Můj cíl cíl je vytvořit velkou vizuální mapu, která má co nejvíc informací o matematice přes ukázání těch nejdůležitějších matematických struktur, definic, rovnic, s co nejmenší textovou omáčkou kolem, chci aby co největší % mapy byly hlavně nejvíc matematický symboly, celý v jednom obřím čitelným plagátu! Což jsem ještě neviděl. Jsou nějaký mapy, tabulky, seznamy, wikiny apod. matematiky, ze kterých se inspiruju, ale neviděl jsem nic jako vizuální mapu tuny definicí a rovnic z hlavně foundations matiky, čistou matiku a aplikovanou matiku: teoretickou fyziku, teorii systémů, matematickou biologii, AI, ostatní matematický aplikovaný vědy a engineering obory, který vidím za nejdůležitější. Často jsou moc obecný nebo moc konkrétní jinde než chci. Existuje třeba mapa matiky od [domain of science](<[The Map of Mathematics - YouTube](https://www.youtube.com/watch?v=OmJ-4B-mS-Y>),) nebo [fyziky](<[The Map of Physics - YouTube](https://www.youtube.com/watch?v=ZihywtixUYo>)) (má jich [víc](<[Než budete pokračovat do Vyhledávání Google](https://www.google.com/search?sca_esv=f032846a98b531f9&sxsrf=ACQVn0976HXiiNvRPJyyV5C4j7DIBC8eyQ:1709279314434&q=map+of+physics&tbm=vid>)),) [Mathematopia](<https://tomrocksmaths.com/2020/12/21/mathematopia-the-adventure-map-of-mathematics/>), [geometric representation of mathematics](<https://imgur.com/Tgd6HmA>) http://srln.se/mapthematics.pdf , od [Zooga](<https://www.reddit.com/r/math/comments/2av79v/map_of_mathematistan_source_in_comments/>), [tahle Laglands nádhera](https://bastian.rieck.me/blog/2020/langlands/), nebo [tady jich pár je listed v math stackexchange](<[big list - Mind maps of Advanced Mathematics and various branches thereof - Mathematics Stack Exchange](https://math.stackexchange.com/questions/124709/mind-maps-of-advanced-mathematics-and-various-branches-thereof>)) nebo [google search nachází nějaký další](https://www.google.com/search?sca_esv=6416b2a2bca84fa5&sxsrf=ACQVn08EYgLRVx_d0OEctey6oKUsAtsrOg:1709276455770&q=map+of+mathematics&tbm=isch&source=lnms&sa=X&ved=2ahUKEwjUgeD_vtKEAxXS0AIHHaQOBFoQ0pQJegQICxAB&biw=1920&bih=878&dpr=1). Plus Peak math staví velkou vizuální interaktivní [mapu](https://www.peakmath.org/peakmath-landscape). Nebo je ještě existují různý wikiny a seznamy: [Wikipedie](<[Outline of science - Wikipedia](https://en.wikipedia.org/wiki/Outline_of_science#Branches_of_science>)) ([Matika](https://en.wikipedia.org/wiki/Mathematics): [category](https://en.wikipedia.org/wiki/Category:Mathematics), [outline](https://en.wikipedia.org/wiki/Outline_of_mathematics), [portal](<https://en.wikipedia.org/wiki/Portal:Mathematics>), [list of topics](https://en.wikipedia.org/wiki/Lists_of_mathematics_topics), [areas](https://en.wikipedia.org/wiki/Template:Areas_of_mathematics), [Category:Fields of mathematics - Wikipedia](https://en.wikipedia.org/wiki/Category:Fields_of_mathematics) nebo [fyzika](https://en.wikipedia.org/wiki/Physics): [category](https://en.wikipedia.org/wiki/Category:Physics), [outline](https://en.wikipedia.org/wiki/Outline_of_physics), [portal](https://en.wikipedia.org/wiki/Portal:Physics)), [encyklopedia of mathematics](https://encyclopediaofmath.org/wiki/Main_Page), [Wolfram math world](https://mathworld.wolfram.com/), [Mathematics Subject Classification](https://zbmath.org/classification/)([na wiki](https://en.wikipedia.org/wiki/Mathematics_Subject_Classification)), [math fandom](https://math.fandom.com/wiki/Math_Wiki), [mathematics atlas](https://web.archive.org/web/20150429140457/http://www.math.niu.edu/%7Erusin/known-math/welcome.html), [awesome math](https://github.com/rossant/awesome-math), [tenhle strom](https://imgur.com/d8KqaFx) apod., ale to nejsou