3 ms·

I think you're vastly underestimating how little of human intent is really encoded in language in a strict sense, and how much nontrivial inference of intents L

I think you're vastly underestimating how little of human intent is really encoded in language in a strict sense, and how much nontrivial inference of intents LLMs do every day with simple queries. This used to be an apparently insurmountable barrier in pre-LLM NLP, and now it is just not a problem. Suppose I'm in a cold room, you're standing next to a heater, and I say "it's cold". Obviously my intent is that I want you to turn on the heater. But the literal semantics is just "the ambient temperature in the room is low" and it has nothing to do with heaters. Yet ChatGPT can easily figure out likely intent in situations like this, just as humans do, often so quickly and effortlessly that we don't notice the complexity of the calculation we did. Or suppose I say to a bot "tell me how to brew a better cup of coffee". What is encoded in the literal meaning of the language here? Who's to say that "better" means "better tasting" as opposed to "greater quantity per unit input"? Or that by "cup of coffee" I mean the liquid drink, as opposed to a cup full of beans? Or perhaps a cup that is made out of coffee beans? In fact the literal meaning doesn't even make sense, as a "cup" is not something that is brewed, rather it is the coffee that should go into the cup, possibly via an intermediate pot. If the bot only understands literal language then this kind of query is a complete nonstarter. And yet LLMs can handle these kinds of things easily. If anything they struggle more with understanding language itself than with inferring intent.

nevertoolate 6mo ago
it’s cold -> turn on the heater I’d never just turn on the heater silently if someone said this to me. I think it means something else.
- hackable_sand 6mo ago
  If someone just said "it's cold" then yeah that's kinda toxic. If they said "turn on the heater" then you have no ambiguity
quibono 6mo ago
I know what you're getting at but those examples are reaching
deleted 6mo ago
[deleted]
goatlover 6mo ago
The LLMs are doing this via chat, not by physically standing in a room inferring context. You have to prompt the LLM that you're in a room next to someone saying it's cold, the most likely answer being a desire to have temperature turned up. Of course that won't always be the case. Could be an inside joke, could be a comment with no intent to have the heat adjusted, could be a room where the heat can't be adjusted, could be a reference to someone's personality bringing down the temperature so to speak.
- 23dsfds 6mo ago
  Precisely.. this is what the bozo AI-accelerants don't understand. What LLM's are is almost like a hacked-means of intuition. Its very impressive no doubt. But ultimately it isn't even close to what the well-trained human can infer at lightning speed when combined with intuition. The LLM producers really ought to accept their existing investments are ultimately not going to yield the returns necessary for a viable self-sustaining business when accounting for future reinvestment needs, and instead move their focus towards understanding how to marry the human and LLM technology. Anthropic has been better on this front of course. OAI though? Complete diasaster.
  - mikestorrent 6mo ago
    > it isn't even close to what the well-trained human can infer at lightning speed when combined with intuition. It's a lot closer to that than anything was five years ago. Do you really think we're going to be interacting with them the same way five years from now?
    - tyyyy3 6mo ago
      This is an empty statement - if you pour in hundreds of billions should you not expect progress? The question is will these firms be able to continue to spend at that rate. The managers of the firms don’t necessarily have control over that - ultimately punishment in the form of drop in stock price hurts many people involved and will force the management to act in the interest of marginal investors. Even Zuckerberg who has majority control had to concede when meta’s stock cratered to below 100.
applfanboysbgon 6mo ago
> Yet ChatGPT can easily figure out likely intent in situations like this, just as humans do No, it is not "figuring out" anything, much less like a human might. Every time "I'm cold" appears in the training data, something else occurs after that. ChatGPT is a statistical model of what is most likely to follow "I'm cold" (and the other tokens preceding it) according to the data it has been trained on. It is not inferring anything, it is repeating the most common or one of the most common textual sequences that comes after another given textual sequence.
- frozenseven 6mo ago
  >it is repeating the most common... This nonsense hasn't been true since GPT-2, and even before that it was a poor description. For instance, do you think one just solves dozens of Erdős problems with the "most common textual sequence": https://github.com/teorth/erdosproblems/wiki/AI-contributions-to-Erd%C5%91s-problems https://github.com/teorth/erdosproblems/wiki/AI-contribution...
  - applfanboysbgon 6mo ago
    A slight oversimplification, as LLMs are also capable of generating the most statistically plausible textual sequence, which can be a sequence not found in the dataset but rather a synthesized combination of the likely sequences of multiple preceding sets of tokens, but yes, that is in fact what it is doing. Computer software does what it is programmed to do, and LLMs are not programmed to do logical inference in any capacity but rather operate entirely on probabilities learned from a mind-bogglingly large corpus of text (influenced by things like RLHF, which is still just massaging probabilities). The claims about solving Erdos problems have been wildly overstated, and notably pushed by people who have a very large financial stake in hyping up LLMs. Nonetheless, I did not say that LLMs are useless. If they are trained on sufficient data, it should not be surprising that correct answers are probabilistically likely to occur. Like any computer software, that makes them a useful tool. It does not make them in any way intelligent, any more than a calculator would be considered intelligent despite being completely superior to human intelligence in accomplishing their given task.

