正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Back in Toronto with the next generation. At our @ps43foundation Data Dunkers program, these kids aren’t just learning the game… they’re learning how data, curiosity, and creativity can open doors on and off the court. Basketball is the entry point. The real win is showing them what’s possible. 🧠🏀... show more

pascal siakam

170,999 subscribers

47,838 次观看 • 7 个月前 •via X (Twitter)

教育健康养生体育

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Imagine a world where imagination is enough 🚀 1500 kids from 80 countries just built their first products with Lovable. Everything from games to AI selfie booths. It’s insane what kids can do when you give them the right tools. The next generation isn’t learning how to code. They’re learning how to imagine. Thanks for sharing and making it possible Fabian Hedin + Thomas Wolf

Imagine a world where imagination is enough 🚀 1500 kids from 80 countries just built their first products with Lovable. Everything from games to AI selfie booths. It’s insane what kids can do when you give them the right tools. The next generation isn’t learning how to code. They’re learning how to imagine. Thanks for sharing and making it possible Fabian Hedin + Thomas Wolf

Felix Haas

22,086 次观看 • 11 个月前

🏀 Future stars, future leaders. 🌟 Before hearing their names called on Draft Night, the top NBA Draft prospects hit the court with New Era Cap & local youth at The NBPA ! From basketball skills and combine drills to injury prevention and off-court learning activities, it's a day focused on developing the next generation—on and off the hardwood. 💪

🏀 Future stars, future leaders. 🌟 Before hearing their names called on Draft Night, the top NBA Draft prospects hit the court with New Era Cap & local youth at The NBPA ! From basketball skills and combine drills to injury prevention and off-court learning activities, it's a day focused on developing the next generation—on and off the hardwood. 💪

Jr. NBA/Jr. WNBA

43,242 次观看 • 3 个月前

The #Browns are hoping to open up the passing game, and one of the guys looking to help is WR Malachi Corley. But who is he off the field? Corley is a guy who enjoys the game, anime and nature—especially the deer in Parma. Get to know him on Next Man Up 👇🏽

The #Browns are hoping to open up the passing game, and one of the guys looking to help is WR Malachi Corley. But who is he off the field? Corley is a guy who enjoys the game, anime and nature—especially the deer in Parma. Get to know him on Next Man Up 👇🏽

Camryn Justice

30,883 次观看 • 11 个月前

meet 10 year old learning data science & AI/ML in India while other kids are still stuck at baby shark doo-doo!! meet Prath Udhnawala from Surat who is in 5th standard and who showed up at Cake Wallet event, currently learning data science and AI/ML while having no tech background in family. this is the future of India🇮🇳 very much impressed with this magical mind!🙌

meet 10 year old learning data science & AI/ML in India while other kids are still stuck at baby shark doo-doo!! meet Prath Udhnawala from Surat who is in 5th standard and who showed up at Cake Wallet event, currently learning data science and AI/ML while having no tech background in family. this is the future of India🇮🇳 very much impressed with this magical mind!🙌

Daavya.btc

10,696 次观看 • 4 个月前

Elon Musk's Ad Astra school is launching in Bastrop, Texas! Applications for the 2024-2025 school year are now open. About the school: The next generation of problem solvers and builders. Ad Astra’s mission is to foster curiosity, creativity, and critical thinking in the next generation of problem solvers and builders. Ad Astra’s approach to education is centered around hands-on, project-based learning, where children are encouraged to explore, experiment, and discover solutions to real-world problems. Ad Astra offers a progressive learning environment that emphasizes the integration of Science, Technology, Engineering, and Mathematics (STEM) into its curriculum. More here:

Elon Musk's Ad Astra school is launching in Bastrop, Texas! Applications for the 2024-2025 school year are now open. About the school: The next generation of problem solvers and builders. Ad Astra’s mission is to foster curiosity, creativity, and critical thinking in the next generation of problem solvers and builders. Ad Astra’s approach to education is centered around hands-on, project-based learning, where children are encouraged to explore, experiment, and discover solutions to real-world problems. Ad Astra offers a progressive learning environment that emphasizes the integration of Science, Technology, Engineering, and Mathematics (STEM) into its curriculum. More here:

Dima Zeniuk

318,644 次观看 • 2 年前

The next DeepTech_Ready cohort is now open, and I encourage Nigerians who are ready to advance their expertise in AI, Data Science, and emerging technologies to apply here - The program is designed to equip you with practical, industry-relevant skills and hands-on experience that prepare you for real opportunities. If you’re ready to grow, learn, and contribute meaningfully to our digital economy, this is another opportunity for you. Applications are open. Register via

