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> This problem is well studied - there are ways to make a neural net explain what parts of the input most influenced the decision.

That's a new area of research, actually.



New areas of research become "well studied" in a year or two in AI. GANs are considered both new and well studied, for example.

Machine Learning works on a different timescale from everything else.


> Machine Learning works on a different timescale from everything else.

To piggyback your comment. I think this is the real major cause for concern, AI disruption occurs at a expotentially high rate than human learn rates affecting issues like career transition.

No one may have been ill intentioned when they applied AI and invented a way to replace anyone any longer having to mnqualy do job X but none-the-less all those who do job X are now stuck and even if they retrain (optomistixally in 2-3 years) it will in all likelyhood distrupt them again.

As technologists maybe we face a bias as we have not been significantly career disrupted by technological progress and so we can't fully see what it's like for those not riding but being swamped by the wave.




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