More exciting IMO isn’t so much the big data aspect, but just the opportunity for “big individual data”: people getting to watch their own brain state for many hours. E.g. learning when you’re rationalizing, when you’re avoiding something, when you’re deluded, when you’re tired, when you’re really thinking about something else, etc.
Yes, this is exactly the innovation I was thinking about. With superconductors that fit in hats, you can also combine that self-observation with big data, predictive analytics, and thousands of neurologists/ML engineers/psychologists to identify trends and formulate standard strategies, to get people get themselves on the right track. You can basically open-source the research, Auto-GPT-style.
A billion 3d frames per year per 300 people will make a lot of internal phenomena stick out like a sore thumb, especially the internal phenomena that typically leads up to/away from peak alignment thoughtflow. Just have a “ding” sound when someone’s mind is going in the right direction, and a “dong” sound for the wrong directions.
Just have a “ding” sound when someone’s mind is going in the right direction, and a “dong” sound for the wrong directions
I’d definitely like to try that. The right UX would be a number that goes up as you get closer to the target headspace, with milestone numbers along the way, which each give you a reward. It should possibly be coupled with a puzzle game or a set of creative exercises or something. (Games are good because they can provide reward. If a person isn’t already productive it may be because they didn’t find practicing engineering deeply rewarding so this part of it might be important.)
E.g. learning when you’re rationalizing, when you’re avoiding something, when you’re deluded, [...] when you’re really thinking about something else, etc.
It seems extremely unlikely that these things could be seen in fMRI data.
More exciting IMO isn’t so much the big data aspect, but just the opportunity for “big individual data”: people getting to watch their own brain state for many hours. E.g. learning when you’re rationalizing, when you’re avoiding something, when you’re deluded, when you’re tired, when you’re really thinking about something else, etc.
Yes, this is exactly the innovation I was thinking about. With superconductors that fit in hats, you can also combine that self-observation with big data, predictive analytics, and thousands of neurologists/ML engineers/psychologists to identify trends and formulate standard strategies, to get people get themselves on the right track. You can basically open-source the research, Auto-GPT-style.
A billion 3d frames per year per 300 people will make a lot of internal phenomena stick out like a sore thumb, especially the internal phenomena that typically leads up to/away from peak alignment thoughtflow. Just have a “ding” sound when someone’s mind is going in the right direction, and a “dong” sound for the wrong directions.
functional Machine Intelligence Research Imaging
I’d definitely like to try that. The right UX would be a number that goes up as you get closer to the target headspace, with milestone numbers along the way, which each give you a reward. It should possibly be coupled with a puzzle game or a set of creative exercises or something. (Games are good because they can provide reward. If a person isn’t already productive it may be because they didn’t find practicing engineering deeply rewarding so this part of it might be important.)
It seems extremely unlikely that these things could be seen in fMRI data.