Snowflake's Arctic LLM challenges industry giants Llama and Mixtral with its open-source approach #affordability #AI #AItechnology #Arctic #artificialintelligence #DenseMoEHybridtransformerarchitecture #Efficacy #enterpriseAI #llm #machinelearning #strategicpartnerships
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BLASTNet-2: Revolutionizing Fundamental Fluid Dynamics with Data Power #AI #AIdriveninsights #artificialintelligence #BLASTNet2 #climatemodeling #climatepredictions #computationalfluiddynamics #dataset #Engineering #fluidbehavior #fluiddynamicsresearch #GPT3 #highdimensionaldata #interdisciplinarycollaborations #Largelanguagemodels #llm #machinelearning #machinelearningready #Oceanography #rocketpropulsion #Scientificcommunity #Stanfordresearchers #turbulencemodels
BLASTNet-2: Revolutionizing Fundamental Fluid Dynamics with Data Power
https://multiplatform.ai
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🌍🌐 Machine Learning is key for the EU's #DestinationEarth initiative. In this video, ECMWF’s Mariana Clare explains how #MachineLearning can help quantify uncertainty when running DestinE’s highly accurate #DigitalTwins’ simulations. This is a method that presents benefits especially to overcome high computational costs. Watch ➡️ https://lnkd.in/e2HztF_c EU Digital & Tech EUMETSAT European Space Agency - ESA Copernicus ECMWF
The role of Machine Learning in DestinE, Mariana Clare
https://www.youtube.com/
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it's a data space oddity 🚀 🛰️ a massive amount of Sentinel 2 satellite imagery ready for computer vision & machine learning 🌐 this is huge for training models and making remote sensing more approachable than ever! European Space Agency - ESA Hugging Face #huggingface #esa #satelliteimagery #remotesensing #computervision #machinelearning #foundationmodels
🗺 Major TOM: Expandable Datasets for Earth Observation 🚨 RECORD-BREAKING EO DATASET released in partnership with Hugging Face: the largest ever ML-ready Sentinel-2 dataset! We tried to cover every single point on Earth captured by the European Space Agency - ESA Copernicus Sentinel-2 satellite, and we got pretty close! Mikolaj Czerkawski and I are thrilled to finally announce the release of Major TOM Core. 🌍 About half of the entire planet is covered. That's 2,245,886 patches of 1068 x 1068 pixels, available in both L1C and L2A. At 10 m resolution, we've got 256 million square km with over 2.5 trillion pixels. It's all yours with a few lines of code. See the paper linked below 🔽 for more info! 🧱 And this is just the beginning. We are currently preparing more datasets from different satellites for the Major TOM framework. TOM stands for Terrestrial Observation Metaset - a simple set of rules for building an ecosystem of ML-ready EO datasets, which can be seamlessly combined as if they were Lego bricks. 💡 Got an idea for a Major TOM dataset, big or small? Anyone can join this effort as a member of the Major TOM organisation on Hugging Face. We are looking forward to building a community of contributors and users, who can help to create a data landscape that ensures EO is collaborative by nature, and open to all. If we want to make sure that EO models are transparent, reproducible, and traceable, then we need to start with the data! 🚴♀️ Want to take the dataset for a spin? Then check out the colab notebook linked below, which shows how to search and filter ~23 TB of data within seconds, and create a local copy that works for your needs. 🤗 HF Org: https://lnkd.in/d4nt2DFt 📰 Preprint paper: https://lnkd.in/dqyZxZ7S 💻 Colab example: https://lnkd.in/d8eWZqRv Thank you to the amazing 🤗Hugging Face team for the support, with special kudos to Omar Sanseviero, Quentin Lhoest and Brigitte Tousignant! Developed at #ESA #philab
Major-TOM (Major TOM)
huggingface.co
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#SpaceTech is Enterprise Tech. #SpaceData is Enterprise Data … AI-ML layered onto space data is the beginning of next-gen data fusion. Previously, satellites and space were thought to be esoteric businesses for governments and telecommunications companies. In 2024, the business of space is driven by data - the capture of unique data, the transfer and storage of that data, and the analytics that make such data useful to the masses. In the next decade, all industries will utilize data generated from space-based assets to unlock unprecedented insights. Some of your favorite corporations already are :-) I'm especially happy to see a leading #ai and enterprise technology company like Hugging Face at the forefront of making space data accessible for B2B use cases. Robert Keith and Emily Zhao, you know how to pick 'em :-) #spacedata #spacetech #innovation #data
🗺 Major TOM: Expandable Datasets for Earth Observation 🚨 RECORD-BREAKING EO DATASET released in partnership with Hugging Face: the largest ever ML-ready Sentinel-2 dataset! We tried to cover every single point on Earth captured by the European Space Agency - ESA Copernicus Sentinel-2 satellite, and we got pretty close! Mikolaj Czerkawski and I are thrilled to finally announce the release of Major TOM Core. 🌍 About half of the entire planet is covered. That's 2,245,886 patches of 1068 x 1068 pixels, available in both L1C and L2A. At 10 m resolution, we've got 256 million square km with over 2.5 trillion pixels. It's all yours with a few lines of code. See the paper linked below 🔽 for more info! 🧱 And this is just the beginning. We are currently preparing more datasets from different satellites for the Major TOM framework. TOM stands for Terrestrial Observation Metaset - a simple set of rules for building an ecosystem of ML-ready EO datasets, which can be seamlessly combined as if they were Lego bricks. 