The next DeepTech_Ready cohort is now open, and I encourage Nigerians who are ready to advance their expertise in AI, Data Science, and emerging technologies to apply here - The program is designed to equip you with practical, industry-relevant skills and hands-on experience that prepare you for real opportunities. If you’re ready to grow, learn, and contribute meaningfully to our digital economy, this is another opportunity for you. Applications are open. Register via

Dr. 'Bosun Tijani

21,380 次观看 • 7 个月前

Our CEO Jensen Huang and Dell Technologies Chairman & CEO Michael Dell kicked things off at #DellTechWorld in Las Vegas with a look at how the Dell AI Factory with NVIDIA is redefining how companies build, run, and scale AI. We saw NemoClaw powering agentic AI on-prem, robots demonstrating physical AI in the real world, and sessions packed with enterprise use cases.

Our CEO Jensen Huang and Dell Technologies Chairman & CEO Michael Dell kicked things off at #DellTechWorld in Las Vegas with a look at how the Dell AI Factory with NVIDIA is redefining how companies build, run, and scale AI. We saw NemoClaw powering agentic AI on-prem, robots demonstrating physical AI in the real world, and sessions packed with enterprise use cases.

NVIDIA

93,829 次观看 • 4 个月前

In China, the next generation is growing up surrounded by artificial intelligence tech. From personal tutoring and robotic pet dogs, to young entrepreneurs making money, @SelinaWangTV reports on how children are learning about AI safety and creativity.

In China, the next generation is growing up surrounded by artificial intelligence tech. From personal tutoring and robotic pet dogs, to young entrepreneurs making money, @SelinaWangTV reports on how children are learning about AI safety and creativity.

World News Tonight

16,449 次观看 • 4 个月前

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 次观看 • 9 个月前

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:

Unreal Engine 5 is becoming the go-to platform for robotics teams, not just as a simulator but as a synthetic data factory powering the next generation of robotics systems🦾 From synthetic data to sim-to-real transfer, read our piece on how UE5 is quietly reshaping how robots learn to see, move, and act in the real world:

Unreal Engine

61,187 次观看 • 5 个月前

One of the biggest things Tesla FSD has changed for my family is how we use road trip time. A lot of parents ask me how my kids spend so much time learning, reading, and asking questions, and why we don’t load up our summers with endless camps. The truth is, some of our best learnings happen right in my Tesla. While Tesla FSD handles 100% of the driving, our road trips become productive family time. We bring books, laptops, and our curiosity on our rides. My kids read, learn new things with Grok, research topics they’re interested in, play educational games, and ask questions about the world around them. With Starlink keeping us connected anywhere, the car becomes literally a mobile classroom. What’s funny is that my kids are growing up thinking this is normal. To them, learning with Daddy while traveling down the highway feels completely natural. Years ago, road trips were mostly about getting from Point A to Point B. Today, they’re an opportunity for our family to learn, create, explore ideas, and spend quality time together. The combination of Tesla FSD, Starlink, computers, books, and AI has completely changed how our family travels.

One of the biggest things Tesla FSD has changed for my family is how we use road trip time. A lot of parents ask me how my kids spend so much time learning, reading, and asking questions, and why we don’t load up our summers with endless camps. The truth is, some of our best learnings happen right in my Tesla. While Tesla FSD handles 100% of the driving, our road trips become productive family time. We bring books, laptops, and our curiosity on our rides. My kids read, learn new things with Grok, research topics they’re interested in, play educational games, and ask questions about the world around them. With Starlink keeping us connected anywhere, the car becomes literally a mobile classroom. What’s funny is that my kids are growing up thinking this is normal. To them, learning with Daddy while traveling down the highway feels completely natural. Years ago, road trips were mostly about getting from Point A to Point B. Today, they’re an opportunity for our family to learn, create, explore ideas, and spend quality time together. The combination of Tesla FSD, Starlink, computers, books, and AI has completely changed how our family travels.

Teslaconomics

55,759 次观看 • 3 个月前

Egor Amosov capped an outstanding week at the adidas Next Generation Tournament Finals with a strong showing in the title game. The 6'8 Russian wing is a high-level shot-maker with terrific vision and passing creativity who seemed to take a step forward defensively in Athens.

Egor Amosov capped an outstanding week at the adidas Next Generation Tournament Finals with a strong showing in the title game. The 6'8 Russian wing is a high-level shot-maker with terrific vision and passing creativity who seemed to take a step forward defensively in Athens.

