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Psychomadeye t1_ja796kk wrote

Reply to comment by just-a-dreamer- in Their future is AI, not ours. by [deleted]

You'd be surprised how small the training space is and how far outside a human reaches. We're talking a litter box to a football stadium in difference. And humans know the difference between true and false vectors, but an AI won't. If there's a chance to policy, you'll need years to retrain that model and will need to somehow find a dataset to use for that. You can't just ask it to use new cover pages on the TPS reports. You need to show it a million TPS reports with those cover pages and hope it generates them properly. Even when you don't create something new, the ability of these models to give you exactly what you want and actually have it work is extremely limited. And again, in order to address these limits, we need infinite space in a finite space, a time machine, or computers that fit inside an atom.

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just-a-dreamer- t1_ja79yrd wrote

Humans are screwed then, for their brains are fairly limited. Yet we manage somehow

I believe AI will optimize it's data over time and learn on the go. Besides, the workflow is designed for human hands and brains, not for AI.

It might be more reasonable to have no TPS reports at all as an example and come up with something that is better suited to AI capabilities.

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Psychomadeye t1_ja851np wrote

>Besides, the workflow is designed for human hands and brains, not for AI.

If we want non human workflow we will need a massive amount of data on that for it to learn the correlation. But I'm at a loss of where anyone would even get data on a non human workflow. These specifically aren't thinking machines. They just know how to generate a point on a graph to look like the rest of the points. They're a really really good dart player. This is why I call them correlation engines. They can't replace workers on their own because the rules of the game change slightly and it'll be months or years of training before it's ready again.

>Humans are screwed then, for their brains are fairly limited.

Our neurons don't suffer the same issues because the sheer size of a human brain expressed as a neural network is larger than we can currently hope to compute yet somehow, training time is seconds and not years, and we have transfer learning at a scale that artificial networks don't have.

>It might be more reasonable to have no TPS reports at all as an example and come up with something that is better suited to AI capabilities.

We need data to train the AI on this new system. This means it will need millions of examples. Then it can spend a few years learning that data. We haven't even gotten into costs yet. Those instances will be costly to run. Newer models might be faster but they are not likely to invent time machines or sub atomic computers without examples of those things.

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