vizuální mapy tím jak jsou to wikiny, a jsou v nějakých věcech nedostatečně specializovaný nebo až moc detailní a dávají tam všemožnou omáčku kolem těch rovnic, a já chci s co nejmenší omáčkou mít v obří mapě co nejvíc jen ty matematický symboly. Nebo ještě rád promptuju AIs a snažím se z nich vydolovat koncepty, rovnice, asociace kolem různých oborů a témat přes prompty typu "write a gigantic list of all subfields in math/physics", "write a gigantic list of the most important structures and equations used in this subfield of physics or mathematics", apod. a ty pak dohledávám. Nebo [The Princeton Companion to Mathematics](<https://www.amazon.com/Princeton-Companion-Mathematics-Timothy-Gowers/dp/0691118809>)([pdf](<https://sites.math.rutgers.edu/~zeilberg/akherim/PCM.pdf>)) https://www.amazon.com/Princeton-Companion-Applied-Mathematics/dp/0691150397?ref=d6k_applink_bb_dls&dplnkId=352f8fc3-ee97-4716-817a-e8feea9cd8c2 kniha vypadá zajímavě, nebo je ještě [Mathematical Promenade](https://arxiv.org/abs/1612.06373). Nebo je ještě [proof wiki](https://proofwiki.org/wiki/Category:Proofs), ale to je hlavně na proofs, já chci hlavně dát na jedno místo ty výsledky co nejvíc kompresovaně, aby se těch výsledných definicí, rovnic a různých propojeních vešlo co nejvíc na co nejmíň místa. Nebo quanta magazine má mapu na trochu [matiky](<https://mathmap.quantamagazine.org/map/>) a [fyziky](<https://www.quantamagazine.org/theories-of-everything-mapped-20150803/>). Wiki má ještě fajn [theoretical physics](<https://en.wikipedia.org/wiki/Theoretical_physics>) a [mathematical physics](https://en.wikipedia.org/wiki/Mathematical_physics) nebo https://en.wikipedia.org/wiki/Mathematical_and_theoretical_biology, trillion [AI theory matiky](https://arxiv.org/abs/2106.10165) (principles of deep learning theory, statistical learning theory), [free energy principle](https://arxiv.org/abs/2201.06387),... Dynamical systems, systems theory,... Nebo je ještě [nlab](https://ncatlab.org/nlab/show/HomePage) ([matika](https://ncatlab.org/nlab/show/mathematics), [fyzika](https://ncatlab.org/nlab/show/higher+category+theory+and+physics)) ale to hlavně magie od šílenců z teorií kategorií, toho chci mít jenom část mý mapy, teorie kategorií svěle umožňuje propojovat jednotlivý matematický vesmíry ([od Math3ma](https://www.math3ma.com/blog/what-is-category-theory-anyway), [od Southwella](https://www.youtube.com/playlist?list=PLCTMeyjMKRkoS699U0OJ3ymr3r01sI08l)). Tenhle týpek má fajn list [konkrétnějších knížek podoborů matiky](https://www.reddit.com/r/math/comments/kqnfn5/suggestions_for_starting_a_personal_library/gi9k4gj/?context=3). p Nejradši bych si to prošel úplně všechno do těch nejmenšejšíšch detailíčků a naučil se všechno, ale potřeboval bych nekonečno času. :smile: Já chci do ruky to nový pro všechny neveřejný Googlí AI s lepším reasoningem a kvalitnější a 100x větší pamětí než ChatGPT a všechno mu to nacpat. 😄 Nebo použít dosavadní veřejný LLMs s databázovou pamětí aka groundingem nebo finetuningem, hmm. [Math Major Roadmaps](https://math.mit.edu/academics/undergrad/roadmaps.html) [Machine Learning - Verify.Wiki - Verified Encyclopedia](http://verify.wiki/wiki/Machine_Learning) [9.520/6.860: Wikipedia entries | The Center for Brains, Minds & Machines](https://cbmm.mit.edu/9-520/wiki) STATISTICAL LEARNING THEORY AND APPLICATIONS [Explainable artificial intelligence - Wikipedia](https://en.m.wikipedia.org/wiki/Explainable_artificial_intelligence) Deep computational neurophenomenology: A methodological framework for investigating the how of experience https://twitter.com/lars_sandved/status/1763552438122942871?t=1cizwMK00bDWQE_VadMcFg&s=19 "much of the