Загрузка видео...

Не удалось загрузить видео

На главную

Introducing Petri Dish Neural Cellular Automata (PD-NCA) 🦠 The search for open-ended complexification, a north star of Artificial Life (ALife) simulations, is a question that fascinates us deeply. In this work we explore the role of continual adaptation in ALife simulation, where the cellular automata in our system do...

SakanaAILabs's profile picture

Sakana AI

138,695 subscribers

105,466 просмотров • 9 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

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: Code:
1:12

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: Code:

Sakana AI

258,187 просмотров • 4 месяцев назад

NCA officers have arrested three people as part of a coordinated operation with French police against a people smuggling organised crime group. The group were involved in transporting Vietnamese migrants to the UK in lorries and small boats. NCA officers apprehended a 25-year-old woman at an address in Wrexham and a 25-year-old man in Newcastle-upon-Tyne this morning. French Police arrested a further 16 people in and around Paris in simultaneous raids, supported by colleagues from Europol, Eurojust and Belgian federal police. The woman arrested today is suspected of assisting that group by laundering money, while the man is believed to have organised facilitation through lower members of the crime group. These arrests are a great example of partnership working with our colleagues in France to tackle the groups behind dangerous crossings to the UK. Tackling people smuggling remains a top priority for the NCA, and this is one of approximately 100 ongoing investigations targeting groups or individuals both directly involved in organising journeys, or those who facilitate that criminality. FULL STORY ➡️
0:40

NCA officers have arrested three people as part of a coordinated operation with French police against a people smuggling organised crime group. The group were involved in transporting Vietnamese migrants to the UK in lorries and small boats. NCA officers apprehended a 25-year-old woman at an address in Wrexham and a 25-year-old man in Newcastle-upon-Tyne this morning. French Police arrested a further 16 people in and around Paris in simultaneous raids, supported by colleagues from Europol, Eurojust and Belgian federal police. The woman arrested today is suspected of assisting that group by laundering money, while the man is believed to have organised facilitation through lower members of the crime group. These arrests are a great example of partnership working with our colleagues in France to tackle the groups behind dangerous crossings to the UK. Tackling people smuggling remains a top priority for the NCA, and this is one of approximately 100 ongoing investigations targeting groups or individuals both directly involved in organising journeys, or those who facilitate that criminality. FULL STORY ➡️

National Crime Agency (NCA)

15,504 просмотров • 4 месяцев назад

Elon Musk: We have to keep our simulation interesting, otherwise we might get terminated. “If the simulation theory is true, then it is actually very likely that the most interesting outcome is the most likely. Because only the simulations that are interesting will continue. The simulators will stop any simulations that are boring because they're not interesting. In this reality that we live in, we run simulations all the time. When we try to figure out if the rocket's going to make it, we run thousands, sometimes millions of simulations just to figure out which path is the good path for the rocket and where can it go wrong? Where can it fail? But when we do these millions of simulations of what can happen with the rocket, we ignore the ones where everything goes right, because we have to address the situations where it goes wrong. And for AI simulations as well, like all these things, we keep the simulations going that are the most interesting to us. So, if the simulation theory is accurate, if it is true, who knows, then the simulators will only continue to run the simulations that are most interesting. Therefore, from a Darwinian perspective, the only surviving simulations will be the most interesting ones. And in order to avoid getting turned off, the only rule is that you must keep it interesting because the boring simulations will be terminated.” The Joe Rogan Experience, October 31, 2025
1:41

Elon Musk: We have to keep our simulation interesting, otherwise we might get terminated. “If the simulation theory is true, then it is actually very likely that the most interesting outcome is the most likely. Because only the simulations that are interesting will continue. The simulators will stop any simulations that are boring because they're not interesting. In this reality that we live in, we run simulations all the time. When we try to figure out if the rocket's going to make it, we run thousands, sometimes millions of simulations just to figure out which path is the good path for the rocket and where can it go wrong? Where can it fail? But when we do these millions of simulations of what can happen with the rocket, we ignore the ones where everything goes right, because we have to address the situations where it goes wrong. And for AI simulations as well, like all these things, we keep the simulations going that are the most interesting to us. So, if the simulation theory is accurate, if it is true, who knows, then the simulators will only continue to run the simulations that are most interesting. Therefore, from a Darwinian perspective, the only surviving simulations will be the most interesting ones. And in order to avoid getting turned off, the only rule is that you must keep it interesting because the boring simulations will be terminated.” The Joe Rogan Experience, October 31, 2025

