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ChristianKl comments on What should normal people do? - Less Wrong
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I think will give three examples of problems with whom I stayed over longer time: Spaced repetition learning, polyphasic sleep and quantified self.
Quantified Self is the example where I have the most to show publically. I did community work in QS. My name is in a dozen mainstream media pieces in a total of three languages. Piece means either newspaper, radio or TV I did all of them multiple times.
Spaced repetition learning would be one problem which is extremly important but has very few people who are working on it.
The Mnemosyth data lies around for years without anyone analysing it. Going through that data and doing a bit of modeling with it should be easy for anyone who's searching a bachlor thesis for computer science or otherwise seeks a project.
Another question would be: How do you calculate a good brainperformance score for a given day given Anki review data? (Anki stores all the review data internally in a SQL database)
You don't need to be a genius to contribute to any of the those two issues. Both problems are pretty straightforward if you can program and have interest in modelling.
Polyphasic sleep is a problem where I would say that I contribute to the discussion. I tried it probably 8/9 years ago and I stayed with the problem intellectually. Last year a friend of mine was trying uberman for a month and in researching the topic he came about something I wrote. When talking with him about the topic he quoted one of my online opinion on the topic to me and at first it surprised me because I haven't made that point in his physical presence.
My highest rated answer on skeptic stackexchange is also about the uberman shedule: http://skeptics.stackexchange.com/questions/999/does-polyphasic-sleep-work-does-it-have-long-term-or-short-term-side-effects/1007#1007
It's not like I contributed a breakthrough in thinking about polyphasic sleep but I did contribute to the knowledge on the topic a bit.
It's a real pain to, though, because it's so big. A month after I started, I'm still only halfway through the logs->SQL step.
That sounds like you do one insert per transaction which is the default way SQL operates. It possible to batch multiple inserts together to one transaction.
If I remember right the data was something in the size of 10GB. I think that a computer should be able to do the logs->SQL step in less than a day provided one doesn't do one insert per transaction.
I believe so, yeah. You can see an old copy of the script at http://github.com/bartosh/pomni/blob/master/mnemosyne/science_server/parse_logs.py (or download the Mnemosyne repo with
bzr). My version is slightly different in that I made it a little more efficient by shifting theself.con.commit()call up into the exception handler, which is about as far as my current Python & SQL knowledge goes. I don't see anything in http://docs.python.org/2/library/sqlite3.html mentioning 'union', so I don't know how to improve the script.The .bz2 logs are ~4GB; the half-done SQL database is ~18GB so I infer the final database will be ~36GB.
EDIT: my ultimate solution was to just spend $540 on an SSD, which finished the import process in a day; the final uploaded dataset was 2.8GB compressed and 18GB uncompressed (I'm not sure why it was half the size I expected).