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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

#91

Post by michannne »

lvxferre wrote: post #87

Caveat lector: I'm analysing this as a linguist. Not as a programmer. (Unless you count a bunch of bash scripts as "programming".) >Let us start first with describing what we call the “missing text phenomenon” (MTP), that we believe is at the heart of all challenges in natural language understanding. The missing text is not the heart of the challenge; it's just a surface issue. The actual problem is deeper - machines…

> machines treat words as if they got intrinsic meaning, when the meaning is actually negotiated between speaker and hearer at the moment of the utterance.

IMO, and I'm not a linguist, I've always felt we need to treat conversations as transactions, with each speaker acting as a party and continually updating some contract over time, with each speaker's goal to use conversational tactics i.e. clarification, questioning, answering a question, diverting the conversation, humor/jokes, lies, as a means to "guess" the contract the opposing parties are operating under, then using the best guess of the contract to later retrieve information relevant to the system when it has some confidence in the answers it can expect.

Something like an exceptionally complex game mixed with financial system theory, like I "bet" that you're going to keep talking about your cat, and maybe that bet is wrong, so I can ask a question, "What do you mean by wug?", but the act of asking that question will cost me some points, maybe you can think of it as though we're at a secret club and you use a phrase meant to represent a key and I'm supposed to respond with the proper answer. Even though I'm asking a question, I'm giving you information that you can use to update your best guess of the contract that allows you to assume I'm not part of the in-group of the club and I shouldn't be allowed in.

I've thought about a system designed like this for a bit but I suppose the biggest challenge has been how do you treat a conversation as a game considering neither party may "win" in any reasonable time frame. We could go years chatting and I could never fully predict what your answer might be because your usage of conversational tactics may be refined over years by speaking to multiple people of backgrounds similar to mine. So if I'm a cop, you might know not to admit to a crime when you figure out I'm a cop.

Right now, I don't think ML can do that, not because it's impossible but because I think conversations are as difficult as predicting the stock market. Fortunately it seems solvable, not everyone is expected to win every conversation in their lifetime

Re: Machine learning won't solve natural language understanding

#92

Post by zR0x »

emodendroket wrote: post #86
zR0x wrote: post #73

Earlier quoted context omitted.

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…

Theoretically, if we had a perfect understanding of human mechanisms of language, we could reproduce it in code. Of course we don't.

I don’t understand how we don’t?

I make sound snapping my fingers. Whistling.

Why is it so hard to accept mirror neurons fired when early humans heard birds and animals, each other’s grunts and over time we refined it?

Everyone has some capacity to refine and strengthen muscle. Why do we need some abstract meta-construct to explain where language comes from? It comes from us. Fleshy meat bags that mutate state over a short period then die.

Chomsky diagrams, conceptual organs and the like are useful for “being on the same page” in a particular context, but there’s no reason to believe language is a requirement for consciousness except our own propensity for romanticizing our existing.

Re: Machine learning won't solve natural language understanding

#93

Post by lvxferre »

shoo wrote: post #88
lvxferre wrote: post #87

Caveat lector: I'm analysing this as a linguist. Not as a programmer. (Unless you count a bunch of bash scripts as "programming".) >Let us start first with describing what we call the “missing text phenomenon” (MTP), that we believe is at the heart of all challenges in natural language understanding. The missing text is not the heart of the challenge; it's just a surface issue. The actual problem is deeper - machines…

> A machine does not do that; instead it'll give words intrinsic, non-negotiable meanings. It won't have "wug" in its dataset, so it won't understand that sentence. > I don't believe that machine learning will solve this issue _on its own_, but it could once we're able to simulate that negotiation of meanings. Fascinating example. Personally, I don't reckon there's any inherent reason that forces all machines to give…

The way I see this working is by adding a second layer of machine learning to the process. The first layer would work as usual (associate words with utterance-independent meanings), but the second layer would compare those meanings with the utterance in question, shave most (or all) of them out, and either add a new meaning or reinforce an old one.

But again, I'm no programmer. I might be saying something that is already done, or unfeasible for some reason.

Re: Machine learning won't solve natural language understanding

#94

Post by shoo »

lvxferre wrote: post #93
shoo wrote: post #88

Earlier quoted context omitted.

> A machine does not do that; instead it'll give words intrinsic, non-negotiable meanings. It won't have "wug" in its dataset, so it won't understand that sentence. > I don't believe that machine learning will solve this issue _on its own_, but it could once we're able to simulate that negotiation of meanings. Fascinating example. Personally, I don't reckon there's any inherent reason that forces all machines to give…

The way I see this working is by adding a second layer of machine learning to the process. The first layer would work as usual (associate words with utterance-independent meanings), but the second layer would compare those meanings with the utterance in question, shave most (or all) of them out, and either add a new meaning or reinforce an old one. But again, I'm no programmer. I might be saying something that is alr…

(this is also not my field) I was thinking about this a little more:

In some cases (such as your excellent wug example) it might be possible to heuristically associate the word "wug" to a probable contextual meaning "your cat" from a shortlist of candidate objects ("you (conversing)", "me (also conversing)", "cat", "bed") without understanding that cats are things that sleep on beds, and some kind of educated guess that "that wug" doesn't refer to the speaker themselves!

