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"Generalization means being able to solve problems that the system hasn't been prepared for." Our latest work in #RSS2025 can automatically invent neural networks as state abstractions, which help robots generalize. Check it out here:

Bowen Li

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19,274 views • 1 year ago •via X (Twitter)

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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

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Many scientific problems hinge on finding interpretable formulas that fit data, but neural networks are the outright opposite! Check out our recent work that make neural networks modular and interpretable. If you have interesting datasets at hand, we're happy to collaborate!

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Elon Musk: “In order to solve full self-driving properly, you have to solve real-world AI. The road networks are designed to work with a biological neural net — our brains — and with vision — our eyes. So, in order to make it work with computers, you need to solve real-world AI and vision. We need cameras and silicon neural nets for self-driving to work in a system designed for eyes and biological neural nets. When you put it that way, it’s quite obvious that the only way to solve full self-driving is to solve real-world AI and sophisticated vision.”

Elon Musk: “In order to solve full self-driving properly, you have to solve real-world AI. The road networks are designed to work with a biological neural net — our brains — and with vision — our eyes. So, in order to make it work with computers, you need to solve real-world AI and vision. We need cameras and silicon neural nets for self-driving to work in a system designed for eyes and biological neural nets. When you put it that way, it’s quite obvious that the only way to solve full self-driving is to solve real-world AI and sophisticated vision.”

Elonogy

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Elon Musk: The only way to solve full self-driving properly is to solve real-world AI. ”What are the road networks designed to work with? They're designed to work with a biological neural net, our brains, and with vision, our eyes. And so, in order to make it work with computers, you basically need to solve real-world AI and vision. We need cameras and silicon neural nets in order to have self-driving work for a system that was designed for eyes and biological neural nets. I guess when you put it that way, it's quite obvious that the only way to solve full self-driving is to solve real-world AI and sophisticated vision.” TED Interview, April 14, 2022

Elon Musk: The only way to solve full self-driving properly is to solve real-world AI. ”What are the road networks designed to work with? They're designed to work with a biological neural net, our brains, and with vision, our eyes. And so, in order to make it work with computers, you basically need to solve real-world AI and vision. We need cameras and silicon neural nets in order to have self-driving work for a system that was designed for eyes and biological neural nets. I guess when you put it that way, it's quite obvious that the only way to solve full self-driving is to solve real-world AI and sophisticated vision.” TED Interview, April 14, 2022

ELON CLIPS

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ELON: TO SOLVE FULL SELF-DRIVING, YOU NEED TO SOLVE REAL-WORLD AI "What are the road networks designed to work with? They're designed to work with a biological neural net, our brains, and with vision, our eyes. And so, in order to make it work with computers, you basically need to solve real-world AI and vision. We need cameras and silicon neural nets in order to have self-driving work for a system that was designed for eyes and biological neural nets. I guess when you put it that way, it's quite obvious that the only way to solve full self-driving is to solve real-world AI and sophisticated vision." Source: Elon Musk TED Interview, April 2022

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“No mortal man, I repeat, can solve the problem of Rivers State. It's not a physical problem. Has the President been able to solve it???” — Pastor David Ibiyeomie says, as he Prays for Peace in Rivers State

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Drop the camera, and bang -- the robots do the job without camera calibration or additional data collection. We want robots to manipulate anywhere! Our new work, **Maniwhere**, enables robots to manipulate objects from any camera view with any background. Isn't this the generalization every roboticist is looking for? How do we achieve this with sim2real? You can check this thread from Zhecheng Yuan ! And the website is here:

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Our latest documentary, which is focused on the House of Among, the 11th Parliament of Uganda, is out. Check it out here.

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This is Cortical Cloud. Live neural networks that you can interact with and train! Now open to the public. What will you discover? Credits: Frank Yang and a big thank you and acknowledgement to the rest of the Cortical Labs team. Sign up for Cortical Cloud: Learn more about us: Check out our API: Check out our API Docs: Check out our Developer Guide: Join our Discord:

This is Cortical Cloud. Live neural networks that you can interact with and train! Now open to the public. What will you discover? Credits: Frank Yang and a big thank you and acknowledgement to the rest of the Cortical Labs team. Sign up for Cortical Cloud: Learn more about us: Check out our API: Check out our API Docs: Check out our Developer Guide: Join our Discord:

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