Perplexed comments on Best career models for doing research? - Less Wrong

27 Post author: Kaj_Sotala 07 December 2010 04:25PM

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Comment author: jsteinhardt 08 December 2010 02:24:21AM *  34 points [-]

I believe that most people hoping to do independent academic research vastly underestimate both the amount of prior work done in their field of interest, and the advantages of working with other very smart and knowledgeable people. Note that it isn't just about working with other people, but with other very smart people. That is, there is a difference between "working at a university / research institute" and "working at a top university / research institute". (For instance, if you want to do AI research in the U.S., you probably want to be at MIT, Princeton, Carnegie Mellon, Stanford, CalTech, or UC Berkeley. I don't know about other countries.)

Unfortunately, my general impression is that most people on LessWrong are mostly unaware of the progress made in statistical machine learning (presumably the brand of AI that most LWers care about) and cognitive science in the last 20 years (I mention these two fields because I assume they are the most popular on LW, and also because I know the most about them). And I'm not talking about impressive-looking results that dodge around the real issues, I'm talking about fundamental progress towards resolving the key problems in artificial intelligence. Anyone planning to do AI research should probably at least understand these first, and what the remaining obstacles are.

You aren't going to understand this without doing a lot of reading, and by the time you've done that reading, you'll probably have identified a research group whose work clearly reflects your personal research goals. At this point it seems like the obvious next step is to apply to work with that group as a graduate student / post doc. This circumvents the problem of having to work on research you aren't interested in. As for other annoyances, while teaching can potentially be a time-sink, the rest of "wasted" time seems to be about publishing your work; I really find it hard to justify not publishing your work, since (a) other people need to know about it, and (b) writing up your results formally oftentimes leads to a noticeably deeper understanding than otherwise. Of course, you can waste time trying to make your results look better than they are, but this certainly isn't a requirement and has obvious ethical issues.

EDIT: There is the eventual problem that senior professors spend more and more of their time on administrative work / providing guidance to their lab, rather than doing research themselves. But this isn't going to be an issue until you get tenure, which is, if you do a post-doc, something like 10-15 years out from starting graduate school.

Comment author: Perplexed 22 January 2011 04:52:52AM 1 point [-]

... my general impression is that most people on LessWrong are mostly unaware of the progress made in statistical machine learning (presumably the brand of AI that most LWers care about) and cognitive science in the last 20 years ... . And I'm not talking about impressive-looking results that dodge around the real issues, I'm talking about fundamental progress towards resolving the key problems in artificial intelligence. Anyone planning to do AI research should probably at least understand these first, and what the remaining obstacles are.

I'm not planning to do AI research, but I do like to stay no more than ~10 years out of date regarding progress in fields like this. At least at the intelligent-outsider level of understanding. So, how do I go about getting and keeping almost up-to-date in these fields. Is MacKay's book a good place to start on machine learning? How do I get an unbiased survey of cognitive science? Are there blogs that (presuming you follow the links) can keep you up to date on what is getting a buzz?

Comment author: jsteinhardt 22 January 2011 09:19:18PM 2 points [-]

I haven't read MacKay myself, but it looks like it hits a lot of the relevant topics.

You might consider checking out Tom Griffiths' website, which has a reading list as well as several tutorials.