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Building on the previous paper, in this study we compare a continuous “smooth return” S2>S1 model with an event-driven one, where long periods of relative calm are punctuated by short, intense episodes of global reorganisation. Both models cover the same time window. Neither uses archaeological data in its construction....

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Craig Stone

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10,899 görüntüleme • 9 ay önce •via X (Twitter)

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What kind of human behavior caused the global warming before the abrupt cooling during the Younger Dryas period 12,000 years ago? Climate change and the Geophysical Event are not caused by human behavior. They are natural processes driven by axial and apsidal precession, geomagnetic excursions, the Sun, and the gravitational interactions between planetary bodies. It is a divine algorithm that periodically resets civilization—and even life on Earth. This is the biggest kept secret.
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What kind of human behavior caused the global warming before the abrupt cooling during the Younger Dryas period 12,000 years ago? Climate change and the Geophysical Event are not caused by human behavior. They are natural processes driven by axial and apsidal precession, geomagnetic excursions, the Sun, and the gravitational interactions between planetary bodies. It is a divine algorithm that periodically resets civilization—and even life on Earth. This is the biggest kept secret.

Open Minded Approach

123,394 görüntüleme • 4 ay önce

According to Plato, Atlantis was the main city of an advanced civilization that was wiped out 9,000 years ago, which places the event right at the Younger Dryas abrupt climate change and the Gothenburg geomagnetic excursion. Some sources about Plato suggest that there were at least a few floods between the event that sank Atlantis and the time when he lived. This also aligns with the 8.2-kiloyear event, the 5.9-kiloyear event, and the collapse of the Bronze Age civilizations — all associated with abrupt climate change and geomagnetic excursions. Right now we are facing the same kind of event, and that’s why the billionaires are building doomsday bunkers.
0:12

According to Plato, Atlantis was the main city of an advanced civilization that was wiped out 9,000 years ago, which places the event right at the Younger Dryas abrupt climate change and the Gothenburg geomagnetic excursion. Some sources about Plato suggest that there were at least a few floods between the event that sank Atlantis and the time when he lived. This also aligns with the 8.2-kiloyear event, the 5.9-kiloyear event, and the collapse of the Bronze Age civilizations — all associated with abrupt climate change and geomagnetic excursions. Right now we are facing the same kind of event, and that’s why the billionaires are building doomsday bunkers.

Open Minded Approach

460,956 görüntüleme • 11 ay önce

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)
0:10

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,308 görüntüleme • 2 yıl önce

We are releasing the fastest TTS model as open source! One of the biggest problems we encountered while optimizing TTS models is optimization itself. Since it is LLM-based, even optimizing with VLLM and SgLang libraries is not enough. Additionally, I made the outputs much better with special optimizations for training. The voices you are listening to are results obtained from only 20% of the model training. We trained these outputs on 8xB200 in 24 hours. Even though the dataset includes some low-quality data, the model's output is much better. We will release the code as open source tomorrow. Data: Emilia-3m Model: EchoDit (custom) Codec: DacVae Opt: Flash-attn + CacheDit
0:12

We are releasing the fastest TTS model as open source! One of the biggest problems we encountered while optimizing TTS models is optimization itself. Since it is LLM-based, even optimizing with VLLM and SgLang libraries is not enough. Additionally, I made the outputs much better with special optimizations for training. The voices you are listening to are results obtained from only 20% of the model training. We trained these outputs on 8xB200 in 24 hours. Even though the dataset includes some low-quality data, the model's output is much better. We will release the code as open source tomorrow. Data: Emilia-3m Model: EchoDit (custom) Codec: DacVae Opt: Flash-attn + CacheDit

Kadir Nar

32,232 görüntüleme • 3 ay önce

We're now in the range where the GRAF 🦒model is picking up the early stages of the weekend storm. It's had wins and losses at this range, but did well with last weekend's event across the Carolinas. #SCwx #NCwx WMBF News
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We're now in the range where the GRAF 🦒model is picking up the early stages of the weekend storm. It's had wins and losses at this range, but did well with last weekend's event across the Carolinas. #SCwx #NCwx WMBF News

Jamie Arnold WMBF

98,576 görüntüleme • 9 ay önce

Those who say that the Younger Dryas was caused by a meteor hitting the Earth or grazing the atmosphere have not done their homework. The Cyclical Geophysical Event is caused by a combination of axial and apsidal precession, which leads to geomagnetic excursions and, together with Grand Solar Minima, causes abrupt climate change, massive floods, volcanic activity, and the reshaping of the continents. The Younger Dryas is not an exception. Similar events occurred during the 8.2-kiloyear event, the 5.9-kiloyear event, the 4.2-kiloyear event, the Bronze Age Collapse, and are happening today.
0:10

