (and too general, it was not an implementation too); in general it looked like another cognitive architecture, which were popular in the cognitive science and the AGI community decades earlier, perhaps I have to reread it.

* I understand that if you dismiss even the German, who is at a comparable status as yours or, say he has more ground to be believed that he is, then you (and almost anyone) wouldn't recognize the claimed "priority" or even just the "contribution" of some obscure "self-proclaimed" "crank" or the mentioned theory, no matter the evidence (maybe you wouldn't even bother to check any evidence or count it as "theory" or anything).

BTW, your recent work about the brain/humans as "not general ..." also matches and is closely related to my prior work/accounts, beginning in early 2000s, however with different interpretation of the observations. The limitations don't deny the concept of general intelligence and the possibility of general principles and modules (prediction-compression etc.) I may address the correspondences in a paper.

*  Stack Theory is yet another Fork of Theory of Universe and Mind, SIGI-2025

https://www.researchgate.net/publication/398934575_Stack_Theory_is_yet_another_Fork_of_Theory_of_Universe_and_Mind_-_Appendix_Volume_to_The_Prophets_of_the_Thinking_Machines_Artificial_General_Intelligence_and_Transhumanism_History_Theory_and_Pioneers 

* The first modern AI strategy was published by an 18-year old in 2003 and repeated and implemented by the whole world 15-20 years later: Bulgarian Prophecies: How would I invest one million for the greatest benefit for the development of my country? https://twenkid.com/agi/Purvata_Strategiya_UIR_AGI_2003_Arnaudov_SIGI-2025_31-3-2025.pdf (Bongard, 1967 vs Chollet,2024 p.169-170)

* BTW, cheers from Kyuchuk Paris - that's the district in the city of Plovdiv, where TUM was created. 🙂

* This is the world's first modern "AI strategy", 2003, repeated and implemented by "the whole world" 15-20 years later: https://twenkid.com/agi/proekt.htm 

* It is "dime a dozen", but people decades older than me who *literaly* repeated and ripped-off my suggestions and observations decades later, got prized with billions to *waste* and I am not even mentioned. They did it even in my own country, where one Bulgarian-Canadian became an "architect" of an institute in Sofia, with statements which were a *20 years late rip-off* of the above-cited essay, which were sold as  "innovative" and ground-breaking :))), "for the first time in Eastern Europe" etc.

* As of "Dime a dozen"--> yes, or even "Five a dozen" -->
The current  "supercomputer" of my lab is called "PETAK I", where "Pet" means "5": from: 1. "Pentium" (historically the CPU and brand on which TUM was created), 2. The CPUs of all nodes: Core i5 (all old ones, 11-14 years old models, LOL); 3. Five nodes of the cluster (the initial full configuration) 4. A parody CPU-name from a science fiction work from 2004 from that theory ("Pentium 5") and 5. In Bulgarian it also means "5 cents"... LMAO

 Also as I predicted in 2013 (counterintuitive to all "experts" up to just a few years ago, I namely wrote this article *because* of clueless "experts" predicted the opposite; they were later cited thousands of times for their *WRONG* world-model and wrong predictions):

"Creative Intelligence will be First Surpassed and Blown Away by the Thinking Machines, not the "low-skill" workers whose jobs require agile and quick physical motion and interactions with human-sized and human-shaped environment"

https://artificial-mind.blogspot.com/2013/10/creative-intelligence-will-be-first.html

" (...) For the intellectual jobs - it's much easier to pick a computer, run the appropriate software or connect it to the service,

and get it thinking - you already have decent cameras, microphones and many sensors even in smartphones. (...) The bottom line is that the "white collars" are more endangered in current-time economy. Perhaps that kind of economy could hardly survive the AGI revolution. I guess it may turn upside down for a while - the low-skill workers could get higher pay, because intellectual activities will be done in 1 ms for free... 😉  We, the smart guys (the smart asses, see "Super Smartasses" the graphical series ) wouldn't be needed by anyone... Not that we are needed now. :))"

 * The prediction of the generative AI (however it could have been created by the late 2000s-early 2010s - it came *too late*, not too quick as Hinton and Bengio "complain"; not with gradient-descent of course):  

