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Interesting.

I wonder if these newly-reported crashes happened with the employee positioned in e-brake or in co-pilot mode.

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Humans are extremely bad at vigilance when nothing interesting is happening. Lookout is a life critical role on the railways you might be assigned as a track worker where your whole job is to watch for railway trains and alert your co-workers when one is coming, so they retreat to a safe position while it passes. That seems easy, and these are typically close friends, you work with them every day rotating roles, you'd certainly not want them injured or killed - but it turns out it's basically impossible to stay vigilant for more than an hour or two tops. Having insisted that you aren't tired, since you're just stood somewhere watching while your mates are working hard on the track, you nevertheless lose focus and oops, a train passes without your conscious awareness and your colleague dies or has a life-changing injury.

This is awkward for any technologies where we've made it boring but not safe and so the humans must still supervise but we've made their job harder. Waymo understood that this is not a place worth getting to.

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> Humans are extremely bad at vigilance when nothing interesting is happening

It would be interesting to try training a non-human animal for this. It would probably not work for learning things like rules of the road, but it might work for collision avoidance.

I know of at least two relevant experiments that suggest it might be possible.

1. During WWII when the US was willing to considered nearly anything that might win the war (short of totally insane occult or crackpot theories that the Nazis wasted money on) they sponsored a project by B.F. Skinner to investigate using pigeons to guide bombs.

Skinner was able to train pigeons to look at an image projected on a screen that showed multiple boats, a mix of US and Japanese boats, and move their heads in a harness that would steer a falling bomb to a Japanese boat. They never actually deployed this, but they had tests in a simulator and the pigeons did a great job.

2. I can't give a cite for this one, because I read it in a textbook over 40 years ago. A researcher trained pigeons to watch some parts coming off an assembly line, and if they had any visible defects peck a switch.

There were a couple really clever things about this. To train an animal to do this you have to initially frequently reward them when they are right. When they have learned the desired behavior you can then start rewarding them less frequently and they will maintain the behavior. You will have to keep occasionally rewarding correct behavior though to keep the behavior from eventually going away.

The way they handled this ongoing occasion reward was to use groups of 3 pigeons. The part rejection system was modified to go with a majority vote. Whenever it was not unanimous the 2 pigeons in the majority got a reward. This happened frequently enough to keep the behavior from going extinct in the birds, but infrequently enough to avoid fat pigeons.

Once they had 3 pigeons trained by a human deciding on the rewards during the initial training when you need frequent rewards and got them so they were working great on the line, they could use those 3 to train more. They did that by adding the trainee as a 4th member of the group. The trainee's vote was not counted, but if the other 3 were unanimous and the trainee agreed the trainee was rewarded. This produced the frequent rewards needed to establish the behavior.

The groups of 3 pigeons could do this all day with an error rate orders of magnitude lower than the error rate of the human part inspector. The human was good at the start of a shift, but rapidly get worse after as their shift goes on.

Ultimately the company that had let the researchers try this decided not to actually have it used in production. They felt that no matter how much better the pigeons did and how much they publicly documented that fact ads from competitors about how that company is using birds to inspect their parts would cost too many sales.

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