I rly liked the point about ctrl-c only being able to be labelled retrocausally. I do think that with enough past context you should be able to know what was copied - in some sense the past does encode the future - but…
I agree that thinking in words is much slower than thinking in concepts would be -- that's the point of training models like this, so that ideally people can always just think in concepts. That said, we do need to get…
We build headsets that lets you control your computer directly with your mind. Initially I expect we can get increased bandwidth / efficiency on common tasks (including coding) - but I think it gets really exciting when…
Yeah we do both text and voice (roughly 70% of data collection is typed, 30% spoken). Partly this is to make sure the model is learning to decode semantic intent (rather than just planned motor movements). Right now,…
Basically correct intuition: the model does much better when we give it, e.g., 30 secs of neural data in the leadup instead of e.g. 5 secs. My sense is also that it's learning in context, so people's neural patterns are…
Yes, the predictions are much better for people with more hours of data in the training set. Usually, we just totally separate the train and val set, so no individual with any sessions in the train set is ever used for…
Yup exactly this. Also Ctrl-W, alt tab, etc.
I rly liked the point about ctrl-c only being able to be labelled retrocausally. I do think that with enough past context you should be able to know what was copied - in some sense the past does encode the future - but…
I agree that thinking in words is much slower than thinking in concepts would be -- that's the point of training models like this, so that ideally people can always just think in concepts. That said, we do need to get…
We build headsets that lets you control your computer directly with your mind. Initially I expect we can get increased bandwidth / efficiency on common tasks (including coding) - but I think it gets really exciting when…
Yeah we do both text and voice (roughly 70% of data collection is typed, 30% spoken). Partly this is to make sure the model is learning to decode semantic intent (rather than just planned motor movements). Right now,…
Basically correct intuition: the model does much better when we give it, e.g., 30 secs of neural data in the leadup instead of e.g. 5 secs. My sense is also that it's learning in context, so people's neural patterns are…
Yes, the predictions are much better for people with more hours of data in the training set. Usually, we just totally separate the train and val set, so no individual with any sessions in the train set is ever used for…
Yup exactly this. Also Ctrl-W, alt tab, etc.