Burny - Effective Curiosity

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Burny - Effective Curiosity

Burny - Effective Curiosity

@burny_tech

On the quest to understand the fundamental mathematics of intelligence and of the universe with curiosity. https://t.co/mMchI2d4pg Upskilling @StanfordOnline

42 googolsth multiverse branch Katılım Haziran 2021
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Burny - Effective Curiosity
Burny - Effective Curiosity@burny_tech·
Hey! Follow me on the quest to understand the fundamental mathematics of intelligence and of the universe with curiosity! I explore how everything works, artificial intelligence, intelligence, mathematics, machine learning, physics, science, technology, engineering, understanding AI using mathematical theory from the perspective of physics or pure mathematics or other sciences and by reverse engineering using mechanistic interpretability from the perspective of neuroscience or other sciences, AGI, superintelligence, LLMs, reinforcement learning, AI beyond LLMs and autoregression and transformers and pure deep learning like neurosymbolic AI or physics inspired AI like diffusion models and liquid neural networks or biology inspired AI like evolutionary AI and selforganizing AI or open-ended novelty search, AI for science like physics and biology, comparing AI and biological intelligence, diversity of minds or intelligences or information processing systems, making AI do what we want, AI engineering, creativity, curiosity, neuroscience, fundamental physics, theoretical mechanics, statistical mechanics, quantum mechanics, biology, foundations of everything, philosophy, philosophy of science, bayesianism, ontology, cognitive science, consciousness, philosophy of mind, foundations of mathematics, theories of everything, interdisciplinarity, transdisciplinarity, multidisciplinary, omnidisciplinarity, grokking, AI for engineering like software engineering and programming, AI for good like healthcare, AI agents, future of humanity and AI, futurology, politics, geopolitics of AI, transhumanism, world views like Effective Altruism and Effective Accelerationism, potential benefits and risks of advanced technologies like AI and everything, forecasting, open science, open source, democratizing technology, concentration vs decentralization of power, applied mathematics, pure mathematics, metamathematics, category theory, probability theory, linear algebra, real analysis, optimization theory, statistics, information theory, systems theory, complexity science, emergence, selforganization, dynamical systems theory, open systems, art, logic, formal ethics, cybernetics, computational neuroscience, qualia, meditation, psychology, mathematical psychology, mathematical psychonautics, identity, wellbeing, memetics, epistemic humility, morphological freedom, cosmology, collective intelligence, and so on. Questions I explore the most - How does the world work? How does everything work? - What is the fundamental mathematics of intelligence? What are all the different types of all the possible current and future intelligent systems? - How does artificial intelligence work? What's the current state of empirical research and mathematical theory in artificial intelligence? What is the state of the art in artificial intelligence engineering practice? - What is the fundamental mathematics of the universe? What are all the equations, and mathematical structures more generally, governing reality across all scales in physics, and in all natural science more generally? - How to apply AI for reverse engineering mathematics behind everything? How to apply AI for good as ideally as possible as much as possible? - How to define and understand artificial general intelligence and superintelligence? How to make it do what we want? - What is the fundamental mathematics of the brain? How to upgrade human intelligence? - How does AI and biological intelligence compare? How can humans and AIs form even greater collective intelligence? - How to connect all sciences, formal and natural? What is the fundamental mathematics behind emergence and complexity? How does biology and other scientific fields emerge from physics and chemistry? - What is the fundamental mathematics of creativity in science and art? How to make machines creative beyond human limitations and comprehension for scientific discovery, physics, mathematics, art, philosophy? - What are all the concepts in mathematics? What are all the possible foundations and mathematics with all sorts of mathematical universes and which ones are the best in what contexts? - What is the fundamental mathematics of consciousness? - What is the fundamental mathematics of building a great future for all where everyone flourishes? How to make the world better for all? How to maximize the benefits, and minimize the disadvantages, of technologies and political systems? What is and what will be the geopolitics of AI? What are the probabilities of different future scenarios? - What are the answers to the problems