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can a neural network learn to walk as a physical object in a physics simulation? here I train walking neural nets with an evolutionary algorithm. The input nodes/feet are activated by sine waves at learned phases & connections between two neurons extend based on their difference

Matt Henderson

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339,340 görüntüleme • 3 yıl önce •via X (Twitter)

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Matt Henderson profil fotoğrafı

Matt Henderson3 yıl önce

aka “embodied neural networks”

Matt Henderson profil fotoğrafı

Matt Henderson3 yıl önce

Remember soda constructor?

Patrick Ꝺoyle profil fotoğrafı

Patrick Ꝺoyle3 yıl önce

Whatever the result, they seem to be enjoying themselves.

Eric Jang profil fotoğrafı

Eric Jang3 yıl önce

this is amazing. would you consider open-sourcing the code? I'd love to extend this to optimizing the architecture of the net itself, so it ends up having to trade off physical bulk with function approximation power

Tom profil fotoğrafı

Tom3 yıl önce

Looks kinda similar

Austen Lamacraft profil fotoğrafı

Austen Lamacraft3 yıl önce

Surely a CNN will work better because of the strides?

c7ddfc profil fotoğrafı

c7ddfc3 yıl önce

reminds me of this, also controlled by a neural net

Mike Vella profil fotoğrafı

Mike Vella3 yıl önce

How do you think of this stuff??

Matt Henderson profil fotoğrafı

Matt Henderson3 yıl önce

I saw a cool demo of a neural net controlling an inverted pendulum, and was thinking it would be cool to make a nnet control something. I was also thinking about that old game soda constructor. Then I thought of this idea like a bad joke about learning to walk

Connor McCormick profil fotoğrafı

Connor McCormick3 yıl önce

You should make it so that the ground is a treadmill with numbers on it, and the fitness function rewards it both for walking and for adding the numbers it's touching correctly.

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Does GPT understand the world? Here is what Ilya Sutskever, co-founder of OpenAI, says during a discussion with Jensen Huang, CEO of Nvidia: (1) When we train a large neural network to accurately predict the next word in lots of different texts from the internet, the AI is learning a world model. (2) On the surface, it may look like learning correlations in text, but it turns out that to 'just learn' statistical correlations in text, to compress information really well, what the neural network learns is some representation of the process that produced the text. (3) This text is a projection of the world...what the neural network is learning is aspects of the world, of people, of the human conditions, their hopes, dreams, motivations, their interactions...the situations we are in. The neural network learns a compressed, abstract, usable representation." Do you think learning representations = understanding? Are large language models simply stochastic parrots, or are they much more?

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Alex Ker 🔭

1,367,117 görüntüleme • 3 yıl önce

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Clunk

80,463 görüntüleme • 19 gün önce

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Mathelirium

56,467 görüntüleme • 6 ay önce

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Jesús Fernández-Villaverde

47,212 görüntüleme • 4 ay önce

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Frank

345,926 görüntüleme • 15 gün önce

This is a world model running locally on an RTX 5090. It was built from scratch by a team in India, lucidml. Like modern video games, you can choose between performance and graphics by choosing how many times the neural network is run to generate each frame. Absolutely wild.

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Inspired by Andrej Karpathy's practice of tinkering with smaller GPTs to gain intuition, I've been training small neural nets to play this simple 2d game I created. On the left is a visualization of the neural net firing in real time. Built with Claude, Three.js, and WebGPU and using JAX from Google DeepMind. I’m piping the neuron activation into Ableton as MIDI to trigger Serum 2.

Inspired by Andrej Karpathy's practice of tinkering with smaller GPTs to gain intuition, I've been training small neural nets to play this simple 2d game I created. On the left is a visualization of the neural net firing in real time. Built with Claude, Three.js, and WebGPU and using JAX from Google DeepMind. I’m piping the neuron activation into Ableton as MIDI to trigger Serum 2.

Aaron Lemke

22,687 görüntüleme • 5 ay önce

Humans learn as toddlers how to extend labels to new items based on their functions, such as applying the word “cup” to both a teacup and a sippy cup even though they look different. But finding this skill in dogs—a species much more evolutionarily distant from humans—suggests “the roots of these capacities are widespread in the evolutionary tree.”

