Speaker unverified: belief

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…Wow, yeah. You said something that I think is worth exploring there a little bit. You said it’s primarily a UX challenge, and I think a large component of it is, but there is also a very interesting machine learning challenge here. Which is given some dataset, including some on average correct behavior, of asking the user to move up, or move down, move right, move left, and given a dataset of neural spikes. Is there a way to infer, in some kind of semi-supervised, or entirely unsupervised way, what that high resolution version of their intention is? And if you think about it, there probably is, because there are enough data points in the dataset, enough constraints on your model. That there should be a way with the right sort of formulation, to let the model figure out itself, for example… At this millisecond, this is exactly how hard they’re pushing upwards, and at this millisecond, this is how hard they’re trying to push upwards. It’s really important to have very clean labels, yes? So the problem becomes much harder from the machine learning perspective if the labels are noisy?…

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