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>okay but look at all it can't do and where the capabilities for what it does come from and how they were achieved No. This is not the case. Everyone has your

>okay but look at all it can't do and where the capabilities for what it does come from and how they were achieved No. This is not the case. Everyone has your attitude. Every programmer is downplaying the significance of it all. Literally look at the threads. Every tom dick and jane is saying it's not a big deal as if they know better. The truth is nobody knows shit. Your contextualization is just restating the obvious re-hashed opinion. "ML has limits it's not as good as the human brain it needs lots of training data, yadayadayada." Your not contextualizing. Your reaffirming the prevailing bias.

didibus 4y ago
> No. This is not the case. Everyone has your attitude. Every programmer is downplaying the significance of it all. Expand on the "significance" part. Because I don't feel like I'm downplaying the significance of it at all, this is revolutionary, and there's probably no stopping it, in 10 years you'll be using AI assistance for coding, writing, and art generation as a common thing, and it'll expand to other disciplines as well. It'll be everywhere in that regard. That could affect the job markets in a big way, this is a huge improvement to automation, either you'll need less artists, writers and programmers to get the same job done and so there will be less demand for that, or it'll lead to even more ambitious bigger projects and keep the demand for those jobs same but the output expectations will be higher. Self-driving cars has some harder long tail to address to be fully trusted on its own, I think it could take more than 10 years for that, but assisted driving is already here and that'll just continue to get better. I don't know about other programmers, but I don't feel like I'm downplaying the significance. The limits I've mentioned are not made up, there's a lot we do know about them. There are theories of why DNN work like the information bottleneck, and we know more and more about them each year. We know now that you need to over parameterized, that deep layers are needed for generalization, we know that the only sizable improvements have been from even larger datasets over even larger networks. Whereas a child for lots of tasks only needs a single example to learn where models need thousands+. Then we have things like the Winograd and Winogrande schemes, and we've observed that it seems the models get the right answers through statistical significance, not reasoning. Each time you adapt the question to remove the statistical significance they fall in accuracy again, until an even larger model is trained which likely just managed to capture even more statistical data points. So the problem of do the model actually understand the concept and can perform reasoning is a real one that currently has more sign of that they do not. Now it's possible that understanding concepts and performing logical reasoning as such is not as good as large statistical inference from giant corpus at performing certain tasks, and I think that's where you'll see models take over, for tasks where reasoning underperforms, or where it'll be an assisted tech where humans perform the reasoning and models the statistical inference. This bit is key, there's still a possibility that models are actually learning concepts and reasoning, maybe if they just grow even larger they will for example. If that turned true, than it would be incredible, but it could also turn false, and models are just performing statistical inference, and what happens is we simply underestimated how good statistical inference can be at a lot of tasks to the point it can fool you in thinking they're understanding concepts and doing full human reasoning. And you're right, we don't know for sure here, nobody knows for sure. In what way do you feel I'm underestimating the significance here? Or what do you see as being "significant" that I haven't listed?

didibus 4y ago

> No. This is not the case. Everyone has your attitude. Every programmer is downplaying the significance of it all. Expand on the "significance" part. Because I don't feel like I'm downplaying the significance of it at all, this is revolutionary, and there's probably no stopping it, in 10 years you'll be using AI assistance for coding, writing, and art generation as a common thing, and it'll expand to other disciplines as well. It'll be everywhere in that regard. That could affect the job markets in a big way, this is a huge improvement to automation, either you'll need less artists, writers and programmers to get the same job done and so there will be less demand for that, or it'll lead to even more ambitious bigger projects and keep the demand for those jobs same but the output expectations will be higher. Self-driving cars has some harder long tail to address to be fully trusted on its own, I think it could take more than 10 years for that, but assisted driving is already here and that'll just continue to get better. I don't know about other programmers, but I don't feel like I'm downplaying the significance. The limits I've mentioned are not made up, there's a lot we do know about them. There are theories of why DNN work like the information bottleneck, and we know more and more about them each year. We know now that you need to over parameterized, that deep layers are needed for generalization, we know that the only sizable improvements have been from even larger datasets over even larger networks. Whereas a child for lots of tasks only needs a single example to learn where models need thousands+. Then we have things like the Winograd and Winogrande schemes, and we've observed that it seems the models get the right answers through statistical significance, not reasoning. Each time you adapt the question to remove the statistical significance they fall in accuracy again, until an even larger model is trained which likely just managed to capture even more statistical data points. So the problem of do the model actually understand the concept and can perform reasoning is a real one that currently has more sign of that they do not. Now it's possible that understanding concepts and performing logical reasoning as such is not as good as large statistical inference from giant corpus at performing certain tasks, and I think that's where you'll see models take over, for tasks where reasoning underperforms, or where it'll be an assisted tech where humans perform the reasoning and models the statistical inference. This bit is key, there's still a possibility that models are actually learning concepts and reasoning, maybe if they just grow even larger they will for example. If that turned true, than it would be incredible, but it could also turn false, and models are just performing statistical inference, and what happens is we simply underestimated how good statistical inference can be at a lot of tasks to the point it can fool you in thinking they're understanding concepts and doing full human reasoning. And you're right, we don't know for sure here, nobody knows for sure. In what way do you feel I'm underestimating the significance here? Or what do you see as being "significant" that I haven't listed?