Humans learn as toddlers how to extend labels to new items based on their functions, such as applying the word “cup” to both a teacup and a sippy cup even though they look different. But finding this skill in dogs—a species much more evolutionarily distant from humans—suggests “the roots of these capacities are widespread in the evolutionary tree.”

Science Magazine

43,098 görüntüleme • 11 ay önce

AI for robotics has some monumental challenges, like high dimensionality and reasoning with the physical world. Ashok Elluswamy, AI lead at Tesla, thinks the hardest problem is Evaluation. - Loss isn't a perfect indicator of Policy Neural Network's quality. - Testing in the real world for all the long-tail failure cases is not possible. To solve the evaluation challenge, Tesla has developed a World Simulator Model. It is trained on state and action pairs, so for a given current camera feed and current action, the model generates the next frame in the cameras. Once the world simulation is good enough, it can be hooked up to the policy network for a closed-loop simulation. In this setup, the model generates the next video frames, and based on those, the policy network comes up with the next action, which feeds back into the world simulation; this continuous loop creates a coherent simulation. The world simulation neural network is not just applicable to self-driving but also to humanoids, because Tesla trains it on common data. The examples show Optimus navigating or manipulating inside the world simulation, with all the pixels being generated by the world simulation model. The robotic agents can be tested in an accurately reproduced edge case and in other potential variations of the same edge case.

AI for robotics has some monumental challenges, like high dimensionality and reasoning with the physical world. Ashok Elluswamy, AI lead at Tesla, thinks the hardest problem is Evaluation. - Loss isn't a perfect indicator of Policy Neural Network's quality. - Testing in the real world for all the long-tail failure cases is not possible. To solve the evaluation challenge, Tesla has developed a World Simulator Model. It is trained on state and action pairs, so for a given current camera feed and current action, the model generates the next frame in the cameras. Once the world simulation is good enough, it can be hooked up to the policy network for a closed-loop simulation. In this setup, the model generates the next video frames, and based on those, the policy network comes up with the next action, which feeds back into the world simulation; this continuous loop creates a coherent simulation. The world simulation neural network is not just applicable to self-driving but also to humanoids, because Tesla trains it on common data. The examples show Optimus navigating or manipulating inside the world simulation, with all the pixels being generated by the world simulation model. The robotic agents can be tested in an accurately reproduced edge case and in other potential variations of the same edge case.

The Humanoid Hub

17,065 görüntüleme • 7 ay önce

In 2022, Elon Musk predicted all human code could be replaced by AI in Tesla's self-driving software. This approach materialized two years later with FSD v12. “The thing that's happened over time with respect to Tesla Autopilot, is that the neural nets have gradually absorbed more and more software. In the limit, of course, you could simply take the videos as seen by the car and compare those to the steering inputs from the steering wheel and pedals, which are very simple inputs. In principle, you could train with nothing in between, because that's what humans are doing with a biological neural net, you could train based on video, and what trains the video is the moving of the steering wheel and the pedals, with no other software in between. We're not there yet, but it's gradually going in that direction.” Tesla AI Day, 2022

In 2022, Elon Musk predicted all human code could be replaced by AI in Tesla's self-driving software. This approach materialized two years later with FSD v12. “The thing that's happened over time with respect to Tesla Autopilot, is that the neural nets have gradually absorbed more and more software. In the limit, of course, you could simply take the videos as seen by the car and compare those to the steering inputs from the steering wheel and pedals, which are very simple inputs. In principle, you could train with nothing in between, because that's what humans are doing with a biological neural net, you could train based on video, and what trains the video is the moving of the steering wheel and the pedals, with no other software in between. We're not there yet, but it's gradually going in that direction.” Tesla AI Day, 2022

