Burny - Effective Curiosity

Hey! Follow me for explorations of intelligence, mathematics, science, engineering, technology, artificial intelligence, machine learning, physics, computer science, (not only computational) neuroscience, cognitive science, transhumanism, AI engineering, AI's benefits, risks, impact and future, reverse engineering AI using mechanistic interpretability, mathematical theory of artificial intelligence, future of humanity, AI, sentience, futurology, politics, movements, Effective Altruism x Effective Accelerationism, longevity, wellbeing, biology, philosophy, consciousness, risks, theory of everything, Artificial Intelligence x Biological Intelligence - large language models, neurosymbolic AI, generalization, AI safety, artificial general intelligence, superintelligence - neurotechnology, immortality, human intelligence amplification, structure of experience, psychotechnology, agency, meaning, motivation, meditation, psychedelics - singularity, longtermism, rationalism, Extropianism, TPOT, hedonistic imperative, cosmism - logic, metamathematics - omnidisciplionarity, omniperspectivity - neural networks, data science, programming, programming languages, software engineering, hardware engineering, electronics - metamodernism, secular spirituality, neurophenomenology, metaphysics, ontology - systems theory, information theory - future, freedom, growth, flourishing, politics - spreading sentience full of intelligence and wellbeing across the whole universe, making our civilization ascend to higher Kardashev scale. 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 - 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 GitHub copilot, Cursor, Replit, and autonomous software engineers), 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. (I like courses by DeepLearning . AI, Practical Deep Learning for Coders - Practical Deep Learning, Stanford CS229: Machine Learning) Mathematical and other fundamentals, steerability - 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! (A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist) 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 - 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? 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? 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? Meditation, experience, meaning, art - How does meditation and psychedelics work? What are the most optimal meditation techniques and actions in the real world for internal metaprogramming, cultivation of wellbeing, intelligence, meaning, connection, freedom, motivation, drive? Can we make spiritual traditions scientific? How to engineer the best psychedelic experience? How to use metamodernism, worldviews, philosophical assumptions, or identities the most efficiently in different contexts? How to balance acceptance and change? How to be, do, and become the most meaningfully? How real is the meaning crisis and loneliness crisis and how to solve it? What are all the possible experiences? Can we map out the statespace of consciousness? What is on the edge of the mind? How to approximate with language and model experiences that go absolutely beyond any conventional conceptual understanding when all mental faculties alter or dissolve? - I love surreal maximalist psychedelic mathematical intellectual art full of complexity that induces awe! 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? 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! Superflourishing! Superunderstanding! Superintelligence! Superwellbeing! Superlongevity! Supercuriousity! Superomni! Effective Omni! Important links: My website with my exocortex in wiki format: burnyverse.com/Exocortex , burnyverse.com/Home Discord: discord.gg/2vyjxYTDMU Youtube channel: @burnytech" target="_blank" rel="nofollow noopener">youtube.com/@burnytech LinkedIn: linkedin.com/in/libor-buria… Github: github.com/BurnyCoder Patreon: patreon.com/BurnyTech Gmail: burian.lib@gmail.com Facebook: facebook.com/burian.libor/ Telegram: t.me/burnytech Calendy: calendly.com/burian-lib Mastodon: @Burny" target="_blank" rel="nofollow noopener">mathstodon.xyz/@Burny BlueSky: burnytech.bsky.social My blog: substack.com/profile/160971…

@ludwigABAP write it!

sheaves as a formalization of "context" / the contextual distributed nature of information, and lenses (in the categorical optics sense) as "agents and their wiring" would most likely have some young ML researchers have massive eureka moments that lead to interesting research but sadly there is no populist around who has the shoulders wide enough to write that masterpiece i think

as is probably known at this point, I loved Scott Aaronson's Why Every Philosopher should care about Computational Complexity Theory If i had magic wand I would have these two exist also: - Why Every Philosopher should care about Homotopy Type Theory - Why Every Philosopher should care about about Sheaves and Categorical Optics

@subcountability Yep got that in my collection of collections

@burny_tech Indeed, here's a survey that was done a few years ago agisi.org/Defs_intellige…

If you ask 10 different intelligence researchers to define intelligence, you will get 10 different answers, but there are some similarities between the answers

Not sure what your point is there exactly. AI as a term is very broad and its mostly about consensus in the academic community what is classified into that bin. And LLMs can technically be also seen as part of numerical analysis, as they can be seen as part of optimization, even though there is more complexity and nuance. You can see them as a tool that attempts to extremize functions F(x) with x ranging over a high dimensional parameter space Omega, that can outperform more traditional optimization algorithms when the parameter space is very high dimensional and the function F (and its extremizers) have non-obvious structural features. Sometimes what LLMs find can later be found by simpler optimization methods in practice, but it does not always seem to be the case.