 -- Creativity is Imitation at the Level of Algorithms - An outline sketch of a possible path of development of the Artificial Intelligence "Emil" 

https://www.researchgate.net/publication/395129890_Creativity_is_Imitation_at_the_Level_of_Algorithms_-_An_outline_sketch_of_a_possible_path_of_development_of_the_Artificial_Intelligence_Emil

 * Petak I: https://github.com/Twenkid/SIGI-2025/blob/main/petaki.md

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Lazar - the volume from The Prophets of the Thinking Machines with a new version with corrections up to 29.4.2026

Read the latest corrected edition of the volume "Lazar", about 130 pages, consisting of an enormous survey and summary of many fields of AI and AGI, historical and recent.

Other updates may be published soon with more narrative introductions, summaries and conclusions.

Download from https://twenkid.com/agi and the Github page of the virtual conference Thinking Machines 2025/Self-Improving General Intelligence 2025 (SIGI-2025):

https://github.com/Twenkid/SIGI-2025


Direct link: https://twenkid.com/agi/download.php?file=Lazar_The_Prophets_of_the_Thinking_Machines_20-8-2025.pdf

Perhaps I will upload it to Academia.edu and ResearchGate perhaps after a few more editing and additions.


A brief contents listing some of the domains:

#lotsofpapers  ... #lazar

Lots of Papers: In AI, ML, CV, ANN, DL, …  throughout history, classical 1950s, 1960s, 1970s, 1980s, 1990s, 2000s, early 2010s to 2020s. Computer Vision, Reinforcement Learning, Program Synthesis. Lifelong Learning, Human-Computer Interaction, Mixed Initiative Interfaces (Agentic Systems); Evolutionary programming, Genetic Algorithms, Self-improving agents; Speech Synthesis, Speech Recognition, Audio Generation etc.  Groundbreaking or important researchers or related to the flow and context of the reviewed topics; and a few Bulgarian researchers who participated in some of the works.

*  Lifelong Learning, Continual Learning, Reinforcement Learning (historical Q-Learning, modern Deep Q-Learning: Atari DeepMind …), RL for LLMs, policy optimizations (DPO, PPO, OREO) … Conditional Random Fields (CRF); Chain-of-Thought prompting …

* Survey of other techniques and research in computer vision,  preceding the explosion of the application of convolutional neural networks after 2012: DBN, RBM, MRF, SIFT etc.

* Survey on Early Deep Learning architectures and Normalizations; seminal papers and PhD theses by pioneers in DL from the schools of LeCun, Hinton, Bengio: M.Ranzato, V.Mnih, A.Krizhevsky, I.Sutskever, A.Mohamed …

*  Survey of Object Recognition and Classification before Deep Learning

* Selected Computer Vision works from 1960s to 2020s

* Exploration and introduction of concepts and techniques in computer vision, machine learning, neural networks

*  Mixed-Initiative Interaction (Agentic Systems)

*  Audio: Speech Synthesis, Audio Generation, Speech Recognition; from 1980s to 2010s

*  Neural Machine Translation, Language Models, LLMs, Text Generation, Text and Language Learning and Representation, Word-Embedding

* Alternative approaches for sequence and next words prediction, instead of neural networks: stochastic memorizer, sequence memorizer* Transformers - the seminal paper from 2017

*  Transformer architectures for images and for reducing the quadratic complexity

*  Neural Program Synthesis

*  Transformer architectures for images and for reducing the quadratic complexity

*  Evolutionary Algorithms | Genetic Algorithms | Genetic Programming
*  Genetic Programming, Genetic Algorithms, Evolutionary Programming: Part II

* Summary and selection of important concepts in Evolutionary Algorithms | Genetic Algorithms | Genetic Programming etc.

*  Vision Transformers – ViT

*  Multimodal Learning, Dialog Learning, Continual Learning

*  Diffusion Models

*  Self-Improving General Intelligence, Recursive Self Improvement

 ...

