Kenneth Stanley
@kenneth0stanley
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SVP of Open-Endedness @LilaSciences. Prev: Maven CEO, Lead@OpenAI, Uber AI, prof@UCF. NEAT,HyperNEAT,novelty search, POET. Book:Why Greatness Cannot Be Planned
San Francisco, CA
Joined March 2016
Important announcement (with job opportunities!): I’m thrilled to share that I just joined @LilaSciences as SVP of Open-Endedness! Lila is a new name in the AI space, but one you will be hearing a lot from. Their unique mission to pursue Scientific Superintelligence could not
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I'm teaching my evolutionary robotics course again, for those who want to follow along. Lecture 1: why make robots? https://t.co/f49dC21RNP
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Introducing Digital Red Queen 🟥👑 Remember Core War? Core War is a classic competitive programming game where tiny assembly-like programs (“warriors”) battle for control of a shared virtual computer. Code and data share the same memory, so warriors freely self-modify,
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A question raised by the “platonic representation hypothesis” that I think I haven’t seen discussed: What if a tendency to converge to nearly identical representations would actually hint at underlying pathology in our toolset rather than signal virtue? The fact that two people
As AI models grow more powerful, they appear to be converging on how they internally represent reality. @benbenbrubaker reports:
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Lux is a great firm. This is a great opportunity for aspiring entrepreneurs and for the world.
We started Lux in 2000 with a simple conviction: the biggest opportunities lie at the frontier of science and technology that others find too hard, too early, or too confusing. Twenty-five years later, that conviction hasn't changed. What has changed is that the world has caught
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We started Lux in 2000 with a simple conviction: the biggest opportunities lie at the frontier of science and technology that others find too hard, too early, or too confusing. Twenty-five years later, that conviction hasn't changed. What has changed is that the world has caught
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Because you won't get there [your goal] unless you stop caring about getting there. And so the only way to eventually solve the problem is to stop caring about the problem, which is what we meant when we said abandoning objectives – @kenneth0stanley on the Night Science Podcast
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We had a very interesting discussion on this @nightsciencepod about the origins of scientific innovation and discovery. Thanks to @ItaiYanai and @MartinJLercher for having me on!
🔥New episode of the Night Science Podcast came out! Kenneth Stanley tells us how "the only way to eventually solve the problem is to stop caring about the problem, which is what we meant when we said abandoning objectives." @kenneth0stanley on Why Greatness Cannot Be Planned.
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🔥New episode of the Night Science Podcast came out! Kenneth Stanley tells us how "the only way to eventually solve the problem is to stop caring about the problem, which is what we meant when we said abandoning objectives." @kenneth0stanley on Why Greatness Cannot Be Planned.
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My top 12 favorite books of 2025: One lesson from each book 🧵
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So much equivocation about AGI these days boils down to pillars of open-endedness: discovery, invention, creativity, self-improvement, research, diversity of thought, long horizons, continual learning. Open-endedness has always been destined to be the climax of the AI saga.
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➡️@FoxNewsSunday calls on @AndrewLBeam’s expertise, as Chief Technology Officer at Lila Sciences and a faculty member at Harvard University, to discuss the true impact of AI and what it could mean for human health. 🔗 Watch full episode and catch Andy’s segment at 30:04:
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2026 will be the year of open-endedness!
I asked NeurLPS attendees for their takeaways from the world's largest gathering of AI researchers featuring @andykonwinski, @Thom_Wolf, @tszzl, @DynamicWebPaige, @natolambert, @ml_angelopoulos and more:
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Natural evolution is such a neglected intuition pump for interrogating assumptions about creative intelligence. No world model, no predictions, no chain of thought, no beliefs, no organism an objective of the process, and phenotypic variation NOT simply random. Yet continual
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Mechanistic/pragmatic interpretability should focus less on how networks “work” and more on measuring, detecting, and visualizing the degree of fractured entangled representation (FER) in large networks.
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With all the talk about interpretability and alignment recently, one consideration missing from the conversation is the risk of paradigm shift rendering large swaths of insight on current architectures with current training conventions obsolete. Whatever ends up achieving
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Grateful for another year of growth and the incredible team powering Lila’s mission. This is only the beginning. Interested in building the future of science with us? Explore roles at Lila: https://t.co/Yx15k2GkXk
#Thanksgiving #Hiring
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Reflecting the gut feeling of many, Ilya says “something important” is missing from current AI models. But what is the concrete nature of this chasm? One candidate: the difference between fractured entangled representation (FER) and unified factored representation (UFR).
One point I made that didn’t come across: - Scaling the current thing will keep leading to improvements. In particular, it won’t stall. - But something important will continue to be missing.
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Introducing 🥚EGGROLL 🥚(Evolution Guided General Optimization via Low-rank Learning)! 🚀 Scaling backprop-free Evolution Strategies (ES) for billion-parameter models at large population sizes ⚡100x Training Throughput 🎯Fast Convergence 🔢Pure Int8 Pretraining of RNN LLMs
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Nice depiction of paths to good (UFR) and bad (FER) representation here by @IntuitMachine !
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