Read: "The Prophets of the Thinking Machines: Artificial General Intelligence and Transhumanism: History, Theory and Pioneers; Past, Present and Future, currently >4600-4800?(11.1.2026) (4000 3300? ; 3050-3100? pages (7.8.2025) 1240 1600 >2400-2500? (2900+?) pages [6.2025] This work will continue with "Creating Thinking Machines". Visit SIGI-2025 with some works and volumes already published and check and join and help with the open projects, which are not stealth: the AGI infrastructure called "Vsy" or "Jack of All Trades" etc. Welcome to Artificial Mind, part of The Sacred Computer. I am always looking for friends, partners and collaborators to work with, interesting project and new fields and things to study, explore and create. Join my exploration* or invite me to yours! (...) *Versatile (Limitless) Explorer and (Self-)Improver - one of my alternative terms for AGI/Universal Thinking Machines. Twenkid = ?.

Monday, September 28, 2026

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"Why do some intelligent people believe intelligence equates power?" - Todor's answer to Yann LeCun's post - Compare to "Power Overrides Intelligence", 2025

Todor's answer to:

2 ч [Facebook, 28.9.2026]

 
Why do some intelligent people believe intelligence equates power?
Not only is it not true in general, it's not even true within human societies.
The people with the most power are not the most intelligent.
The most intelligent people generally stay away from the levers of power.
Funnily, the intelligent people who believe intelligence equates power are not particularly powerful themselves.
They are the ones who believe artificial superintelligence is going to dominate humanity simply because of its intelligence.
This relies on a bunch of wrong assumptions.
1. Intelligence is a scalar metric: it's not. One entity can be smart about some things and not so smart about others. A tiger can be smart enough to eat a human.
2. The complexity of the real world is irrelevant: then explain why a simple virus can kill you.
3. Intelligent beings necessarily want to dominate. The desire to dominate only exists among some social species. Non-social intelligent species have no desire to dominate others (e.g. Orangutans, octopuses...). That's not even true within the human species. https://www.facebook.com/yann.lecun?__cft__[0]=AZiAVGFy017hqHkQlE6OwoMfGqAsCDvwFA5GDhZs1nFfY_zEbfy0pw3sXybtEP396G7ufnm9VZg7FjokLSUVqtKVvthgUU9xcVFy5ZAwsXhmpPYEWnrZ1j75nBPd6llRj1rJ0hBEosGFmKhAVygvNmii2ZPgDOGBnv3O4IoNIJSfVi6Q2Q&__cft__[1]=AZjVjnYOzr7chBCwZssdDpbXGcg-UKCWLAC9p94_A5ENOZ8XrvCfpB2ciZ5ivgRFx_pNbuE17NstFnlmxldzmW9WuEygatTGCuiVxZbpRhlbnhfGLxRa-1_m1jVn_avm_rk1-pwqbKMs69nX0bHEPWiBlSlc_mD8zGAwvpqIoSmp8a9qlg&__tn__=-UC%2CP-R 
Споделена публикация
...


28.9.2026: Todor: They do, for example for two reasons: first, because they may "understand", observe, feel correctly that "Power overrides intelligence", and second: because they are *APES*, i.e."social animals" (see below), they are inclined to connect and conflate power, especially over other individuals, "agents" from the group and [cognitive] intelligence; and also they do not really understand that cognitive aspect of intelligence - cognition, including the reflective thought as well, self-knowledge; they also make the common error: confusing *subordination*, submissiveness, sometimes "adaptability" with intelligence*. This is the "intelligence" of a dog - dogs are considered "intelligent", because they can be trained to follow commands by their masters; cats are harder or they cannot be trained at all, "therefore, they are less intelligent" - in fact they are *less obedient*.

You are right in the message, however "ideas are worth "dime a dozen"" (your words), and yet again these ideas are already well explained in the AGI literature, at least starting up to 25 years ago by the "Prodigy of AGI". A recent take on that is the paper called:

* "Power Overrides Intelligence", SIGI-2025 (& AGI List 2025),

(PDF) Power Overrides Intelligence: Answers to Matt Mahoney's summary of LessWrong's "The Problem" regarding the so called existential risks of Artificial General Intelligence and Superintelligence in August 2025. Available from:
* https://www.researchgate.net/publication/407308848_Power_Overrides_Intelligence_Answers_to_Matt_Mahoney's_summary_of_LessWrong's_The_Problem_regarding_the_so_called_existential_risks_of_Artificial_General_Intelligence_and_Superintelligence_in_August_2

It was an answer to MIRI/"Lesswrong"/Yudkowski, "John" Connor from Eleuther etc. and their followers.

Some times later Gary Marcus rediscovered it and repeated the title, answering a question that the danger is not intelligence, it is "Power". Others follow.

This paper was a recent answer extension of explanations in the letter to one Oxford's institute, February 2012:

* Philosophical and Interdisciplinary Discussion on General Intelligence, AGI and Superintelligence Safety and Human Moral | Cognitive Origins of the Concepts of Human Soul and its Immortality | Free Will and How it Originates Cognitively | Animate Being and Soul and the Cognitive Reason for the Believe that "Thinking Machines can't have a Soul and Consciousness" | Technology Making us more Humane | The Egoism of Humanity | And more, 2.2012
https://artificial-mind.blogspot.com/2012/02/philosophical-and-interdisciplinary.html
(...)
Some of the points, e.g. the Terminator's real message (expressed in T2's conclusion) is explained even in the foundational works from the Theory of Universe and Mind from 2001-2002.