Those who say that the Younger Dryas was caused by a meteor hitting the Earth or grazing the atmosphere have not done their homework. The Cyclical Geophysical Event is caused by a combination of axial and apsidal precession, which leads to geomagnetic excursions and, together with Grand Solar Minima, causes abrupt climate change, massive floods, volcanic activity, and the reshaping of the continents. The Younger Dryas is not an exception. Similar events occurred during the 8.2-kiloyear event, the 5.9-kiloyear event, the 4.2-kiloyear event, the Bronze Age Collapse, and are happening today.

Open Minded Approach

14,895 görüntüleme • 4 ay önce

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.
0:14

1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.

Sundar Pichai

864,989 görüntüleme • 1 yıl önce

They lie to you by claiming that humans cause climate change as rapidly as it is happening today, and that naturally driven climate change is slow and lasts for thousands of years. Every Bond Event was quick; the 5.9-kiloyear event and the desertification of the Sahara occurred within a 100-year period. There are tipping points where all hell breaks loose, and we are heading toward one. We are in a geomagnetic excursion.
0:11

They lie to you by claiming that humans cause climate change as rapidly as it is happening today, and that naturally driven climate change is slow and lasts for thousands of years. Every Bond Event was quick; the 5.9-kiloyear event and the desertification of the Sahara occurred within a 100-year period. There are tipping points where all hell breaks loose, and we are heading toward one. We are in a geomagnetic excursion.

Open Minded Approach

128,904 görüntüleme • 8 ay önce

The Great Flood during the Xia Dynasty describes a catastrophic, decades-long deluge and giant waves that covered mountains and wiped out entire regions. The official narrative places the Xia Dynasty around 2200–2000 BC, right at the time of the 4.2-kiloyear event and the collapse of the ancient Egyptian, Akkadian, and Indus Valley civilizations. During the 4.2-kiloyear event, there was abrupt climate change, a Grand Solar Minimum, and a collapse of major ocean currents. It is also possible that this flood is connected to the 5.9-kiloyear event, the Solovki Geomagnetic Excursion, with supporting evidence found in Chinese sediments, and the desertification of the Sahara. The same event is coming soon!
0:12

The Great Flood during the Xia Dynasty describes a catastrophic, decades-long deluge and giant waves that covered mountains and wiped out entire regions. The official narrative places the Xia Dynasty around 2200–2000 BC, right at the time of the 4.2-kiloyear event and the collapse of the ancient Egyptian, Akkadian, and Indus Valley civilizations. During the 4.2-kiloyear event, there was abrupt climate change, a Grand Solar Minimum, and a collapse of major ocean currents. It is also possible that this flood is connected to the 5.9-kiloyear event, the Solovki Geomagnetic Excursion, with supporting evidence found in Chinese sediments, and the desertification of the Sahara. The same event is coming soon!

Open Minded Approach

171,791 görüntüleme • 11 ay önce

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 görüntüleme • 3 ay önce

Every ancient civilization knew that the safest place during a large catastrophic event was underground. That is why we find places like Derinkuyu in Turkey, the Longyou Caves in China, Ellora in India and many other massive underground structures around the world. The legends about Shambhala, Agartha, Belovodye and other underground cities where human knowledge was protected from catastrophic events are not just random myths. They are memories of a real breakaway civilization that survived previous cataclysms underground. Even the Zoroastrian religion tells us the same story. Humanity is warned about a coming catastrophic winter and Ice Age, and an underground bunker or vault is built to preserve people, animals, plants and seeds until the disaster is over. This is not a coincidence. Ancient civilizations knew that catastrophic events happen again and again, and when the surface becomes uninhabitable, the survivors go underground. That is exactly why underground cities were built in the past — and why massive underground facilities are still being built today.
0:10

Every ancient civilization knew that the safest place during a large catastrophic event was underground. That is why we find places like Derinkuyu in Turkey, the Longyou Caves in China, Ellora in India and many other massive underground structures around the world. The legends about Shambhala, Agartha, Belovodye and other underground cities where human knowledge was protected from catastrophic events are not just random myths. They are memories of a real breakaway civilization that survived previous cataclysms underground. Even the Zoroastrian religion tells us the same story. Humanity is warned about a coming catastrophic winter and Ice Age, and an underground bunker or vault is built to preserve people, animals, plants and seeds until the disaster is over. This is not a coincidence. Ancient civilizations knew that catastrophic events happen again and again, and when the surface becomes uninhabitable, the survivors go underground. That is exactly why underground cities were built in the past — and why massive underground facilities are still being built today.

