[madsbuch]

There is an immensely strong dogma that, to my best knowledge, is not founded in any science or philosophy:

        First we must lay down certain axioms (smart word for the common sense/ground rules we all agree upon and accept as true).
        
        One of such would be the fact that currently computers do not really understand words. ...

The author is at least honest about his assumptions. Which I can appreciate. Most other people just has it as a latent thing.

For articles like this to be interesting, this can not be accepted as an axiom. It's justification is what's interesting,

[mensetmanusman]

It’s a reasonable axiom, because for many people understanding involves qualia. If you believe LLM have qualia, you also believe a very large Excel sheet with the right numbers has an experience of consciousness and feels pain or something where the document is closed.

[madsbuch]

As I wrote, I appreciate that the author wrote it out as they did. It might be reasonable in the context of the article. But fixing it as an axiom just makes the discussion boring (for me).

> If you believe LLM have qualia, you also believe a ...

You use the word believe twice here. I am actively not talking about beliefs.

I just realise, that the author indeed gave themselves an out:

> ... currently computers do not really understand words.

The author might believe that future computers can understand words. This is interesting. Questions being _what_ needs to be in order for them to understand? Could that be an emergent feature of current architectures? That would also contradict large parts of the article.

[shkkmo]

Amusingly, the author does not appear to fully understand the meaning of "axiom".

While practice, axioms are often statements that we all agree on and accept as true, that isn't necessarily true and isn't the core of it's meaning.

Axioms are something we postulate as true, without providing an argument for its truth, for the purposes of making an argument.

In this case, the assertion isn't really used as part of a argument, but to bootstrap an explanation of how words are represented in LLMs.

Edit: I find this so amusing because it is an example of learning a word without understanding it.

[LtWorf]

> Axioms are something we postulate as true, without providing an argument for its truth, for the purposes of making an argument.

Uhm… no?

They are literally things that can't be proven but allow us to prove a lot of other things.

[madsbuch]

It seems like you fully agree with the parent.

I also agree, that the author probably not meant to establish an axiom: The axiom being established, while not having any support right now, does seem like something we can reduce in the future. The author also uses the word "currently" in their axiom, which contradicts axioms (or is temporal axioms a thing?).

I think the author merely meant to establish the scene for the article. Something I truly appreciate.

[shkkmo]

"unprovability" is not a property that it is necessary to prove to pick something as an axiom.

There is generally a project to reduce axioms to the simplest and weakest forms required to make a proof. This is does result in axioms that are unprovable but does not mean the "unprovable" is a necessary property of axioms.

[matwood]

Yeah, for axioms like the above my next question is define 'understand'. Does my dog understand words when it completes specific actions because of what I say? I'm also learning a new language, do I understand a word when I attach a meaning (often a bunch of other words to it) to it? Turns out computers can do this pretty well.

[southernplaces7]

Oh please, enough with the semantics. It reminds me of a post modernist asking me to define what "is" is. The LLM does not understand words in the way a human understands them and that's obvious. Even the creators of LLMs implicitly take this as a given and would rarely openly say they think otherwise no matter how strong the urge to create a more interesting narrative.

Yes, we attach meaning to certain words based on previous experience, but we do so in the context of a conscious awareness of the world around us and our experiences within it. An LLm doesn't even have a notion of self, much less a mechanism for attaching meaning to words and phrases based on conscious reasoning.

Computers can imitate understanding "pretty well" but they have nothing resembling a pretty good or bad or any kind of notion of comprehension about what they're saying.

[logicallee]

It's the most incredible coincidence. Three million paying OpenAI customers spend $20 per month (compare: NetFlix standard: $15.49/month) thinking they're chatting with something in natural language that actually understands what they're saying, but it's just statistics and they're only getting high-probability responses without any understanding behind it! Can you imagine spending a full year showing up to talk to a brick wall that definitely doesn't understand a word you say? What are the chances of three million people doing that! It's the biggest fraud since Theranos!! We should make this illegal! OpenAI should put at the bottom of every one of the millions of responses it sends each day: "ChatGPT does not actually understand words. When it appears to show understanding, it's just a coincidence."

You have kids talking to this thing asking it to teach them stuff without knowing that it doesn't understand shit! "How did you become a doctor?" "I was scammed. I asked ChatGPT to teach me how to make a doctor pepper at home and based on simple keyword matching it got me into medical school (based on the word doctor) and when I protested that I just want to make a doctor pepper it taught me how to make salsa (based on the word pepper)! Next thing you know I'm in medical school and it's answering all my organic chemistry questions, my grades are good, the salsa is delicious but dammit I still can't make my own doctor pepper. This thing is useless!

