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Machine learning won't solve natural language understanding

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Re: Machine learning won't solve natural language understanding

#21

Post by drdeca »

emodendroket wrote: post #14
burnte wrote: post #10

Earlier quoted context omitted.

> Even humans are only probably approximately correct. This is very true, more true than we realize. Notice how much more "could you repeat that?" we have with masks on. It's not JUST the mild muffling of the speaker's voice, it's not seeing their lips move. We're all lip readers to a small degree, and it helps inform our decoding to see the lips. Fff and th sound similar but look very different. Even without that, t…

That's a different problem, isn't it? That's more about transcription -- getting the speech into words -- than about what he's talking about, making sense of the words once you have them.

The two tasks are interconnected. The reasoning flows both ways.

Re: Machine learning won't solve natural language understanding

#24

Post by 2muchcoffeeman »

lalaithion wrote: post #5

> In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. But, can humans do this? I think not; I still disagree with the author about what "Do we have a retired BBC reporter that was based in an East European country duri…

Maybe your computer science brain kicked in took a CA interpretation of the question once they mentioned a query. Eg:

Q: Are you a man or a woman? A: Yes

Despite your interpretations of the article’s question. I bet you know which one is the more likely answer.

Re: Machine learning won't solve natural language understanding

#25

Post by evrydayhustling »

The author seems hung up on the idea that because NLU involves apparent "discontinuities" -- places where small variations in interpretation completely transform tasks -- it will not be amenable to smooth, continuous notions like PAC learning, compressibility, and so on. While there's a directional insight there, the terms aren't well defined. And the big story of ML-based NLP in the last decade has been that many tasks that were presumed too jagged for curve fitting are in fact tractable, given large data sets and clever shifts of the way discontinuities can be modeled (e.g. attention-based techniques like Transformers).

Finally, since humans don't perform perfectly at these contrived tasks either, we must ask acknowledge that there is some degree of "approximate correctness" that satisfies our ideas of intelligence.

Re: Machine learning won't solve natural language understanding

#26

Post by gok »

> However, language understanding does not admit any degrees of freedom. A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey.

What a laughable claim. By this definition NLU is easily shown to be impossible by the existence of ambiguity in natural language.

Re: Machine learning won't solve natural language understanding

#27

Post by axiom92 »

mnky9800n wrote: post #17
criticaltinker wrote: post #7

Earlier quoted context omitted.

> An armchair effort Given the authors credentials and publication history [1] it's a bit disingenuous to call this an 'armchair effort'. [1] https://scholar.google.com/citations?user=i5sEc1YAAAAJ&hl=en...

My h index is twice as high and that's a terrible h index still lol

This person's h-index is 10x yours: https://scholar.google.com/citations?user=OHbcFkcAAAAJ&hl=en...!

Source: https://www.nature.com/articles/d41586-019-02479-7

Re: Machine learning won't solve natural language understanding

#29

Post by cs702 »

I DISAGREE with the author's certainty.

Consider a modified question: Can artificial agents that incorporate trillion-parameter NLP models learn to understand natural language in order to perform a wide range of useful tasks in open-ended environments?

It sure seems possible to me, at least judging by recent progress in both reinforcement learning in open-ended environments (e.g., DeepMind's recent paper on open-ended self-play) and NLP (e.g., with models such as DALL-E and GPT-3, which are trained to learn only a very narrow set of simple tasks, but could be trained in service of more complex and diverse agent objectives).

At a minimum, I'd say no one today can answer the NLU question with such certainty.

Re: Machine learning won't solve natural language understanding

#30

Post by axiom92 »

criticaltinker wrote: post #7
visarga wrote: post #2

An armchair effort to redefine the goalposts and judge NLP, but proof is in the pudding. For now, large language models are the best flavor. NLP models are already useful even in this early stage.

> An armchair effort Given the authors credentials and publication history [1] it's a bit disingenuous to call this an 'armchair effort'. [1] https://scholar.google.com/citations?user=i5sEc1YAAAAJ&hl=en...

In case you were highlighting the person's h-index or citation count, there are a few shortcomings that these metrics suffer from [1], making it very hard to judge scientific impact.

EDIT:

Specifically, self-citation is the biggest issue, though often there are very good reasons to do it. For example, if you are working on a new area there might not be a lot of work other than yours. However, mindless self-citation grows the number of citation at O(N), as your n^th paper cites the (n - 1) papers before it. The total citation count grows by O(N^2)).

[1] https://www.nature.com/articles/d41586-019-02479-7

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