Oracle Unveils AI Data Platform for US Federal Government - Finviz
<a href="https://news.google.com/rss/articles/CBMikgFBVV95cUxOQ2ItLThXeDdERXZwTF9GcFJQQXdTdUtMMUFVbTVtSHduUXhPbEc3ZFE0dXNtNVJ5Qy1WMWJiNUEwUk94ZHNKcEFNeGlKMjZ0WE1rNEc2UGFoR2JEeVRScnVOS2tnX1lRLW00TC1PRVZJVXczUVIwOTJXNGZ2b0Y1QzNVWVlORGlVcFl5OTdYY3BOQQ?oc=5" target="_blank">Oracle Unveils AI Data Platform for US Federal Government</a> <font color="#6f6f6f">Finviz</font>
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Nvidia market share in China falls to less than 60% — Chinese chip makers deliver 1.65 million AI GPUs as the government pushes data centers to use domestic chips - Tom's Hardware
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Covalo raises €3.5M to become the shared data infrastructure for an industry where 80% of products will need reformulating by 2030
The Zurich platform, which connects 1,500+ ingredient suppliers and 6,000 brands including Givaudan, Symrise, PUIG, and La Prairie, is evolving from a discovery marketplace into a data backbone that plugs directly into suppliers’ PIM systems and brands’ R&D workflows. Hi inov led the round. Covalo, the Zurich-based platform connecting personal care ingredient suppliers with brands, […] This story continues at The Next Web
Generative AI Search and Data Summarization Now Available in Power Pages for U.S. Government Clouds - Microsoft
<a href="https://news.google.com/rss/articles/CBMi9gFBVV95cUxQckVZdm9WSVdBeFZvZmFGTzIyZkw5TGVUb0E3cWlSMUdNTVExSFRTNGlFNldpNTEyOS1hTUtFV0IyNDlZdnBpdk84QWRsbi1kdUo5TENNcXVNUktLdkZvU1hYaTh3WXJZblMzRzMzd0FjelgwcDdLZGk5dk9PYmRYRnhnZGR4RkpYWFE0WVNzazBhTU1VUkdiTlNKYjEzYXJuWTg4Z1VfTloxWU5RSHZYQldtSmZBY21hM0UzdnBDSGlIOTljRFlwZ2RZTU1mcGxPaUpQbWVVV1Y4Q2dhS01kUFhwZHNBUFkzUGdnS1NCdkRCZms0dHc?oc=5" target="_blank">Generative AI Search and Data Summarization Now Available in Power Pages for U.S. Government Clouds</a> <font color="#6f6f6f">Microsoft</font>
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This Defense Company Made AI Agents That Blow Things Up - WIRED
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[D] Why I abandoned YOLO for safety critical plant/fungi identification. Closed-set classification is a silent failure mode
<!-- SC_OFF --><div class="md"><p>I’ve been building an open-sourced handheld device for field identification of edible and toxic plants wild plants, and fungi, running entirely on device. Early on I trained specialist YOLO models on iNaturalist research grade data and hit 94-96% accuracy across my target species. Felt great, until I discovered a problem I don’t see discussed enough on this sub.</p> <p>YOLO’s closed set architecture has no concept of “I don’t know.” Feed it an out of distribution image and it will confidently classify it as one of its classes at near 100% confidence. In most CV cases this can be annoyance. In foraging, it’s potentially lethal.</p> <p>I tried confidence threshold fine-tuning at first, doesn’t work. The confidence scores on OOD inputs are indistinguishable f
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