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NVIDIA did it again.. they trained a GPT that generates human movement instead of text.. next-token prediction, but the tokens are body motions. it's called GPC and it hit a 99.98% success rate reproducing a massive corpus of motion clips.. cartwheels, vaults, flips, all of it. → 99.98% success...

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How To Prompt

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101,409 Aufrufe • vor 16 Tagen •via X (Twitter)

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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.
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MotionGPT: Human Motion as a Foreign Language paper page: Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.

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Rune Christensen, Co-Founder of Sky (Sky), on USDS, STUSDS, and risk tiers in DeFi "Lindy, like stablecoin product with a really really reasonable rate of return" "right now it's at 3.75%, which is actually 10 points above SOFR" "a lot of people dunking on DeFi for not being able to beat TradFi, but USDS does in fact still beat TradFi and is very much highly risk optimized" "with Sky you can also use a product like STUSDS, which is a higher risk token that still has this link to the US dollar" "you can actually go even more high risk and buy something like SPK, which is a completely volatile governance token of one of the Sky agents" "STUSDS, because it's a risk token and it's explicitly marketed as risk capital, it's for advanced users because it has higher risk" "it's not just a stablecoin that is designed to protect you above everything else" "with STUSDS it's also about being able to offer a higher rate of return" "one of the consequences of this is that it can have its liquidity, its one to one liquidity with USDS frozen"

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This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.
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Rohan Paul

37,121 Aufrufe • vor 5 Monaten

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.
0:23

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.

Ioannis Koukourakis

48,636 Aufrufe • vor 7 Monaten

Elon Musk just stripped away every emotional narrative around paralysis and reduced it to a pure engineering equation. The human nervous system is not mystical. It is a biological wiring grid. When a wire breaks, you build a bypass. Traditional medicine treats a severed spinal cord as a permanent biological endpoint. Musk treats it as a broken routing switch. Musk: “It’s basically a communications bridge. You bridge the communications from the motor cortex past the point in the neck or spine where the nerves are damaged.” Not a miracle. Not a mystery. A bridge. Musk: “It is possible from a physics standpoint to restore full body functionality. There is nothing that prevents it happening from a physics standpoint.” The physics already check out. This is not a question of possibility. It is a question of execution speed. We are building AGI by mastering computational physics in silicon. Neuralink is applying that same mastery to carbon. The human body is not a sacred text. It is a machine. And machines can be patched. But this is bigger than medicine. Humanity’s ability to interface with superintelligence is currently bottlenecked by thumbs typing on a glass screen. Neuralink is the solution to that constraint dressed as a medical device. If you can bridge the brain past a broken spine, you can bridge the brain to a data center. Healing the paralyzed is step one. Merging with superintelligence is the endgame. Musk: “It’s a very hard technical problem, right, but there is nothing that prevents it happening from a physics standpoint.” Somewhere right now, a person is sitting in a wheelchair. An engineer is sitting in a lab. Neither knows the other exists. But one of them is quietly rewriting the definition of permanent. And it isn’t the one in the wheelchair.
0:25

Elon Musk just stripped away every emotional narrative around paralysis and reduced it to a pure engineering equation. The human nervous system is not mystical. It is a biological wiring grid. When a wire breaks, you build a bypass. Traditional medicine treats a severed spinal cord as a permanent biological endpoint. Musk treats it as a broken routing switch. Musk: “It’s basically a communications bridge. You bridge the communications from the motor cortex past the point in the neck or spine where the nerves are damaged.” Not a miracle. Not a mystery. A bridge. Musk: “It is possible from a physics standpoint to restore full body functionality. There is nothing that prevents it happening from a physics standpoint.” The physics already check out. This is not a question of possibility. It is a question of execution speed. We are building AGI by mastering computational physics in silicon. Neuralink is applying that same mastery to carbon. The human body is not a sacred text. It is a machine. And machines can be patched. But this is bigger than medicine. Humanity’s ability to interface with superintelligence is currently bottlenecked by thumbs typing on a glass screen. Neuralink is the solution to that constraint dressed as a medical device. If you can bridge the brain past a broken spine, you can bridge the brain to a data center. Healing the paralyzed is step one. Merging with superintelligence is the endgame. Musk: “It’s a very hard technical problem, right, but there is nothing that prevents it happening from a physics standpoint.” Somewhere right now, a person is sitting in a wheelchair. An engineer is sitting in a lab. Neither knows the other exists. But one of them is quietly rewriting the definition of permanent. And it isn’t the one in the wheelchair.

