Hackernews likes this as a practicable application of transfer learning. Fans of machine learning want to see transfer learning as more than a cool trick.<p>Unfortunately, there is really no good reason for someone seriously interested in accurate information, like a researcher or journalist, to use machine learning for this particular task. Labeling a couple thousand images yourself or with friends is not that big of a task. Do it over a few evenings while watching TV and drinking beer. You could have mechanical turk workers do it for you for a few hundred dollars. In either case you will get extremely reliable information. If you use multiple judges you will have a good estimate of uncertainty for every classification. There is no way transfer learning can provide this uncertainty information.<p>The main advantage of this technique remains the ability to quickly label very large amounts of data on the order of hundreds of thousands of rows, or thousands of columns. For smaller data, machine learning can sometimes achieve marginal improvements in predictive performance through model complexity. However prediction in smaller data regimes is mainly useful for out-of-sample prediction. The machine learning paradigm offers limited support for measuring uncertainty for out of sample predictions, which is super important if you are a researcher.<p>One capability of transfer learning could be to support many many applications from one model, but I have yet to see demonstrations of this in practice. The problem is that knowing how well learning has transfered requires measuring generalizabity and so cannot be done blindly.
It might be better if the title were this line from the subtitle:<p><i>A brief analysis of gender distribution in visual representations of everyday life in North Korea</i><p>The actual title gave me the impression this was a run of the mill, shallow complaint about sexism (with, possibly, some ugly politics thrown in to boot). It took me a while to get curious about it based on other cues that this might not be the case. This piece is interesting for reasons having nothing to do with the sexism angle. It is a rich piece about history, culture, AI and possibly other things, given that I don't have time to read all the way through it at this time.
This makes sense to me, because North Korea is still technically at war with South Korea. During WWII, when all members of the male-dominated workforce were overseas fighting, the propaganda posters were mostly centered around female participation in the workforce.<p>If your country has been at war for over 60 years, I suppose your society would develop a kind-of "gender-agnostic" workforce, or perhaps a total reversal of demographics from a country who is at peace.
Most interesting. It'd be nice to see this sorted by age as well, to infer whether the distribution of gender had changed over time. And...well it's easy to imagine many other contexts that it would be good to see similar analyses applied to.<p>I wonder if/how this will change now that Kim Jong Un has promoted his sister to more visible public roles.