in philosophy? Artificial intelligence research and engineering - How does artificial intelligence (and intelligence more generally) that might soon become very general and superintelligent, works from an interdisciplinary lens using all sorts of mathematics and methodologies from computer science, statistics, information theory, physics, mechanistic interpretability, cognitive science, geometry, pure mathematics and so on, what is the mathematical theory of artificial intelligence? How to mathematically and empirically understand current and future AI systems, why and how they work, and how to make them much more reliable, robust, steerable, creative, intelligent, safe etc. across all levels of their development, such as with formal verification? What are the current state of the art results and methods in AI engineering and research in academia and industry, such as mathematical results, empirical results, and tools used like PyTorch? What will be future developments in scaling, data, algorithms, architecture, hardware, wrappers, agents, multiagent systems, etc.? How good in what usecases are various AI paradigms such as statistical AI, connectionist AI with deep learning, symbolic AI, neurosymbolic AI, evolutionary AI, cognitive AI, bayesian AI, biologically-inspired AI such as neuromorphic AI, quantum AI, embodied AI, distributed AI, etc.? How can we merge different paradigms? What would be considered as Artificial General Intelligence and Superintelligence? How to define, measure and build intelligence? How much does AI safety play a role? What is the intersection of artificial intelligence x biological intelligence x collective intelligence? How to create theory of everything in intelligence? State of the art of AI - I am interested in the current state of the art top artificial Intelligence systems (machine learning, data science, statistics, deep learning, generative AI (large language models, image/sound/video models, multimodal models), reinforcement learning models, expert systems, neurosymbolic AI, etc. I want to use them in practice for the benefit of others, such as for automating mundane tasks (dishes, laundry), healthcare (AMIE, AlphaFold, SLIViT), programming (coding AI copilots such as Claude Code, Codex), science (autonomous science such as AI scientist), physics (FermiNet), mathematics (AlphaProof), technology development (AlphaChip, virtual reality), chatbot assistants grounded in reality, education, information searching, minimizing various risks and crises, transportation, manufacturing, security, cybersecurity, energy optimization, supply chain optimization, weather forecasting, agriculture, translation, recommendations, finance, call centers, entertainment, legal services, games, robotics for good, altruism, etc. by predicting, forecasting, generating, classification, analysis, clustering, segmentating etc., with AI engineering methods by building and training models, finetuning, prompt engineering, retrieval augmented generation, agent and multiagent frameworks, etc. using PyTorch, Keras, Scikit-learn, FastAI, OpenAI or Anthropic API, Llama locally or deployed, Llamaindex, Langchain, Autogen, LangGraph, vector databases, etc. Mathematical and other fundamentals, steerability of AI - I am interested in trying to mathematically and empirically understand current and future AI systems, why and how they work, and how to make them much more reliable, robust, steerable, creative, intelligent, safe etc. across all levels of their development! Better steering wheel for AI systems would be great! RLHF, prompt engineering, systems made of LLMs, and current reverse engineering methods don't seem to be enough! Mechanistic interpretability, neurosymbolic AI, weak to strong generalization paradigm, and formal verification sound promising! I'm curious about the mathematical theory of artificial intelligence! Big picture of artificial intelligence - I love AI for science like biology and physics, mathematics, healthcare, education, technology development for good, understanding the nature of intelligence, increasing the standards of living for all, progress of civilization and so on. I want to see more of that please! I want to see AI applied much more in science, technology, engineering, mathematics, healthcare, altruistic usecases, etc. I want to see it as a tool that generates abundance for everyone. I want the technology to build better future for all. I want the technology to fight poverty and other world problems and risks. I want the research to help understand the nature of intelligence. I want the technology to empower all humans that don't want to see the world burn or are not dictators. I want the power of it be used for good. I want the power to not be concentrated. I want to see it developed safely and ethically in steerable way. I want people to get compensated properly. I'm trying to push that and help to work towards these goals more! AI can be used for both bad, good, and neutral things. Let's maximize the good usecases! - What are the