psychology i was taught was science (dual-process theories, social psychology, box-and-arrow cognitive science) now seems like bunk to me and much of the psychology i was taught was bunk (psychodynamic, object-relations, phenomenology) now makes eminent sense" https://twitter.com/JakeOrthwein/status/1763576652024492542?t=2UGw2-c_B5T7iVNGuOUmfg&s=19 [IS THE MIND REALLY FLAT? - YouTube](https://youtu.be/5cBS6COzLN4?si=If5qPSY4ZfqDGXRK) Robotics [Unitree H1 Breaking humanoid robot speed world record [full-size humanoid] Evolution V3.0 - YouTube](https://youtu.be/83ShvgtyFAg?si=X-11QpsKXAJNJ33B) https://arxiv.org/abs/2402.11753 https://twitter.com/fchollet/status/1763692655408779455 "There are roughly four levels of generalization: 0. No generalization (e.g. a database) 1. Having memorized *the answers* for a static set of tasks and being able to interpolate between them. Most LLM capabilities are at that level. 2. Having encoded generalizable programs to robustly solve tasks within a static set of tasks. LLMs can do some of that, but as displayed below, they suck at it, and fitting programs via gradient descent is ridiculously data-inefficient. 3. Being able to synthesize new programs on the fly to solve never-seen-before tasks. This is general intelligence." [Percolation: a Mathematical Phase Transition - YouTube](https://www.youtube.com/watch?v=a-767WnbaCQ) "GPT-4 with simple engineering can predict the future around as well as crowds: arxiv.org/abs/2402.18563 On hard questions, it can do better than crowds. If these systems become extremely good at seeing the future, they could serve as an objective, accurate third-party. This would help us better anticipate the longterm consequences of our actions and make more prudent decisions. "The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom." - Asimov I didn't write this paper, but we called for AI forecasting research in Unsolved Problems in ML Safety some years back (arxiv.org/abs/2109.13916), and concretized as a research avenue a year later in Forecasting Future World Events with Neural Networks (arxiv.org/abs/2206.15474). Hopefully AI companies will add this feature as the election season begins." https://www.lesswrong.com/posts/YsFZF3K9tuzbfrLxo/counting-arguments-provide-no-evidence-for-ai-doom Gravitace je newtonovská síla (klasicka mechanika), teda ne je to zakriveni casoprostoru (teorie relativity), teda spis je to mozna kvantova castice graviton (quantum gravity), nebo mozna vsechno je ze strun (teorie strun), a nebo casoprostor je ze smycek (loop quantum gravity)... Uaaaaaaaaa [First quantum measurement of gravity: What does it mean? - YouTube](https://youtu.be/M0Xh-bGDVJ0?si=DTZawD4nQHegv_hZ) Applying all parts of math to AI [First quantum measurement of gravity: What does it mean? - YouTube](https://youtu.be/M0Xh-bGDVJ0?si=67g93OQyrX8-udd7) https://arxiv.org/abs/2402.17764 [What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond | The Transmitter: Neuroscience News and Perspectives](https://www.thetransmitter.org/defining-representations/what-are-we-talking-about-clarifying-the-fuzzy-concept-of-representation-in-neuroscience-and-beyond/) "Trust technical staff when they hint at AGI. It probably exists, and the world will shudder when it drops. Then, it will quickly be unimpressive. The 4-minute mile will break; a flood of competitors will emerge with more efficient, specialized, or uncensored systems. Smart sapiens will still dominate for some time, and AI research will march on. New algorithms will break ground. Mistral is so gonna make it. Businesses will shop for the right AGI as I shop for shampoo on Amazon. It will be incredible and mundane; rural