In other cases, figuring this out might require a fair bit of side knowledge about the meanings of what is being discussed, and how they relate to each other, to almost logically deduce what entity is being labelled, given knowledge of how those entities work. That might be dramatically harder to get get a machine to do well. Perhaps a machine being able to to this would be a leap forward in machine intelligence from today's ML "stochastic parrots".

Re: Machine learning won't solve natural language understanding

#95

Post by tjs8rj »

shoo wrote: post #88
lvxferre wrote: post #87

Caveat lector: I'm analysing this as a linguist. Not as a programmer. (Unless you count a bunch of bash scripts as "programming".) >Let us start first with describing what we call the “missing text phenomenon” (MTP), that we believe is at the heart of all challenges in natural language understanding. The missing text is not the heart of the challenge; it's just a surface issue. The actual problem is deeper - machines…

> A machine does not do that; instead it'll give words intrinsic, non-negotiable meanings. It won't have "wug" in its dataset, so it won't understand that sentence. > I don't believe that machine learning will solve this issue _on its own_, but it could once we're able to simulate that negotiation of meanings. Fascinating example. Personally, I don't reckon there's any inherent reason that forces all machines to give…

Who cares if the machine understands wug? Maybe the issue is more to do with trying to teach language learning as a end on its own. Nobody studies language without purpose, there’s always a next step.

If you’re friend is telling you about their cat and what a wug it is, the goal is to conversate, make the other feel heard, entertain them, kill time until something next happens, impress them, and so on.

Most of these don’t require even picking up on “wug”, they require understanding the other’s state of mind: “my new cat is so cute - I love them!”. Getting caught on solving wug seems like an uneccessary triviality, more important is the tone they say it with, they body language, your past interactions with them. You could form a proper reaction if you only heard every other word and had those other details.

Humans don’t rely on deciphering “wug” - they don’t need to. The whole interaction is just negotiating mental states, intentions, and actions. Deciphering “wug” is unnecessary.

Re: Machine learning won't solve natural language understanding

#96

Post by visarga »

lvxferre wrote: post #87

Caveat lector: I'm analysing this as a linguist. Not as a programmer. (Unless you count a bunch of bash scripts as "programming".) >Let us start first with describing what we call the “missing text phenomenon” (MTP), that we believe is at the heart of all challenges in natural language understanding. The missing text is not the heart of the challenge; it's just a surface issue. The actual problem is deeper - machines…

GPT-3 can figure out a nonsensical word and use it in the same example.

The prompt is between quotes, everything else is generated.

> "I got a cat. That wug sleeps on my bed." On every night. He’s really nice. But he isn’t small.

> I got a dog. That wug sleeps at my house. On every night. She’s really nice. But she isn’t small.

~~~

Another one in dialogue format.

> Human: I got a cat. That wug sleeps on my bed.

> AI: It has a great personality.

> Human: Define a wug based on the previous lines.

> AI: A wug is a small, impulsive, and clumsy creature.

Re: Machine learning won't solve natural language understanding

#97

Post by abecedarius »

lvxferre wrote: post #87

Caveat lector: I'm analysing this as a linguist. Not as a programmer. (Unless you count a bunch of bash scripts as "programming".) >Let us start first with describing what we call the “missing text phenomenon” (MTP), that we believe is at the heart of all challenges in natural language understanding. The missing text is not the heart of the challenge; it's just a surface issue. The actual problem is deeper - machines…

fwiw, I just pasted your example into GPT-3, ran it twice, and both times the continuation acted like 'wug' meant a cat. (Once as the cat's name, once generically.)

Re: Machine learning won't solve natural language understanding

#98

Post by visarga »

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…

> unable to understand how or why it works

We do understand how and why it works to a certain degree (gradient based input to output approximation). But if you want the meaning of the third neuron in the 10th layer, then yes... But in the same sense we don't understand thermodynamics because we don't account for each particle, just a statistical aggregate of them.

And on the other hand, we don't understand much about how people work or how we are motivated, even though we have first and third person perspectives. Psychology seems to be no further advanced than AI. And yet we work with what we have.

Re: Machine learning won't solve natural language understanding

#99

Post by rubatuga »

joe_the_user wrote: post #78
agravier wrote: post #71

Earlier quoted context omitted.

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.

It could list all possible solutions based on the response to the followup question it would have asked.

Re: Machine learning won't solve natural language understanding

#100

Post by visarga »

why-el wrote: post #74
suifbwish wrote: post #67

Earlier quoted context omitted.

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!

You can't understand why without considering the environment, the problem is that it's too expensive to train AI agents in reality and simulated environments are too simplistic.

But if such a simulated environment is available, agents can learn general skills.

> Deep mind: Generally capable agents emerge from open-ended play

https://deepmind.com/blog/article/generally-capable-agents-e...

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