benefits, risks, impact and future of artificial intelligence? How will the current artificial intelligence revolution transform humanity technologically, economically, culturally, governmentaly? How to make sure that AI benefits everyone, such as by automating mundane tasks (dishes, laundry), science (AlphaFold in biology), physics (FermiNet), mathematics (AlphaProof), healthcare (diagnosis), technology development (recursive self-improvement), programming (copilots, autonomous software engineers), preventing various risks (biorisks), useful chatbot assistants and robots factually grounded in reality etc.? How does existing technology already make us cyborgs? - Is artificial intelligence a tool like scissors, or like internet, or like electricity, or as powerful as nuclear weapons, or even more powerful and AI systems will populate the whole galaxy, or are we growing new species that will require moral rights? How can we collectively create optimistic stories about our future and build that great future together? Will there be post-scarcity economy where technology generates abundance for all, not just for select few? How to make sure that people in the AI and in general the fourth industrial revolution with exponential automation don't suffer? Maybe something like universal basic income or services will be needed to catch up with lob loss with increasing automation? Is universal basic income or services realistic? How to minimize power concentration in the hands of the few? How to prevent realistic risks? Is rogue superintelligent AI likely? How to prevent regulatory capture? Is singularity near? How will singularity look like? Science, Technology, Engineering, Mathematics, Physics, Biology, STEM - How does reality, science, technology, engineering, mathematics work? What is the structure of everything, what equations govern everything across all scales, what is the source code of our reality? How does astrophysics, celestial mechanics, etc. emerge from lower scales? How does sociology emerge from neuroscience and biology, and how that emerges from chemistry, and that from physics? What are the answers to the questions in cosmology? What is the best simplest most predictive and explanatory, most useful, integrating, unifying model in all natural sciences, using all its applied mathematics methods, like linear algebra, calculus, differential equations, geometry, topology, discrete mathematics, probability theory, statistics, graph theory, etc. with the help of pure mathematics? How can we use the methods of physics in as many fields as possible? How to effectively map all of knowledge and follow state of the art in many fields at once? How to most effectively create a generalist synthesis, but also narrowly model reality concretely on each level of abstraction on all scales, scruting the seemingly inscrutable reality's quantum fields with it's emergent laws? How do we turn the seeming alchemy of empirical sciences into deep understanding of its underlying mechanisms by the most optimal mathematical compression? How can AI accelerate this process? How can we infer the best tools for different domains for different usecases? How can we understand and unify all mathematical subfields using abstract mathematics such as category theory? How can we integrate together all methods from different physics subfields, such as tools from classical mechanics, statistical mechanics, quantum mechanics, quantum field theory, and so on? How to solve quantum gravity? Are string theory and loop quantum gravity good solutions to quantum gravity? How to create a theory of everything, theory of everything in fundamental physics, theory of everything in all of physics, theory of everything in natural sciences, theory of everything in all of science? Why did big bang happen? What if alternatives to big bang like big crunch happened instead? Did it actually happen? Why is universe governed by few fundamental forces between tens of elementary particles? Why is the standard model and general relativity the best current description of it that we have so far? Why do we struggle with unifying quantum mechanics and general relativity so much? Is theory of everything even possible? What even is space? What even is time? Is there such thing as "before the big bang" if time might not have existed before it? Why and how did chemical elements exactly emerge? Why and how did life exactly emerge and how does it work? Why is evolution such unreasonably effective algorithm? Why and how exactly is there such mindblowing specialized diversity of life? Brain, mind, and body - What is life? How does the brain work? How to integrate the lenses of biology, neuroscience, chemistry, computational neuroscience, statistics, probability theory, physics, machine learning, systems theory or other mathematics in terms of understanding the brain? How does learning work, what kind of intelligence is biological intelligence compared to artificial Intelligence, what is the intersection between biological and artificial intelligence? How to