Solving NaN Tensors and Pickling Errors in a ZeroGPU Space
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My Hypothesis:
Concepts like entropy, energy, and the second law of thermodynamics are not intrinsic to physical matter but are emergent properties of any sufficiently complex system where probabilistic decision-making, optimization, and information flow occur. These principles arise naturally in artificial environments that are structured with rules governing uncertainty, even without explicit definitions of physical thermodynamic laws.
Proven Via:
The Second Law of Thermodynamics
Geometric Langlands Program
Lagrangean Mechanics
TL;DR: When I create a simulated environment, I do not need to code entropy and energy into the simulated environment. I can utilize Entropy and the Second Law of Thermodynamics and I can use Conservation of Energy, but I do not need to explicitly code these into the environment. That is peculiar.
I made a video with a clickbait title but a bunch of code that breaks this observation down further. Would love for someone to prove my simple observation false: https://youtu.be/8n7SXLj7P1o
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TuringsSolutions's
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9 days ago
My Hypothesis:
Concepts like entropy, energy, and the second law of thermodynamics are not intrinsic to physical matter but are emergent properties of any sufficiently complex system where probabilistic decision-making, optimization, and information flow occur. These principles arise naturally in artificial environments that are structured with rules governing uncertainty, even without explicit definitions of physical thermodynamic laws.
Proven Via:
The Second Law of Thermodynamics
Geometric Langlands Program
Lagrangean Mechanics
TL;DR: When I create a simulated environment, I do not need to code entropy and energy into the simulated environment. I can utilize Entropy and the Second Law of Thermodynamics and I can use Conservation of Energy, but I do not need to explicitly code these into the environment. That is peculiar.
I made a video with a clickbait title but a bunch of code that breaks this observation down further. Would love for someone to prove my simple observation false: https://youtu.be/8n7SXLj7P1o
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TuringsSolutions's
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9 days ago
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My Hypothesis:
Concepts like entropy, energy, and the second law of thermodynamics are not intrinsic to physical matter but are emergent properties of any sufficiently complex system where probabilistic decision-making, optimization, and information flow occur. These principles arise naturally in artificial environments that are structured with rules governing uncertainty, even without explicit definitions of physical thermodynamic laws.
Proven Via:
The Second Law of Thermodynamics
Geometric Langlands Program
Lagrangean Mechanics
TL;DR: When I create a simulated environment, I do not need to code entropy and energy into the simulated environment. I can utilize Entropy and the Second Law of Thermodynamics and I can use Conservation of Energy, but I do not need to explicitly code these into the environment. That is peculiar.
I made a video with a clickbait title but a bunch of code that breaks this observation down further. Would love for someone to prove my simple observation false: https://youtu.be/8n7SXLj7P1o
Concepts like entropy, energy, and the second law of thermodynamics are not intrinsic to physical matter but are emergent properties of any sufficiently complex system where probabilistic decision-making, optimization, and information flow occur. These principles arise naturally in artificial environments that are structured with rules governing uncertainty, even without explicit definitions of physical thermodynamic laws.
Proven Via:
The Second Law of Thermodynamics
Geometric Langlands Program
Lagrangean Mechanics
TL;DR: When I create a simulated environment, I do not need to code entropy and energy into the simulated environment. I can utilize Entropy and the Second Law of Thermodynamics and I can use Conservation of Energy, but I do not need to explicitly code these into the environment. That is peculiar.
I made a video with a clickbait title but a bunch of code that breaks this observation down further. Would love for someone to prove my simple observation false: https://youtu.be/8n7SXLj7P1o
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TuringsSolutions's
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9 days ago
here is my proof that i did some months ago along the same principals you bring up.
https://gist.github.com/p3nGu1nZz/3acdf783cf14ec21d678db6fc04cc8ee
left and right click adds and removes bubs and tensors to the network
without programming nuclear forces directly, only by setting the attraction weights of the bub (dots), the system will exhibit and learn core physics and thermal dynamics properties, as emergent properties.
these are similar to "free lunch" in mathematics.
however, correlation does not equal causation, so it's important to develop a falsifiable experiment testing and measuring these phenomena.
thanx for sharing the brain candy and nice video.