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Wolpert’s Theorems about Mutual Unpredictability and the Impossibility of Subuniverses to Predict with Highest Resolution of Causality-Control are Rediscoveries of Concepts from Theory of Universe and Mind – Entangled with Unnecessary Mathematical Notation and Unsatisfiable Premises

Wolpert’s Theorems about Mutual Unpredictability and the Impossibility of Subuniverses to Predict with Highest Resolution of Causality-Control are Rediscoveries of Concepts from Theory of Universe and Mind – Entangled with Unnecessary Mathematical Notation and Unsatisfiable Premises

https://www.researchgate.net/publication/403842042_Wolpert's_Theorem_about_Mutual_Unpredictability_and_the_Impossibility_of_Subuniverses_to_Predict_with_Highest_Resolution_of_Causality-Control_are_Rediscoveries_of_Concepts_from_Theory_of_Universe_and

 In a discussion from 8.2025 on AGI List, a part of which I archived at SIGI-2025 as a paper, titled "Power overrides intelligence", Matt Mahoney mentioned some "Wolpert's law" of which I didn't know at the time. Later that year I discovered it without searching for it from other publications, and I found out that in fact this is a fork of my own "Arnaudov's laws" (principles), published and explained 4-5 years before the first paper of Wolpert (2007-2008) and up to 16 years before a consequent paper from 2018*. There is a match even on "Liar's paradox" - it is addressed in my work as well, however I "scaled" it to even more absurd form, ridiculed it and explained why it was nonsense and it didn't prove what the "cheaters" intended. It is related to the concept in TUM "resolution of causality-control and perception"; comparison of  representation with different and incompatible RCCP. "What's the color of the rainbow?" (one color) These are ill-posed problems, presented as well-posed by "cheaters". In this circumstances, any higher-resolution answer is correct, as the lower-resolution causality-control doesn't have capacity to distinguish the answers.

The original "law" from TUM also reflects the hierarchical structure of the predictions and CCUs, which work as and are created by hierarchical universal simulators of virtual universes, i.e. predictors and causers, which are multi-resolution, multi-range, multi-scale, multi-domain, multi-precision, ... multi- ...  Wolpert has a corresponding concept "a general-purpose prediction device, capable of correctly predicting different aspects of the universe’s future",  however his "devices", at least so long as I interpreted the paper, are flat and they predict/not predict, true/false", "A/not A" - 1-bit nonsense in the real Universe, as explained in TUM. 

This paper is a chapter from the book "Reflections on Everything", or "Listove", the second-largest volume from "The Prophets of the Thinking Machines: AGI & Transhumanism: History, Theory and Pioneers; Past, Present and Future", SIGI-2025, 10.2025.

Abstract

This work presents a critical and comparative analysis of the theoretical limits of prediction, inference, and control in physical systems, focusing on the framework introduced by David H. Wolpert and its relationship to the Theory of Universe and Mind (TUM). Wolpert’s theorems establish that no inference device embedded within a universe can achieve complete and error-free prediction, observation, or control of other devices, and that mutual perfect predictability between independent agents is impossible. These results are often interpreted as formal limits on knowledge, extending earlier ideas such as the impossibility of Laplace’s demon.

The present paper argues that these conclusions correspond to principles previously articulated within TUM, where the universe is modeled as a hierarchical computational structure composed of interacting causality-control units (CCUs). In this framework, predictive and causal capacities are determined by the resolution of causality-control and perception (RCCP), with higher-level subsystems operating on compressed representations of lower-level dynamics. As a consequence, all subsystems exhibit bounded predictive power, limited memory, and partial control, while only the universe as a whole achieves maximal resolution and completeness.

The analysis further examines the assumptions underlying formal inference models, particularly the treatment of devices as independent entities. It is argued that, in physically realized systems, all subsystems are inherently correlated due to shared origin, continuous interaction, and embedding within a common dynamical structure. This challenges the applicability of certain formal premises and suggests that observed limits on prediction arise from structural and hierarchical constraints rather than solely from logical or computational restrictions.

Additionally, the paper critiques the use of highly abstract logical formulations – such as binary query models and paradox-based arguments – as insufficient for capturing the multi-scale, continuous, and physically grounded nature of real-world systems. Instead, it advocates for models that incorporate hierarchical organization, varying resolutions, and the interplay between compression and prediction.

The conclusion is that while formal results on the limits of inference are valid and significant, they can be more comprehensively interpreted within a broader framework that accounts for the hierarchical and embedded nature of cognition and physical processes.

...

