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>I would say these versions did exceed their training data Well, this is obviously incorrect because the training data included all the games it played against
by trention 4y ago
>I would say these versions did exceed their training data
Well, this is obviously incorrect because the training data included all the games it played against itself (good luck replicating that in the domain of programming though).
We were not talking about "exceeding human abilities in at least some domains", we were talking about whether ML (DL really) systems were able to "go beyond" their training data - but your deflection and going off point is noted.
- ALittleLight 4y agoTheir training data is also part of their output. The training games produced last exceed the earlier games. So, yes, obviously the model can exceed its training data. If you're trying to argue that the model can't be better than it is, i.e. the last training data it produces, then that's true but tautologically so. ML has conquered some domains, like Go or Chess or birdwatching, and is making progress in others - like programming. There is no reason to assume ML systems can't exceed human ability at programming or any other task.
- trention 4y ago>Their training data is also part of their output. More deflection and going off point. >is making progress in others - like programming I already explained that programming is not amenable to the approach that allowed alphago to learn from the games it played against itself. This is already getting pointless and tedious, should in the future know better than to respond to people that are obviously in the "scale is all you need" nonsense camp, nothing meaningful comes out of it.