reverse engineer and amplify human intelligence, agency, wellbeing and longevity to catch up to exponentially increasing machine intelligence using neurotechnology, biotechnology, and other methods? How does wellbeing, agency, drive, productivity, meaning etc. work on the level of the brain and the whole society according to cognitive science? On what level are factors influencing these phenomena genetic and on what level are they environmental? What is the physical substrate of experience? How does experience arise? What's the best philosophy of mind position? What are the best psychotechnologies and neurotechnologies strenghtening or transforming the neuroscience and software of the brain, for example intelligence, wellbeing and longevity, like brain computer interfaces, meditation, philosophy, psychotherapy, selfhelp, culture, substances, cold exposure, or just healthy lifestyle, what are the realistic limits? How can we upgrade our sentient substrate collectively? How to achieve longevity? Is immortality solvable engineering problem and how? Technorealism, future of humanity, AI, sentience, futurology, politics - How to be not naively technooptimistic or technopessimistic when it comes to technology, but something in the middle, technorealistic? How can we gather the benefits of technology and minimize the risks of artificial intelligence and technology in general? How can we merge the ideas of Effective Altruism and Effective Accelerationism? How can we the most effectively use science, technology and other methods to adapt or solve the biggest world problems and prevent risks, from suffering from for example poverty, wars, injustice, crises? How to prevent existential risks such as natural or engineered pandemics, nuclear war, environmental collapse? How is polycrisis actually real and how do we solve it? How to use AI and other technology to solve and prevent all these problems? How to prevent technology itself becoming too powerful or in the hands of the wrong people like dictators? Should it be open source? What is the best political system? Is it liberalism, collectivism, individualism, global governance, benevolent dictators, AI assisted governance, global AGI governance, decentralization, democracy, anarchism, minimal state, and so on? Should we regulate more, or less, and regulate what and when? - How do we collectively steer all of sentience not into oppressive dystopia without democracy, or complete extinction, or plateau without progress, but into collective protopia, collective growth of science, technology, intelligence, wellbeing, connection, knowledge, drive, motivation, freedom, agency, survival, longterm adaptability, stability, sustainability, love, peace, safety, meaning, fulfillment, selfactualization? Metamathematics and philosophy - What is the most useful logic and foundations of mathematics and metamathematics like set theory, homotopy type theory or category theory? What are the most beautiful, novel and exotic parts of formal sciences? Can we mathematize ethics? What is the best positions in metaphysics with ontology? What is the best epistemology? What is the best interpretation of quantum mechanics? How does identity work? What are the best positions in philosophy for science, meaning, wellbeing and freedom? Why is there something rather than nothing? Why can we ask this question? Does asking this even make sense? How to create theory of everything, theory of everything in philosophy, theory of everything in culture, meta theory of everything? Beyond polarization - How can we collectively effectively communicate and understand what is empirically true by steelmanning eachother, accelerating omniperspectivity, bridging between eachother, instead of polarizing tribalism more disconnected from reality, leading to improved collective decision making? How do we understand our limited computational power of our brain, limited data and perspective, when we consider ourselves as information processing agents that model the world's enormous growing complexity together to collectively flourish in the future, future of humanity, AI, sentience, futurology, politics, and future of universe? How can artificial intelligence help us in this process? The ultimate existential challenge - How to beat the ultimate existential challenge, the second law of thermodynamics, how to survive the death of the universe? How can we together achieve resistance to entropy, optional transhumanistic merging with eachother or machines or the universe, and so on, for every being, using the most optimal collective emergent selforganized sentient coordinated thermodynamic cybernetic architecture that might expand into the whole universe and become a beautiful cosmic constellation of linked posthumanist clusters of sentient matter of any form it wishes to shapeshift into, such as raw computronium? Infinite curiosity - Why is there something rather than nothing? Why can we ask this question? Does asking this even make sense? Why did big bang happen? What if alternatives to big bang