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Google Research
Trends
- 1Google's Project Suncatcher to put ML infrastructure in space●Google’s Project Suncatcher to put ML infrastructure in space
Google has announced Project Suncatcher, a research initiative exploring the deployment of machine learning infrastructure in space. The plan involves placing solar-powered computing hardware, including TPUs, on satellites to take advantage of near-constant sunlight and reduce terrestrial energy demands for AI. The initiative remains in early testing stages, with prototype launches planned, and observers are debating its technical feasibility and costs.
- 2Timnit Gebru Rejects Claims of Existential AI Threat●Timnit Gebru Believes There Is No ‘Existential Threat’ From AI
AI researcher Timnit Gebru says there is no 'existential threat' from artificial intelligence, arguing in an interview with WIRED that doomsday rhetoric from tech founders is really about making money rather than saving humanity. Gebru, one of AI's most prominent critics after her high-profile exit from Google, argues the focus on speculative extinction scenarios distracts from AI's present-day harms.
- 3Google report examines AI's growing role in science●AI in Science - scientists use it lot, which can then be reused by the AI models. https:// ai.google/static/documents/AI
Google has published 'AI in Science: Early Insights', a 42-page report by researchers including Mihai Codreanu, Alex Imas, Juan Mateos-Garcia, Joseph Emmens, Evalyne Muiruri, Arthur Turrell and Julian Jacobs. It looks at how heavily scientists are using AI in their work and how the outputs of that research can in turn be reused to train future AI models, creating a feedback loop between science and machine learning.
- 4NVIDIA's Physis-Lang Pushes Cosmos 3 Past Veo 3.1 on Physics●NVIDIA Researchers Introduce Physis-Lang: Self-Evolving Physical Language That Lifts Cosmos 3 Past Veo 3.1 on Physics Benchmarks
NVIDIA researchers have introduced Physis-Lang, a self-evolving physical language designed to improve how AI models understand and simulate physical dynamics. According to reports, the technique lifted NVIDIA's Cosmos 3 video generation model past Google's Veo 3.1 on physics benchmarks, suggesting stronger real-world motion and interaction fidelity in generated video.
Repos
- google-research/rrsi
- tensorflow/tensorflow An Open Source Machine Learning Framework for Everyone
- zhengkid/Dream-RSI The offical repo for "Dream-RSI: Recursive Self-Improvement through Evolving Worlds"