Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

LLMs understand nullability

dmodel.ai

Post reply on HN
Print view View original

21–30 of 143 posts

Re: LLMs understand nullability

#21

Post by nativeit »

nsingh2 wrote: post #6
EncomLab wrote: post #2

This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.

Where is the boundary where this becomes semantically correct? It's easy for these kinds of discussions to go in circles, because nothing is well defined.

Hard to define something that science has yet to formally outline, and is largely still in the realm of religion.

Re: LLMs understand nullability

#22

Post by mubou »

aSanchezStern wrote: post #17
mubou wrote: post #14

Earlier quoted context omitted.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…

Many people don't think we have any good evidence that our brains aren't essentially the same thing: a stochastic statistical model that produces outputs based on inputs.

Of course, you're right. Neural networks mimic exactly that after all. I'm certain we'll see an ML model developed someday that fully mimics the human brain. But my point is an LLM isn't that; it's a language model only. I know it can seem intelligent sometimes, but it's important to understand what it's actually doing and not ascribe feelings to it that don't exist in reality.

Too many people these days are forgetting this key point and putting a dangerous amount of faith in ChatGPT etc. as a result. I've seen DOCTORS using ChatGPT for diagnosis. Ignorance is scary.

Re: LLMs understand nullability

#23

Post by nativeit »

aSanchezStern wrote: post #17
mubou wrote: post #14

Earlier quoted context omitted.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…

Many people don't think we have any good evidence that our brains aren't essentially the same thing: a stochastic statistical model that produces outputs based on inputs.

Care to share any of this good evidence?

Re: LLMs understand nullability

#24

Post by nativeit »

fallingknife wrote: post #8
EncomLab wrote: post #2

This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.

Or like saying the photoreceptors in your retina understand when it's dark. Or like claiming the temperature sensitive ion channels in your peripheral nervous system understand how hot it is.

Describing the mechanics of nervous impulses != describing consciousness.

Re: LLMs understand nullability

#25

Post by mubou »

gwd wrote: post #20
mubou wrote: post #14

Earlier quoted context omitted.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…

> Each word/token is chosen as that which is statistically most likely to follow the previous. The best way to predict the weather is to have a model which approximates the weather. The best way to predict the results of a physics simulation is to have a model which approximates the physical bodies in question. The best way to predict what word a human is going to write next is to have a model that approximates human…

LLMs don't approximate human thought, though. They approximate language. That's it.

Please, I'm begging you, go read some papers and watch some videos about machine learning and how LLMs actually work. It is not "thinking."

I fully realize neural networks can approximate human thought -- but we are not there yet, and when we do get there, it will be something that is not an LLM, because an LLM is not capable of that -- it's not designed to be.

Re: LLMs understand nullability

#27

Post by wongarsu »

mubou wrote: post #14
robotresearcher wrote: post #7

Earlier quoted context omitted.

You declare this very plainly without evidence or argument, but this is an age-old controversial issue. It’s not self-evident to everyone, including philosophers.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…

That argument only really applies to base models. After that we train them to give correct and helpful answers, not just answers that are statistically probable in the training data.

But even if we ignore that subtlety, it's not obvious that training a model to predict the next token doesn't lead to a world model and an ability to apply it. If you gave a human 10 physics books and told them that in a month they have a test where they have to complete sentences from the book, which strategy do you think is more successful: trying to memorize the books word by word or trying to understand the content?

The argument that understanding is just an advanced form of compression far predates LLMs. LLMs clearly lack many of the facilities humans have. Their only concept of a physical world comes from text descriptions and stories. They have a very weird form of memory, no real agency (they only act when triggered) and our attempts at replicating an internal monologue are very crude. But understanding is one thing they may well have, and if the current generation of models doesn't have it the next generation might

Re: LLMs understand nullability

#28

Post by nativeit »

nativeit wrote: post #26

We’re all just elementary particles being clumped together in energy gradients, therefore my little computer project is sentient—this is getting absurd.

Sorry, this is more about the discussion of this article than the article itself. The moving goal posts that acolytes use to declare consciousness are becoming increasingly cult-y.

Re: LLMs understand nullability

#30

Post by wongarsu »

nativeit wrote: post #28
nativeit wrote: post #26

We’re all just elementary particles being clumped together in energy gradients, therefore my little computer project is sentient—this is getting absurd.

Sorry, this is more about the discussion of this article than the article itself. The moving goal posts that acolytes use to declare consciousness are becoming increasingly cult-y.

We spent 40 years moving the goal posts on what constitutes AI. Now we seem to have found an AI worthy of that title and instead start moving the goal posts on "consciousness", "understanding" and "intelligence".
Post reply on HN