ELON CLIPS

16,989 просмотров • 9 месяцев назад

Introducing Continuous Thought Machines New Blog: Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: Full Paper: GitHub :
0:57

Introducing Continuous Thought Machines New Blog: Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: Full Paper: GitHub :

Sakana AI

289,996 просмотров • 1 год назад

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?
3:10

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?

Alex Ker 🔭

1,367,117 просмотров • 2 лет назад

Friends, our readings for the fourth and final Sunday of Advent are all about maybe the central motif of the spiritual life. Our culture today is so self-oriented: It’s all about me and my choice. But that attitude is directly repugnant to the Bible; in fact, the Bible is constantly trying to move us out of that space and into a different space—namely, one of surrender to the higher purpose of God.
15:07

Friends, our readings for the fourth and final Sunday of Advent are all about maybe the central motif of the spiritual life. Our culture today is so self-oriented: It’s all about me and my choice. But that attitude is directly repugnant to the Bible; in fact, the Bible is constantly trying to move us out of that space and into a different space—namely, one of surrender to the higher purpose of God.

Bishop Robert Barron

25,384 просмотров • 8 месяцев назад

ELON: ONLY INTERESTING SIMULATIONS SURVIVE “If the simulation theory is true, then it is actually very likely that the most interesting outcome is the most likely. Because only the simulations that are interesting will continue. The simulators will stop any simulations that are boring because they're not interesting. In this reality that we live in, we run simulations all the time. When we try to figure out if the rocket's going to make it, we run thousands, sometimes millions of simulations just to figure out which path is the good path for the rocket and where can it go wrong? Where can it fail? But when we do these millions of simulations of what can happen with the rocket, we ignore the ones where everything goes right, because we have to address the situations where it goes wrong. And for AI simulations as well, like all these things, we keep the simulations going that are the most interesting to us. So, if the simulation theory is accurate, then the simulators will only continue to run the simulations that are most interesting. Therefore, from a Darwinian perspective, the only surviving simulations will be the most interesting ones. And, in order to avoid getting turned off, the only rule is that you must keep it interesting because the boring simulations will be terminated.” Source: Elon Musk on The Joe Rogan Experience Joe Rogan, October 31, 2025
1:41

ELON: ONLY INTERESTING SIMULATIONS SURVIVE “If the simulation theory is true, then it is actually very likely that the most interesting outcome is the most likely. Because only the simulations that are interesting will continue. The simulators will stop any simulations that are boring because they're not interesting. In this reality that we live in, we run simulations all the time. When we try to figure out if the rocket's going to make it, we run thousands, sometimes millions of simulations just to figure out which path is the good path for the rocket and where can it go wrong? Where can it fail? But when we do these millions of simulations of what can happen with the rocket, we ignore the ones where everything goes right, because we have to address the situations where it goes wrong. And for AI simulations as well, like all these things, we keep the simulations going that are the most interesting to us. So, if the simulation theory is accurate, then the simulators will only continue to run the simulations that are most interesting. Therefore, from a Darwinian perspective, the only surviving simulations will be the most interesting ones. And, in order to avoid getting turned off, the only rule is that you must keep it interesting because the boring simulations will be terminated.” Source: Elon Musk on The Joe Rogan Experience Joe Rogan, October 31, 2025

Mario Nawfal

2,080,365 просмотров • 9 месяцев назад

I disagree, Eckhart. The 'world' is here to put humanity in low-vibrational states for the creators of the simulation to feed off ('loosh') and it is not about awakening to the True Self, but PREVENTING the awakening to the True Self. Every experience is an opportunity to learn from, but that is not the point of the simulation which is there to control, not awaken. The 'nature' you speak of here is a systematically-designed killing field - that's why animals and birds are, as you say, constantly 'alert'. This is misleading people into believing that their suffering is good for them - absolutely not the case. See The Road Map. What did Palestinian kids learn from their experience after October 7th? If we don't see through the illusion of suffering being there as a 'learning experience' we go on justifying the control system designed to keep us asleep.
13:18

