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Sebastian Risi

@risi1979 • 10,227 subscribers

Researching @SakanaAILabs, ITU, Co-founder @ https://t.co/EeVHNpCcDq. Collective Intelligence | Self-Organizing AI | Neuroevolution. ERC GROW-AI.

Shorts

Introducing Self-Organising Digital Circuits! Can a simulated chip heal itself, not with a spare part, but by rewiring around the wound? Work led by Marcello Barylli and Gabriel Béna w/ Eleni Nisioti and Alex Mordvintsev. Check out Gabriel's blog for more details: PDF: Will be presented today at ALIFE Conference 2026!

Introducing Self-Organising Digital Circuits! Can a simulated chip heal itself, not with a spare part, but by rewiring around the wound? Work led by Marcello Barylli and Gabriel Béna w/ Eleni Nisioti and Alex Mordvintsev. Check out Gabriel's blog for more details: PDF: Will be presented today at ALIFE Conference 2026!

11,700 views

Introducing Neural Developmental Programs (NDPs)🧬🧠Instead of neural networks with fixed architectures, we allow neural networks to grow through a dynamic self-organizing process, inspired by how biological nervous systems develop👇 PDF:

Introducing Neural Developmental Programs (NDPs)🧬🧠Instead of neural networks with fixed architectures, we allow neural networks to grow through a dynamic self-organizing process, inspired by how biological nervous systems develop👇 PDF:

239,970 views

Biological brains 🧠 are characterized by neurons of many different classes. Yet most artificial neural networks only use one activation function throughout. We show that through neural diversity alone we can solve RL problems in networks with random weights 🤯

Biological brains 🧠 are characterized by neurons of many different classes. Yet most artificial neural networks only use one activation function throughout. We show that through neural diversity alone we can solve RL problems in networks with random weights 🤯

96,957 views

RL is back! But is it always the best choice? In a new paper, we investigate under what circumstances neuroevolution outperforms RL in transfer learning tasks. See more details in the thread below 🧵 While NE performs best in simpler domains, it will be interesting to see if the lessons learned here can also be applied to more complex systems/tasks (LLMs?).

RL is back! But is it always the best choice? In a new paper, we investigate under what circumstances neuroevolution outperforms RL in transfer learning tasks. See more details in the thread below 🧵 While NE performs best in simpler domains, it will be interesting to see if the lessons learned here can also be applied to more complex systems/tasks (LLMs?).

11,220 views

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Excited to share our latest work: “Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots” 🪲🦎 We show that Hebbian learning outperforms LSTM-based adaptation for real-world transfer. It even works without domain randomization! It can handle: ✅ Uneven terrain ✅ Morphological damage ✅ Sim-to-real gaps

Excited to share our latest work: “Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots” 🪲🦎 We show that Hebbian learning outperforms LSTM-based adaptation for real-world transfer. It even works without domain randomization! It can handle: ✅ Uneven terrain ✅ Morphological damage ✅ Sim-to-real gaps

14,390 views • 1 year ago

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