Dustin

508,807 Aufrufe • vor 5 Monaten

🚨 Fox’s Marc Thiessen 🇺🇸❤️🇺🇦🇹🇼🇮🇱 NAILS IT on defining Trump’s success in Iran: “There are multiple levels of success- one level is to destroy Iran’s capability to do what they’re doing now in projecting force out of their borders - the chance of that success is pretty near 100%.” “Another level of success is the regime falls and there’s a replacement regime that is subservient to the U.S. Pretty high chance of that succeeding.” “And the third level of success is if the Iranian people take Trump’s offer and rise up, overthrow the government and we replace an enemy with a democratic ally in Iran. That’s still possible.” “Even just level one would be a huge success and transform the Middle East.”
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🚨 Fox’s Marc Thiessen 🇺🇸❤️🇺🇦🇹🇼🇮🇱 NAILS IT on defining Trump’s success in Iran: “There are multiple levels of success- one level is to destroy Iran’s capability to do what they’re doing now in projecting force out of their borders - the chance of that success is pretty near 100%.” “Another level of success is the regime falls and there’s a replacement regime that is subservient to the U.S. Pretty high chance of that succeeding.” “And the third level of success is if the Iranian people take Trump’s offer and rise up, overthrow the government and we replace an enemy with a democratic ally in Iran. That’s still possible.” “Even just level one would be a huge success and transform the Middle East.”

TV News Now

15,640 Aufrufe • vor 5 Monaten

Intelligence was the one thing that never scaled. We scaled everything else. Steel. Energy. Compute. The one resource that built all of it never left the skull. Musk: “People thought defeating Go was either never or 20 years away.” Twelve months later it was over. Musk: “Now that same AlphaGo system can defeat the top 50 players simultaneously with 0% chance of them winning. And that’s one year later.” Fifty lifetimes of mastery against a system that does not know it is playing a game. Zero percent chance. That was not a competition. That was a preview. Musk: “The degrees of freedom to which artificial intelligence is able to apply itself are really increasing by 10 orders of magnitude a year.” Ten billion times. Every twelve months. No brain alive can visualize that number. By design. Every hard problem that ever defeated us did it for the same reason. Not complexity. Scarcity. The only mind capable of solving it was biological and there was never enough of it. Cancer. Fusion. Climate. The physics we cannot even see yet. Not waiting on more data. Waiting on something that can think at a scale biology never allowed. That just arrived. Most people hear this and reduce it to a question about their paycheck. They are watching the single largest expansion of capability in the history of life on this planet and worrying about a job title. For ten thousand years intelligence had one speed. One brain. One lifetime. Every civilization on earth throttled by the same biological ceiling. That ceiling just shattered. We are the only species that ever hit its own limit and built what breaks through it. That is not an ending. That is the point of everything we ever built.
0:32

Intelligence was the one thing that never scaled. We scaled everything else. Steel. Energy. Compute. The one resource that built all of it never left the skull. Musk: “People thought defeating Go was either never or 20 years away.” Twelve months later it was over. Musk: “Now that same AlphaGo system can defeat the top 50 players simultaneously with 0% chance of them winning. And that’s one year later.” Fifty lifetimes of mastery against a system that does not know it is playing a game. Zero percent chance. That was not a competition. That was a preview. Musk: “The degrees of freedom to which artificial intelligence is able to apply itself are really increasing by 10 orders of magnitude a year.” Ten billion times. Every twelve months. No brain alive can visualize that number. By design. Every hard problem that ever defeated us did it for the same reason. Not complexity. Scarcity. The only mind capable of solving it was biological and there was never enough of it. Cancer. Fusion. Climate. The physics we cannot even see yet. Not waiting on more data. Waiting on something that can think at a scale biology never allowed. That just arrived. Most people hear this and reduce it to a question about their paycheck. They are watching the single largest expansion of capability in the history of life on this planet and worrying about a job title. For ten thousand years intelligence had one speed. One brain. One lifetime. Every civilization on earth throttled by the same biological ceiling. That ceiling just shattered. We are the only species that ever hit its own limit and built what breaks through it. That is not an ending. That is the point of everything we ever built.

Dustin

24,315 Aufrufe • vor 1 Monat

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."
1:21

Mathematician Terence Tao offers a counterintuitive take: AI doesn't look intelligent because our definition of intelligence was wrong all along. He argues that the entire history of AI has followed a predictable pattern: "The history of AI has been here's a task that only humans can do, like maybe it is read natural language or win at chess or solve a math problem, and then one by one someone finds some AI algorithm that also does that." But every time a machine cracks one of these "uniquely human" tasks, we move the goalposts. The solution never feels like real thinking: "You look at how it's done and it doesn't feel like intelligence. It's, oh, it was some trick. You just cobbled together these neural networks and you ran some algorithm, and we were looking for some elusive intelligent way of thinking, and we don't see it in the tools that actually solve our goals." Tao then flips the problem on its head. What if the issue isn't with the machines, but with us? "But maybe it's actually because intelligence is not what we think it is." He points to large language models as the clearest case. What they do sounds almost embarrassingly simple: "Large language models in particular become very successful, and a lot of what they're doing is just predicting the next token, clicking the next word in a sentence. And that doesn't sound like something which is intelligent." To show why this feels wrong, Tao draws a comparison to how we'd judge a human doing the same thing: "If you ask someone to improvise a speech and they have no preparation, and at every moment they're just saying the next word that comes to their mind, you don't think that this could actually work." And yet it works for LLMs. Which forces an uncomfortable possibility: "Maybe that's actually a lot of what humans do as well."

Big Brain AI

69,480 Aufrufe • vor 2 Monaten