Omnidisciplionary metamathemagics is my favorite field! Omnidisciplionary metamathemagician of physical reality! AGI/ASI singularity transhumanist! Omniperspectivity! Infinite freedom, growth, flourishing for all! https://twitter.com/burny_tech/status/1762190079252926471 https://twitter.com/burny_tech/status/1762187995262714295 "Using variations of gradient descent to minimize loss function over predicting tons of text data with attention! (now often with better routing of information thanks to mixture of experts architecture)" based tons of inscrutable matrices s triliardama emergentníma vzorama v dynamice kterým matematicky rozumíme nedostatečně. Co se uvnitř děje a ovládatelnost řeší celej mechanistic interpretability obor [GitHub - JShollaj/awesome-llm-interpretability: A curated list of Large Language Model (LLM) Interpretability resources.](<https://github.com/JShollaj/awesome-llm-interpretability>), nebo [Statistical learning theory](<https://en.wikipedia.org/wiki/Statistical_learning_theory>) a deep learning theory obor, [[2106.10165] The Principles of Deep Learning Theory](<https://arxiv.org/abs/2106.10165>), nebo jiný alignment a empirický alchemistický metody [[2309.15025] Large Language Model Alignment: A Survey](<https://arxiv.org/abs/2309.15025>) And humans may be podobně reduktivně "just doplňovač predikcí input signálů that are compared to actual signals" (using a version of bayesian inference) [Predictive coding](<https://en.wikipedia.org/wiki/Predictive_coding>) nebo "jenom bioeletrika a biochemie" nebo "jenom částice" Teď je asi největší limitace do AI systémů nacpat víc complex systematic coherent reasoning, planning, generalizing, agentnost (autonomita), memory, factual groundedness, online learning, human like etický uvažování, ovládatelnost, což mají celkem weak na složitější tasky když se škálují jejich capabilities, ale děláme v tom progress, buď přes composing LLMs v multiagent systémech, škálování, kvalitnější data a trénování, šťourání jak uvnitř fungujou a tak je ovládat, přes lepší matematický modely jak learning funguje, nebo poupravenou nebo překopanou architekturu, apod.... a nebo se ještě řeší fungující robotika... a všechny top AI laby na těchto věcech v různých mírách pracují/investují. Tady jsou nějaký práce: Survey of LLMs: [[2312.03863] Efficient Large Language Models: A Survey](<https://arxiv.org/abs/2312.03863>), [[2311.10215] Predictive Minds: LLMs As Atypical Active Inference Agents](<https://arxiv.org/abs/2311.10215>), [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](<https://arxiv.org/abs/2402.07927>) Reasoning: [Human-like systematic generalization through a meta-learning neural network | Nature](<https://www.nature.com/articles/s41586-023-06668-3>), [[2305.20050] Let's Verify Step by Step](<https://arxiv.org/abs/2305.20050>), [[2302.00923] Multimodal Chain-of-Thought Reasoning in Language Models](<https://arxiv.org/abs/2302.00923>), [[2310.09158] Learning To Teach Large Language Models Logical Reasoning](<https://arxiv.org/abs/2310.09158>), [[2303.09014] ART: Automatic multi-step reasoning and tool-use for large language models](<https://arxiv.org/abs/2303.09014>), [AlphaGeometry: An Olympiad-level AI system for geometry - Google DeepMind](<https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/>) Robotics: [Mobile ALOHA - A Smart Home Robot - Compilation of Autonomous Skills - YouTube](<[Mobile ALOHA - A Smart Home Robot - Compilation of Autonomous Skills - YouTube](https://www.youtube.com/watch?v=zMNumQ45pJ8>),) [Eureka! Extreme Robot Dexterity with LLMs | NVIDIA Research Paper - YouTube](<[Eureka! Extreme Robot Dexterity with LLMs | NVIDIA Research Paper - YouTube](https://youtu.be/sDFAWnrCqKc?si=LEhIqEIeHCuQ0W2p>),) [Shaping the future of advanced robotics - Google DeepMind](<https://deepmind.google/discover/blog/shaping-the-future-of-advanced-robotics/>), [Optimus - Gen 2 - YouTube](<[Optimus - Gen 2 | Tesla - YouTube](https://www.youtube.com/watch?v=cpraXaw7dyc>),) [Atlas Struts - YouTube](<https://www.youtube.com/shorts/SFKM-Rxiqzg>), [Figure Status Update - AI Trained Coffee Demo - YouTube](<[Figure Status Update - AI Trained Coffee Demo - YouTube](https://www.youtube.com/watch?v=Q5MKo7Idsok>),) [Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks - YouTube](<[Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks - YouTube](https://www.youtube.com/watch?v=Qob2k_ldLuw>)) Multiagent systems: [[2402.01680] Large Language Model based Multi-Agents: A Survey of Progress and Challenges](<https://arxiv.org/abs/2402.01680>) Pozměněný/alternativní architektury: [Mamba (deep learning architecture) - Wikipedia](<https://en.wikipedia.org/wiki/Mamba_(deep_learning_architecture)>), [[2305.13048] RWKV: Reinventing RNNs for the Transformer Era](<https://arxiv.org/abs/2305.13048>), [V-JEPA: The next step toward advanced machine intelligence](<https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/>), [Active