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Oct 6Tue
  1. Google Research34

    Google Earth AI's PDFM geospatial foundation model validated across five global public health challenges

    Google Earth AI's Population Dynamics Foundation Model (PDFM) provides plug-and-play location embeddings that matched or improved conventional epidemiological inputs across five public health challenges without task-specific fine-tuning. Partners including Mount Sinai, NYU, Oxford, and WHO AFRO reported gains such as +36% explained variance in cross-border MMR vaccination and +18.1% Precision@5 for cholera outbreak prediction at 8 weeks.

  2. NVIDIA Blog22

    Why Telecom Operators Are Building Their AI Strategy on Open Models

    NVIDIA's State of AI in Telecommunications report finds 89% of respondents say open source models and software are important to their AI strategy. NVIDIA announced the 30-billion-parameter Nemotron 3 Large Telco Model, fine-tuned by AdaptKey on open source telecom datasets, plus a full fine-tuning recipe via NeMo. SoftBank, AT&T and Indosat Ooredoo Hutchison are using open models for telecom-specific AI.

Oct 5Mon
Oct 3Sat
  1. Hugging Face Blog69

    Microsoft and Hugging Face release ThinkingBox, a benchmark that grades AI agents on backend state across 507 workflows

    Microsoft and Hugging Face released ThinkingBox, an agent benchmark that grades terminal backend state and side effects rather than final responses or tool-call validity.

    Why it matters: The paper's 20-run repeat metric and failure breakdown show why a clean tool-call trace can still leave the wrong database state.

Oct 2Fri
  5. Hugging Face Blog42

    AutoSynthData: Generating Training Data for Enterprise Agents

    ServiceNow CoreAI built AutoSynthData, a pipeline that turns a target model's failures and a stronger teacher's successes into new training tasks for enterprise agents. It generates tasks as system specification, user prompt, and verifier, then validates them in the environment and uses accepted samples for post-training, with the curriculum shifting toward remaining weaknesses. The pipeline is illustrated with EnterpriseOps Gym.

Oct 1Thu
Sep 30Wed
  5. Google DeepMind71

    Google DeepMind introduces SynthID Bio for watermarking AI-generated proteins

    Google DeepMind introduced SynthID Bio, a family of watermarking methods that embeds a verifiable signature into AI-generated biological code while preserving protein function in laboratory testing. In wet-lab tests across VEGF-A, the SARS-CoV-2 spike protein RBD and PD-L1, watermarked binder designs matched the hit rate, binding affinity and natural sequence diversity of unwatermarked versions, and for protein folding the method fine-tunes part of AlphaFold 3's diffusion network so predicted 3D coordinates carry a detectable signature. DeepMind is publishing the methods paper and open-sourcing the code, in vitro data and model weights, and says key challenges include making the watermark more robust against deliberate tampering.

    Why it matters: The post details how a watermark is embedded into protein sequences and structures and what wet-lab tests showed about function.

Sep 29Tue
Sep 28Mon
Sep 25Fri
  1. GitHub Blog · AI & ML32

    GitHub Copilot app for Beginners: How to build custom workflows with canvases

    GitHub Copilot app's canvases are customizable, bidirectional interfaces you create by running the /create-canvas skill and describing the workflow in plain English. The agent builds the UI in the right-side panel, and both you and the agent can update its shared state at the same time. Canvases are saved as extensions for reuse or team sharing, and ready-made ones are available via Awesome Copilot.