like big crunch happened instead? Did it actually happen? Why is universe governed by few fundamental forces between tens of elementary particles? Why is the standard model of particle physicsand general relativity the best current description of it that we have so far? Why do we struggle with unifying quantum mechanics and general relativity so much? Is theory of everything even possible? What even is space? What even is time? Is there such thing as "before the big bang" if time might not have existed before it? Why and how did chemical elements exactly emerge? Why and how did life exactly emerge and how does it work? Why is evolution such unreasonably effective algorithm? Why and how exactly is there such mindblowing specialized diversity of life? Why and how did intelligence emerge and how does it work? What are the best definitions of intelligence? Why are brains and artificial intelligence systems so unreasonably effective in different complementary ways? How can they be upgraded? What happens to consciousness after death? Why and how did consciousness and experience emerge and how does it work? What are the best definitions of consciousness? What is the solution to the hard problem of consciousness? Does this question even make sense? What even is consciousness in the first place? Why are be able to design so many technologies that allow us to manipulate the universe to such degree? Why does emergence happen in the first place? How will the universe end? Is there such a thing as end of the universe? Is the multiverse theory true? Why is mathematics so unreasonably effective at describing and predicting nature? Is there a better mathematical foundation than set theory, type theory or category theory? Is mathematics invented or discovered? Is mathematics fundamental language of reality or just our mental tool to survive? What even is reality? What is being? Why can we even ask all of these questions? Do many of these questions even make sense and are they any final answers to them, or answers we get are just getting closer to to us incomprehensible "truth", or they have many parallel answers, or many answers are differently relatively valid depending on the assumptions we start with, or are they fundamentally unanswerable? Effective Curiosity, Effective Omni - Effective Curiosity = Maximizing the total understanding of reality by building models of as many physical phenomena as possible across as many scales of the universe as possible, that are as comprehensive, unified, simple, and empirically predictive as possible! Intelligence and fundamental physics, which are subsets of this, are the most fascinating to me! - Effective Omni = Steelman and if possible verify all models from all disciplines, all theoretical, applied, natural, formal, social sciences, all movements with worldviews shaping the future, all overall philosophical or other perspectives, and synthesize them all on a higher level of complexity as compatibly as possible for collective survival, wellbeing, flourishing and growth of all of sentience, increasing intelligence and ascending the Kardashev scale! Let's integrate it all into one useful framework! Reduce suffering in the universe! Increase prosperity in the universe! Increase understanding in the universe! The best way to do that is with AI and other technologies from the fourth industrial revolution! Trying to understand the equations of intelligence, our world, and the universe, and applying them to build technology for the benefit of all! Effective Curiousity (Supercuriousity)! Effective Understanding (Superunderstanding)! Effective Intelligence (Superintelligence)! Effective Longevity (Superlongevity)! Effective Wellbeing (Superwellbeing)! Effective Flourishing (Superflourishing)! Effective Omni (Superomni)! Effective Omni! - Effective Curiousity (Supercuriousity), Effective Understanding (Superunderstanding) = The quest to uncover all mathematical patterns of the universe and all of physical and platonic reality! - Effective Intelligence (Superintelligence) = Intelligence so advanced in every dimension that it surpasses human comprehension, vastly more advanced than any currently existing physical system! - Effective Longevity (Superlongevity) = Extending lifespan, achieving immortality, and overcoming the heat death of the universe! - Effective Wellbeing (Superwellbeing) = Complete fulfillment of Maslow’s hierarchy of needs, and similar models of wellbeing, at their highest possible levels! - Effective Flourishing (Superflourishing) = Collective superwellbeing! Contact and links - My website with my exocortex in wiki format: burnyverse.com, burnyverse.com/Exocortex , burnyverse.com/Home - Substack: substack.com/profile/160971… - BlueSky: burnytech.bsky.social - Youtube channel: @burnytech" target="_blank" rel="nofollow noopener">youtube.com/@burnytech - Discord: burnytech - Discord server: discord.gg/2vyjxYTDMU - Patreon: patreon.com/BurnyTech - LinkedIn: linkedin.com/in/libor-buria… - Telegram: @burnytech - Mastodon: @Burny" target="_blank" rel="nofollow noopener">mathstodon.xyz/@Burny - Facebook: facebook.com/burian.libor/ - Gmail: burian.lib@gmail.com - Github: github.com/BurnyCoder - HuggingFace: huggingface.co/BurnyCoder