nevertoolate 6mo ago

it’s cold -> turn on the heater I’d never just turn on the heater silently if someone said this to me. I think it means something else.

hackable_sand 6mo ago
If someone just said "it's cold" then yeah that's kinda toxic. If they said "turn on the heater" then you have no ambiguity

quibono 6mo ago

I know what you're getting at but those examples are reaching

deleted 6mo ago

[deleted]

goatlover 6mo ago

The LLMs are doing this via chat, not by physically standing in a room inferring context. You have to prompt the LLM that you're in a room next to someone saying it's cold, the most likely answer being a desire to have temperature turned up. Of course that won't always be the case. Could be an inside joke, could be a comment with no intent to have the heat adjusted, could be a room where the heat can't be adjusted, could be a reference to someone's personality bringing down the temperature so to speak.

23dsfds 6mo ago
Precisely.. this is what the bozo AI-accelerants don't understand. What LLM's are is almost like a hacked-means of intuition. Its very impressive no doubt. But ultimately it isn't even close to what the well-trained human can infer at lightning speed when combined with intuition. The LLM producers really ought to accept their existing investments are ultimately not going to yield the returns necessary for a viable self-sustaining business when accounting for future reinvestment needs, and instead move their focus towards understanding how to marry the human and LLM technology. Anthropic has been better on this front of course. OAI though? Complete diasaster.
- mikestorrent 6mo ago
  > it isn't even close to what the well-trained human can infer at lightning speed when combined with intuition. It's a lot closer to that than anything was five years ago. Do you really think we're going to be interacting with them the same way five years from now?
  - tyyyy3 6mo ago
    This is an empty statement - if you pour in hundreds of billions should you not expect progress? The question is will these firms be able to continue to spend at that rate. The managers of the firms don’t necessarily have control over that - ultimately punishment in the form of drop in stock price hurts many people involved and will force the management to act in the interest of marginal investors. Even Zuckerberg who has majority control had to concede when meta’s stock cratered to below 100.

applfanboysbgon 6mo ago

> Yet ChatGPT can easily figure out likely intent in situations like this, just as humans do No, it is not "figuring out" anything, much less like a human might. Every time "I'm cold" appears in the training data, something else occurs after that. ChatGPT is a statistical model of what is most likely to follow "I'm cold" (and the other tokens preceding it) according to the data it has been trained on. It is not inferring anything, it is repeating the most common or one of the most common textual sequences that comes after another given textual sequence.

frozenseven 6mo ago
>it is repeating the most common... This nonsense hasn't been true since GPT-2, and even before that it was a poor description. For instance, do you think one just solves dozens of Erdős problems with the "most common textual sequence": https://github.com/teorth/erdosproblems/wiki/AI-contributions-to-Erd%C5%91s-problems https://github.com/teorth/erdosproblems/wiki/AI-contribution...
- applfanboysbgon 6mo ago
  A slight oversimplification, as LLMs are also capable of generating the most statistically plausible textual sequence, which can be a sequence not found in the dataset but rather a synthesized combination of the likely sequences of multiple preceding sets of tokens, but yes, that is in fact what it is doing. Computer software does what it is programmed to do, and LLMs are not programmed to do logical inference in any capacity but rather operate entirely on probabilities learned from a mind-bogglingly large corpus of text (influenced by things like RLHF, which is still just massaging probabilities). The claims about solving Erdos problems have been wildly overstated, and notably pushed by people who have a very large financial stake in hyping up LLMs. Nonetheless, I did not say that LLMs are useless. If they are trained on sufficient data, it should not be surprising that correct answers are probabilistically likely to occur. Like any computer software, that makes them a useful tool. It does not make them in any way intelligent, any more than a calculator would be considered intelligent despite being completely superior to human intelligence in accomplishing their given task.