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> Take self-driving accidents, for example. Pretty much all of them happen in situations that no human would fail in, and that's obvious to most people, but the

> Take self-driving accidents, for example. Pretty much all of them happen in situations that no human would fail in, and that's obvious to most people, but then we're forgetting about all the other mistakes similar "AI" systems make, and don't realize that they're also failures no human would make. Amen? I've tried to explain precisely this to many brilliant AI/ML folks in lots of variations, with little success. They look at me funny, as if I'm a crank for believing that that probabilities don't capture the whole story. It seems that, to far too many of them, a computer that has half the accident rate of a human is a strictly better replacement, end of story. The notion of just how spectacular the failure mode might be, or the degree of control that a human might have in the process, or any other human factor you can think of besides the accident rate, just seems like a completely nonsensical notion to many of them. For the life of me I have yet to find a way to convey this thought in a compelling manner.

jrumbut 7y ago
To me, one of the best uses of this generation of AI would be as an aid to decision making. Your personal Skynet learns representations of data that help you make decisions, and watches out for worrying signs that you may be missing (perhaps based on your known mistake patterns, adapting as those change). Meanwhile, human domain experts still look at the details and are able to add their own understanding of broader context, human nature, etc. In this world, AI/ML primarily increases quality by augmenting human abilities rather than decreasing cost by automating humans out of the system. It's a smaller market maybe, but there are areas where better work can fetch a premium. I think part of this is the lack of well understood patterns for the plumbing and UI of a system like this that makes it easy and useful and non-threatening to users. It's nothing that someone couldn't figure out, but not as well paved a road as integrating a caching, search, or image processing subsystem.
- arvinsim 7y ago
  So I guess we aim for augmentation first before shooting for a complete replacement.
  - jrumbut 7y ago
    It has a reasonable sound to it! Although I think the complete replacement scenario has proven a little harder than it looked on some tasks (driving could be an example). Yet it seems like there is a lot more all or nothing efforts (or seeing human workers as a level of escalation), I see fewer projects aimed at being helpful in a user directed way without taking control.
henrikschroder 7y ago
Statistics is incredibly hard and unintuitive, and if you understand statistics, it might be hard to understand that the majority of people absolutely don't. I get that if all cars were self-driving, and the error rate of the self-driving system was half of the average human error rate, it would be better for humanity. But if the errors of those self-driving cars would be obviously avoidable for a human, the absolute majority of people would never ever ever in a million years trust that system. It's a complete fucking no-go. Most people would much rather take shittier odds, if the failures are human, than better odds, but where the failures are alien and bizarre. Most people would automatically think that that risk is worse. I would probably too, even though I understand the statistics. It's the same reason people are afraid of flying, even though it's much, much safer than driving a car.

jrumbut 7y ago

To me, one of the best uses of this generation of AI would be as an aid to decision making. Your personal Skynet learns representations of data that help you make decisions, and watches out for worrying signs that you may be missing (perhaps based on your known mistake patterns, adapting as those change). Meanwhile, human domain experts still look at the details and are able to add their own understanding of broader context, human nature, etc. In this world, AI/ML primarily increases quality by augmenting human abilities rather than decreasing cost by automating humans out of the system. It's a smaller market maybe, but there are areas where better work can fetch a premium. I think part of this is the lack of well understood patterns for the plumbing and UI of a system like this that makes it easy and useful and non-threatening to users. It's nothing that someone couldn't figure out, but not as well paved a road as integrating a caching, search, or image processing subsystem.

arvinsim 7y ago
So I guess we aim for augmentation first before shooting for a complete replacement.
- jrumbut 7y ago
  It has a reasonable sound to it! Although I think the complete replacement scenario has proven a little harder than it looked on some tasks (driving could be an example). Yet it seems like there is a lot more all or nothing efforts (or seeing human workers as a level of escalation), I see fewer projects aimed at being helpful in a user directed way without taking control.

henrikschroder 7y ago

Statistics is incredibly hard and unintuitive, and if you understand statistics, it might be hard to understand that the majority of people absolutely don't. I get that if all cars were self-driving, and the error rate of the self-driving system was half of the average human error rate, it would be better for humanity. But if the errors of those self-driving cars would be obviously avoidable for a human, the absolute majority of people would never ever ever in a million years trust that system. It's a complete fucking no-go. Most people would much rather take shittier odds, if the failures are human, than better odds, but where the failures are alien and bizarre. Most people would automatically think that that risk is worse. I would probably too, even though I understand the statistics. It's the same reason people are afraid of flying, even though it's much, much safer than driving a car.