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As far as I understand the underlying model is not multimodal. Maybe a quite naive question but can't we improve the performance by a joint embedding of EEG and MEG data? If it scales with log-linear data, maybe it would also improve with other data as well.
As far as I understand the underlying model is not multimodal. Maybe a quite naive question but can't we improve the performance by a joint embedding of EEG and MEG data? If it scales with log-linear data, maybe it would also improve with other data as well.
As far as I understand the underlying model is not multimodal. Maybe a quite naive question but can't we improve the performance by a joint embedding of EEG and MEG data? If it scales with log-linear data, maybe it would also improve with other data as well.
As far as I understand the underlying model is not multimodal. Maybe a quite naive question but can't we improve the performance by a joint embedding of EEG and MEG data? If it scales with log-linear data, maybe it would also improve with other data as well.
As far as I understand the underlying model is not multimodal. Maybe a quite naive question but can't we improve the performance by a joint embedding of EEG and MEG data? If it scales with log-linear data, maybe it would also improve with other data as well.
As far as I understand the underlying model is not multimodal. Maybe a quite naive question but can't we improve the performance by a joint embedding of EEG and MEG data? If it scales with log-linear data, maybe it would also improve with other data as well.
Please see my bio for the full rant. The key take-away is: > While we missed the boat on Internet tracking, there is still time to avoid sailing through the final frontier of neural tracking. > Thanks to the BCI, we will soon be offered the trade of our privacy for the convenience of password-free login and faster typing. Next, there will be a quick TSA neural scan prior to boarding...
Earlier quoted context omitted.
The topic is research into brain-wave decoding. To take some specific piece of research going on in one corner of $BigCorp, and tar it with the brush of general sentiment about $BigCorp, is a classic example of generic tangent: where the topic goes from something specific-and-more-interesting, to something generic-and-more-indignant. We've learned over the years that this is exactly the wrong direction for HN threads…
It's not a 'general sentiment' or tangent though, it's specifically THE technology that has the potential to make privacy nonexistent. Funded by the company that in the recent past, got exposed trying to track employees computer interactions and grabbing screens. Not long before that, it was leaked that employees and contractors were watching videos recorded from meta/rayban smartglasses. I'm a little bit baffled by…
Not only that but less-interesting discussion is usually more-indignant, which makes sense when discussion abandons the actual content of an article (https://hn.algolia.com/?dateRange=all&page=0&prefix=true&sor...). So we end up losing along two axes: loss of information and rise of inflammation.