I disagree, Eckhart. The 'world' is here to put humanity in low-vibrational states for the creators of the simulation to feed off ('loosh') and it is not about awakening to the True Self, but PREVENTING the awakening to the True Self. Every experience is an opportunity to learn from, but that is not the point of the simulation which is there to control, not awaken. The 'nature' you speak of here is a systematically-designed killing field - that's why animals and birds are, as you say, constantly 'alert'. This is misleading people into believing that their suffering is good for them - absolutely not the case. See The Road Map. What did Palestinian kids learn from their experience after October 7th? If we don't see through the illusion of suffering being there as a 'learning experience' we go on justifying the control system designed to keep us asleep.

David Icke

177,337 просмотров • 7 месяцев назад

Elon Musk reveals why he believes the most interesting outcome is always the most likely "If simulation theory is true, it is very likely that the most interesting outcome is the most likely, because only the simulations that are interesting will continue" "The simulators will stop any simulations that are boring because they're not interesting" "In this reality, we run simulations all the time. When we try to figure out if the rocket's going to make it, we run thousands, sometimes millions of simulations" "When we do millions of simulations of what can happen with the rocket, we ignore the ones where everything goes right. We only care about where it goes wrong" "We keep the simulations going that are the most interesting to us" "From a Darwinian perspective, the only surviving simulations will be the most interesting ones" "In order to avoid getting turned off, the only rule is you must keep it interesting, or you will be terminated" "Video games have gone from Pong, with two rectangles in a square, to photorealistic with millions of people playing simultaneously, and all of that has occurred in our lifetime" "If that trend continues, video games will be indistinguishable from reality. You don't know if what you're seeing is a real video or a fake video" "If we're creating millions, if not billions, of photorealistic simulations of reality, then what are the odds that we're in base reality versus someone else's simulation?" "The most interesting and usually ironic outcome is the most likely. That's a good predictor of the future"
6:02

Elon Musk reveals why he believes the most interesting outcome is always the most likely "If simulation theory is true, it is very likely that the most interesting outcome is the most likely, because only the simulations that are interesting will continue" "The simulators will stop any simulations that are boring because they're not interesting" "In this reality, we run simulations all the time. When we try to figure out if the rocket's going to make it, we run thousands, sometimes millions of simulations" "When we do millions of simulations of what can happen with the rocket, we ignore the ones where everything goes right. We only care about where it goes wrong" "We keep the simulations going that are the most interesting to us" "From a Darwinian perspective, the only surviving simulations will be the most interesting ones" "In order to avoid getting turned off, the only rule is you must keep it interesting, or you will be terminated" "Video games have gone from Pong, with two rectangles in a square, to photorealistic with millions of people playing simultaneously, and all of that has occurred in our lifetime" "If that trend continues, video games will be indistinguishable from reality. You don't know if what you're seeing is a real video or a fake video" "If we're creating millions, if not billions, of photorealistic simulations of reality, then what are the odds that we're in base reality versus someone else's simulation?" "The most interesting and usually ironic outcome is the most likely. That's a good predictor of the future"

Jaynit

10,440 просмотров • 1 месяц назад

We are excited to share that “Continuous Thought Machines” has been accepted as a Spotlight at #NeurIPS2025! 🧠✨ The CTM is an AI that mimics biological brains by using neural dynamics & synchronization to think over time. It can solve complex mazes by building internal maps, gaze around images to classify them, and learn algorithms—all emergent from its core design. This is just the beginning. A hint of what we're exploring next… (video attached!) The team: Luke Darlow Ciaran@ALIFE🇨🇦 Sebastian Risi Jeffrey Seely Llion Jones
0:55

