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Google DeepMind released Perch 2.0, a bioacoustics foundation model trained primarily on bird and terrestrial animal sounds, which unexpectedly performed well at classifying marine mammal vocalizations despite containing no underwater audio in its training data. The model achieved top or second-best performance across three marine datasets (NOAA PIPAN, ReefSet, and DCLDE) when evaluated with few-shot learning using 4 to 32 labeled examples per class. The findings enable researchers to rapidly create custom whale vocalization classifiers using transfer learning, reducing computational requirements and supporting faster analysis of newly discovered marine sounds through a publicly available end-to-end tutorial on Google Colab.
Google's Gemini Deep Think mode solved research-level problems in mathematics, physics, and computer science through agentic reasoning workflows guided by expert researchers. The model scored 90% on IMO-ProofBench Advanced tests and autonomously solved four open problems from the Erdős Conjectures database, with results published in peer-reviewed venues including an ICLR '26 acceptance. This shifts scientific workflows by enabling AI to handle knowledge retrieval and proof verification, allowing researchers to focus on conceptual innovation and creative direction.
OpenAI has deployed a custom version of ChatGPT on GenAI.mil, a platform designed for U.S. defense and government personnel. The system runs on a government-controlled infrastructure with security protocols tailored to defense requirements. This allows military and defense teams to access large language model capabilities without sending data through commercial cloud services.
Transformers.js v4 was released on NPM with a new WebGPU runtime written in C++ that enables AI models to run with hardware acceleration across browsers, Node.js, Bun, and Deno. The build system migration from Webpack to esbuild reduced build times from 2 seconds to 200 milliseconds and cut the default bundle size by 53%. The library now supports advanced model architectures like Mamba and Mixture of Experts, adds production-ready features like ModelRegistry for inspecting pipeline assets before loading, and extracts tokenization logic into a separate 8.8kB library.
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