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> 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 sente

by shoo 5y ago

> 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 words intrinsic non-negotiable meanings. The machine is merely fitting or predicting based on some model that someone has cooked up, subject to some particular modelling assumptions. If someone can cook up a new model then the machine will be able to negotiate wug-meanings too. (perhaps some kind of hierarchical or parallel model that incorporates a baseline model assuming intrinsic meanings learned from training data, but overlays some additional wug-negotiation subsystem that can kick in when it encounters a new word or starts to infer that a word is being used in a rather nonstandard way). In the world of parsing text for programming languages (which should have well-defined grammars, hopefully an incredibly well defined and easy problem, unless the language is insane/c++) in order for a computer to output helpful error messages when a programmer types something incoherent, a helpful parser needs to be carefully built to handle all kinds of weird crap and attempt to make some kind of sense out of it ("there's no 'ffo' defined. Did you mean 'foo' or this other thing 'bar' that is sitting right here defined the current context?").

lvxferre 5y ago
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.
- shoo 5y ago
  (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".
tjs8rj 5y ago
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.
- goatlover 5y ago
  As a human, I certainly would ask what a wug is, or try to figure it out in context, or look up the definition. I think that's typical for most conversations.

by shoo 5y ago

lvxferre 5y ago

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.

shoo 5y ago
(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".

tjs8rj 5y ago

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.

goatlover 5y ago
As a human, I certainly would ask what a wug is, or try to figure it out in context, or look up the definition. I think that's typical for most conversations.