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!

I thought that kind of career change only happened in The Sims :-)

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, it's doing better on the typed part, but I expect that's just because we have more data of that kind.

It does generalize between typed and spoken, i.e. it does much better on spoken decoding if we've also trained on the typing data, which is what we were hoping to see.

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 quite different but there's a higher-level generator that lets the model learn in context (or probably multiple higher-level patterns, each of which the model can learn from in context).

We only got any generalization to new users after we had >500 individuals in the dataset, fwiw. There's some interesting MRI studies also finding a similar thing that when you have enough individuals in the dataset, you start seeing generalization.

Presumably it's more like an errant Ctrl-C.

Yeah I think the way we trained the embedding model focused a lot on how to make it as efficient as possible, since it is such a data-limited regime. So I think based on (early) scaling results, it'll be closer to 50-70k hours, which we should be able to get in the next months now we've already scaled up a lot.

That said, the way to 10-20x data collection would be to open a couple other data collection centers outside SF, in high-population cities. Right now, there's a big advantage in just having the data collection totally in-house, because it's so much easier to debug/improve it because we're so small. But now we've mostly worked out the process, it should also be very straightforward for us to just replicate the entire ops/data pipeline in 3-4 parallel data collection centers.

1. The predictions get better with more data - and we don't seem to be anywhere near diminishing returns. 2. The thing we care about is generalization between people. For this, less data from more people is much better.

Tangential to your point, if you collect 10,000 hours of brain scanning in exactly one damp basement, I wonder if perhaps the model would become very, very specialized for all of the flavors of "this room seems colder."

Exactly. And honestly both this example and the one about the woman seemed to be what I would actually think/feel vs what I say.

Very interesting!

Yeah, agreed

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)

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 people start designing new software / interaction paradigms with this in mind.

Sell it to an ad agency.

EEG has very good temporal resolution, but quite bad spacial resolution, and other modalities have different tradeoffs

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

The second most useful by far is Indeed, where we post an internship opportunity for participants interested in doing 10 sessions over 10 weeks. Other things that work pretty well are asking professors to send out emails to students at local universities, putting up ~300-500 fliers (mostly around universities and public transit), and posting on Nextdoor. We also just texted a lot of groupchats/posted on linkedin/ gave out fliers and the signup link to kind of everyone we talked to in cafes and similar. We take on some participants as ambassadors as well, and pay them to refer their friends.

We tried google/facebook/instagram ads, and we tried paying for some video placements. Basically none of the explicit advertisement worked at all and it wasn't worth the money. Though for what it's worth, none of us are experts in advertising, so we might have been going about it wrong -- we didn't put loads of effort into iterating once we realized it wasn't working.

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.

Wild world we live in