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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

#71

Post by agravier »

SavantIdiot wrote: post #70
canjobear wrote: post #39

The history of NLP is littered with people claiming on theoretical grounds that XYZ is unattainable using purely statistical methods, and that some notion of the logical structure of language needs to be brought in. And yet one by one, the XYZ have been attained by statistical methods. If you think there's something NLP can't do using machine learning, make a challenge dataset. That would be much more useful than yet…

Will the AI be able to ask for clarification? Translators do. In fact, even people speaking the same language need to ask for clarification sometimes. It is literally impossible to discern some sentences without follow up, why would someone think an AI can do it one shot?

Why wouldn't an AI be able to ask for clarification in a live setting? I don't see your point.

Re: Machine learning won't solve natural language understanding

#72

Post by qayxc »

nonameiguess wrote: post #41
amirkdv wrote: post #18

Earlier quoted context omitted.

> But, can humans do this? I think not; > Even humans are only probably approximately correct. Fair point. But how about this: It's true that what John would do with the sentence is technically an "approximation" of what Alice would do because they have slightly different understandings of correct behavior. However, for humans to do what they do, they still do build an absolutely correct model of meaning in their min…

This seems to be misunderstanding what PAC learnable means. It's saying that for parameters epsilon and delta, you can get an algorithm for learning a hypothesis that, with probability 1 - delta, has an average error rate less than epsilon, given some number of training samples polynomial in 1/delta and 1/epsilon. This has nothing to do with the mental model. The language model doesn't have to spit out probabilities.…

There's a very important difference, though: humans can and often do qualify their interpretation.

One major flaw of current PAC-based models is the inability to express certainty and being able to present the rationale behind their (lack of) confidence.

One crucial aspect of human language processing is the ability to question or interrogate oneself to adjust one's interpretation. During a dialog this can be done by actively asking for more context or confirmation.

So unlike current ML models, humans know that their interpretation/models might be wrong and can actively seek confirmation or correction.

A trivial example would be asking a digital assistant "What's the capital of Georgia?" There are two possible answers and there's no way of knowing what the "correct" one is without further context. So a human would likely either ask whether you mean the country of Georgia or the US state, or qualify their answer ("the capital of the country/the state capital is Tbilisi/Atlanta").

This is what GPT-J-6B returns:

  Q: What's the capital of Georgia?
  A: Atlanta
When given just the prompt "What's the capital of Georgia", GPT-J-6B also returns Atlanta and generates further text about the US state and its capital.

And it's not as if the model doesn't "know":

  Q: What is Tbilisi?
  A: Tbilisi is the capital of Georgia.
It's even more interesting when logic is involved:

  Q: If Albert is a pig and all pigs are blue, what colour is Albert?
  A: Albert is a blue pig.
I only asked for the colour, but close enough. Can GPT deal with ambiguity?

  Q: If Albert is a pig and all pigs are blue, is Albert happy?
  A: No.
Awesome! How about some common sense then:

  Q: If Alice is a dog and Bob just finished high school, can they have kids together?
  A: Yes, but they will have to get married first.
So yeah, GPT obviously has no actual language understanding. Bob having finished high school implies that he's a young human man while Alice is a dog. Humans and dogs can't have kids together.

But if the model doesn't understand language, why did it do so well on the logic test? In order to find out, I just asked :)

  Q: If Albert is a pig and all pigs are blue, why is Albert not happy?
  A: Because he is a pig.
Hm. So are all pigs unhappy then? Let's take them out of the equation and ask again:

  Q: If Albert is blue, is Albert unhappy?
  A: No, Albert is not unhappy.
Bingo! So the model only appeared to know the subtlety of "blue" having multiple meanings. It's as dumb as a rock and simply tried to match the tokens "is blue" against "is unhappy". Replacing "blue" with "pig" confirms this:

  Q: If Albert is a pig, is Albert unhappy?
  A: No, Albert is not unhappy. Albert is a pig.
Dad humour. Well, either that or just a dumb model capable of fooling us humans because we tend to interpret more intelligence into its replies than is actually there...

EDIT: in case you want to test this yourself (you might get different results, it's probabilistic after all):

https://6b.eleuther.ai

When using the Q/A-format, make sure not add a space after the "A:" prompt.

Re: Machine learning won't solve natural language understanding

#73

Post by zR0x »

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.

We had to learn to collaborate before “language”.

Perhaps thinking language is a key aspect of consciousness is wrong.

Chomsky has said human languages themselves are just random sounds we’ve been polishing the meaning of.

So yeah I have no doubt a machine can sort them correctly if we tell it to. They mean what we want and see in them.

That’s hardly proving anything about consciousness. Just that a computer can sort and count syntax, and guess at meaning by frequency of relative placement.

It’s acting on English data sets. Not human biology.

Re: Machine learning won't solve natural language understanding

#74

Post by why-el »

suifbwish wrote: post #67
schoen wrote: post #56

Earlier quoted context omitted.

