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I am wondering (knowing nothing about this) if there is an issue with the approach to acquire data that it putting AI in a difficult position. This is akin to trying to train and AI to walk in the footsteps of a geophysicist, rather than making new footsteps for the AI. I guess I would extend this to radiology too since it seems to be the same issue.

Let me give an example:

People often mention that truck drivers are safe from automation because lots of last mile work is awkward and non-standard, requiring humans to navigate the bizarre atypical situations the truck encounter. Training an AI to handle all this is far harder than getting it to drive on a highway.

What is often left out though is the idea that the infrastructure can/will change to accommodate the short comings of AI. This could look like warehouses having a "conductor" on staff who commandeers trucks for the tricky last bit of getting on the dock. Or perhaps preset radar and laser path guidance for the tight spots. I'd imagine most large volume shippers would build entire new warehouses just to accommodate automated trucks.

A long time ago people noted that horses offered much more versatility than cars since roads were rocky and muddy. How do you make a car than can traverse the terrain a horse does? You don't, you pave all the roads.




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