Luke Darlow @LearningLukeD

@hive_echo @jeffreyseely If I understand the issue correctly (which is a sizeble caveat, but perhaps @jeffreyseely can elucidate further), it isn't a question of maybes, but more of certainties, simply owing to causality.

This is too cool! @YesThisIsLion

Zuzanna Stamirowska @zuzanna_pathway

2 weeks ago

The full Transformer vs Post-Transformer debate is live. 80 minutes. Seven rounds. No slides. Real disagreement. @lukaszkaiser came to defend the Transformer. @adrian_pathway, @YesThisIsLion, and @mlech26l made the case for what comes next. 00:00 Contenders enter the ring 06:30

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@jeffreyseely All cats found.

@MLStreetTalk Interesting take. Your point on AI creating more complexity resonates with me because of What I Do (build funky ML models). Being able to push the envelope of complexity owing to AI tools really opens up some new doors for me.

We are honored to be featured in the latest @twominutepapers video! You all can watch the full video here: youtu.be/QzZ4VwDHAT4 Here’s a short clip from it:

Sakana AI @SakanaAILabs

2 months ago

What happens when you put competing neural networks in a Petri Dish and start changing the rules while they adapt? Last year we released Petri Dish NCA, where neural nets are the organisms that learn during simulation. Today we're releasing Digital Ecosystems: a browser-based

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Thank you so much @twominutepapers for featuring my work. I'm so happy that you had "an almost illegal amount of fun"!

@jeffreyseely captain cryptic strikes again

@SakanaAILabs @blaiseaguera your book (whatisintelligence.antikythera.org) was instrumental in guiding my thinking when building this out. I think that there are similarities to "Computational Life" (arxiv.org/abs/2406.19108): self-replication, emergent symbiosis, and robust dynamic equilibria.

Huge thanks to Hannah Erlebach @hannaherlebach, Lukas Seiers @l_seier, Antonio León Villares @alv314159, Michael Beukman @mcbeukman for their great feedback that improved the paper. @LearningLukeD for the early support and for releasing the amazing substrate pub.sakana.ai/pdnca together with the awesome @_ivyzhang

@bytecrafter_1 @SakanaAILabs Sensitivity to rules is far higher. Try it for yourself. Hitting "reset" resets the models randomly with a different seed. You'll note that there is some sensitivity to seed, but stability in the system is more rule-dependent.

@leo_os8 @SakanaAILabs It certainly works as a tool for intuitively understanding how things like optimisers and learning rates function,. There are so many advances and updates you can make to this (for example, pitting optimisers against one another) that I never got to. Its open source, though...

@SakanaAILabs Sometimes the most fascinating science happens by accident. I was changing (and breaking..) something in the simulation a few days ago, accidentally stumbling across a different simulation landscape. In my opinion, one of the most diverse and interesting yet. So mesmerizing!

@leo_os8 @SakanaAILabs Exactly. But the most powerful thing I learned when building it was that real-time changes to these hyper parameters let's you navigate to edge of chaos regimes, and that's where the interesting stuff happens. It's impossible to get there with hyper parameter search alone.

Super happy to share my new research! I've been building out this web demo purely for the sake of understanding how the system works, but I thought I'd share it with the world. Enjoy!

Sakana AI @SakanaAILabs

2 months ago

What happens when you put competing neural networks in a Petri Dish and start changing the rules while they adapt? Last year we released Petri Dish NCA, where neural nets are the organisms that learn during simulation. Today we're releasing Digital Ecosystems: a browser-based

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What happens when you put competing neural networks in a Petri Dish and start changing the rules while they adapt? Last year we released Petri Dish NCA, where neural nets are the organisms that learn during simulation. Today we're releasing Digital Ecosystems: a browser-based platform for interactive artificial life research. The setup: several small CNNs share a 2D grid, each seeing only a 3x3 neighborhood. No global plan. They compete for territory by attacking neighbours and defending against incoming attacks, learning via gradient descent online while the simulation runs. What we didn't expect was the role of the learning itself. Gradient descent isn't just optimising each species' strategy. Instead, it acts to stabilize the whole system during simulation. Species that overextend get pushed back by the loss. Species that stagnate get nudged to grow. This means you can push parameters toward edge-of-chaos regimes: a zone characterised by emergent complexity. Letting the neural networks learn acts to hold the complex system together while you explore and interact. The platform lets you steer all of this interactively. You can draw walls to create niches, erase parts of the system online, and tune 40+ system parameters to explore the most interesting configurations. We find it mesmerizing to watch species carve out territories and reorganise when you perturb them. Everything runs client-side in your browser, no install needed. Blog: pub.sakana.ai/digital-ecosys… Code: github.com/SakanaAI/digit…

This is some very cool work that builds on our original Petri Dish NCA (pub.sakana.ai/pdnca), using population-based meta optimization to enrich the complexity of the system. Their paper and website are both very excellent: pbt-nca.github.io

Whereas before it wouldn't be unheard of for me to "understanding while coding". Several months ago I was convinced that part of my design and articulation process was enveloped in the actual coding. I think I was wrong.

Conversations about the impact of agentic work on humans is largely doomerist, and I dislike that. An unexpected positive is that I'm now required to design, think deeply, and articulate clearly what I want a system to do. This feels a lot like studying and learning.

"That feeling" of typing out a plan prompt for Claude that you couldn't fall asleep with last night because it was rattling around in your head. Several paragraphs, many enumerations, verbose instructions. Finally hitting enter and sipping on coffee feels so much like sci-fi.