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>Alphago, and similar, are all examples of ML systems exceeding their training data Alphago basically generated its own training data (hence, "exceeding" is no

>Alphago, and similar, are all examples of ML systems exceeding their training data Alphago basically generated its own training data (hence, "exceeding" is not correct) because the domain was amenable to a specific form of reinforcement learning. Programming isn't such a domain.

ALittleLight 4y ago
An early version of Alphago was trained on expert human games and did go on to defeat the human champion. Later versions of Alphago are stronger still and developed entirely through selfplay to become superhuman. I would say these versions did exceed their training data because they started training on basically random games and got increasingly better as they learned. What is that if not exceeding what they started from? Alphago is proof that ML systems can exceed human abilities in at least some domains. Other domains, like identifying animal breeds/species by sight, I believe ML models are also super human. It's just not correct to say models are limited by their training data to something less than human quality. We have multiple counterexamples already.
- 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 ago
    Their 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.

ALittleLight 4y ago

An early version of Alphago was trained on expert human games and did go on to defeat the human champion. Later versions of Alphago are stronger still and developed entirely through selfplay to become superhuman. I would say these versions did exceed their training data because they started training on basically random games and got increasingly better as they learned. What is that if not exceeding what they started from? Alphago is proof that ML systems can exceed human abilities in at least some domains. Other domains, like identifying animal breeds/species by sight, I believe ML models are also super human. It's just not correct to say models are limited by their training data to something less than human quality. We have multiple counterexamples already.

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 ago
  Their 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.