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Solving NaN Tensors and Pickling Errors in a ZeroGPU Space
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๐ ๐๐ก๐ ๐๐ข๐ซ๐ฌ๐ญ ๐๐ฏ๐๐ซ ๐
๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง ๐ฐ๐๐๐ญ๐ก๐๐ซ ๐ฆ๐จ๐๐๐ฅ: ๐๐ซ๐ข๐ญ๐ก๐ฏ๐ข ๐๐ฑ๐ ๐๐ง๐๐๐ฅ๐๐ฌ ๐ฅ๐ข๐๐-๐ฌ๐๐ฏ๐ข๐ง๐ ๐ฐ๐๐๐ญ๐ก๐๐ซ ๐ฉ๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง๐ฌ
Hurricane Katrina killed hundreds of people as it made landfall on New Orleans in 2005 - many of these deaths could have been avoided if alerts had been given one day earlier. Accurate weather forecasts are really life-saving.
๐ฅย Now, NASA and IBM just dropped a game-changing new model: the first ever foundation model for weather! This means, it's the first time we have a generalist model not restricted to one task, but able to predict 160 weather variables!
Prithvi WxC (Prithvi, โเคชเฅเคฅเฅเคตเฅโ, is the Sanskrit name for Earth) - is a 2.3 billion parameter model, with an architecture close to previous vision transformers like Hiera.
๐กย But it comes with some important tweaks: under the hood, Prithvi WxC uses a clever transformer-based architecture with 25 encoder and 5 decoder blocks. It alternates between "local" and "global" attention to capture both regional and global weather patterns.
๐๐ฒ๐ ๐ถ๐ป๐๐ถ๐ด๐ต๐๐:
๐ฎ Nails short-term forecasts - Prithvi WxC crushed it on 6-12 hour predictions, even outperforming some traditional numerical weather models
๐ Tracks hurricanes like a champ - For Hurricane Ida, it predicted the landfall location within 5 km (vs 20+ km errors from other AI models), which is a huge progress!
๐ 6x downscaling power - Can zoom in on weather data to 6x higher resolution with 4x lower error than basic methods
๐ Models elusive gravity waves - Accurately simulates these crucial but hard-to-capture atmospheric oscillations
As climate change intensifies, tools like Prithvi WxC will become more and more crucial to avoid disasters!
Announcement post ๐ https://newsroom.ibm.com/2024-09-23-ibm-and-nasa-release-open-source-ai-model-on-hugging-face-for-weather-and-climate-applications
Model on the Hub ๐ https://huggingface.co/Prithvi-WxC
Thank you @clem for highlighting it!
Hurricane Katrina killed hundreds of people as it made landfall on New Orleans in 2005 - many of these deaths could have been avoided if alerts had been given one day earlier. Accurate weather forecasts are really life-saving.
๐ฅย Now, NASA and IBM just dropped a game-changing new model: the first ever foundation model for weather! This means, it's the first time we have a generalist model not restricted to one task, but able to predict 160 weather variables!
Prithvi WxC (Prithvi, โเคชเฅเคฅเฅเคตเฅโ, is the Sanskrit name for Earth) - is a 2.3 billion parameter model, with an architecture close to previous vision transformers like Hiera.
๐กย But it comes with some important tweaks: under the hood, Prithvi WxC uses a clever transformer-based architecture with 25 encoder and 5 decoder blocks. It alternates between "local" and "global" attention to capture both regional and global weather patterns.
๐๐ฒ๐ ๐ถ๐ป๐๐ถ๐ด๐ต๐๐:
๐ฎ Nails short-term forecasts - Prithvi WxC crushed it on 6-12 hour predictions, even outperforming some traditional numerical weather models
๐ Tracks hurricanes like a champ - For Hurricane Ida, it predicted the landfall location within 5 km (vs 20+ km errors from other AI models), which is a huge progress!
๐ 6x downscaling power - Can zoom in on weather data to 6x higher resolution with 4x lower error than basic methods
๐ Models elusive gravity waves - Accurately simulates these crucial but hard-to-capture atmospheric oscillations
As climate change intensifies, tools like Prithvi WxC will become more and more crucial to avoid disasters!
Announcement post ๐ https://newsroom.ibm.com/2024-09-23-ibm-and-nasa-release-open-source-ai-model-on-hugging-face-for-weather-and-climate-applications
Model on the Hub ๐ https://huggingface.co/Prithvi-WxC
Thank you @clem for highlighting it!
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