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Pushmeet Kohli
Pushmeet Kohli@pushmeet·
AI agents are advancing research-level math. 🚀 I’m thrilled to share @GoogleDeepMind’s AlphaProof Nexus - an agentic framework for formal proof search powered by Gemini. When applied to a set of open formal math problems, our agent autonomously solved: ✅ 9 open Erdős problems (including two open for 56 years!) ✅ 44 Online Encyclopedia of Integer Sequences (OEIS) problems ✅ A 15-year-old open problem in algebraic geometry ✅ A 7-year-old open question in min-max optimization We are collaborating with mathematicians across disciplines - from combinatorics and graph theory to quantum optics. Ultimately, these results show the massive potential of even simple agentic loops powered by Gemini. Read the paper here: arxiv.org/abs/2605.22763…
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Burny - Effective Curiosity
OpenAI's model disproved a central conjecture in discrete geometry which was an Erdos problem. I liked these comment from the mathematicians: "I believe it would be fair to say that every mathematician working in Combinatorial Geometry thought about this problem, and lots of mathematicians working in other areas spent at least some time thinking about it… The fact that the correct answer is not n^(1+o(1))is surprising, and the construction and its analysis apply fairly sophisticated tools from algebraic number theory in an elegant and clever way." "On examining the construction, it becomes more clear how people had missed this before – it requires the confluence of several different unlikely events: that a good mathematician is (1) spending significant time in thinking about the unit distance conjecture in the first place; (2) seriously trying to disprove it, despite the oft-repeated belief of Erdős that it is true; (3) believes that there is mileage in generalising the original construction to other number fields, and so is willing to expend significant time in exploring such constructions; and (4) sufficiently familiar with the relevant parts of class field theory to recognise that the appropriately phrased question about infinite towers of number fields with appropriate parameters can be solved using existing theory. The AI met all of these criteria, and its success here echoes previous achievements: it often produces the most surprising results by persevering down paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery. When assessing the importance and influence of an AI-generated proof, a question I ask myself is: has this taught us something new about the problem? Do we understand discrete geometry better now? I think the answer is a moderated yes: this shows that there is a lot more that number theoretic constructions have to say about these sorts of questions than we suspected; moreover, that the number theory required can be very deep. No doubt many algebraic number theorists will be taking a close look at other open problems in discrete geometry in the coming months. On the other hand, perhaps some in the area will be a little disappointed with how little this tells us: it does not introduce any powerful new geometric tools, or hitherto unsuspected structural results, that a proof of the unit distance conjecture would likely have called for. Still, while perhaps not the proof of a conjecture that we had hoped for, no doubt this construction and the ideas involved will have a major impact in discrete geometry." "All the same, I would consider this to be a very "human" proof, though a extremely ingenious one. The model’s CoT is deeply interesting. It is noteworthy that a significant majority of the thoughts are trying to construct a counterexample to the widely believed upper bound, rather than trying to prove it. This argues that the model has some combination of good intuition, willingness to try approaches considered long-shot by the community, and a predisposition to attempt constructions. The CoT showed the model trying out a vast array of ideas from a wide range of mathematics for the required construction. The model went through ideas pretty quickly, but when it reached the crucial idea (in the paragraph starting with "Suppose optimistically that..."), it honed in on the proof quite methodically. In my opinion this paper demonstrates that current AI models go beyond just helpers to human mathematicians – they are capable of having original ingenious ideas, and then carrying them out to fruition. This is a really impressive piece of work, and I would accept it for any journal without hesitation." "A novel ingredient of the AI argument is to take [K : Q] → ∞"
Josie Zayner@josiezayner

We get it. Every AI model has solved Erdos problems. But like maybe the 1,217 Erdos problems aren't as complicated as people thought and/or no one has just really ever tried to solve many of them?

