The bad industries ones will be transformed before the good ones. What I mean by that is that computer vision applied to medical imaging would be huge. But the detection/classification isn’t accurate enough for that field, just yet. Yes, results are amazing on standard datasets such as ImageNet but they fail to become equally good when there are orders of magnitudes less amount of data. And in the field, accuracy is very important, a net classifying cancer correctly 90 % of the time is likely useless.
One exception is automated language translation which is getting very good. I’m noticing that some of the articles papers I’m reading are machine translated. They appear to apply machine translation to English articles and then have some editor doing manual touch-ups which seldom is enough.
The “bad” industries such as spam and SEO can definitely benefit from ML as it exists today. There are ML algorithms (LSTM) that can generate faked web sites with images that, from Googlebot’s point of view, are completely indistinguishable from real sites. Another use would be to generate realistic looking accounts in social media to steer the conversation, perhaps for political purposes. Porn obviously, could also use ML due to the huge amount of data (the porn itself and user interactions) available.
.. I think it’s pretty safe to say finance will be a big one. Finance has a large amount of individuals and firms researching the applications of ML methodologies to financial indicators. With the semi-recent rise of quant firms, I think this research is only going to get more aggressive, and HFT will become more lucrative and more automated as long as regulation does not get in the way.
.. HFT – yes. But longer-term investment (i.e. Buffett – or even with a horizon of a couple of years) is unlikely to be transformed soon – ML needs vast historical data, which is very slow to generate. Waiting 10 years only gives you 10 years of history, which is 5 non-overlapping 2-year forward returns, and maybe 1 or 2 economic/financial regimes.
This is also a problem with new datasets being generated – there is not nearly enough history available to test them or feed them to a ML system.
Furthermore, arguably, longer-term investment requires forward-looking modelling of scenarios, based on the kinds of inputs that were not seen in history. ML is not very applicable when you get big covariate shifts.
So I would say human financial analysts are not going anywhere, and any improvements would be relatively small and incremental... HFT is not profitable. Its completely commoditized.
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