@EpochAIResearch There are many problems that mathematicians have tried and failed to solve. There is a whole family of algorithms used instead - en.wikipedia.org/wiki/Numerical…. Maybe they are #AI too?.. :trollface:

AI has solved one of the problems in FrontierMath: Open Problems, our benchmark of real research problems that mathematicians have tried and failed to solve. See thread for more.

@anonimus8ib @DotCSV A lot of problems in mathematics are much easier to verify than figure out.

@DotCSV “Y como saben que está bien si no lo habían resuelto antes?”

¡ÉPICO! Se confirma que la IA ha resuelto el primer problema matemático del benchmark FrontierMath: Open Problems, que se compone de problemas que aún no estaban resueltos tras intentos de la comunidad matemática. El primero de muchos más por venir!

and it sounds like you could make the same first part of Julia's argument for google search

@rao2z and it sounds like you could make the same first part of Julia's argument for google search

🔥

Hyperagents This paper introduces hyperagents: "self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program." "Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements." "Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes." "Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve." "The Darwin Gödel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains." They "instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task." "Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems." "Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs." "DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve."

i wonder if they didnt do all sorts of the openclaw features sooner because they didnt get the idea, or it was low priority to implement, or because of safety risks in the sense of more autonomous claude agents having more potential to do mess

@testingcatalog @M1Astra i wonder if they didnt do all sorts of the openclaw features sooner because they didnt get the idea, or it was low priority to implement, or because of safety risks in the sense of more autonomous claude agents having more potential to do mess

BREAKING 🚨: Anthropic is also likely working on a Phone Use, so Claude will be able to make calls and execute tasks on your mobile device. Orbit 🪐 Discovered by @M1Astra

Introducing Hyperagents: an AI system that not only improves at solving tasks, but also improves how it improves itself. The Darwin Gödel Machine (DGM) demonstrated that open-ended self-improvement is possible by iteratively generating and evaluating improved agents, yet it relies on a key assumption: that improvements in task performance (e.g., coding ability) translate into improvements in the self-improvement process itself. This alignment holds in coding, where both evaluation and modification are expressed in the same domain, but breaks down more generally. As a result, prior systems remain constrained by fixed, handcrafted meta-level procedures that do not themselves evolve. We introduce Hyperagents – self-referential agents that can modify both their task-solving behavior and the process that generates future improvements. This enables what we call metacognitive self-modification: learning not just to perform better, but to improve at improving. We instantiate this framework as DGM-Hyperagents (DGM-H), an extension of the DGM in which both task-solving behavior and the self-improvement procedure are editable and subject to evolution. Across diverse domains (coding, paper review, robotics reward design, and Olympiad-level math solution grading), hyperagents enable continuous performance improvements over time and outperform baselines without self-improvement or open-ended exploration, as well as prior self-improving systems (including DGM). DGM-H also improves the process by which new agents are generated (e.g. persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. This work was done during my internship at Meta (@AIatMeta), in collaboration with Bingchen Zhao (@BingchenZhao), Wannan Yang (@winnieyangwn), Jakob Foerster (@j_foerst), Jeff Clune (@jeffclune), Minqi Jiang (@MinqiJiang), Sam Devlin (@smdvln), and Tatiana Shavrina (@rybolos).

pretty insane result. A specific subset of 7 layers, discovered by evaluating on TWO dumb simple items, can just be repeated without training, and massively improve the performance on a bunch of benchmarks. Great validation of "denoising circuits" intuition

We introduce the first open-source, scalable Cross-Layer Transcoder (CLT) framework — a step toward real mech interp. CLTs extend SAEs across layers to capture causal structure, not just representations. Previously closed (Anthropic, 2025), now open. 💻 github.com/LLM-Interp/CLT…

Mech interp or representation interp? We need to decode the causal computational graph of #LLMs—not just cataloguing representations (steering vectors etc). Analogy: we can’t understand biology by just blood composition. We need to understand how the body works. Same for LLMs.

Preprint: How Out-of-Equilibrium Phase Transitions can Seed Pattern Formation in Trained Diffusion Models A synthesis of our work on symmetry bearking (ft @gaboraya) and @MasonKamb and @SuryaGanguli work on pattern formation from locality Highly AI powered, many thanks to GPT!

- end-to-end image only JEPA world model training with SIGReg (no teacher-student, no EMA, no problem) - beats DINO-WM and PLDM - similar "physics breaking detection" as the VJEPA models through prediction loss - single hyper-hyparameter - 50X planning speedup - all open-source

@askalphaxiv I'm surprised people haven't really tried this?

"Exclusive Self Attention" This paper proposed Exclusive Self-Attention (XSA), which is a tiny two-line change that stops attention from looking at itself. This forces it to focus on the rest of the sequence, and can make transformers more effective! This improves the performance at long context at almost no extra cost.