ELON CLIPS

6,068,111 görüntüleme • 1 yıl önce

End-to-end neural networks racing drones in Abu Dhabi! 🚁 Check out the drone racing team from Delft University of Technology! A completely end-to-end neural network solution, from pixels to direct motor commands. No Kalman filters. No computer vision feature detectors. Just neurons flying the drone. The challenge is extreme. These drones fly at high speeds and need split-second decisions with minimal onboard resources: a single rolling-shutter camera and an IMU. Their approach is called SkyDreamer, based on the Dreamer-v3 reinforcement learning algorithm. First, a world model is trained in simulation. Then, the neural network learns how to fly in its dreams through reinforcement learning. The network's internal state can be read out to see where it thinks it is on the track or how fast it's going. Even better, the drone estimates some of its own body characteristics during flight, like the camera angle relative to the body, eliminating time-consuming manual calibration. The system uses only a single camera and the gyros from the IMU, ignoring the accelerometers, just like human FPV pilots do. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

End-to-end neural networks racing drones in Abu Dhabi! 🚁 Check out the drone racing team from Delft University of Technology! A completely end-to-end neural network solution, from pixels to direct motor commands. No Kalman filters. No computer vision feature detectors. Just neurons flying the drone. The challenge is extreme. These drones fly at high speeds and need split-second decisions with minimal onboard resources: a single rolling-shutter camera and an IMU. Their approach is called SkyDreamer, based on the Dreamer-v3 reinforcement learning algorithm. First, a world model is trained in simulation. Then, the neural network learns how to fly in its dreams through reinforcement learning. The network's internal state can be read out to see where it thinks it is on the track or how fast it's going. Even better, the drone estimates some of its own body characteristics during flight, like the camera angle relative to the body, eliminating time-consuming manual calibration. The system uses only a single camera and the gyros from the IMU, ignoring the accelerometers, just like human FPV pilots do. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

207,500 görüntüleme • 7 ay önce

As we all know, Valve is working on an AI based Anticheat called VACNet however there are already AI based Aimbots ruining the game experience. According to the dev, these aimbots doesn't modify the game files or interfere with the game at all. Here is a working AI based Aimbot in CS2. It works by real-time object detection with neural networks and recognizes the patterns similar to human movements. I am sure with the amount of data Valve has, they will be able to ban these aimbots however it's gonna be a tough battle to fight. What do you think about this? 🤔

As we all know, Valve is working on an AI based Anticheat called VACNet however there are already AI based Aimbots ruining the game experience. According to the dev, these aimbots doesn't modify the game files or interfere with the game at all. Here is a working AI based Aimbot in CS2. It works by real-time object detection with neural networks and recognizes the patterns similar to human movements. I am sure with the amount of data Valve has, they will be able to ban these aimbots however it's gonna be a tough battle to fight. What do you think about this? 🤔

Thour

378,721 görüntüleme • 2 yıl önce

“Originally conceived as a social experiment, the idea grew out of a wish to stimulate dialogue between two parts of the on-chain, generative art communities. Each particle cloud operates as an autonomous system governed by physics-based algorithms until paired with another instance where behavioural synchronisation emerges” entangled by 𝕭𝖏ø𝖗𝖓 𝕾𝖙𝖆𝖆𝖑 is so good, its a release I’d love to see in a physical setting one day (like in the next post). it was released with fxhash

“Originally conceived as a social experiment, the idea grew out of a wish to stimulate dialogue between two parts of the on-chain, generative art communities. Each particle cloud operates as an autonomous system governed by physics-based algorithms until paired with another instance where behavioural synchronisation emerges” entangled by 𝕭𝖏ø𝖗𝖓 𝕾𝖙𝖆𝖆𝖑 is so good, its a release I’d love to see in a physical setting one day (like in the next post). it was released with fxhash

Chet Woodside

49,132 görüntüleme • 8 ay önce

Rulani here spoke about the difference between North African & SA supporters. “In North Africa, the fans lift the team when it’s suffering, while in SA, the fans tend to suffer with the team” I think as the Kaizer Chiefs fanbase we can learn a thing or two from that.

Rulani here spoke about the difference between North African & SA supporters. “In North Africa, the fans lift the team when it’s suffering, while in SA, the fans tend to suffer with the team” I think as the Kaizer Chiefs fanbase we can learn a thing or two from that.

Zakhele Mtolo

92,077 görüntüleme • 9 ay önce