leading ML researchers of the biggest labs, for example Shane Legg from DeepMind, or Dario Amodei from Anthropic, have their timelines of AGI arriving around 2026-2028. [Shane Legg (DeepMind Founder) - 2028 AGI, Superhuman Alignment, New Architectures - YouTube]([Shane Legg (DeepMind Founder) - 2028 AGI, Superhuman Alignment, New Architectures - YouTube](https://www.youtube.com/watch?v=Kc1atfJkiJU)) [Dario Amodei (Anthropic CEO) - $10 Billion Models, OpenAI, Scaling, & Alignment - YouTube](https://www.youtube.com/watch?v=Nlkk3glap_U) And Ilya Sutskever, Chief scientist in OpenAI, when asked if models are creative, if they can produce nontrivial mathematical conjectures, is like "Are you sure they cannot already do that" [Q* - Clues to the Puzzle? - YouTube](https://youtu.be/ARf0WyFau0A?si=FHp30hqXQKQeiG6T&t=651) [[2311.10215] Predictive Minds: LLMs As Atypical Active Inference Agents]([[2311.10215] Predictive Minds: LLMs As Atypical Active Inference Agents](https://arxiv.org/abs/2311.10215)) Predictive Minds: LLMs As Atypical Active Inference Agents [MT Bench - a Hugging Face Space by lmsys]([Hugging Face – The AI community building the future.](https://huggingface.co/)spaces/lmsys/mt-bench) [Imgur: The magic of the Internet](https://imgur.com/j1lmlvZ) There are pretty relatively small models with performance better compared to much bigger ones [LMSys Chatbot Arena Leaderboard - a Hugging Face Space by lmsys]([LMSys Chatbot Arena Leaderboard - a Hugging Face Space by lmsys]([LMSys Chatbot Arena Leaderboard - a Hugging Face Space by lmsys]([Hugging Face – The AI community building the future.](https://huggingface.co/)spaces/lmsys/chatbot-arena-leaderboard))) SSRIs increase neuroplasticity: Effects of escitalopram on synaptic density in the healthy human brain: a randomized controlled trial [Effects of escitalopram on synaptic density in the healthy human brain: a randomized controlled trial | Molecular Psychiatry]([Effects of escitalopram on synaptic density in the healthy human brain: a randomized controlled trial | Molecular Psychiatry](https://www.nature.com/articles/s41380-023-02285-8)) [How plants can perform feats of quantum mechanics - Big Think]([How plants can perform feats of quantum mechanics - Big Think](https://bigthink.com/hard-science/plants-quantum-mechanics/)) Plants perform quantum mechanics feats that scientists can only do at ultra-cold temperatures Bose-Einstein condensates - [Imgur: The magic of the Internet]([Imgur: The magic of the Internet](https://imgur.com/a/7yhe23f)) https://imgur.com/a/9fcHohf [Imgur: The magic of the Internet]([Imgur: The magic of the Internet](https://imgur.com/a/7yhe23f)) https://imgur.com/a/9fcHohf vectorization implementation Shallow brain hypothesis [How deep is the brain? The shallow brain hypothesis | Nature Reviews Neuroscience]([How deep is the brain? The shallow brain hypothesis | Nature Reviews Neuroscience](https://www.nature.com/articles/s41583-023-00756-z)) [[2311.00117] BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B]([[2311.00117] BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B](https://arxiv.org/abs/2311.00117)) BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B [So What Should We Do About AI? Let’s Count The Ways | by The Society Library | Nov, 2023 | Medium](https://societylibrary.medium.com/so-what-should-we-do-about-ai-lets-count-the-ways-c383cefe0c55) [Imgur: The magic of the Internet](https://imgur.com/RT6BZKI) TRANSFORMERS IS ALL YOU NEED FOR AGI Illya https://twitter.com/burny_tech/status/1725578088392573038 ,ITS GROKKING CIRCUITS [A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube]([A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube]([A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube]([A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube](https://www.youtube.com/watch?v=ob4vuiqG2Go)))), SCALING LAWS ARE EXPLAINEDBY DATA FRACTAL MANIFOLD DIMENSIONS [Scaling Laws from the Data Manifold Dimension]([Scaling Laws from the Data Manifold Dimension]([Scaling Laws from the Data Manifold Dimension]([Scaling Laws from the Data Manifold Dimension](https://jmlr.org/papers/v23/20-1111.html)))) and [Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning]([Towards Monosemanticity: Decomposing Language Models With Dictionary Learning](https://transformer-circuits.pub/2023/monosemantic-features/index.html)))))))))) generate