                This work is a chapter from the book “Reflections on Everything”, or “Listove” (Листове по всичко), which is an appendix and the second-biggest volume from the hyperbook “The Prophets of The Thinking Machines: Artificial General Intelligence and Transhumanism: History, Theory and Pioneers; Past, Present and Future”, T.Arnaudov, 2025-1.2026 – all published at the yearlong virtual conference “Self-Improving General Intelligence/Thinking Machines” 2025, organized by The Sacred Computer: Thinking Machines, Creativity and Human Development, a virtual multi- and interdisciplinary AGI and Transhumanism research and development laboratory, created in 2000. The first classic works of Theory of Universe and Mind were published between 2001 and 2004. Core ideas from the theory were presented in a lecture at Technical University of Sofia in September 2009 and in the world’s first university course in AGI at the University of Plovdiv “Paisii Hilendarski”, Bulgaria in 2010 and 2011.

The Sacred Computer: Thinking Machines, Creativity and Human Development: 2000-2026

https://github.com/twenkid

...

* See references to my work in the paper and in the referred books

* Physical limits of inference, David H. Wolpert, MS 269-1, NASA Ames Research Center, Moffett Field, CA 94035, USA, https://arxiv.org/pdf/0708.1362  [Submitted on 10 Aug 2007 (v1), last revised 23 Oct 2008 (this version, v2)]

* Theories of Knowledge and Theories of Everything, D.Wolpert, February 2018

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Common Confusion About Agency, Will, Soul etc. of Humans and Biological and Artificial Agents - Todor's comment on The Asymptotic Illusion: On the Ontological Limitations of Artificial Agency

A discussion about the paper on Academia.edu:
* The Asymptotic Illusion: On the Ontological Limitations of Artificial Agency, Mohamad Al-Zawahreh, 25.12.2025
https://www.academia.edu/s/06c854dde5

Todor Arnaudov
10 days ago

Hi, this is a common confusion - building a straw-man of Turing machines or "computation" as defined in this abstract sense, while the counterpart argument refers to the full "stack" definition of the systems, down to the lowest level virtual universes (See Theory of Universe and Mind, T.A. 2001-2004+, e.g. "Stack Theory is yet another fork of Theory of Universe and Mind", 2025). Neither computers, nor LLMs, AI (in whatever form) or whatever are "just" algorithms, "computation" etc. - they are immersed in the Universe and their implementation includes those "non-computable" parts as well. On the other hand, all these advanced thinking, theories, logic, reasoning about "quantum" etc. would have been impossible without all the science and technology, all "mechanical" processing etc., i.e. the mighty "conscious" being wouldn't have been capable to even formulate these problems and their [personal] "superiority", they would have been just silly apes. There are a lot of discussions about that in TOUM, including the latest additions in the hyperbook "The Prophets of the Thinking Machines: AGI & Transhumanism: History, theory and pioneers; past, present and future". One point which I addressed in a letter to the cognitive semioticists Jordan Zlatev: the LLMs have no consciousness, but "just" etc. [see in appendix "Reflections on Everything" (Listove) from "The Prophets"]. However I haven't read anything but my own thoughts regarding the following problem, which is valid for humans and their mind as well: WHAT[an LLM is] and WHERE *exactly* the LLM is located, why and who (evaluator-observer) sets the boundary exactly there (at what resolution of causality-control and perception) and what *exactly* it is when fully defined in order to function. It is not "just" the weights of the NN (what the h* is "NN" in the physical universe - it includes the whole hardware, all chips, all connected computers - your PC or smartphone, the routers, the Internet servers, the main GPU servers; it included the original data, the data in the memory (other representation); it includes also the *HUMANS* who interact, their biology, their states; it is a result of a chain of operations and activities of all related humans and other technologies and ultimately it requires the whole universe to be what it is. However, this is refuted and "vulgarized" to "they are algorithmic, they are "just 1s and 0s", "switches" (the earlier nonsense about computers, still defended e.g. by F.Faggin). No, computers are not "just 1s and 0s", and they emerged and operate in the same Universe as "humans"; besides humans are created and built also *WITH* the participation of all computers and technologies, i.e. humans are *PARTS of the machines* as well. "We are merged" without the need of "physical"/"short-molecular-distance"* way of interaction. See also an article about that in "The Prophets ..." in volumes: "Science Fiction. Futurology. Cybernetics. Transhumanism" and cited also as an appendix in the small monograph "Universe and Mind 6", which is related to this discussion. * Another school with this confusions (or choices) is "Relevance Realization", which is explaining definitions of Theory of Universe and Mind, published more than 20 years earlier, however they put a stamp "this is not computable", "the opposite of computation", while the original [work] claims it is computable, but hierarchical, multi-scale, multi-domain, multi-precision, preemptive, has "interrupts" etc., they are built by the same fabric of the Universe computer and they interact with it and fetch "sensory" data from the common "pool". The real computers are like that, they are not the primitive and abstract strictly serial, one scale, "fixed symbols" Turing machine outputting AAABBBBBAAAAC-HALT. --- References: * Stack Theory is yet another Fork of Theory of Universe and Mind, T.A. 2025, SIGI-2025, https://artificial-mind.blogspot.com/2025/09/stack-theory-is-yet-another-fork-of.html

