NVIDIA Cosmos3-DROID was used to build an end-to-end streaming robotics learning pipeline that reads Parquet and video data without downloading the full dataset locally. The pipeline avoids downloading a 707 GB repository and uses byte-range Parquet reads plus seek-based AV1 video decoding to fetch only needed data windows. It then trains and evaluates a multimodal behavior-cloning policy and saves the resulting policy checkpoint for downstream use.
Neuralink has collected over 50,000 hours of unlabeled neural data since its clinical trials began to improve its machine-learning decoders. The article says it used more than 50,000 hours of freeform neural data. Decoders are expected to need less calibration and become more accurate and longer-lasting for individual users.
The paper designs two open-ended probes, using a Wizard of Oz setup, to let people train personalized machine learning systems on phenomena they define in their daily lives. The week-long study found ontological boundaries were negotiated at four sites. This shifts evaluation from usability or technical feasibility toward designing methods that support how users set boundaries around the phenomenon, data, and objectivity.
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