different responces, apply reflection tokens [[2310.11511] Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection]([[2310.11511] Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection]([[2310.11511] Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection]([[2310.11511] Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://arxiv.org/abs/2310.11511)))) (trainable by selfevaluation, feedback from human or artificial agents), reprompt or vector steer in inference, [Steering GPT-2-XL by adding an activation vector — AI Alignment Forum]([Steering GPT-2-XL by adding an activation vector — AI Alignment Forum]([Steering GPT-2-XL by adding an activation vector — AI Alignment Forum](https://www.alignmentforum.org/posts/5spBue2z2tw4JuDCx/steering-gpt-2-xl-by-adding-an-activation-vector))) [Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency]([Representation Engineering: A Top-Down Approach to AI Transparency](https://www.ai-transparency.org/)))))))) according to their score (creating giant database of them), or changing weights, try again until reflections tokens are as happy as possible LLMs are more statistically efficient, even when its less size and energy efficients [CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube](https://www.youtube.com/watch?v=Gg-w_n9NJIE))))) There's no reason AI can't have our strong bayesian prior [CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube]([CBMM10 Panel: Research on Intelligence in the Age of AI - YouTube](https://www.youtube.com/watch?v=Gg-w_n9NJIE))))) https://www.anthropic.com/index/constitutional-ai-harmlessness-from-ai-feedback Safety: Metody jsou mechanistic interpretability, redteaming, evaluating dangerous capabilities, process supervision [Improving mathematical reasoning with process supervision]([Improving mathematical reasoning with process supervision](https://openai.com/research/improving-mathematical-reasoning-with-process-supervision)) feature selection [Q* - Clues to the Puzzle? - YouTube]([Q* - Clues to the Puzzle? - YouTube](https://youtu.be/ARf0WyFau0A?si=cfp17bD5xgl8ZaF0&t=888)) Transformers plus recurence in depth [Q* - Clues to the Puzzle? - YouTube]([Q* - Clues to the Puzzle? - YouTube](https://youtu.be/ARf0WyFau0A?si=cfp17bD5xgl8ZaF0&t=888)) Ale hledat zákony ve společnosti, počasí a klima je dost těžký, ale něco máme. Často se tam používají různý numerický a statistický metody, fluid dynamics jsou super. Hází se na to AI která bývá lepší než klasický modely. [Umělá inteligence už umí předpovídat počasí. Dělá to skvěle, ale dopouští se i hrubých chyb — ČT24 — Česká televize]([Umělá inteligence už umí předpovídat počasí. Dělá to skvěle, ale dopouští se i hrubých chyb — ČT24 — Česká televize](https://ct24admin.ceskatelevize.cz/veda/3631915-umela-inteligence-uz-umi-predpovidat-pocasi-dela-skvele-ale-dopousti-se-i-hrubych-chyb)) [ECMWF | Charts]([ECMWF | Charts]([ECMWF | Charts](https://charts.ecmwf.int/?facets=%7B%22Product%20type%22%3A%5B%22Experimental%3A%20Machine%20learning%20models%22%5D%7D))) AI computing feynman diagrams [François Charton | Transformers for maths, and maths for transformers - YouTube]([François Charton | Transformers for maths, and maths for transformers - YouTube](https://youtu.be/Sc6k06wVX3s?si=Oz545XT5qX_rq5aM)) “Quadratic attention has been indispensable for information-dense modalities such as language... until now. Announcing Mamba: a new SSM arch. that has linear-time scaling, ultra long context, and most importantly--outperforms Transformers everywhere we've tried.” [GitHub - state-spaces/mamba](https://github.com/state-spaces/mamba) I have become mathematics, the structure of all possible realities https://twitter.com/LangChainAI/status/1724099234574811149?t=621wZqUPz2i6N6OQL-lnPw&s=19 [AI Jason - YouTube]([AI Jason - YouTube](https://www.youtube.com/@AIJasonZ/videos)) Ben Shapiro w.linkedin.com/posts/llamaindex_new-multi-modal-llm-abstractions-the-activity-7127684117600604162-XgIn?utm_source=share&utm_medium=member_android https://twitter.com/sairahul1/status/1721822547065688097 [Short Courses | Learn Generative AI from DeepLearning.AI]([DeepLearning.AI: Start or Advance Your Career in AI](https://www.deeplearning.ai/)short-courses/) [Courses - DeepLearning.AI]([DeepLearning.AI: Start or Advance Your Career in