Jeff Dean, Google's longest-serving AI executive, is leaving the company along with fellow researchers Sanjay Ghemawat, Quoc Le, and Oriol Vinyals to start Discovery Loop, a startup focused on using AI to automate and accelerate scientific research. The startup has secured funding co-led by Radical Ventures and Khosla Ventures, with participation from Alphabet, Kleiner Perkins, Lightspeed, and Doerr Capital. The company aims to run thousands of simultaneous experiments and explore recursive self-improvement, potentially replacing sequential human iteration in the research process.
Four senior Google engineers—Jeff Dean, Sanjay Ghemawat, Oriol Vinyals, and Quoc Le—are leaving to found Discovery Loop, an automated research lab that uses AI to solve problems in machine learning, science, and engineering. Google is investing in the startup and providing cloud infrastructure, with DeepMind's Demis Hassabis stepping back to focus on long-term AGI strategy while Koray Kavukcuoglu takes over Gemini development. The departures follow earlier losses of key researchers to competitors and occur as Google consolidates its AI leadership structure under single oversight.
IEEE has launched an online course teaching AI applications for modernizing power grids, addressing the urgent need for automation as U.S. electrical infrastructure operates at capacity. The course consists of five modules covering machine learning fundamentals, grid control, forecasting, physics-informed AI, and generative AI, developed by power systems experts to train engineers and data scientists. Utilities can now deploy AI-driven automation to process sensor data in real time, reduce equipment downtime by up to 50 percent, and balance volatile renewable energy sources without human intervention.
Researchers published a paper arguing that scientific communication should shift from human-written papers to an AI-native format called Agent-Native Research Artifacts (ARA) to accommodate AI agents as autonomous contributors to research. The paper, authored by 37 scientists from top institutions, proposes that this new format would eliminate the "storytelling tax" where 80 percent of research information is lost in traditional papers. If adopted, ARA could fundamentally restructure how scientific findings are documented and enable AI systems to reproduce and extend research without human intermediaries.
AI workflow automation platforms are increasingly targeting physical and science sectors like materials discovery and drug development, moving beyond digital-only applications. European advanced materials startups raised €3bn this year versus €1.6bn last year, with drug discovery startups on pace to match €4.7bn annual funding as AI adoption accelerates. Success requires embedding physics constraints into AI models, deep domain expertise, and redesigning workflows from the ground up rather than adding AI as an afterthought to existing processes.
Ai2 and Hugging Face expanded their partnership to give Ai2 substantially more storage and bandwidth for open-source AI models and datasets. Hugging Face is tripling Ai2's storage to nearly two petabytes and removing rate limits, following more than 50 million downloads of Ai2's artifacts since spring 2024. The added capacity enables Ai2 to continue publishing complete research materials including training data, checkpoints, and benchmarks as its portfolio grows across robotics, climate science, and other domains.
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