Google DeepMind
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8 months ago
Google released AlphaEarth Foundations, an AI model that consolidates satellite imagery and Earth observation data into unified digital representations for global mapping and monitoring. The system creates summaries requiring 16 times less storage space than competing AI systems while achieving a 24% lower error rate on average across various mapping tasks. Scientists and organizations can now generate detailed, consistent maps on-demand for applications including crop monitoring, deforestation tracking, and ecosystem classification without relying on single satellite passes.
Google DeepMind
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8 months ago
Google introduced Backstory, an experimental AI tool built on Gemini that analyzes online images to determine whether they are AI-generated, where they have previously appeared, and if they have been digitally altered. The tool responds to written prompts about images and generates reports combining AI-detection technologies with contextual information like metadata and usage history. Users can now access additional context about images they encounter online to make more informed assessments of trustworthiness, though the company notes that determining if an image is AI-generated does not automatically indicate whether it is misleading or untrustworthy.
Google DeepMind
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8 months ago
Researchers released Aeneas, an AI model that helps historians interpret, restore, and contextualize fragmentary ancient Roman inscriptions by identifying textual and contextual parallels. The model was trained on a dataset of 176,000 Latin inscriptions and achieves 73% accuracy when restoring damaged text gaps up to ten characters and 72% accuracy in attributing inscriptions to one of 62 ancient Roman provinces. The tool is freely available to researchers and students at predictingthepast.com, with historians in testing reporting increased confidence and faster identification of parallel texts for their research.
Google DeepMind
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8 months ago
Researchers developed Deep Loop Shaping, an AI method using reinforcement learning to stabilize gravitational wave detectors at LIGO observatories. The technique reduces noise in the observatory's most unstable feedback loop by 30 to 100 times, allowing mirrors to remain still enough to measure distortions from cosmic events. Applying this across all of LIGO's mirror control systems could enable detection of hundreds more gravitational wave events annually, improving observations of intermediate-mass black holes and other cosmic phenomena.
