
Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B - bookofjoe
https://arxiv.org/abs/1906.06718
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benrbray
I only skimmed this, so perhaps there is more to this than meets the eye. But
after reading I'm left with the question: Why does this need to be "neural"?

Given that there is a history of using classical NLP methods for sequence
alignment / noisy channel decoding, I would've expected a more extensive
discussion of how NNs might be able to overcome limitations of simpler
methods.

But it seems the opposite is true--here they're using classical approaches to
overcome limitations of their neural approach. The paper concludes by
observing the "utmost importance of injecting prior linguistic knowledge" into
the model. This "linguistic knowledge" is outlined in Section 3, and basically
appears in the model as a regularization term based on a classical noisy
channel / word alignment model. These regularization terms basically just
encourage the neural network to behave like the classical models. And "neural"
approach only performs marginally better than the (Berg-Kilpatrick & Klein
2011) paper they're comparing to, which takes a more classical combinatorial
approach.

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olliej
this only gets 67% accuracy, which is obviously an improvement, but it's still
not close to sufficient accuracy to be useful. Historically a good test was
how many "gods" or what not were invented in a translation.

E.g. at 67% accuracy a translation of Linear A would probably being
meaningless

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Shorel
It would be great to have a more complete reconstruction of Proto-Indo-
European.

This is a step on that way.

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mongol
If / when AI deciphers Linear A, then we are talking proper strong AI, right?

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salty_biscuits
Strong AI is a shifting goal post isn't it? Just whatever we can do now that
machines can't. As soon as someone works out how to turn a human problem into
a mathematical optimization problem it turns into "just mechanical maths".

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maxander
Generally “strong AI” means _general_ intelligence, such as a human’s ability
to figure out the solution to problems in general, as opposed to the single-
purpose AI tools that we have today. So solving Linear A (by a method like
this one) wouldn’t be “Strong AI”- merely profoundly powerful “weak AI.”

(“Strong AI” is also sometimes used to refer to _conscious_ AI, but that’s a
different issue again.)