I'm sad that statistical methods have gained so much ground over more formal and logical methods (edit: maybe I should instead say "that formal and logical methods have lost so much ground compared to statistical methods"), and, while I can see ways to construct examples that the statistical methods ought to have trouble with, I also notice how incredibly well they've done and how many barriers they've blown past in…

The main problem with using stat models or approaches such as deep learning is not that we are unable to do it. Though possibly trivial, the real problem is we are unable to understand how or why it works which can lead to unintended consequences or lack of ability to support/continue further development (aside from not being able to leverage the new fundamental understanding and apply it to related fields). Imagine…

I think this is the crux of the issue. People like Chomsky argue the same way, that is, sure a statistical model can mimic a phenomenon to an increasingly likely degree, but the question he concerns himself with is "how and why it [language] works that way", not that I can somehow approach it. His example scenario of a filming bees and statistically re-engineer their dance is poignant: sure you get impressive results, but you will never understand why they dance!

Once this is clear, the discussion becomes healthier: linguists continue to try and understand fundamentals; ML NLP continues to try to get impressive, possibly lucrative results. Logical research can borrow from ML results, or re-use them, and so forth, but I agree with Chomsky that it can't be a shot in the dark always, because once you deploy it and pass it some tough decisions to make, well, what then?

I was pleased to see at least one idea, the recursive structures found in his theories, which is increasingly being accepted by people like Hinton, so that's great.

Re: Machine learning won't solve natural language understanding

#75

Post by abeppu »

> we are convinced that the ‘big data’ approach to NLU is not only psychologically, cognitively, and even computationally implausible

I wish they didn't just breeze by the psychological and cognitive perspectives. Because even if the giant corpus/giant language model approach can do a lot of understanding-related tasks under the right conditions ... you would never claim to be teaching a child English by having them look at endless piles of documents. You interact with them. You _show_ them things in the world that the words name. They get what they want faster when they learn to say what they want. They "understand" words and sentences in reference to things and experiences. What does it even mean for a network to "understand" language when not exposed to other representations of objects, actions, etc?

Suppose you could time travel and bring English language technical documents back to a Mesopotamian cult (I only care that they haven't seen the latin alphabet), and you got a community of priests to study them as scripture, and after years of study they could tell you which symbols were missing from a sequence, or which symbols appear in the same contexts, or generate a plausible sequence of symbols and flag implausible ones, but they had no idea what the words referred to -- would they have "understood" anything?

Suppose you read a scifi book which makes new words or even has some weird typographic trick to represent something that's definitely out of corpus (e.g. the Language in Embassytown), you can understand it regardless, not because your model tells you about the relationships between those words and other words, but because you have conceptual frames for the situations, actions, intents etc.

Re: Machine learning won't solve natural language understanding

#76

Post by djoldman »

"The man saw a boy with a telescope."

There is NO exact interpretation of the above sentence. It is possible that the man peered through a telescope to see the boy, or that the man saw a boy who had a telescope.

One may reasonably argue that one interpretation is more likely but either may be true.

Ambiguity is an immense problem in semantic analysis, compounded by assumed speaker intent, etc.

Ultimately there is no exact interpretation or meaning of all language unfortunately.

Re: Machine learning won't solve natural language understanding

#77

Post by danielmarkbruce »

suifbwish wrote: post #67
schoen wrote: post #56

Earlier quoted context omitted.

I'm sad that statistical methods have gained so much ground over more formal and logical methods (edit: maybe I should instead say "that formal and logical methods have lost so much ground compared to statistical methods"), and, while I can see ways to construct examples that the statistical methods ought to have trouble with, I also notice how incredibly well they've done and how many barriers they've blown past in…

The main problem with using stat models or approaches such as deep learning is not that we are unable to do it. Though possibly trivial, the real problem is we are unable to understand how or why it works which can lead to unintended consequences or lack of ability to support/continue further development (aside from not being able to leverage the new fundamental understanding and apply it to related fields). Imagine…

I'm not a physicist or mechanical engineer and this is a genuine question - why would we not be able to reliably design anything if we only had statistical models? Empirical formulas are used in various places in science/engineering and seem to work well (with exceptions). As an example - would we not arrive at something like f = ma using statistics and then use it without really knowing why (or maybe all the implications of) f = ma?

Re: Machine learning won't solve natural language understanding

#78

Post by joe_the_user »

agravier wrote: post #71
SavantIdiot wrote: post #70

Earlier quoted context omitted.

Will the AI be able to ask for clarification? Translators do. In fact, even people speaking the same language need to ask for clarification sometimes. It is literally impossible to discern some sentences without follow up, why would someone think an AI can do it one shot?

Why wouldn't an AI be able to ask for clarification in a live setting? I don't see your point.

Well, the approach of solving a corpus (advocated by GP) doesn't leave room for the possibility of asking a follow-up question so you can't train for it that way.

Re: Machine learning won't solve natural language understanding

#80

Post by wutbrodo »

zR0x wrote: post #66
canjobear wrote: post #39

The history of NLP is littered with people claiming on theoretical grounds that XYZ is unattainable using purely statistical methods, and that some notion of the logical structure of language needs to be brought in. And yet one by one, the XYZ have been attained by statistical methods. If you think there's something NLP can't do using machine learning, make a challenge dataset. That would be much more useful than yet…

If it’s just stats why do I need Google? I can write code to bubble up stats across text sources in any language. I’m reminded of a behind the scenes video of Batman 1989, where they discuss a complicated layering of Joker makeup to achieve the effect in the scene where he wipes off fleshy colored face paint; white makeup on Nicholson, special coating to be able to apply the next layer, on and on. No one thought to j…

> If it’s just stats why do I need Google? I can write code to bubble up stats across text sources in any language.

I'm not sure I understand your framing here, but in it, aren't you just describing what Google _is_?

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