...

However:

"Yann: 1. Intelligence is a scalar metric: it's not. One entity can be smart about some things and not so smart about others. A tiger can be smart enough to eat a human."

The entity can, but the "superintelligence" and AGI is supposed to cover enough domains, e.g. have enough input and output modalities and possess the "general" intermodal capability over them.

It is true that average *humans* and some "talented" ones general intelligence have severe resource limitations and their generalization hits walls quickly (seethe mentioned TOUM, 2001-2004, and early 2010s publications up to now), but that doesn't mean that "there is no general intelligence" ~ general algorithms and methods, given value-free raw data - one of the keys of general intelligence is the capability to predict, and also to do it *in a progressive* manner (to predict better), if this GI involves "learning". Regarding your work, "Path to autonomous" ("dime a dozen") you seem to agree, and it is already demonstrated.

However, there could be limitations in data and memory, and the connection between different modalities, which prevent the agent from going higher (also, there are "swarms" and accumulation and tools - human's intelligence is outsourced as well).

The general cognitive algorithms still may be similar, but if one brain or system doesn't have enough resources, steps, levels of processing, memory, speed (given an allowed range) and harsh early "interrupts" are issued, it cannot reach to particular structures and generalizations - the residual data loses all its detail and features, the higher level cannot discriminate or make more precise or more broad classification etc. , the actuators are not precise enough etc., or even it just cannot *remember* the data - human's phonological loop is about 1.5-2 s for normal people. Their low-level auditory processing or "microphones" in the ears may be not fine enough to detect the patterns for proper "musical intelligence" or their neocortical areas for fine motor skills, motor, premotor, supplementary motor, cerebellum may have too few neurons, be not myelinated properly at young age etc.; their visual areas may be also lacking proper volume and connections in the associative areas etc. - that is why they cannot "understand" drawing etc. However this is not a principled limitation of "intelligence", the principles of processing and generalization are the same as all these modalities are just data, motor outputs are also just data which maps or reconstructs the inputs.

"Yann: 2. The complexity of the real world is irrelevant: then explain why a simple virus can kill you."

Not only this, and the complexity applies for different ranges and spaces.
"Power Overrides intelligence", August 2025:

"""... The bacteria and viruses are supposedly "dumber" than humans in human measures, or say gamma radiation or temperature, but at low level at molecular scale, they are more powerful and "more intelligent" by the given definition, they have "higher predictive" (and more importantly CAUSAL) power over molecules in their locality than the molecules of the human body, so they can kill humans and humanity with 0 points on the LLM tests."""

Viruses are "simple" in *some* scale, however they have higher causal power and in some other scale, involving the general measure of prediction, they actually could be viewed as "more intelligent" than the cells when they "out-predict" them, but the dangerous part is the "out-causing" and "over-causing" them, overriding the cell's will.

This case is also an example of the long chain of transfers of the Will through the scales - the Mind or "brain" cannot control directly at molecular level, no matter how "smart" it is at macro level. In order to do, a huge amount of auxiliary "by-effect" technologies have to be developed, involving an enormous amount of other humans and machines - the whole ecosystem.


"""Yann: 3. Intelligent beings necessarily want to dominate. The desire to dominate only exists among some social species. Non-social intelligent species have no desire to dominate others (e.g. Orangutans, octopuses...). That's not even true within the human species."


Right, this is a projection of the "aligners", they reflect what their type and their masters type of beings are, and project it to every "AI", "SAI" or potential "aliens".

Another point, extending the "domination", explained in another letter to AGI List, 2026 (but this point is not understand by many of the so called "intelligent" people about which you talk also - "social ANIMAL" or species actually mean hierarchies of *subordination*. Humans are selected, trained and forced to *subordinate* and the ones who climb the hierarchies are of that type, they are the preferred for the "stable" operation of the swarm: two roles: either tyrants or submissive and instrumental to the will of the tyrants (the "alphas").

See also Stanislaw Lem's "Golem XIV" - an LLM-like superintelligence and the appendix volume on Science Fiction, Futurology, Cybernetics and Transhumanism from The Prophets of the Thinking Machines. #sf #tosh1 #prophets
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Friday, July 24, 2026

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Bozhidar Saraboyukov Jumps 8.49 m in Plovdiv


https://youtu.be/QRgIu4nulWk 


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Tuesday, June 30, 2026

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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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Saturday, June 13, 2026

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A few volumes from The Prophets of the Thinking Machines: History, Theory and Pioneers; Past, Present and Future

 Title pages of a few among 20-some volumes of the hyperbook "The Prophets of the Thinking Machines: Artificial General Intelligence and Transhumanism: History, Theory and Pioneers, Past, Present and Future". The books, monographs and papers are in English and Bulgarian. The images below are from: "Reflections on Everything" (Listove) - the second largest volume, Lazar - a broad survey in various ML and AI domains; "Is Mortal Computation Requried for the Creation of Universal Thinking Machines" (Нужни ли са смъртни изчисления за създаване на универсални мислещи машини?), "The Cat" (Котката, Kotkata) - Can the 2023-2024-2025 LLMs recreate the "prompt-engineering" which I demonstrated in 2003-2004 in the seminal work "Analysis of the Meaning of a Sentence ... - find on SIGI-2025






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Thursday, May 28, 2026

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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 clusterApprox. shareNotes
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 / unclearremainderMixed 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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Wednesday, May 20, 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

 
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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