We are excited to share that “Continuous Thought Machines” has been accepted as a Spotlight at #NeurIPS2025! 🧠✨ The CTM is an AI that mimics biological brains by using neural dynamics & synchronization to think over time. It can solve complex mazes by building internal maps, gaze around images to classify them, and learn algorithms—all emergent from its core design. This is just the beginning. A hint of what we're exploring next… (video attached!) The team: Luke Darlow Ciaran@ALIFE🇨🇦 Sebastian Risi Jeffrey Seely Llion Jones

Sakana AI

169,801 просмотров • 10 месяцев назад

❤️W3 thought of the day🌈 Today I want to share with you a vision that goes beyond the boundaries of our imagination. A vision where wellness becomes a fundamental part of our existence, where individuals are empowered to live their best lives and reach their highest potential. This is the vision that drives us at Yogapetz Imagine a world where every person is in tune with their mind, body, and spirit, where inner peace and balance are not elusive concepts, but a daily reality. With the power of $PRANA , our vision becomes attainable. We believe that wellness is not a luxury reserved for the few, but a birthright of every individual. Through our innovative ecosystem, we are creating a platform where people can embark on their wellness journey, earn rewards for their dedication, and experience a true sense of community. $PRANA is the fuel that drives this vision forward, igniting the fire within each person to live a life filled with purpose, vitality, and joy. Together, let us embrace this vision of a world where wellness is not a fleeting trend, but an essential part of our existence. Let us breathe life into this vision, awakening our potential and inspiring others to do the same. Join us on this transformative journey, and together, let's create a future where wellness knows no boundaries. #PRANA #YogapetzSummer #VisionaryWellness
0:36

❤️W3 thought of the day🌈 Today I want to share with you a vision that goes beyond the boundaries of our imagination. A vision where wellness becomes a fundamental part of our existence, where individuals are empowered to live their best lives and reach their highest potential. This is the vision that drives us at Yogapetz Imagine a world where every person is in tune with their mind, body, and spirit, where inner peace and balance are not elusive concepts, but a daily reality. With the power of $PRANA , our vision becomes attainable. We believe that wellness is not a luxury reserved for the few, but a birthright of every individual. Through our innovative ecosystem, we are creating a platform where people can embark on their wellness journey, earn rewards for their dedication, and experience a true sense of community. $PRANA is the fuel that drives this vision forward, igniting the fire within each person to live a life filled with purpose, vitality, and joy. Together, let us embrace this vision of a world where wellness is not a fleeting trend, but an essential part of our existence. Let us breathe life into this vision, awakening our potential and inspiring others to do the same. Join us on this transformative journey, and together, let's create a future where wellness knows no boundaries. #PRANA #YogapetzSummer #VisionaryWellness

Kakarot ❤️ WELL3 🌈

11,651 просмотров • 3 лет назад

It is no accident that we are witnessing a complete shift in our world—a profound unraveling and reweaving of how we experience each other, the creatures we share this planet with, and the quiet, intricate beauty of the universe itself. This transformation is a call to remember our place in the web of existence, not as masters of it, but as participants—deeply entwined, urgently invited to feel more, see more, and live with reverence for all that surrounds us.
1:04

It is no accident that we are witnessing a complete shift in our world—a profound unraveling and reweaving of how we experience each other, the creatures we share this planet with, and the quiet, intricate beauty of the universe itself. This transformation is a call to remember our place in the web of existence, not as masters of it, but as participants—deeply entwined, urgently invited to feel more, see more, and live with reverence for all that surrounds us.

armygirl

123,574 просмотров • 1 год назад

SPRING. This year’s Mental Health Awareness Week, we are celebrating humanity’s longstanding connection to nature, and its capacity to inspire us and help us to heal and grow in mind, body and spirit. As we confront the challenges of an increasingly complex and digital world, the importance of the connection between humanity and nature takes on even more significance. The Mother Nature series is a reminder and reflection of the beauty and complexity of the human experience. It is a tribute to the lessons we can learn from nature, helping us to foster our own growth, strengthen our bonds with the world around us and each other. C
2:36

SPRING. This year’s Mental Health Awareness Week, we are celebrating humanity’s longstanding connection to nature, and its capacity to inspire us and help us to heal and grow in mind, body and spirit. As we confront the challenges of an increasingly complex and digital world, the importance of the connection between humanity and nature takes on even more significance. The Mother Nature series is a reminder and reflection of the beauty and complexity of the human experience. It is a tribute to the lessons we can learn from nature, helping us to foster our own growth, strengthen our bonds with the world around us and each other. C

