The Game of Life on steroids! This model learns to grow complex patterns in an entirely local way. Each cell is trained to listen to its neighbors and update itself in a way such that, collectively, an overall goal is reached. Fascinating and interactive! https://distill.pub/2020/growing-ca/ https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life Abstract: Most multicellular organisms begin their life as a single egg cell - a single cell whose progeny reliably self-assemble into highly complex anatomies with many organs and tissues in precisely the same arrangement each time. The ability to build their own bodies is probably the most fundamental skill every living creature possesses. Morphogenesis (the process of an organism’s shape development) is one of the most striking examples of a phenomenon called self-organisation. Cells, the tiny building blocks of bodies, communicate with their neighbors to decide the shape of organs and body plans, where to grow each organ, how to interconnect them, and when to eventually stop. Understanding the interplay of the emergence of complex outcomes from simple rules and homeostatic 1 feedback loops is an active area of research. What is clear is that evolution has learned to exploit the laws of physics and computation to implement the highly robust morphogenetic software that runs on genome-encoded cellular hardware. This process is extremely robust to perturbations. Even when the organism is fully developed, some species still have the capability to repair damage - a process known as regeneration. Some creatures, such as salamanders, can fully regenerate vital organs, limbs, eyes, or even parts of the brain! Morphogenesis is a surprisingly adaptive process. Sometimes even a very atypical development process can result in a viable organism - for example, when an early mammalian embryo is cut in two, each half will form a complete individual - monozygotic twins! The biggest puzzle in this field is the question of how the cell collective knows what to build and when to stop. The sciences of genomics and stem cell biology are only part of the puzzle, as they explain the distribution of specific components in each cell, and the establishment of different types of cells. While we know of many genes that are required for the process of regeneration, we still do not know the algorithm that is sufficient for cells to know how to build or remodel complex organs to a very specific anatomical end-goal. Thus, one major lynch-pin of future work in biomedicine is the discovery of the process by which large-scale anatomy is specified within cell collectives, and how we can rewrite this information to have rational control of growth and form. It is also becoming clear that the software of life possesses numerous modules or s ... https://www.youtube.com/watch?v=9Kec_7WFyp0

#mlnews #openai #ai Everything important going on in the ML world right here! Sponsor: Paperspace https://www.paperspace.com/?src=yannic OUTLINE: 0:00 - Introduction 0:20 - Whisper: Open-Source Speech Transcription 6:30 - Sponsor: Paperspace 9:30 - Meta: How the brain hears audio 11:25 - PyTorch moves to Linux Foundation 12:15 - French Government uses AI to find unlicensed swimming pools 13:35 - AlphaFold extends database 14:10 - John Carmack raises 20M to build AGI0729970510422016 16:10 - Cerebras achieves model size record 17:40 - Andrej Karpathy on YouTube 18:35 - ColabPro changes pricing 19:15 - Huggingface runs evaluation on the hub 20:35 - AI wins art fair 22:50 - PaLI: Multilingual Language-Image Learning 23:40 - Operationalizing Machine Learning: An Interview Study 24:35 - LAION OpenCLIP: New Models 25:10 - BlenderBot 3 175B Released 25:45 - OWL-ViT on the Hub 26:10 - GLM-130B 26:35 - Ernie-ViLG 27:10 - Digitizing Smell using Molecular Maps 28:00 - AlexaTM 20B 29:00 - Audio-LM 29:45 - Useful Things 37:20 - Raycasting in JAX 38:00 - GPT-3 Prompt Injection 39:20 - GPT-3 plus Python 40:45 - Game Emulation via DNN References here (external bc too long for YT): https://early-hair-c20.notion.site/ML-News-Whisper-References-17e51ca488ef4eb6b8be12749c10870c Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n ... https://www.youtube.com/watch?v=S-7r0-oysaU

#minecraft #neuralnetwork #backpropagation I built an analog neural network in vanilla Minecraft without any mods or command blocks. The network uses Redstone wire power strengths to carry the signal through one hidden layer, including nonlinearities, and then do automatic backpropagation and even weight updates. OUTLINE: 0:00 - Intro & Overview 1:50 - Redstone Components Explained 5:00 - Analog Multiplication in Redstone 7:00 - Gradient Descent for Square Root Computation 9:35 - Neural Network Demonstration 10:45 - Network Schema Explained 18:35 - The Network Learns a Datapoint 20:20 - Outro & Conclusion I built this during a series of live streams and want to thank everyone who helped me and cheered for me in the chat! World saves here: https://github.com/yk/minecraft-neural-network Game here: https://www.minecraft.net Multiplier Inspiration: https://www.youtube.com/channel/UCLmzk4TlnLXCXCHcjuJe2ag Credits to Lanz for editing! Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/ BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n ... https://www.youtube.com/watch?v=7OdhtAiPfWY

#ai #accel #evolution This is an interview with the authors Jack Parker-Holder and Minqi Jiang. Automatic curriculum generation is one of the most promising avenues for Reinforcement Learning today. Multiple approaches have been proposed, each with their own set of advantages and drawbacks. This paper presents ACCEL, which takes the next step into the direction of constructing curricula for multi-capable agents. ACCEL combines the adversarial adaptiveness of regret-based sampling methods with the capabilities of level-editing, usually found in Evolutionary Methods. OUTLINE: 0:00 - Intro 1:00 - Start of interview 4:45 - How did you get into this field? 8:10 - What is minimax regret? 11:45 - What levels does the regret objective select? 14:20 - Positive value loss (correcting my mistakes) 21:05 - Why is the teacher not learned? 24:45 - How much domain-specific knowledge is needed? 29:30 - What problems is this applicable to? 33:15 - Single agent vs population of agents 37:25 - Measuring and balancing level difficulty 40:35 - How does generalization emerge? 42:50 - Diving deeper into the experimental results 47:00 - What are the unsolved challenges in the field? 50:00 - Where do we go from here? Website: https://accelagent.github.io Paper: https://arxiv.org/abs/2203.01302 ICLR Workshop: https://sites.google.com/view/aloe2022 Book on topic: https://www.oreilly.com/radar/open-endedness-the-last-grand-challenge-youve-never-heard-of/ Abstract: It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent's capabilities. These methods benefit from their generality, with theoretical guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces. By contrast, evolutionary approaches seek to incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. In this paper we propose to harness the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive versio ... https://www.youtube.com/watch?v=16BsJI5I-Yw