this post was submitted on 18 Feb 2026
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That's the key when you're looking at applications for machine learning. If you can find a task that's simple but hard to scale because it requires a human expert then it is very likely that a trained neural network can do 'good enough' work at 1,000x the speed.
The results won't be perfect but, then again, they wouldn't be perfect even if you assigned the project to undergraduates with two decades of training. You still need an expert human supervisor who's validating the results and tweaking the system.
In these limited cases, machine learning tools are pretty amazing and they give us capabilities that simply were not available to the average person 5 years ago. I'm not on the AI hype train in terms of the current capitalist casino bubble (chatbots and image generators are toys, not an industry), but from an academic point of view these tools are astonishingly powerful in the right context.