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Physical Literacy is about learning to move well before learning to play well. It is the foundation upon which both sporting excellence and lifelong health are built. With The Physical Literacy Project, in association with Fumos, the PE learning app, we begin a national conversation on why movement education... show more

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The Agboyi Community Secondary School is rapidly progressing, and we are getting closer to seeing this transformative project come to life! The roof column is currently being prepared, and soon, we will move to the next phase of construction. This school is more than just a building! It is a beacon of hope for the riverine Agboyi community, designed to provide quality education to our future stars. The school will feature a Modern Classrooms for a conducive learning environment; Fully Equipped Science and ICT Laboratories to drive innovation and digital literacy and a Library for research and academic excellence For too long, students in Agboyi have had to travel great distances to access quality secondary education. This school will bridge that gap, ensuring that no child is left behind due to location or accessibility challenges. Education remains a top priority for us, and we are proud to be delivering a project that will empower the next generation with knowledge and opportunities. We’ll update you every step of the way!

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Deep learning works extraordinarily well. And we still largely don't know why. A new paper from Jamie Simon, Daniel Kunin, and 12 co-authors argues that a scientific theory of deep learning is emerging, and coins a name for the emerging field: learning mechanics. We sat down with Jamie and Dan on Generally Intelligent to talk about what a physics of deep learning would actually look like, why now, and what's left to figure out. 3:05 Learning mechanics as the physics to mechanistic interpretability's biology 4:13 Why deep learning needs a theory 7:07 Why deep learning is uniquely hard to engineer 12:11 How a week in the woods became a paper 25:59 The barrier to theory isn't opacity, but complexity 36:26 Deep learning's first gas law 47:22 Why more particles makes the problem easier 56:22 The discretization hypothesis 1:01:50 The strongest signal that a compact theory exists 1:05:07 The Platonic Representation Hypothesis 1:15:41 Why learning mechanics and mech interp need each other 1:25:29 Theory as safety infrastructure

Imbue

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The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

Mathelirium

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Every time I found myself in an unfamiliar situation in my cricketing career, I never let it get me down. Instead, I adapted to it by learning. Early in my career, the T20 format was introduced. I trained hard for it and learnt to play it. Learning made me confident in something I had never done. It is this very habit of learning that made me confident enough to play all the formats in different conditions around the world. The learning didn’t stop even after I retired. I learnt to be a coach and a mentor. That’s why I like what I have done with the people at CoinDCX : India Ka Crypto Coach. We share a common belief “Learning is the only way to succeed.” #ConfidentCryptoStart #Ad

Every time I found myself in an unfamiliar situation in my cricketing career, I never let it get me down. Instead, I adapted to it by learning. Early in my career, the T20 format was introduced. I trained hard for it and learnt to play it. Learning made me confident in something I had never done. It is this very habit of learning that made me confident enough to play all the formats in different conditions around the world. The learning didn’t stop even after I retired. I learnt to be a coach and a mentor. That’s why I like what I have done with the people at CoinDCX : India Ka Crypto Coach. We share a common belief “Learning is the only way to succeed.” #ConfidentCryptoStart #Ad

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☄️ Newton’s Room is a Mixed Reality physics education experience. ⚛️ Users have to apply Newton's laws of physics to solve puzzles that adapt and respond to their physical environment. 🆓 Download the app today for Free on Meta Quest! 🥽 Enjoy learning physics in your room.

☄️ Newton’s Room is a Mixed Reality physics education experience. ⚛️ Users have to apply Newton's laws of physics to solve puzzles that adapt and respond to their physical environment. 🆓 Download the app today for Free on Meta Quest! 🥽 Enjoy learning physics in your room.

Treeview

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Education is one of the most crucial pillars of our THEMES+ agenda. It is not just a priority for our administration, it is a passion of mine. Education lays the foundation for the future of our children and, ultimately, the growth and development of our society. That’s why we are committed to renovating and reconstructing our schools to meet global standards. We want our educational institutions to be places where children can thrive, learn, and be inspired. Every child deserves access to a conducive learning environment, equipped with the necessary resources to help them excel academically and personally. Together, we are building a brighter future for the next generation, and I am excited to see our children flourish in an environment that encourages learning and innovation.

Education is one of the most crucial pillars of our THEMES+ agenda. It is not just a priority for our administration, it is a passion of mine. Education lays the foundation for the future of our children and, ultimately, the growth and development of our society. That’s why we are committed to renovating and reconstructing our schools to meet global standards. We want our educational institutions to be places where children can thrive, learn, and be inspired. Every child deserves access to a conducive learning environment, equipped with the necessary resources to help them excel academically and personally. Together, we are building a brighter future for the next generation, and I am excited to see our children flourish in an environment that encourages learning and innovation.

Babajide Sanwo-Olu

20,591 次观看 • 1 年前

This isn’t about fat shaming anyone, but an issue that needs to be addressed. Police and MNDF are responsible for protecting our nation from both internal and external threats. So they should be among the fittest individuals in the country. If these frontline services are struggling with health and fitness, it’s a reflection of a wider national problem. An unhealthy population places a significant financial strain on Aasandha. Much of this is preventable. That’s why we need to have a serious conversation about it. We need a national survey to understand why so many people are facing health and weight issues. Is it cause of our dietary habits, like having curry every day? Is it due to limited space for physical activity? Are high rise buildings discouraging people from going out once they’re home? Or is it something to do with the ingredients we commonly use? Is it the oil that STO is selling? What is it? A chronically unhealthy workforce weakens national productivity and security.

This isn’t about fat shaming anyone, but an issue that needs to be addressed. Police and MNDF are responsible for protecting our nation from both internal and external threats. So they should be among the fittest individuals in the country. If these frontline services are struggling with health and fitness, it’s a reflection of a wider national problem. An unhealthy population places a significant financial strain on Aasandha. Much of this is preventable. That’s why we need to have a serious conversation about it. We need a national survey to understand why so many people are facing health and weight issues. Is it cause of our dietary habits, like having curry every day? Is it due to limited space for physical activity? Are high rise buildings discouraging people from going out once they’re home? Or is it something to do with the ingredients we commonly use? Is it the oil that STO is selling? What is it? A chronically unhealthy workforce weakens national productivity and security.

Hassan Kurusee

50,997 次观看 • 1 年前