Why Google’s Researchers are Intentionally Poisoning Datasets and More

Researchers reported two dataset-poisoning methods that could manipulate web-scale training data at low cost by exploiting trust assumptions and temporary editing windows in sources such as Wikipedia, while also proposing countermeasures to dataset maintainers. Separate work found that incorporating human preference signals during language-model pretraining can reduce harmful, privacy-revealing, and noncompliant outputs without long-term performance loss, with conditional training identified as a particularly effective approach. Google researchers also showed that policy-gradient fine-tuning can optimize vision models for complex evaluation metrics across tasks such as object detection and panoptic segmentation. Product news included Samsung’s Korean-language AI voice feature for Bixby call handling, Spotify’s AI-powered DJ combining personalized music with spoken commentary, and Elon Musk’s indication that Twitter’s recommendation algorithm could be open-sourced. Community updates covered fixes and contribution efforts in Lightning, while Lightning AI highlighted talks on foundation models and a Nixtla demonstration of transfer learning for cold-start time-series forecasting. The roundup also noted approaching submission deadlines for IROS, Interspeech, CoLLAs, and ICCV 2023.

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