# NASA Published articles for NASA. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error DevFeed: [NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:43:16Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [lunar foundation model](), [foundation-models](), [ibm](), [Open Source](), [hugging face](), [dataset](), [multimodal](), [Benchmark]() Tags: [ai](), [benchmark](), [data](), [enterprise](), [fine-tuning](), [foundation-models](), [hugging-face](), [ibm](), [lunar-foundation-model](), [machine-learning](), [multimodal](), [nasa](), [open-source](), [space]() ### AI overview IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source on Hugging Face, along with its weights, technical report, and training dataset. Built on TerraMind, the model uses multimodal lunar observations for tasks including ice-deposit mapping, volcanic-feature detection, and crater detection. Reported benchmarks show up to 22% lower ice-mapping error than SwinV2-B, while the accompanying dataset contains roughly 2 million image tiles from nine instruments across four lunar missions. ### Source excerpt IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source, one of the first publicly available foundation models built for scientific study of the Moon. The weights, a technical report, and the machine-learning-ready dataset it was trained on are up on Hugging Face under the Prithvi family, which already covers Earth observation, The post NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error appeared first on StorageReview.com. ## NASA and IBM open source lunar mapping tools DevFeed: [NASA and IBM open source lunar mapping tools]() Original publisher: [Read original article]() Author: Dan Robinson Published: 2026-09-10T16:09:12Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Open Source]() Tags: [ai-and-ml](), [artificial-intelligence](), [ibm](), [lunar-foundation-model](), [mapping](), [nasa](), [open-source]() ### AI overview NASA and IBM open-source lunar mapping tools intended to help identify locations on the Moon and support scientific discoveries. ### Source excerpt Can help you pick the perfect spot for your future evil lair, or boffins make discoveries about Earth's satellite ## Introducing IBM and NASA's new foundation model for the Moon DevFeed: [Introducing IBM and NASA's new foundation model for the Moon]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-09-10T12:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [lunar foundation model](), [ibm](), [foundation-models](), [data](), [Artificial Intelligence](), [AI Architecture]() Tags: [accelerated-discovery](), [ai](), [ai-architecture](), [algorithms](), [ibm](), [lunar-foundation-model](), [mapping](), [model](), [nasa](), [release](), [science](), [space](), [us]() ### AI overview IBM and NASA are open-sourcing the NASA-IBM Lunar Foundation Model, a multimodal AI model that integrates lunar observations from US and Japanese missions across viewing angles, spatial scales, and measurement types. The model is intended to support lunar mapping, volcanic-history research, and searches for polar ice. ### Source excerpt The multi-modal model could help astronauts navigate craters, investigate ancient lava, and search for ice, as the US plans for a long-term lunar presence. ## Mapping global methane emissions from space with deep learning DevFeed: [Mapping global methane emissions from space with deep learning]() Original publisher: [Read original article]() Published: 2026-09-01T18:40:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep neural networks](), [Google]() Tags: [climate-sustainability](), [deep-learning](), [earth-ai](), [framework](), [global](), [google](), [machine-intelligence](), [mapping](), [nasa](), [research](), [space]() ### AI overview The article presents MAPL-EMIT, a deep-learning framework for automating global detection, enhancement prediction, and source estimation of methane plumes from EMIT hyperspectral satellite measurements. ### Source excerpt Climate & Sustainability ## Proposing the SAFE Working Group: An Open Community Effort to Improve AI Security DevFeed: [Proposing the SAFE Working Group: An Open Community Effort to Improve AI Security]() Original publisher: [Read original article]() Author: andrewb@proximabiz.com (The Linux Foundation) Published: 2026-08-04T13:00:01Z Content type: article Language: en Sources: [Linux Foundation - Blog]() Topics: [ai security](), [Artificial Intelligence](), [Security](), [Open Source](), [Critical Infrastructure]() Tags: [ai](), [ai-security](), [aviation](), [cisco](), [hugging-face](), [linux-foundation](), [nasa](), [nvidia](), [open-source](), [red-hat](), [security]() ### AI overview The article proposes the Shared AI Findings Exchange (SAFE) Working Group, an open community effort to help organizations confidentially share lessons from AI security incidents and near misses. Its draft RFC outlines collecting and analyzing incidents, notifying affected organizations, identifying recurring control failures, and developing evidence-based defensive guidance. ### Source excerpt The open source community has shown time and again that collaboration produces better software, stronger security, and more resilient infrastructure. As AI becomes increasingly integrated into enterprise applications, developer workflows, and critical infrastructure, we have an opportunity to apply those same principles to one of the industry's newest challenges: learning from AI security incidents. ## NeuralGCM harnesses AI to better simulate long-range global precipitation DevFeed: [NeuralGCM harnesses AI to better simulate long-range global precipitation]() Original publisher: [Read original article]() Published: 2026-01-12T17:52:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Artificial Intelligence](), [Neural Network](), [Google](), [Earth AI](), [AI Research]() Tags: [ai](), [climate-sustainability](), [earth-ai](), [general-science](), [global](), [google](), [machine-intelligence](), [machine-learning](), [nasa](), [open](), [open-source-models-datasets](), [research](), [science](), [sustainability]() ### AI overview Google Research presents NeuralGCM, a hybrid atmospheric model that combines physics-based modeling with a neural network trained on NASA satellite precipitation observations. The model improves global precipitation simulations, including average rainfall, extreme events, and daily weather cycles, while supporting longer-range weather and climate research. ### Source excerpt Climate & Sustainability ## Should robots be building our homes? DevFeed: [Should robots be building our homes?]