💡 Got an idea for a Major TOM dataset, big or small? Anyone can join this effort as a member of the Major TOM organisation on Hugging Face. We are looking forward to building a community of contributors and users, who can help to create a data landscape that ensures EO is collaborative by nature, and open to all. If we want to make sure that EO models are transparent, reproducible, and traceable, then we need to start with the data! 🚴♀️ Want to take the dataset for a spin? Then check out the colab notebook linked below, which shows how to search and filter ~23 TB of data within seconds, and create a local copy that works for your needs. 🤗 HF Org: https://lnkd.in/d4nt2DFt 📰 Preprint paper: https://lnkd.in/dqyZxZ7S 💻 Colab example: https://lnkd.in/d8eWZqRv Thank you to the amazing 🤗Hugging Face team for the support, with special kudos to Omar Sanseviero, Quentin Lhoest and Brigitte Tousignant! Developed at #ESA #philab
Major-TOM (Major TOM)
huggingface.co
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Check this out!! The largest ever ML ready Sentinel 2 dataset has just been published. Huge achievement from our brilliant research fellows!
🗺 Major TOM: Expandable Datasets for Earth Observation 🚨 RECORD-BREAKING EO DATASET released in partnership with Hugging Face: the largest ever ML-ready Sentinel-2 dataset! We tried to cover every single point on Earth captured by the European Space Agency - ESA Copernicus Sentinel-2 satellite, and we got pretty close! Mikolaj Czerkawski and I are thrilled to finally announce the release of Major TOM Core. 🌍 About half of the entire planet is covered. That's 2,245,886 patches of 1068 x 1068 pixels, available in both L1C and L2A. At 10 m resolution, we've got 256 million square km with over 2.5 trillion pixels. It's all yours with a few lines of code. See the paper linked below 🔽 for more info! 🧱 And this is just the beginning. We are currently preparing more datasets from different satellites for the Major TOM framework. TOM stands for Terrestrial Observation Metaset - a simple set of rules for building an ecosystem of ML-ready EO datasets, which can be seamlessly combined as if they were Lego bricks. 💡 Got an idea for a Major TOM dataset, big or small? Anyone can join this effort as a member of the Major TOM organisation on Hugging Face. We are looking forward to building a community of contributors and users, who can help to create a data landscape that ensures EO is collaborative by nature, and open to all. If we want to make sure that EO models are transparent, reproducible, and traceable, then we need to start with the data! 🚴♀️ Want to take the dataset for a spin? Then check out the colab notebook linked below, which shows how to search and filter ~23 TB of data within seconds, and create a local copy that works for your needs. 🤗 HF Org: https://lnkd.in/d4nt2DFt 📰 Preprint paper: https://lnkd.in/dqyZxZ7S 💻 Colab example: https://lnkd.in/d8eWZqRv Thank you to the amazing 🤗Hugging Face team for the support, with special kudos to Omar Sanseviero, Quentin Lhoest and Brigitte Tousignant! Developed at #ESA #philab
Major-TOM (Major TOM)
huggingface.co
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Senior Data Scientist | Specializing in Generative AI for EdTech and Assistive Technology | Committed to Leveraging AI for Social Good
A picture is worth a thousand words. Emphasizing the profound impact of visual representation, Hans Rosling's enlightening video, "200 Countries, 200 Years, 4 Minutes: Joy of Stats," effectively underscores the role of Data Visualization in simplifying intricate information for a broader audience. Earth Observation Data emerges as a valuable resource, providing accurate and continuous insights into our land, water bodies, and atmosphere. The potential applications of this data are vast, offering solutions to address societal challenges positively. I'm excited to share that DrivenData is currently hosting the Pale Blue Dot: Visualization Challenge, spotlighting how these datasets can contribute to three crucial UN Sustainable Development Goals (SDGs): Zero Hunger (SDG 2), Clean Water and Sanitization (SDG 6), and Climate Action (SDG 13). For more details about this impactful challenge, check out https://buff.ly/40OLGyi. If you're enthusiastic about leveraging AI for social good, this competition provides an excellent starting point, welcoming submissions from participants across all skill levels. As we delve into this realm, I'm curious: what are some of your go-to open-source tools for analyzing space data? #DataVisualization #EarthObservation #SDGs #AIforGood #Drivendata #PaleBlueDotChallenge #OpenSourceTools #SpaceDataAnalysis #LiPostingChallengeIndia
Open Earth Observation Data for the Pale Blue Dot: Visualization Challenge
drivendata.co
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I'm really excited to share this news article about BLASTNet, the first large dataset for fundamental fluid dynamics. 🔺This dataset can enable machine learning models to tackle complex problems in fluid flows, such as turbulence, combustion, and multiphase flows. These problems are relevant for many scientific and engineering domains, such as biomedical science, physics, energy, and neuroscience. 🔺I believe this dataset can lead to exponential breakthroughs in these fields and help us better understand the natural phenomena around us. 👨🏾💻Check out the article and the dataset on GitHub. Kudos to the Stanford HAI team for creating this amazing resource! Shout out to Paul Golding for finding this.
