Hello people

I've been reading up on online estimation algorithms.

In computer-science lingo, an "online algorithm" is one in which data is processed one sample at a time instead of all at once.

Mainly useful for real-time applications, with the key benefit being that your data storage capacity can be much smaller than the amount of data being fed through the machine.

@PhiNotPhi: Also useful when you have data-sets too large to fit in memory

@Difficult to Google for though - wish they'd been called something else

@Sycorax on the rep cap: there's a shiny gold badge for doing that 150 times

@Sycorax ... which has been awarded exactly once on this site and is unlikely to be achieved by anyone else any time soon ;-).

@PhiNotPi We have a tag for that, online, with over a hundred hits.

I'll check out that tag.

@whuber I believe I've hit the reputation cap every day so far this week, so if all future days are like this one, I should have the gold badge before too long.

*if all future weeks

Some background: I'm an undergrad student doing research in psychology and learning / decision making. One way to view certain psych experiments is that subjects are performing an online estimation algorithm: take a sample, condense it to memory, output a guess, repeat.

Most questions I see take the form of "what's an online algorithm to compute ___?" whereas I guess I'm more interested in the theoretical limits... when can/can't sufficiently-efficient online algorithms exist, and what's the theoretical tradeoff between memory usage and accuracy?

Hello!

@Sycorax I shared your NN thread on the Seattle ML user group. You're getting some serious virtual hi-fives.

Well done.

@MatthewDrury Thank you! Most of the stuff that I wrote is about my 1 year sojourn that was trying to get a generative language model to do well. Do you like being a data scientist in Seattle? I'm wondering if our nation's capital is the best place for a data scientist to have a career...

@Sycorax: It would be interesting to hear your definition of "better" (and "best")! Having collaborated with people from Seattle and D.C. I think one would gain -and lose- different things from working in different environments (I was based in California at the time).

@usεr11852 I just meant that data science in DC tends to be focused on the local industry, i.e. government. I've worked on gov't projects before; not sure that I'd like to do that as my next job.

I'd love to hear your input on this