* Reflections on Everything/Listove (Листове, Листове по всичко) - a volume from The Prophets of The Thinking Machines: Artificial General Intelligence and Transhumanism: History, Theory and Pioneers; Past, Present and Future, T.Arnaudov, 2025 https://github.com/twenid/sigi-2025 See the cited excerpt from Todor's letter to Jordan Zlatev on what and where the LLMs are (it will be published as an article/paper as well), after p.340 "* Sentience or consciousness of another “entity” is in the eyes of the evaluator * Thoughts from a letter by Todor Arnaudov to the cognitive semioticist Jordan Zlatev , 16.8.2025" * "We are merged..." - see my article, answering Tim Tyler's video, published both in Reflections on Everything and in the volume "Science Fiction. Futurology. Cybernetics. Transhumanism" from The Prophets ... *Коментар на Тодор Арнаудов към видеото на Тим Тайлър „Да се слеем [с машините]“ от 10.2023 Tim Tyler: Let's merge! (...), p. ~175 in Listove The text in English, posted on Youtube: Todor: IMO humans and machines - actually the technologies, the recognizable systems, entities in the Universe (and the ones which are not recognizable for now) are merged anyway. The human individuals as bodies, entities are one "view", "rendering", a way of sampling of the actual intrinsic representation of the underlying properties and processes. The same goes for any piece of hardware, computer, robot, any object. They are what they are under a particular sampling of the data, in another sampling they are physically, causally, energic-based etc. connected and part of the causality, influence, events network and interrelated. In one philosophical school humans are defined as the set of "social relations". Also human individuals without technology and starting from scratch with no previous culture and language, which is also technology, are not very much more capable than apes in the first generations, or in the first hundreds of thousands of years. Technology, the environment and previous recorded knowledge and the social, scientific and technological "software" that gets loaded into our minds, all from the whole universe allow human beings to be so clever etc. So we are part of the human-machine (technology) system anyway.

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The distributed representation of humans and everything in a field-like form, the causal id/causal tags* theory etc. is discussed also in other volumes, such as "Universe and Mind 6" and "Is Mortal Computation Required for the Creation of Universal Thinking Machnes" at SIGI-2025. ** Note that "causal IDs" in my explanation are not referring to Michael T. Bennet's "causal identity" concept, but the match of similarly titled concepts in his fork (with unrecognized prior work) is funny. The causal IDs in my speculations are about hypothetical information and interaction entities or signals which allow or make the wholes, beings, systems to "feel" as a whole. I derived them from the insight that all systems in all scales and multi-scales encounter inevitable lags between their constituent parts, and these lags are varied. One way of a given causality-control unit - the building blocks in TOUM - to know that a signal is from a given whole, could be by special interactions and information, these "causal IDs", which could be updated and kept active for particular reasons, e.g. a chain of particular chemical, physical or whatever interactions within given .... etc. and fade. E.g. on the page on Github about TOUM in late 2023; most of "Universe and Mind 6" was written in the Spring of 2023 and in 2023-early 2024, but it was published as a completed monograph in 2025. https://github.com/Twenkid/Theory-of-Universe-and-Mind

  

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