() Original publisher: [Read original article]() Author: Jenny Xie Published: 2024-06-26T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Robotics](), [Artificial Intelligence](), [contact-rich manipulation](), [Software](), [Cosmos](), [FIRST]() Tags: [3d](), [3d-printing](), [ai](), [building](), [homes](), [nasa](), [robotics](), [robots](), [software](), [space](), [supply-chain]() ### AI overview Jason Ballard of Icon discusses how AI, software systems, and 3D-printing robots could make housing more affordable. Icon's robotic construction system 3D-prints cement-based walls, while its Vitruvius AI system is intended to eventually produce buildable designs, plans, budgets, and schedules. The company is also collaborating with NASA on infrastructure for the Moon. ### Source excerpt Jason Ballard, co-founder and CEO of Icon, explains why AI and his fleet of 3D-printing robots may be our ticket to more affordable housing. ## Trino at Open Source Summit North America 2023 DevFeed: [Trino at Open Source Summit North America 2023]() Original publisher: [Read original article]() Author: Manfred Moser Published: 2023-05-15T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [Open Source](), [data](), [large-language-models]() Tags: [developers](), [event](), [government](), [linux](), [nasa](), [open-source](), [opensearch](), [risc-v]() ### AI overview The article recounts attendance at Open Source Summit North America 2023 in Vancouver, highlighting open source communities and projects such as OpenSearch, RISC-V, government DevHub initiatives, and NASA open source and open data projects. It also discusses Trino, query engines, lakehouses, Starburst, data use cases, large language models, and the upcoming free virtual Trino Fest. ### Source excerpt Last week, I had the pleasure to attend Open Source Summit North America 2023 in Vancouver. A quick hop across the Strait of Georgia got me right into the event and into the midst of my peers of open source developers, advocates, and enthusiasts. ## How Ignoring Mistakes Creates Overconfidence and Organizational Risk DevFeed: [How Ignoring Mistakes Creates Overconfidence and Organizational Risk]() Original publisher: [Read original article]() Author: andreacorbellini Published: 2023-03-18T08:20:00Z Content type: opinion Language: en Sources: [Andrea Corbellini]() Topics: [Resilience](), [Learning]() Tags: [behavior](), [leadership](), [learning](), [mistakes](), [nasa](), [organization](), [resilience]() ### AI overview The article explains how successful outcomes can conceal flawed execution, causing people and organizations to repeat mistakes and develop overconfidence. It discusses normalization of deviance through examples including the Challenger and Columbia shuttle disasters, where NASA leadership ignored engineering concerns. ### Source excerpt As a scuba diver who often explores new places, I can say that I have found myself in some dangerous situations, but I always made it back to the surface without facing any negative consequences. Does this mean that I never made any mistakes? Absolutely not: mistakes were made, and lessons were learned. We can all agree that learning from mistakes is good, but ... ## Space News DevFeed: [Space News]() Original publisher: [Read original article]() Author: Adam Published: 2022-12-18T00:00:00Z Content type: news Language: en Sources: [Shostack & Friends Blog]() Topics: [3D](), [Rocket](), [Software](), [iteration]() Tags: [3d](), [3d-printed](), [iteration](), [model](), [nasa](), [news](), [software](), [space]() ### AI overview A commentary post discusses spacecraft shown in a NASA schematic and a 3D-printed aerospike rocket engine from Hyperganic. It notes that the engine's claimed efficiency has not been demonstrated by firing it and describes the company's software-driven approach to rapid iteration and analysis. ### Source excerpt Interesting space news, including ISS and 3d printed engines ## Software failures, invisible complexity, and the limits of code DevFeed: [Software failures, invisible complexity, and the limits of code]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2022-06-18T07:00:00Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Code](), [Software](), [Hardware](), [Visual Studio](), [Fortran]() Tags: [code](), [hardware](), [nasa](), [software](), [visual-studio]() ### AI overview This commentary examines how software can fail despite executing its instructions correctly, arguing that failures often arise from misunderstood requirements and the invisible complexity of computing. It discusses software-related safety investigations, Visual Studio's scale, and how programming languages and interfaces shape how people understand code. ### Source excerpt A good friend of mine who's worked on some pretty pivotal hardware/software integrations for our time told me: "it feels like a miracle when I see the code I wrote drive physical bits. I almost can't believe it. No seriously... how does this even work?". If this sentiment resonates with you to some level, you will love reading this article: The coming software apocalypse ## This entire article is fascinating. It took a lot of restraint not to just pull-quote every paragraph from this article. The software did exactly what it was told to do. In fact it did it perfectly. The reason it failed is that it was told to do the wrong thing. Software failures are failures of understanding, and of imagination. This is the trouble with making things out of code, as opposed to something physical. "The complexity," as Leveson puts it, "is invisible to the eye." ..."Computing is fundamentally invisible," Gerard Berry said in his talk. "When your tires are flat, you look at your tires, they are flat. When your software is broken, you look at your software, you see nothing." The National Highway Traffic Safety Administration enlisted software experts from NASA to perform an intensive review of Toyota's code. After nearly 10 months, the NASA team hadn't found evidence that software was the cause -- but said they couldn't prove it wasn't. ... They showed that as little as a single bit flip -- a one in the computer's memory becoming a zero or vice versa -- could make a car run out of control." "Visual Studio is one of the single largest pieces of software in the world," he said. "It's over 55 million lines of code. And one of the things that I found out in this study is more than 98 percent of it is completely irrelevant. All this work had been put into this thing, but it missed the fundamental problems that people faced. And the biggest one that I took away from it was that basically people are playing computer inside their head. ... So the students who did well -- in fact the only ones who survi