Pennsylvania will still look like 1975. AGI very most certainly does NOT equate to a wavefront of magical rearrangement of reality sweeping across the world at the speed of light rearranging it into quantum foam computronium" https://twitter.com/MikePFrank/status/1763645175774216503 "omg.. whaat? this sounds, potentially *extremelly* useful for intracortical BCIs. bacteria-thick probably-self-assemblying conducting filaments? some synthetic biologist + neurotech person need to look at thiss!" https://twitter.com/guillefix/status/1763443372038279197 https://arxiv.org/abs/2212.13836 https://www.inference.org.uk/mackay/itila/ Quantum eraser Quantum bayesianism I tend to abstain from a lot of easy pleasures to make my brain reinforce seeing most available pleasure and meaning sources in gaining knowledge and creating projects and art [Evo: DNA foundation modeling from molecular to genome scale | Arc Institute](https://arcinstitute.org/news/blog/evo) biology AI stripedhyena dosavadní modely jsou nic v porovnání s potenciálníma budoucíma mnohem víc capable modelama viz https://openai.com/research/weak-to-strong-generalization alignment jde zobecnit na controllability controllability metody se zároveň používají pro zlepšování capabilities minimálně bych i argumentoval že u dosavadně capable dnešních modelů fakt nechceš pro masy (normies) releasnout chatbota co je completně unhinged off the rails, to nechci aby moje babička dostala nebo to nechceš pro research nebo programovani tam chceš forcnout konkretni behavior tím unhinged může být i generování šílenýho procenta blbostí proto jsem napsal "potenciálními" v teorii je replikovat humanlike inteligenci možný, humanlike inteligence je čistě fyzikalistiká replikovatelná věc naší biologický mašiny a potenciálně mnohem víc upgradovatelná (i když dosavadní AI systémy od biologie časem celkem divergujou), podobně jako v robotice replikujeme líp a líp všechny ostatní aspekty naší biologický mašiny u dosavadní AI vlně zatím nevidím slow down, ale zároveň vidím jako možnost kdyby dosavadní AI systémy fundamentálně hitnuli limit kvůli až moc velkým odlišnostem od optimální inteligence, vedoucí k další AI winter, a dostali bychom se k lepším inteligencím eventulně až o dost později spíš např přes něco mnohem víc symbolickýho, neuromorphic apod. dosavadní AI systémy jsou dost biased humanlike inteligencí přes slabou podobnost k naší architektuře, přes naše trénovací data apod., ale v dost věcech jsou od náš hodně alien inteligence, např v některých věcech jsou horší než batolata, a v některých už jsou better than all humans ale tomu dávám relativně malou pravděpobonost, architekturální apod. změny/additions si myslím budou potřeba, jako např explicitnější dodání chain of thought, searchu, planningu,... ale ne až tak radikální Francois má dle mě asi nejlepší typologii inteligencí tough it kind of feels like according to it 99% of humanity is not generally intelligent https://twitter.com/fchollet/status/1763692655408779455 https://arxiv.org/abs/1911.01547 [Resources for Foundation Models - Foundation Model Development Cheatsheet](https://fmcheatsheet.org/) Foundation Model Development Cheatsheet https://arxiv.org/abs/2402.19155?fbclid=IwAR0Ew9rsgxdHeZo5T85TqSOyp0l_1QzNf3_Kd0anIdlFcP-oVFhAGLwngvU Let's technologically, politically, culturally, economically push more for technology generating abundance and growth for everyone and suppress generating dystopian or catastrophic phenomena https://neurosciencenews.com/ai-creative-thinking-25690/ [The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks | Scientific Reports](https://www.nature.com/articles/s41598-024-53303-w) "Modeling the world for action