AI](https://www.deeplearning.ai/)courses/) [290+ Machine Learning Projects with Python | by Aman Kharwal | Coders Camp | Medium](https://medium.com/coders-camp/230-machine-learning-projects-with-python-5d0c7abf8265) Play with SoTA open source LLMs https://twitter.com/omarsar0/status/1717896775720116546?t=xLnUVSEhyldx2s5VJOteIA&s=19 [🚀 Abhishek Thakur on LinkedIn: Zero-shot image classification entirely in the browser (no server) in 17… | 15 comments](https://www.linkedin.com/posts/abhi1thakur_zero-shot-image-classification-entirely-in-activity-7121065021597450240-OuD_?utm_source=share&utm_medium=member_android) [AutoGEN + MemGPT + Local LLM (Complete Tutorial) 😍 - YouTube](https://www.youtube.com/watch?v=bMWXXPoDnDs) AutoGEN + MemGPT + Local LLM (Complete Tutorial) 😍 [Courses - DeepLearning.AI]([DeepLearning.AI: Start or Advance Your Career in AI](https://www.deeplearning.ai/)courses/)generative-ai-for-everyone/ [Paper Replication Walkthrough: Reverse-Engineering Modular Addition — Neel Nanda]([Neel Nanda](https://www.neelnanda.io/)mechanistic-interpretability/modular-addition-walkthrough) [Getting started with Llama 2 - AI at Meta](https://ai.meta.com/llama/get-started/?utm_source=linkedin&utm_medium=organic_social&utm_campaign=llama2&utm_content=image) [LlamaIndex on LinkedIn: Hyperparameter Tuning for RAG 🦾🔎 A HUGE issue with building LLM apps is… | 12 comments](https://www.linkedin.com/posts/llamaindex_hyperparameter-tuning-for-rag-a-huge-activity-7126976402960105472-oJ2W?utm_source=share&utm_medium=member_desktop) nVidia stuff? https://twitter.com/rharang/status/1725161975976497627?t=ZB9P7AQh0NxEhE3bu_vl7w&s=19 [A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube]([A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube]([A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube]([A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) - YouTube](https://www.youtube.com/watch?v=ob4vuiqG2Go)))) neel nanda A Walkthrough of Reverse-Engineering Modular Addition: Model Training (Part 1/3) neel nanda A Walkthrough of A Mathematical Framework for Transformer Circuits [A Walkthrough of A Mathematical Framework for Transformer Circuits - YouTube]([A Walkthrough of A Mathematical Framework for Transformer Circuits - YouTube]([A Walkthrough of A Mathematical Framework for Transformer Circuits - YouTube](https://www.youtube.com/watch?v=KV5gbOmHbjU))) neel nanda A Walkthrough of Interpretability in the Wild [A Walkthrough of Interpretability in the Wild Part 1/2: Overview (w/ authors Kevin, Arthur, Alex) - YouTube]([A Walkthrough of Interpretability in the Wild Part 1/2: Overview (w/ authors Kevin, Arthur, Alex) - YouTube](https://www.youtube.com/watch?v=gzwj0jWbvbo)) [A Walkthrough of Finding Neurons In A Haystack w/ Wes Gurnee Part 1/3 - YouTube]([A Walkthrough of Finding Neurons In A Haystack w/ Wes Gurnee Part 1/3 - YouTube]([A Walkthrough of Finding Neurons In A Haystack w/ Wes Gurnee Part 1/3 - YouTube](https://www.youtube.com/watch?v=r1cfSpVAeqQ))) transformer circuits [Transformer Circuits [rough early thoughts] - YouTube]([Transformer Circuits [rough early thoughts] - YouTube]([Transformer Circuits [rough early thoughts] - YouTube](https://www.youtube.com/playlist?list=PLoyGOS2WIonajhAVqKUgEMNmeq3nEeM51))) [Concrete Steps to Get Started in Transformer Mechanistic Interpretability — Neel Nanda]([Concrete Steps to Get Started in Transformer Mechanistic Interpretability — Neel Nanda]([Concrete Steps to Get Started in Transformer Mechanistic Interpretability — Neel Nanda]([Neel Nanda](https://www.neelnanda.io/)mechanistic-interpretability/getting-started))) [Interpretability quickstart resources & tutorials]([Interpretability quickstart resources & tutorials]([Interpretability quickstart resources & tutorials](https://alignmentjam.com/interpretability))) [A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist]([A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist]([A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J))) [GitHub - jacobhilton/deep_learning_curriculum: Language model alignment-focused deep learning curriculum]([GitHub - jacobhilton/deep_learning_curriculum: Language model alignment-focused deep learning curriculum]([GitHub - jacobhilton/deep_learning_curriculum: Language model alignment-focused deep learning curriculum](https://github.com/jacobhilton/deep_learning_curriculum))) [[2311.00117] BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B]([[2311.00117] BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B](https://arxiv.org/abs/2311.00117)) (BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B) https://twitter.com/NeelNanda5/status/1716772823916859416 contribute to open source https://twitter.com/garrytan/status/1728793817472463101 [GitHub - OthersideAI/self-operating-computer: A framework to enable multimodal models to operate a computer.]