/s

[shkkmo]

Maps are useful, but they don't understand the geography they describe. LLMs are maps of semantic structures and as such, can absolutely be useful without having an understanding of that which they map.

If LLMs were capable of understanding, they wouldn't be so easy to trick on novel problems.

[logicallee]

> If LLMs were capable of understanding, they wouldn't be so easy to trick on novel problems.

Got it, so an LLM only understands my words if it has full mastery of every new problem domain within a few thousand milliseconds of the first time the problem has been posed in the history of the world.

Thanks for letting me know what it means to understand words, here I was thinking it meant translating them to the concepts the speaker intended.

Neat party trick to have a perfect map of all semantic structures and use it to trick users to get what they want through simple natural-language conversation, all without understanding the language at all.

[shkkmo]

> Got it, so an LLM only understands my words if it has full mastery of every new problem domain within a few thousand milliseconds of the first time the problem has been posed in the history of the world.

That's not what I said. Please try to have a good faith discussion. Sarcastically misrepresenting what I said does not contribute to a healthy discussion.

There have been plenty of examples of taking simple, easy, problems, and then presenting them in a novel way that doesn't occure in the training material, and having the LLM get the answer wrong.

[logicallee]

Sounds like you want the LLM to get the answer right in all simple, easy cases before you will say it understands words. I hate to break it to you but people do not meet that standard either and misunderstand each other plenty. For three million paying customers, ChatGPT understands their questions well enough and they are happy to pay more than for any other widespread Internet service for the chance to ask it questions in natural language, and even though there is a free tier available with high amounts of free usage.

It is as though you said a dog couldn't really play chess if it plays legal moves all day every day from any position and for millions of people, but sometimes fails to see obvious mates in one in novel positions that never occur in the real world.

You're entitled to your own standard of what it means to understand words but for millions of people it's doing great at it.

[shkkmo]

> I hate to break it to you but people do not meet that standard either and misunderstand each other plenty

Sure, and there are ways to tell when people don't understand the words they use.

One of the ways to check how well people understand a word or concept is to ask them a question they haven't seen the answer for.

It is the difference in performance on novel tasks that allows us to separate understanding from memorization in both people and computer models.

The confusing thing here is that these LLMs are capable of memorization at a scale that makes the lack of understanding less immediately apparent.

> You're entitled to your own standard of what it means to understand words but for millions of people it's doing great at it.

It's not mine, the distinction I am drawing is widespread and common knowledge. You see it throughout education and pedagogy.

> It is as though you said a dog couldn't really play chess if it plays legal moves all day every day from any position and for millions of people, but sometimes fails to see obvious mates in one in novel positions that never occur in the real world.

While I would say chess engines can play chess, I would not say the chess engines understands chess. Conflating utility with understanding simply serves to erase an important distinction.

I would say that LLMs can talk and listen. And perhaps even that it understand how people use language. Indeed, as you say, millions people show this every day. I would however not say that LLMs understand what they are saying or hearing. The words are themselves meaningless to the LLM beyond their use in matching memorized patterns.

Edit: Let me qualify my claims a little further. There may indeed be some words that are understood by some LLMs, but it seems pretty clear there are definitely some important ones that aren't. Given the scale of memorized material, demonstrating understanding is hard but assuming it is not safe.

[arolihas]

Some of us care about actual understanding and intelligence. Other people just want something useful enough that can mimic it. I don't know why he feels the need to be an ass about it though.

[crabmusket]

> Maps are useful, but they don't understand the geography they describe. LLMs are maps of semantic structures and as such, can absolutely be useful without having an understanding of that which they map.

That's a really interesting analogy I've never heard before! That's going to stick in my head right alongside Simon Willison's "calculator for words".

[madsbuch]

i am not sure where this comment fits as an answer to my comment.

Firstly, do understand that I am not saying that LLMs (or ChatGPT) do understand.

I am merely saying that we don't have any sound frameworks to assess it.

For the rest of your rant: I definitely see that you don't derive any value from ChatGPT. As such I really hope you are not paying for it - or wasting your time on it. What other people decide to spend their money on is really their business. I don't think any normal functioning people have the expectation that a real person is answering them when they use ChatGPT - as such it is hardly a fraud.

[logicallee]

I added an /s tag to my comment.

[madsbuch]

Sorry, I was too fast to answer to see that.