Inference](<https://mitpress.mit.edu/9780262045353/active-inference/>) Agency: [[2305.16291] Voyager: An Open-Ended Embodied Agent with Large Language Models](<https://arxiv.org/abs/2305.16291>), [[2309.07864] The Rise and Potential of Large Language Model Based Agents: A Survey](<https://arxiv.org/abs/2309.07864>), [Agents | Langchain](<https://python.langchain.com/docs/modules/agents/>), [GitHub - THUDM/AgentBench: A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)](<https://github.com/THUDM/AgentBench>), [[2401.12917] Active Inference as a Model of Agency](<https://arxiv.org/abs/2401.12917>), [CAN AI THINK ON ITS OWN? - YouTube](<[The Free Energy Principle approach to Agency - YouTube](https://www.youtube.com/watch?v=zMDSMqtjays>),) [Artificial Curiosity Since 1990](<https://people.idsia.ch/~juergen/artificial-curiosity-since-1990.html>) Factual groundedness: [[2312.10997] Retrieval-Augmented Generation for Large Language Models: A Survey](<https://arxiv.org/abs/2312.10997>), [Perplexity](<https://www.perplexity.ai/>), [ChatGPT - Consensus](<https://chat.openai.com/g/g-bo0FiWLY7-consensus>) Memory: větší context window [Gemini 10 mil token context window](<https://twitter.com/mattshumer_/status/1759804492919275555>), nebo [vektorový databáze](<https://en.wikipedia.org/wiki/Vector_database>) Online learning: <https://en.wikipedia.org/wiki/Online_machine_learning> Meta learning: [Meta-learning (computer science) - Wikipedia](https://en.wikipedia.org/wiki/Meta-learning_(computer_science)) Planning: [[2402.01817] LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks](<https://arxiv.org/abs/2402.01817>), [[2401.11708v1] Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs](<https://arxiv.org/abs/2401.11708v1>), [[2305.16151] Understanding the Capabilities of Large Language Models for Automated Planning](<https://arxiv.org/abs/2305.16151>) Generalizing: [[2402.10891] Instruction Diversity Drives Generalization To Unseen Tasks](<https://arxiv.org/abs/2402.10891>), [Automated discovery of algorithms from data | Nature Computational Science](<https://www.nature.com/articles/s43588-024-00593-9>), [[2402.09371] Transformers Can Achieve Length Generalization But Not Robustly](<https://arxiv.org/abs/2402.09371>), [[2310.16028] What Algorithms can Transformers Learn? A Study in Length Generalization](<https://arxiv.org/abs/2310.16028>), [[2307.04721] Large Language Models as General Pattern Machines](<https://arxiv.org/abs/2307.04721>), [A Tutorial on Domain Generalization | Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining](<https://dl.acm.org/doi/10.1145/3539597.3572722>), [[2311.06545] Understanding Generalization via Set Theory](<https://arxiv.org/abs/2311.06545>), [[2310.08661] Counting and Algorithmic Generalization with Transformers](<https://arxiv.org/abs/2310.08661>), [Neural Networks on the Brink of Universal Prediction with DeepMind’s Cutting-Edge Approach | Synced](<https://syncedreview.com/2024/01/31/neural-networks-on-the-brink-of-universal-prediction-with-deepminds-cutting-edge-approach/>), [[2401.14953] Learning Universal Predictors](<https://arxiv.org/abs/2401.14953>), [Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks | Nature Communications](<https://www.nature.com/articles/s41467-021-23103-1>) Je dost možný (a velký % researchers si myslí) že research snažící se ovládat tyhle šílený inscrutabe matrices nemá dostatečně rychlej development v porovnávání s capabilities researchem (zvětšování množství věcí čeho ty systémy jsou schopny) Potom nemáme tušení jak vypnout behaviors s dosavadníma metodama [Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training \ Anthropic](<https://www.anthropic.com/news/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training>), což teď poslední dny šlo vidět s tím jak GPT4 začlo outputovat totální chaos po updatu [OpenAI’s ChatGPT Went Completely Off the Rails for Hours](<https://www.thedailybeast.com/openais-chatgpt-went-completely-off-the-rails-for-hours>), Gemini bylo víc woke než bylo intended, nebo každou chvíl vidím novej jailbreak co obchází zábrany :PepeLaugh~1: [[2307.15043] Universal and Transferable Adversarial Attacks on Aligned Language Models](<https://arxiv.org/abs/2307.15043>). Co se týče definicí AGI, tohle je dobrý od DeepMindu [Levels of AGI: Operationalizing Progress on the Path to AGI](https://arxiv.org/abs/2311.02462), nebo ještě mám rád i když celkem vague tak celkem dobrou definici od OpenAI: Highly autonomous systems that outperform humans at most economically valuable work, nebo tohle je fajn thread různých definicí a jejich výhod a nevýhod [9 definitions of Artificial General Intelligence (AGI) and why they are flawed](<https://twitter.com/IntuitMachine/status/1721845203030470956>), nebo ještě [Universal Intelligence: A Definition of Machine Intelligence](<https://arxiv.org/abs/0712.3329>), nebo Karl Friston má dobrý definice [KARL FRISTON - INTELLIGENCE 3.0](<[KARL FRISTON - INTELLIGENCE 3.0 - YouTube](https://youtu.be/V_VXOdf1NMw?si=8sOkRmbgzjrkvkif&t=1898>))) Pokud chcete jiný zdroj predikcí o AI od dalších AI lidí než od Metaculusu, LessWrong/EA, Singularity komunity a různých vlivných v top AI labech, co mají hodně short timelines v příštích 10 letech. [Singularity Predictions 2024 by some people big in the field](https://www.reddit.com/r/singularity/comments/18vawje/singularity_predictions_2024/kfpntso/), [Metaculus: When will the first weakly general AI system be devised, tested, and publicly announced?](<[Date Weakly General AI is Publicly Known | Metaculus](https://www.metaculus.com/questions/3479/date-weakly-general-ai-is-publicly-known/>)) Tak existují ještě tyhle priority a predikce, jejíž intervaly se v těchto questionares každým rokem ~dva krát zmenšují: [AI experts make predictions for 2040. I was a little surprised. | Science News](<[AI experts make predictions for 2040. I was a little surprised. | Science News - YouTube](https://www.youtube.com/watch?v=g7TghURVC6Y>),) [Thousands of AI Authors on the Future of AI](https://arxiv.org/abs/2401.02843): "In the largest survey of its kind, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace of AI progress and the nature and impacts of advanced AI systems The aggregate forecasts give at least a 50% chance of AI systems achieving several milestones by 2028, including autonomously constructing a payment processing site from scratch, creating a song indistinguishable from a new song by a popular musician, and autonomously downloading and fine-tuning a large language model. If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar survey we conducted only one year earlier [Grace et al., 2022]. However, the chance of all human occupations becoming fully automatable was forecast to reach 10% by 2037, and 50% as late as 2116 (compared to 2164 in the 2022 survey). Most respondents expressed substantial uncertainty about the long-term value of AI progress: While 68.3% thought good outcomes from superhuman AI are more likely than bad, of these net optimists 48% gave at least a 5% chance of extremely bad outcomes such as human extinction, and 59% of net pessimists gave 5% or more to extremely good outcomes. Between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that "substantial" or "extreme" concern is warranted about six different AI-related scenarios, including misinformation, authoritarian control, and inequality. There was disagreement about whether faster or slower AI progress would be better for the future of humanity. However, there was broad agreement that research aimed at minimizing potential risks from AI systems ought to be prioritized more." [Brilliant Light Power - Wikipedia](https://en.wikipedia.org/wiki/Brilliant_Light_Power) Job interviewer: Where do you see yourself in the next few years? Me: 10: make impact for longtermism, transhumanist policy 100: survive, help make radical tech transition work 1000: help build Dyson sphere, long reflection, seed universe 10,000: various copies of me do different things (art, science, invention, exploration...) 100,000: help coordinate galactic settlement 1e6: start local stellar rearrangement 1e9: help biosphere maintenance against solar brightening, begin interstellar rearrangement into hypercluster, some copies long-range dispersal 1e12: prepare for post-stelliferous infrastructure 1e14: post-stelliferous hypercluster computational and energy infrastructure up and running. 1e15: start to get antsy about proton decay, work on fix. 1e36+: hopefully help fixing it. 1e500+: retire. This makes it sound like I plan on doing everything myself. That is unrealistic: I want to be a part of a civilization, or tree of civilizations, that achieves good and great things. Even if I am the counterpart to the janitor. https://twitter.com/anderssandberg/status/1554846691848687617 [The Undeniable Common Sense of UBI - Felonious Monk](https://thefeloniousmonk.com/the-undeniable-common-sense-of-ubi/) [CyberGaia: Earth as cyborg | Humanities and Social Sciences Communications](https://www.nature.com/articles/s41599-024-02822-y) ising model [Polymaths Are Late Bloomers - by Robin Hanson](https://www.overcomingbias.com/p/polymaths-are-late-bloomers) Gcn, gin, gat -> geometric deep learning -> categorical deep learning -> talk like a graph https://twitter.com/michael_galkin/status/1761949271375651139 https://twitter.com/tsitsulin_/status/1761962660730462455 https://arxiv.org/abs/2310.04560 [Category:Futures studies stubs - Wikipedia](https://en.m.wikipedia.org/wiki/Category:Futures_studies_stubs) https://arxiv.org/abs/2402.05232 List all mathematical definitions, from foundations to concrete structures, from theorems to physical equations, from general principles to concrete models, all of it, in one gigantic graph [From artificial intelligence to hybrid intelligence - with Catholijn Jonker - YouTube](https://youtu.be/vb_Os_AJXjY?si=bZ0rJ_kA5jQvZ5pF)