By collecting data from the field of computational fluid dynamics into a single dataset, AI researchers at Stanford hope to do for rocket science, oceanography, and climate modeling what web-scale data did for language. https://lnkd.in/gtdqRsea
BLASTNet – The First Large Machine Learning Dataset for Fundamental Fluid Dynamics
hai.stanford.edu
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IBM and Hugging Face release AI foundation model for climate science Great news! #IBM and #HuggingFace have announced the release of the watsonx.ai geospatial foundation model, built from #NASAs satellite data, to democratise the access to AI technology for climate science. This marks the first-ever open-source AI foundation model developed in collaboration; a crucial tool for advancing our understanding of the science behind climate change. #AI #ClimateChange #Satellites #Data #Science https://lnkd.in/ggef2s_R
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🗺 Major TOM: Expandable Datasets for Earth Observation 🚨 RECORD-BREAKING EO DATASET released in partnership with Hugging Face: the largest ever ML-ready Sentinel-2 dataset! We tried to cover every single point on Earth captured by the European Space Agency - ESA Copernicus Sentinel-2 satellite, and we got pretty close! Mikolaj Czerkawski and I are thrilled to finally announce the release of Major TOM Core. 🌍 About half of the entire planet is covered. That's 2,245,886 patches of 1068 x 1068 pixels, available in both L1C and L2A. At 10 m resolution, we've got 256 million square km with over 2.5 trillion pixels. It's all yours with a few lines of code. See the paper linked below 🔽 for more info! 🧱 And this is just the beginning. We are currently preparing more datasets from different satellites for the Major TOM framework. TOM stands for Terrestrial Observation Metaset - a simple set of rules for building an ecosystem of ML-ready EO datasets, which can be seamlessly combined as if they were Lego bricks. 💡 Got an idea for a Major TOM dataset, big or small? Anyone can join this effort as a member of the Major TOM organisation on Hugging Face. We are looking forward to building a community of contributors and users, who can help to create a data landscape that ensures EO is collaborative by nature, and open to all. If we want to make sure that EO models are transparent, reproducible, and traceable, then we need to start with the data! 🚴♀️ Want to take the dataset for a spin? Then check out the colab notebook linked below, which shows how to search and filter ~23 TB of data within seconds, and create a local copy that works for your needs. 🤗 HF Org: https://lnkd.in/d4nt2DFt 📰 Preprint paper: https://lnkd.in/dqyZxZ7S 💻 Colab example: https://lnkd.in/d8eWZqRv Thank you to the amazing 🤗Hugging Face team for the support, with special kudos to Omar Sanseviero, Quentin Lhoest and Brigitte Tousignant! Developed at #ESA #philab
Major-TOM (Major TOM)
huggingface.co
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Digital business manager, Certified LinkedIN marketing Insider, X website traffic and sales campaigns badge. Certified AI and Data science professional skilled in Power BI, Tableau, SQL, ML, DL, LLM, NLP, computer Vision
Python project: Raptor engin Specifications analysis with AI https://lnkd.in/g5imUevQ The SpaceX Raptor is a cryogenic staged combustion rocket engine intended to power the high-performance lower and upper stages for the Interplanetary Transport System. It has more than three times the thrust of SpaceX’s Merlin 1D engines propelling the Falcon 9 and Falcon Heavy rockets and steps away from a Kerosene-based propellant.Raptor consumes a combination of Liquid Methane and Liquid Oxygen in a Full-Flow Staged Combustion Cycle . #rocket #spacex #raptorengin #artificialintelligence #machinelearning #deeplearning #spacescience
Raptor engin Specifications analysis with AI
kaggle.com
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