The problem I have with this idea is that ML systems usually only repeat patterns they've seen before. Sure, you can get quirky output by going to unused portions of the latent space, but that's also more likely to yield degenerate results (things that look 40% shirt, 40% pants, and 20% "other"). While these types of results are usually the most interesting, they're also the most likely to require professional "cleaning"; removing artifacts in a way that produces good results still requires skilled labor. I would expect an ML system to be able to do fine adjustments like spacing and repetition of minor motifs, coarse adjustments like combining motifs in novel ways, or even simple block patterns like the one in the article, but it's unlikely you'll find novel motifs solely via the latent space. Even for the most interesting potential use case, novel combinations of existing motifs, you're likely going to need human discretion as a final pass.<p>When a human designs something, there's often intent involved; there are design constraints and social context involved. I don't expect statistical ML (which is good at interpolation) to cross these gaps without integration with symbolic ML (which is good at extrapolation).<p>Though maybe I'm biased since I work in a symbolic AI lab.
A more in-depth view of the StitchFix model that I found really interesting: <a href="https://algorithms-tour.stitchfix.com/" rel="nofollow">https://algorithms-tour.stitchfix.com/</a>
Perhaps the risk isn’t that technology will actually take over white collar jobs, but that managers will use minimally viable examples of technology to layoff massive numbers of people for short-term gains.<p>Of course when the companies then implode because the technology can no way make up for the resulting skill drain, said managers will have moved on to a new position, having sold the layoffs/technology as evidence of their “superior business accumen.”
There’s plenty of uninspiring (not sure about high skilled) white collar work...I would not be surprised if a lot of what early career lawyers, investment bankers, accountants, consultants do could be automated. I’m also bullish on radiology being the first area of medical practice that can be largely automated by machines.<p>In the future (and even now) careers are going to be defined by how well you can form relationships (and therefore sell) i.e. the things that will be hardest for machines to do.
We're a long, long, long way from machines taking over white collar work to any reasonable degree. Even extremely basic commands are routinely misunderstood by voice assistants.<p>What is going to continue to happen is what is already happening now: Less work wasted on bullshit, more potentially interesting findings surfaced for humans to examine. Same type of thing I was working on 2011 (helping lawyers with discovery, edit: no I confused things. Back then I was helping companies scan internal communication for automatic skill mapping), only better.
<i>The first algorithm generated random images that it tried to pass off as clothing. The second had to distinguish between those images and clothes...</i><p>Sounds like a long winded way of describing a GAN.