Block, Inc.

Marc Tyndel

Rayhane Mama

Hashiam Kadhim

Ragavan Thurairatnam

Joseph Palermo

A computer-implemented method includes receiving, from a first device of a first participant of a videoconference, a first compressed video stream and a customized first machine-trained decoding model. The first compressed video stream is compressed using a customized first machine-trained encoding model. The first decoding model and the first compressed video stream are transmitted to a second device of a second participant in the videoconference. A second compressed video stream and a customized second machine-trained decoding model are received from the second device. The second compressed video stream is compressed using a customized second machine-trained encoding model. The second decoding model and second compressed video stream are transmitted to the first device by the server. The first compressed video stream is decoded on the second device using the first decoding model and the second compressed video stream is decoded on the first device using the second decoding model.

Machine-learning based data compression for streaming media

Dynamically optimizing routing within a decentralized network is described. In accordance with the described techniques, a node operator accesses historical state data associated with a decentralized network layered on top of a blockchain network. Using machine learning, the node operator trains one or more models based on the historical state data. For example, the node operator trains the one or more models to optimize routing within the decentralized network. In near-real time and using the one or more models, the node operator monitors state data (e.g., current state data) associated with the decentralized network. Based at least in part on the monitoring, the node operator performs one or more actions to optimize the routing within the decentralized network.

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Toronto, Canada

Marc Tyndel