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Burny - Effective Curiosity
OpenAI's model disproved a central conjecture in discrete geometry which was an Erdos problem. I liked these comment from the mathematicians: "I believe it would be fair to say that every mathematician working in Combinatorial Geometry thought about this problem, and lots of mathematicians working in other areas spent at least some time thinking about it… The fact that the correct answer is not n^(1+o(1))is surprising, and the construction and its analysis apply fairly sophisticated tools from algebraic number theory in an elegant and clever way." "On examining the construction, it becomes more clear how people had missed this before – it requires the confluence of several different unlikely events: that a good mathematician is (1) spending significant time in thinking about the unit distance conjecture in the first place; (2) seriously trying to disprove it, despite the oft-repeated belief of Erdős that it is true; (3) believes that there is mileage in generalising the original construction to other number fields, and so is willing to expend significant time in exploring such constructions; and (4) sufficiently familiar with the relevant parts of class field theory to recognise that the appropriately phrased question about infinite towers of number fields with appropriate parameters can be solved using existing theory. The AI met all of these criteria, and its success here echoes previous achievements: it often produces the most surprising results by persevering down paths that a human may have dismissed as not worth their time to explore, combining superhuman levels of patience with familiarity with a vast array of technical machinery. When assessing the importance and influence of an AI-generated proof, a question I ask myself is: has this taught us something new about the problem? Do we understand discrete geometry better now? I think the answer is a moderated yes: this shows that there is a lot more that number theoretic constructions have to say about these sorts of questions than we suspected; moreover, that the number theory required can be very deep. No doubt many algebraic number theorists will be taking a close look at other open problems in discrete geometry in the coming months. On the other hand, perhaps some in the area will be a little disappointed with how little this tells us: it does not introduce any powerful new geometric tools, or hitherto unsuspected structural results, that a proof of the unit distance conjecture would likely have called for. Still, while perhaps not the proof of a conjecture that we had hoped for, no doubt this construction and the ideas involved will have a major impact in discrete geometry." "All the same, I would consider this to be a very "human" proof, though a extremely ingenious one. The model’s CoT is deeply interesting. It is noteworthy that a significant majority of the thoughts are trying to construct a counterexample to the widely believed upper bound, rather than trying to prove it. This argues that the model has some combination of good intuition, willingness to try approaches considered long-shot by the community, and a predisposition to attempt constructions. The CoT showed the model trying out a vast array of ideas from a wide range of mathematics for the required construction. The model went through ideas pretty quickly, but when it reached the crucial idea (in the paragraph starting with "Suppose optimistically that..."), it honed in on the proof quite methodically. In my opinion this paper demonstrates that current AI models go beyond just helpers to human mathematicians – they are capable of having original ingenious ideas, and then carrying them out to fruition. This is a really impressive piece of work, and I would accept it for any journal without hesitation." "A novel ingredient of the AI argument is to take [K : Q] → ∞"
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Burny - Effective Curiosity
I think future will include some big culture wars around people who are pro technology/transhumanism and anti technology/transhumanism. I think we're starting to see begginings of that now.
