rio-popper

vor 7 Monatendiscuss

We actually did try making a pulley-system to reduce weight at one point. The issue was that it moved the headset kind of oddly WRT the person's head, which reduced sensor contact. One thing we intend to try is pulley system + good chinstrap, which might make the headset stay still and let us reduce the weight at the same time. Good ideas!

rio-popper

vor 7 Monatendiscuss

For the record, it was two basements -- we moved office in the middle -- and a bigger issue was actually overheating. But your point is basically right! The model is a lot better at certain kinds of ideas than others. Particularly concerning was the fact that the first cluster I noticed getting good was all the different variations of 'the headset is uncomfortable/heavy' etc. But this makes sense -- what participants talk about has a lot to do with what kinds of ideas the model can pick up, and this was more or less what we expected

rio-popper

vor 7 Monatendiscuss

Good point. We're a team of 7 right now (3 engineering, 4 running data collection across shifts). We've been spending ~all our time on the data and model side, so the “About us” page lagged behind, but we’ll add one this week. Appreciate the feedback!

rio-popper

vor 7 Monatendiscuss

so the neural quality real-time checking was the most important thing here. Before we rewrote the backend, between 58-64% of participant hours were actually usable data. Now, it's between 90-95%

If you mean the text quality scoring system, then when we added that, it improved the amount of text we got per hour of neural data by between 30-35%. (That includes the fact that we filter which participants we have return based on their text quality scores)

rio-popper

vor 7 Monatendiscuss

Yeah we did consider this. For now, there's an advantage to having the data collection in the same building as the whole eng team, but once we hire a couple more engs, I expect we'll just replicate the collection setup in other countries as well

rio-popper

vor 7 Monatendiscuss

edit: oops sorry misread - the neural data is tokenised by our embedding model. the number of tokens per second of neural data varies and depends on the information content.

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