by generating pixel is as wasteful and doomed to failure as the largely-abandoned idea of "analysis by synthesis". Decades ago, there was a big debate in ML about the relative advantages of generative methods vs discriminative methods for classification. Learning theorists, such as Vapnik, argued against generative methods, pointing out that training a generative modeling was a way more difficult than classification (from the sample complexity standpoint). Regardless, a whole community in computer vision was arguing that recognition should work by generating pixels from explanatory latent variables. At inference time, one would infer the configuration of latent variables that generated the observed pixels. The inference method would use optimization: e.g. use a 3D model of an object and try to find the pose parameters that reproduce the image. This never quite worked, and it was very slow. Later, some people converted to the Bayesian religion and tried to use Bayesian inference for the latent (e.g. using variational approximations and/or sampling). At some point, when Non-Parametric Bayes and Latent Dirichlet Allocation became the rage in text modeling, some folks heroically attempted to apply that to object recognition from images. >>> THIS WAS A COMPLETE AND UTTER FAILURE <<< If your goal is to train a world model for recognition or planning, using pixel-level prediction is a terrible idea. Generation happens to work for text because text is discrete with a finite number of symbols. Dealing with uncertainty in the prediction is easy in such settings. Dealing with prediction uncertainty in high-dimension continuous sensory inputs is simply intractable. That's why generative models for sensory inputs are doomed to failure." https://twitter.com/ylecun/status/1759486703696318935?t=MA5twJf06rfC6Web1s7ZZw&s=19 [Dysregulation of oxytocin and dopamine in the corticostriatal circuitry in bipolar II disorder | Translational Psychiatry](https://www.nature.com/articles/s41398-020-00972-6) Equations of AI and robotics [ChatGPT](https://chat.openai.com/share/4b5e580d-c533-4c74-b82a-49444a1b3010) Finally understanding mathematical definition or equation more is the most orgasmic experience Yes, with meditation or psychedelics so many things are possible to experience in the brain's world simulation https://arxiv.org/abs/2402.18041 I just had a dream where I woke up out of dream where I woke up out of dream where I woke up out of dream,... Such dreams really make one feel that the most frequent reality is just another dream really strongly, or that it's being simulated. In a sense yes it's a useful world simulation generated by the brain with useful for evolutionary survival notion of things, events, causes, self, others, space, time,... But that useful notion of physical brain with dreams is itself part of that dream as well... I never understood this reasoning: "I give zero probability to the possibility of us replicating our biological machine's intelligence in other types of machines and upgrading it when we've replicated so many mechanisms that work in nature already with technology, often in much stronger ways by slightly alternative methods, because our intelligence is somehow special" Unless you believe this will possible in more than 100 years, which I IMO see low evidence for when extrapolating the trends, but I don't blindly rule that possibility out, I just don't think that's probable. [Breakthrough Could Reduce Cultivated Meat Production Costs by up to 90%](https://scitechdaily.com/breakthrough-could-reduce-cultivated-meat-production-costs-by-up-to-90/?fbclid=IwAR2pmLzmEdHk5sZtf2fYbyuiVd-cHT22oSN7TQsoCUWvUW40J11Jjb5Z7zA)