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Matryoshka Representation Learning (MRL) is a super exciting approach to improving the quality and efficiency of embedding models and strategies ✨ MRL allows models to store more information in the earlier dimensions of their data vectors. This method not only boosts performance in tasks like classification and retrieval, but... show more

Victoria Slocum

9,845 subscribers

120,470 görüntüleme • 2 yıl önce •via X (Twitter)

Sanat Bilim & Teknoloji Eğitim

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Parul Pandey profil fotoğrafı

Parul Pandey2 yıl önce

Great video and explanation. Also love how you color-coded the equation to make it simple to understand 💡

Leonie profil fotoğrafı

Leonie2 yıl önce

Love the new format! 🔥🔥🔥

Philip Vollet profil fotoğrafı

Philip Vollet2 yıl önce

seesh 9000

Edward profil fotoğrafı

Edward2 yıl önce

This video is

Suman Das 👨🏽‍💻 ⚽ profil fotoğrafı

Suman Das 👨🏽‍💻 ⚽2 yıl önce

Thanks for sharing 🙌

Radheshyam profil fotoğrafı

Radheshyam2 yıl önce

Awesome explanation thanks 🙏

Species ⚡️ 🌘 🌍 profil fotoğrafı

Species ⚡️ 🌘 🌍2 yıl önce

How do you make video like this? Shareable?

Victoria Slocum profil fotoğrafı

Victoria Slocum2 yıl önce

Green screen, Adobe Premiere, and lots of failed attempts and practice 😅

Flavio Milazzo, MSc profil fotoğrafı

Flavio Milazzo, MSc2 yıl önce

Great share, thank you.

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Matryoshka dolls 🪆 = the key to AI efficiency. Gemini Embedding 2 leverages Matryoshka Representation Learning (MRL) so you can: 🔹 Dynamically truncate vectors for high-speed candidate matching without losing precision 🔹Slash database costs by choosing a smaller storage footprint without re-indexing 🔹 Adapt to any latency budget or accuracy requirement 📈💸 Learn how this helps build more efficient RAG, semantic search, and more:

Matryoshka dolls 🪆 = the key to AI efficiency. Gemini Embedding 2 leverages Matryoshka Representation Learning (MRL) so you can: 🔹 Dynamically truncate vectors for high-speed candidate matching without losing precision 🔹Slash database costs by choosing a smaller storage footprint without re-indexing 🔹 Adapt to any latency budget or accuracy requirement 📈💸 Learn how this helps build more efficient RAG, semantic search, and more:

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Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

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Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

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World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

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Today we’re releasing V-JEPA, a method for teaching machines to understand and model the physical world by watching videos. This work is another important step towards Yann LeCun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks. Details ➡️ We're releasing a collection of V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information. It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency. The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training. Our results show that, using a frozen backbone, our top V-JEPA models achieve 82.0% on Kinetics-400, 72.2% on Something-Something-v2 and 77.9% on ImageNet1K — competitive with or exceeding previous leading video models. We believe that this work is an important milestone on the path to advancing machine intelligence.

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(a little late but) Happy 1 year to H4R !!! 🩷 It’s been so much fun creating so many memories and experiencing things with you guys. Thank you for all the love & support, always. Quite literally could nit do this without you guys✨ Love y’all and i’m super excited to see what this next year has in store for me/us! It’s only up

(a little late but) Happy 1 year to H4R !!! 🩷 It’s been so much fun creating so many memories and experiencing things with you guys. Thank you for all the love & support, always. Quite literally could nit do this without you guys✨ Love y’all and i’m super excited to see what this next year has in store for me/us! It’s only up

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Introducing Reinforcement-Learned Teachers (RLTs): Transforming how we teach LLMs to reason with reinforcement learning (RL). Blog: Paper: Traditional RL focuses on “learning to solve” challenging problems with expensive LLMs and constitutes a key step in making student AI systems ultimately acquire reasoning capabilities via distillation and cold-starting. Enter our RLTs—a new class of models prompted with not only a problem’s question but also its solution, and directly trained to generate clear, step-by-step “explanations” to teach their students. Remarkably, an RLT with only 7B parameters produces superior results when distilling and cold-starting students in competitive and graduate-level reasoning tasks than orders-of-magnitude larger LLMs. RLTs are as effective even when distilling 32B students, much larger than the teacher itself—unlocking a new standard for efficiency in developing reasoning language models with RL. Code:

Sakana AI

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New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

