For 2, it's worth looking at streaming audio input; we often use rnn's, which update state continually over time, and can make multi scale representations of th

For 2, it's worth looking at streaming audio input; we often use rnn's, which update state continually over time, and can make multi scale representations of the audio to help guide decisions. (eg, speaker embeddings which apply to whole sentences, and guide sample-level speech separation.). Audio's a great place to experiment with time; it's much cheaper than dealing with video, and there are lots of great data sets. Transformers and attention are also tools for responding to the current context with a pretrained network. A core problem is that if you update the weights during inference, there's seemingly very little garauntee of keeping prior quality high, and the models are already occult magic as it is. Federated learning might be another interesting area to look into for ways to address that issue.