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LLMs aren't bitter lesson pilled enough. Humans created the architecture, the learning algorithm, the training data, the system prompts, the agentic scaffolds. Even with reinforcement learning, there are also by humans carefully crafted reinforcement learning environments with human crafted rewards (human feedback or symbolic verifiable rewards code or LLM as judge setups with prompts) and tasks. And human crafted tools, bash, compliers, etc. in tool calling. Many people in LLM scaling land praise Sutton for LLMs being bitter lesson pilled while the father of bitter lesson itself himself thinks LLMs are dead end because of all those human biases everywhere in these systems across their development. I would say bitter lesson is a spectrum and LLMs are generally partially bitter lesson pilled, and definitely more than pure expert systems. I think bitter lesson in the limit escapes human biases at all levels. No human handholding anywhere. Even autonomous self play RL systems bootstrapping themselves to superhuman performance are defined by humans at the beginning, their architecture, the learning algorithm, the rewards, the task, the environment, etc., Even if they are slightly more bitter lesson pilled than more by humans hand holded RL approaches. And they don't work in general domains outside of games that much yet. If you take bitter lesson to the limit: Current AI is still definitely far from architecture and learning algorithm itself being selforganized from evolution or something like that. What sensory data to pay attention to and what rewards to pay attention to being inferred by the system. Deciding on its own what open ended things to pursue. How much compute to add to itself. How to self modify itself. Etc. Without human biases. Similar to how nobody designed humans. We emerged from physics, from evolution. We set out goals, not some external agent. Etc.
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Richard Sutton
Richard Sutton@RichardSSutton·
The bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.
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Burny - Effective Curiosity
LLMs aren't bitter lesson pilled enough. Humans created the architecture, the learning algorithm, the training data, the system prompts, the agentic scaffolds. Even with reinforcement learning, there are also by humans carefully crafted reinforcement learning environments with human crafted rewards (human feedback or symbolic verifiable rewards code or LLM as judge setups with prompts) and tasks. And human crafted tools, bash, compliers, etc. in tool calling. Many people in LLM scaling land praise Sutton for LLMs being bitter lesson pilled while the father of bitter lesson itself himself thinks LLMs are dead end because of all those human biases everywhere in these systems across their development. I would say bitter lesson is a spectrum and LLMs are generally partially bitter lesson pilled, and definitely more than pure expert systems. I think bitter lesson in the limit escapes human biases at all levels. No human handholding anywhere. Even autonomous self play RL systems bootstrapping themselves to superhuman performance are defined by humans at the beginning, their architecture, the learning algorithm, the rewards, the task, the environment, etc., Even if they are slightly more bitter lesson pilled than more by humans hand holded RL approaches. And they don't work in general domains outside of games that much yet. If you take bitter lesson to the limit: Current AI is still definitely far from architecture and learning algorithm itself being selforganized from evolution or something like that. What sensory data to pay attention to and what rewards to pay attention to being inferred by the system. Deciding on its own what open ended things to pursue. How much compute to add to itself. How to self modify itself. Etc. Without human biases. Similar to how nobody designed humans. We emerged from physics, from evolution. We set out goals, not some external agent. Etc.
Richard Sutton@RichardSSutton

The bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.

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Burny - Effective Curiosity
I'm not claiming anywhere any oneshotting. You keep giving arguments in my mouth that I never said. And you're constantly parroting wrong bullshit about Lean. Pls learn Curry–Howard correspondence before talking about something you don't know anything about. en.wikipedia.org/wiki/Curry%E2%…
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Burny - Effective Curiosity
@DesignCntrl "Bad" inputs are classified as incorrect by Lean, once you've setup your axiomatic foundations and problem, which was already set up for the LLM ;) Have you ever used Lean btw? :) Did you check the AlphaProof Nexus architecture? :)
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DesignCntrl Inc. / Alwyn Bunsie
Lean doesn't magically make bad inputs correct. It forces you to be explicit and rigorous, but wrong premises → wrong (or useless) conclusions. This is why people say "if it type-checks in Lean, it's correct relative to your formalization." Always double-check your foundations. AI is famous for doing things wrong that look right.
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