([GitHub - OthersideAI/self-operating-computer: A framework to enable multimodal models to operate a computer.](https://github.com/othersideAI/self-operating-computer)) [GitHub - stas00/ml-engineering: Machine Learning Engineering Online Book]([GitHub - stas00/ml-engineering: Machine Learning Engineering Online Book](https://github.com/stas00/ml-engineering)) [Chris Olah - Looking Inside Neural Networks with Mechanistic Interpretability - YouTube]([Chris Olah - Looking Inside Neural Networks with Mechanistic Interpretability - YouTube]([Chris Olah - Looking Inside Neural Networks with Mechanistic Interpretability - YouTube]([Chris Olah - Looking Inside Neural Networks with Mechanistic Interpretability - YouTube]([Chris Olah - Looking Inside Neural Networks with Mechanistic Interpretability - YouTube](https://www.youtube.com/watch?v=2Rdp9GvcYOE))))) [First steps in spiking neural networks | by Andrey Urusov | Medium](https://medium.com/@tapwi93/first-steps-in-spiking-neural-networks-da3c82f538ad) [Simulated Annealing Explained By Solving Sudoku - Artificial Intelligence - YouTube]([Simulated Annealing Explained By Solving Sudoku - Artificial Intelligence - YouTube](https://www.youtube.com/watch?v=FyyVbuLZav8)) sim annealing [STM - State-space models - filtering, smoothing and forecasting]([STM - State-space models - filtering, smoothing and forecasting](https://statisticssu.github.io/STM/tutorial/statespace/statespace.html)) [🤗 Transformers]([🤗 Transformers]([Hugging Face – The AI community building the future.](https://huggingface.co/)docs/transformers/index)) [Mobile ALOHA](https://mobile-aloha.github.io)/?fbclid=IwAR2nm-ih_iej5JOH3sWDKfCMJxGK9hghA4qw_n_kr3CTDspB1GGX8fYPk4A [GitHub - nrimsky/LM-exp: LLM experiments done during SERI MATS - focusing on activation steering / interpreting activation spaces]([GitHub - nrimsky/LM-exp: LLM experiments done during SERI MATS - focusing on activation steering / interpreting activation spaces](https://github.com/nrimsky/LM-exp/tree/main)) Hmm this looks good, but includes almost nothing on Transformers [[2310.20360] Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory]([[2310.20360] Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory](https://arxiv.org/abs/2310.20360)) Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory [AGI Can Be Safe]([AGI Can Be Safe]([AGI Can Be Safe]([AGI Can Be Safe](https://dataskeptic.com/blog/episodes/2023/agi-can-be-safe)))) [Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia]([Meta-learning (computer science) - Wikipedia](https://en.wikipedia.org/wiki/Meta-learning_(computer_science)))))) [From Language to Consciousness (Guest: Joscha Bach) - YouTube]([From Language to Consciousness (Guest: Joscha Bach) - YouTube]([From Language to Consciousness (Guest: Joscha Bach) - YouTube]([From Language to Consciousness (Guest: Joscha Bach) - YouTube](https://www.youtube.com/watch?v=ApHnqHfFWBk)))) from language to consciousness presentation by joscha bach [Explainable artificial intelligence - Wikipedia]([Explainable artificial intelligence - Wikipedia]([Explainable artificial intelligence - Wikipedia](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence))) David Deutsch (beggining of infinity) [David Deutsch - AI, America, Fun, & Bayes - YouTube]([David Deutsch - AI, America, Fun, & Bayes - YouTube]([David Deutsch - AI, America, Fun, & Bayes - YouTube](https://www.youtube.com/watch?v=EVwjofV5TgU))) [Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube]([Nick Bostrom: How AI will lead to tyranny - YouTube](https://www.youtube.com/watch?v=_Oo-m893-xA)))))) Nick Bostrom: How AI will lead to tyranny [Our AI Future: Hopes and Hurdles Ahead - YouTube]([Our AI Future: Hopes and Hurdles Ahead - YouTube](https://www.youtube.com/watch?v=IeVY_Ag8JI8)) Our AI Future: Hopes and Hurdles Ahead recursive selfimporvement