Jonathan Givony

89,325 次观看 • 4 个月前

In-context learning is the holy grail of robot learning. It is challenging because: 1. long-context training and infra (ICL can easily max out disk I/O) 2. need model to follow multimodal (sensorimotor) condition 3. data collection strategy and how to pair data Tried to get it work in 2024, where we proposed in-context robot transformer, an autoregressive formulation: Congrats on the launch! Looking forward to seeing few-shot improvement via in-context learning in the future!

In-context learning is the holy grail of robot learning. It is challenging because: 1. long-context training and infra (ICL can easily max out disk I/O) 2. need model to follow multimodal (sensorimotor) condition 3. data collection strategy and how to pair data Tried to get it work in 2024, where we proposed in-context robot transformer, an autoregressive formulation: Congrats on the launch! Looking forward to seeing few-shot improvement via in-context learning in the future!

Max Fu

10,235 次观看 • 1 个月前

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Today we are excited to open up Neuracore to the academic community! Neuracore is a new data foundation built to accelerate robot learning by removing one of the field’s biggest bottlenecks: capturing and working with high-fidelity multimodal robotics data. For the first time, researchers can store, view, and work with robotics data in a cloud-native system built specifically for large-scale learning, and we are making this core platform completely free for academia. The platform lets teams capture every sensor at its native rate, store and visualize data without loss, and then train and deploy models locally using our open-source code (Link in the comments). We are rolling out access to select academic institutions first. Anyone with an academic email can sign up, and if your institution is not part of the initial rollout, you will be able to join the waitlist directly. Beyond providing this infrastructure, we see an opportunity to build a global community where engineers and researchers can share, collaborate, and advance the frontier of robot learning together. Supported by our recent $3M pre-seed round led by Earlybird VC, we are excited to take this mission even further. Our long-term goal is for Neuracore to become the natural home for cutting-edge robot learning algorithms and real-world robotics experimentation, helping accelerate the next wave of Physical AI.

Neuracore

40,810 次观看 • 10 个月前

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

DeepLearning.AI

20,833 次观看 • 2 年前

Italy's Defense Minister Guido Crosetto: On these new technologies, China is ahead of everyone, especially in medical technologies and those involving the study of human beings. And here, too, it is helped by the fact that it is not a democracy and does not have privacy. If you are an autocratic state and there is no privacy, you can simply go in and obtain the health data of one billion people. In the U.S. and Italy, the state rightly cannot simply obtain the health data of any of us. But if you have the health data of one billion people and put them into quantum computers, with artificial intelligence you can process them and extract things faster and obtain more results than a West that instead has its own rules.

Italy's Defense Minister Guido Crosetto: On these new technologies, China is ahead of everyone, especially in medical technologies and those involving the study of human beings. And here, too, it is helped by the fact that it is not a democracy and does not have privacy. If you are an autocratic state and there is no privacy, you can simply go in and obtain the health data of one billion people. In the U.S. and Italy, the state rightly cannot simply obtain the health data of any of us. But if you have the health data of one billion people and put them into quantum computers, with artificial intelligence you can process them and extract things faster and obtain more results than a West that instead has its own rules.

Clash Report

22,502 次观看 • 8 天前

Inferact CEO Simon Mo says open and closed models are converging. The moat is environment: "What really differentiates open-weight models from closed-weight models? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy. Capability-wise, I don't really see a big gap, not even today." "One of the most important parts is just the data. It's about who gets what data, and then what are the environments you are building to let the model improve on itself." "For Moonshot, they have built some of the best environments for front-end coding... the ability for this model to code and then see what the rendered code is, and then continue looping in this iterative process." "Now, this is about their environment to improve the model. It's not about just source data. It's not about where they get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "The next year is all gonna be about that: how open-weight model labs are differentiating and really getting the model to meet the real world." Simon Mo

Inferact CEO Simon Mo says open and closed models are converging. The moat is environment: "What really differentiates open-weight models from closed-weight models? In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy. Capability-wise, I don't really see a big gap, not even today." "One of the most important parts is just the data. It's about who gets what data, and then what are the environments you are building to let the model improve on itself." "For Moonshot, they have built some of the best environments for front-end coding... the ability for this model to code and then see what the rendered code is, and then continue looping in this iterative process." "Now, this is about their environment to improve the model. It's not about just source data. It's not about where they get the data from. Rather, it's who can build the best environment and who can make the most optimization and algorithmic choices to leverage all this learning from this environment." "The next year is all gonna be about that: how open-weight model labs are differentiating and really getting the model to meet the real world." Simon Mo

a16z

39,424 次观看 • 1 个月前