New PNAS paper. Historical GDP per capita data is scarce, but data on the places of birth, death, and occupations of famous individuals is abundant. In this paper we estimate the historical GDP per capita of hundreds of regions in Europe and North America using a machine learning model that leveraged data on about 500k famous biographies. Our estimates more-or-less quadruple the availability of historical GDP per capita estimates for the last 700 years. So why use biographies to augment historical GDP per capita data? Biographical data contains information about people who might have contributed directly to economic growth, like James Watt, or that were attracted to wealthy places looking for patrons, like Michelangelo. So we--mainly Philipp (Philipp Koch)--used this data to construct hundreds of features describing each European region. Then, we trained a machine learning model to find the features that explained most of the variance in a cross-validation test, where we split regions multiple times into a training set and a test set. On average, the model explained about 90% of the variance in GDP per capita of the regions it had not seen during training. But we wanted to go further, and Philipp really went to town by looking at different ways to validate our estimates. We found our estimates correlate positively with historical measures of wellbeing, church building activity, urbanization, and body height. We also used these measures to reproduce the basic Atlantic trade result of Acemoglu, Johnson, and Robison and to explore the economic consequences of the famous Lisbon earthquake of 1755. But what I personally loved most about this project, other than working with Philipp Koch and V, is that it shows that we can use machine learning methods not only to explore the future, but the past. There is a bright and growing future in the use of machine learning for economic history. Hope you enjoy the paper and the data. You can find links to the paper and a data exploration tool in the first comment.

César A. Hidalgo

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All the best coaching in the world at the youngest ages is rendered useless if a child hasn’t developed the ability to focus their attention. You can bring the best coach in the world to your child’s training session and if they don’t pay full attention absolutely nothing takes place. Similar to inside a classroom where you can bring the teacher of the year to your child’s classroom and if they don’t focus and pay attention, learning does not take place. So it’s not always the case of good or bad coaching it’s often the case a child’s lack of ability to learn by not focusing on what is being taught. This is especially true for the youngest ages. This video is an example of a 5yr old child focusing their attention, trying to control an object with their feet, the ball. This becomes a mental task, married together with the action of movement, making it a physical task as well. The brain loves to learn while moving. Combining, mind and body, thinking and feeling, this allows the cerebellum, the seat of the unconscious mind, to create a chemical signature of this experience, which is emotions. Emotions are the on off switch for learning. Couple in a parent being present, this becomes a shared experience together, where the child is constantly seeking the parents approval, attention, and praise which creates a chemical electrical process in the body which is emotions. This facilitates, deep learning, and long-term memory, all disguised as playtime. A parent just being present allows for this experience to take place. A child this young rarely starts playing or exercising with a ball without someone being present. Being present and sharing this experience together is key for the learning process to take place. These movements are being stored in the non-declared memory which makes this implicit learning when you do something so many times it becomes natural and outside your conscious awareness. Like riding a bicycle or driving a car.

All the best coaching in the world at the youngest ages is rendered useless if a child hasn’t developed the ability to focus their attention. You can bring the best coach in the world to your child’s training session and if they don’t pay full attention absolutely nothing takes place. Similar to inside a classroom where you can bring the teacher of the year to your child’s classroom and if they don’t focus and pay attention, learning does not take place. So it’s not always the case of good or bad coaching it’s often the case a child’s lack of ability to learn by not focusing on what is being taught. This is especially true for the youngest ages. This video is an example of a 5yr old child focusing their attention, trying to control an object with their feet, the ball. This becomes a mental task, married together with the action of movement, making it a physical task as well. The brain loves to learn while moving. Combining, mind and body, thinking and feeling, this allows the cerebellum, the seat of the unconscious mind, to create a chemical signature of this experience, which is emotions. Emotions are the on off switch for learning. Couple in a parent being present, this becomes a shared experience together, where the child is constantly seeking the parents approval, attention, and praise which creates a chemical electrical process in the body which is emotions. This facilitates, deep learning, and long-term memory, all disguised as playtime. A parent just being present allows for this experience to take place. A child this young rarely starts playing or exercising with a ball without someone being present. Being present and sharing this experience together is key for the learning process to take place. These movements are being stored in the non-declared memory which makes this implicit learning when you do something so many times it becomes natural and outside your conscious awareness. Like riding a bicycle or driving a car.

Tom Byerトム•バイヤー

26,691 görüntüleme • 2 yıl önce

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.

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 görüntüleme • 8 ay önce

Lebron didn't only want team up with Wade And Bosh, he also wanted to team up with Carmelo Anthony and Also wanted to have a Super Bench And tried to blame the Bench players for losing in 2011 to the Mavericks SMH This is one of the reasons hes NOT The GOAT Imagine being this pathetic and needing this much help 😭😭

Lebron didn't only want team up with Wade And Bosh, he also wanted to team up with Carmelo Anthony and Also wanted to have a Super Bench And tried to blame the Bench players for losing in 2011 to the Mavericks SMH This is one of the reasons hes NOT The GOAT Imagine being this pathetic and needing this much help 😭😭

TheTruth

50,589 görüntüleme • 2 ay önce