Open Minded Approach

23,055 görüntüleme • 1 ay önce

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference
0:14

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

Sasha Malysheva

15,156 görüntüleme • 2 ay önce

HEEBOO redefines how entertainment is created, owned, and experienced. The industry is broken. By validating ideas with Superfans, we’re building a market-driven franchise model where IPs start digital-first, monetize early, scale globally, and are co-owned by their communities. Built by the Claynosaurz team.
0:20

HEEBOO redefines how entertainment is created, owned, and experienced. The industry is broken. By validating ideas with Superfans, we’re building a market-driven franchise model where IPs start digital-first, monetize early, scale globally, and are co-owned by their communities. Built by the Claynosaurz team.

HEEBOO

357,925 görüntüleme • 1 yıl önce

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.
0:19

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,517 görüntüleme • 11 ay önce

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.
0:10

This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.

Physical Intelligence

121,658 görüntüleme • 10 ay önce

In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead to curved trajectories as the model averages over many conflicting pairings. However, if we use optimal transport on batches of pairs, this leads to fewer ambiguous intersections that the model must resolve, leading to straighter trajectories at inference time.
0:18

In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead to curved trajectories as the model averages over many conflicting pairings. However, if we use optimal transport on batches of pairs, this leads to fewer ambiguous intersections that the model must resolve, leading to straighter trajectories at inference time.

Alec Helbling

65,798 görüntüleme • 5 ay önce

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.
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Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 görüntüleme • 2 yıl önce

Let me take you back 12,000 years… to the exact border between the Age of Virgo and the Age of Leo. The planet was warming fast. Then, almost overnight, it plunged back into near-ice-age cold: the Younger Dryas. So tell me: what human behavior caused that warming? None. It was driven by forces far bigger than us: axial and apsidal precession, geomagnetic excursions, the Sun, and the gravitational pull between the planets. The Geophysical Event follows the same laws. A divine algorithm that periodically resets civilization… and sometimes life on Earth itself. The best-kept secret in history. And the clock is still ticking. ⏳
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Let me take you back 12,000 years… to the exact border between the Age of Virgo and the Age of Leo. The planet was warming fast. Then, almost overnight, it plunged back into near-ice-age cold: the Younger Dryas. So tell me: what human behavior caused that warming? None. It was driven by forces far bigger than us: axial and apsidal precession, geomagnetic excursions, the Sun, and the gravitational pull between the planets. The Geophysical Event follows the same laws. A divine algorithm that periodically resets civilization… and sometimes life on Earth itself. The best-kept secret in history. And the clock is still ticking. ⏳

Open Minded Approach

16,986 görüntüleme • 29 gün önce

You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.
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You don’t have to use one model (or one provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.

Workshop AI

1,046,048 görüntüleme • 6 ay önce

Russian scientists breakthrough in safer rocket design with model that predicts when space fuel ignites The new mathematical model calculates ignition delay time with high accuracy (within 10–15% error) for hypergolic fuels — propellants that ignite on contact without a spark or heat 🔸The model accounts for heat exchange, evaporation, vapor mixing, and chemical reactions — both in liquid and gas phases 🔸Ignition timing depends on droplet size and temperature; the model accurately predicts both 🔸Larger droplets evaporate faster, but beyond a certain size (about 3 mm), ignition delay time stops changing 🔸The technology allows real-time monitoring of temperature and mixture composition during ignition
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Russian scientists breakthrough in safer rocket design with model that predicts when space fuel ignites The new mathematical model calculates ignition delay time with high accuracy (within 10–15% error) for hypergolic fuels — propellants that ignite on contact without a spark or heat 🔸The model accounts for heat exchange, evaporation, vapor mixing, and chemical reactions — both in liquid and gas phases 🔸Ignition timing depends on droplet size and temperature; the model accurately predicts both 🔸Larger droplets evaporate faster, but beyond a certain size (about 3 mm), ignition delay time stops changing 🔸The technology allows real-time monitoring of temperature and mixture composition during ignition

Sputnik

23,699 görüntüleme • 3 ay önce