The Prince and Princess of Wales

404,055 просмотров • 1 год назад

We are excited to share our #CORL2024 paper (oral) on "Learning Quadruped Locomotion Using Differentiable Simulation" done in collaboration with Sangbae Kim Massachusetts Institute of Technology (MIT). We present a new way to learn to walk in minutes without parallelization, outperforming PPO in sample efficiency! PDF: Video: We present a new framework for learning quadruped locomotion. By leveraging differentiable simulation for policy optimization, our approach achieves fast convergence and stable training, significantly outperforming model-free #ReinforcementLearning methods like PPO in sample efficiency. The key enabler is to combine a high-fidelity, non-differentiable simulator for forward dynamics with a simplified surrogate model for gradient backpropagation. Our framework enables learning quadruped walking in simulation in minutes without parallelization. When augmented with GPU parallelization, our approach allows the quadruped robot to master diverse locomotion skills on challenging terrains in minutes. This work highlights one of the first successful real-world applications of differentiable simulation for quadruped robots, offering a compelling alternative to traditional RL methods. Kudos to Yunlong Song! UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) Massachusetts Institute of Technology (MIT)MechE
1:13

We are excited to share our #CORL2024 paper (oral) on "Learning Quadruped Locomotion Using Differentiable Simulation" done in collaboration with Sangbae Kim Massachusetts Institute of Technology (MIT). We present a new way to learn to walk in minutes without parallelization, outperforming PPO in sample efficiency! PDF: Video: We present a new framework for learning quadruped locomotion. By leveraging differentiable simulation for policy optimization, our approach achieves fast convergence and stable training, significantly outperforming model-free #ReinforcementLearning methods like PPO in sample efficiency. The key enabler is to combine a high-fidelity, non-differentiable simulator for forward dynamics with a simplified surrogate model for gradient backpropagation. Our framework enables learning quadruped walking in simulation in minutes without parallelization. When augmented with GPU parallelization, our approach allows the quadruped robot to master diverse locomotion skills on challenging terrains in minutes. This work highlights one of the first successful real-world applications of differentiable simulation for quadruped robots, offering a compelling alternative to traditional RL methods. Kudos to Yunlong Song! UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) Massachusetts Institute of Technology (MIT)MechE

Davide Scaramuzza

15,554 просмотров • 1 год назад

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
1:05

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 просмотров • 8 месяцев назад

We often hear that machine learning models "learn patterns in data". But what does that actually look like in geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a self-organizing map...a simple unsupervised neural model...learn the shape of a two-dimensional dataset arranged in a spiral arm. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud...it knows nothing about the structure underneath. Learning is a repeated game: pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML
0:59

We often hear that machine learning models "learn patterns in data". But what does that actually look like in geometry? If you dropped a little elastic mesh into a cloud of points and let it learn, how would it fold itself to match the shape of the data? In this scene we watch a self-organizing map...a simple unsupervised neural model...learn the shape of a two-dimensional dataset arranged in a spiral arm. On top of this, we lay down a square grid of neurons whose weights live in the same plane. At the start, this grid is just a flat net floating across the cloud...it knows nothing about the structure underneath. Learning is a repeated game: pick a random data point, find the neuron whose weight is closest, and then nudge that neuron and its neighbours toward the point. Do this again and again, while slowly shrinking how far the neighbourhood influence spreads. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML

Mathelirium

139,757 просмотров • 9 месяцев назад

New Paper: Continuous Thought Machines 🧠 Neurons in brains use timing and synchronization in the way that they compute, but this is largely ignored in modern neural nets. We believe neural timing is key for the flexibility and adaptability of biological intelligence. We propose a new neural architecture, “Continuous Thought Machines” (CTMs), which is built from the ground up to use neural dynamics as a core representation for intelligence. By using neural dynamics as a first-class representational citizen, CTMs naturally perform adaptive computation. Many emergent, interesting behaviors arise as a result: CTMs solve mazes by observing a raw maze image and producing step-by-step instructions directly from its neural dynamics. When tasked with image recognition, the CTM naturally takes multiple steps to examine different parts of the image before making its decision. This step-by-step approach not only makes its behavior more interpretable but also improves accuracy: the longer it “thinks,” the more accurate its answers become. We also found that this allows the CTM to decide to spend less time thinking on simpler images, thus saving energy. When identifying a gorilla, for example, the CTM’s attention moves from eyes to nose to mouth in a pattern remarkably similar to human visual attention. I think this work underscores an important, yet often lost, synergy between neuroscience and AI. While modern AI is ostensibly brain-inspired, the two fields often operate in surprising isolation. By starting with such inspiration and iteratively following the emergent, interesting behaviors, we developed a model with unexpected capabilities, such as its surprisingly strong calibration in classification tasks, a feature that was not explicitly designed for. When we initially asked, “why do this research?”, we hoped the journey of the CTM would provide compelling answers. By embracing light biological inspiration and pursuing the novel behaviors observed, we have arrived at a model with emergent capabilities that exceeded our initial designs. We are committed to continuing this exploration, borrowing further concepts to discover what new and exciting behaviors will emerge, pushing the boundaries of what AI can achieve.
0:40

New Paper: Continuous Thought Machines 🧠 Neurons in brains use timing and synchronization in the way that they compute, but this is largely ignored in modern neural nets. We believe neural timing is key for the flexibility and adaptability of biological intelligence. We propose a new neural architecture, “Continuous Thought Machines” (CTMs), which is built from the ground up to use neural dynamics as a core representation for intelligence. By using neural dynamics as a first-class representational citizen, CTMs naturally perform adaptive computation. Many emergent, interesting behaviors arise as a result: CTMs solve mazes by observing a raw maze image and producing step-by-step instructions directly from its neural dynamics. When tasked with image recognition, the CTM naturally takes multiple steps to examine different parts of the image before making its decision. This step-by-step approach not only makes its behavior more interpretable but also improves accuracy: the longer it “thinks,” the more accurate its answers become. We also found that this allows the CTM to decide to spend less time thinking on simpler images, thus saving energy. When identifying a gorilla, for example, the CTM’s attention moves from eyes to nose to mouth in a pattern remarkably similar to human visual attention. I think this work underscores an important, yet often lost, synergy between neuroscience and AI. While modern AI is ostensibly brain-inspired, the two fields often operate in surprising isolation. By starting with such inspiration and iteratively following the emergent, interesting behaviors, we developed a model with unexpected capabilities, such as its surprisingly strong calibration in classification tasks, a feature that was not explicitly designed for. When we initially asked, “why do this research?”, we hoped the journey of the CTM would provide compelling answers. By embracing light biological inspiration and pursuing the novel behaviors observed, we have arrived at a model with emergent capabilities that exceeded our initial designs. We are committed to continuing this exploration, borrowing further concepts to discover what new and exciting behaviors will emerge, pushing the boundaries of what AI can achieve.

hardmaru

257,518 просмотров • 1 год назад

Fei-Fei Li on what simulation can do for robotics that real-world data can't: "You play out events that haven't happened, or cannot happen... And while you play it out, you learn how to act in it." "That's really important in robotics, because we cannot possibly have enough real-world data for that." "Here is a real life example: the industry of self-driving cars. Waymo has officially said they use billions of hours of simulation." "Cars are the simplest kind of robots. So clearly simulation plays a huge role in robotic learning." Fei-Fei Li Yunzhu Li World Labs martin_casado
1:30

Fei-Fei Li on what simulation can do for robotics that real-world data can't: "You play out events that haven't happened, or cannot happen... And while you play it out, you learn how to act in it." "That's really important in robotics, because we cannot possibly have enough real-world data for that." "Here is a real life example: the industry of self-driving cars. Waymo has officially said they use billions of hours of simulation." "Cars are the simplest kind of robots. So clearly simulation plays a huge role in robotic learning." Fei-Fei Li Yunzhu Li World Labs martin_casado

a16z

35,480 просмотров • 28 дней назад