[EncomLab]

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.

[aSanchezStern]

The post includes this caveat. Depending on your philosophical position about sentience you might say that LLMs can't possibly "understand" anything, and the post isn't trying to have that argument. But to the extent that an LLM can "understand" anything, you can study its understanding of nullability.

[keybored]

People don’t use “understand” for machines in science because people may or may not believe in the sentience of machines. That would be a weird catering to panpsychism.

[nsingh2]

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.

[nativeit]

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

[EMIRELADERO]

That depends entirely on whether you believe understanding requires consciousness.

I believe that the type of understanding demonstrated here doesn't. Consciousness only comes into play when we become aware that such understanding has taken place, not on the process itself.

[stefl14]

Shameless plug of personal blog post, but relevant. Still not fully edited, so writing is a bit scattered, but crux is we now have the framework for talking about consciousness intelligently. It's not as mysterious as in the past, considering advances in non-equilibrium thermodynamics and the Free Energy Principle in particular.

https://stefanlavelle.substack.com/p/i-am-therefore-i-feel

[fallingknife]

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.

[throw4847285]

This is a fallacy I've seen enough on here that I think it needs a name. Maybe the fallacy of Theoretical Reducibility (doesn't really roll off the tongue)?

When challenged, everybody becomes an eliminative materialist even if it's inconsistent with their other views. It's very weird.

[throwuxiytayq]

Or like saying that the tangled web of neurons receiving signals from these understands anything about these subjects.

[nativeit]

Describing the mechanics of nervous impulses != describing consciousness.

[LordDragonfang]

Which is the point, since describing the mechanics of LLM architectures do not inherently grant knowledge of whether or not it is "conscious"

[robotresearcher]

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.

[fwip]

Philosophers are often the last people to consider something to be settled. There's very little in the universe that they can all agree is true.

[mubou]

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, and that's notably not the frontal lobe. That's required for intelligence, reasoning, self-awareness, etc.

So, no, it's not a question of philosophy. For an AI to enter that realm, it would need to be more than just an LLM with some bells and whistles; an LLM plus something else, perhaps, something fundamentally different which does not yet currently exist.

[aSanchezStern]

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.

[mubou]

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.

[goatlover]

Do biologists and neuroscientists not have any good evidence or is that just computer scientists and engineers speaking outside of their field of expertise? There's always been this danger of taking computer and brain comparisons too literally.

[root_axis]

If you're willing to torture the analogy you can find a way to describe literally anything as a system of outputs based on inputs. In the case of the brain to LLM comparison, people are inclined to do it because they're eager to anthropomorophize something that produces text they can interpret as a speaker, but it's totally incorrect to suggest that our brains are "essentially the same thing" as LLMs. The comparison is specious even on a surface level. It's like saying that birds and planes are "essentially the same thing" because flight was achieved by modeling planes after birds.

[SJC_Hacker]

Thats probably the case 99% of the time.

But that 1% is pretty important.

For example, they are dismal at math problems that aren't just slight variations of problems they've seen before.

Here's one by blackandredpenn where ChatGPT insisted the solution to problem that could be solved by high school / talented middle school students was correct, even after trying to convince it it was wrong. https://youtu.be/V0jhP7giYVY?si=sDE2a4w7WpNwp6zU&t=837

Rewind earlier to see the real answer

[LordDragonfang]

> For example, they are dismal at math problems that aren't just slight variations of problems they've seen before.

I know plenty of teachers who would describe their students the exact same way. The difference is mostly one of magnitude (of delta in competence), not quality.

Also, I think it's important to note that by "could be solved by high school / talented middle school students" you mean "specifically designed to challenge the top ~1% of them". Because if you say "LLMs only manage to beat 99% of middle schoolers at math", the claim seems a whole lot different.

[jquery]

ChatGPT o1 pro mode solved it on the first try, after 8 minutes and 53 seconds of “thinking”:

https://chatgpt.com/share/67f40cd2-d088-8008-acd5-fe9a9784f3...

[SJC_Hacker]

The problem is how do you know that its correct ...

A human would probably say "I don't know how to solve the problem". But ChatGPT free version is confidentially wrong ..

[nativeit]

Care to share any of this good evidence?

[gwd]

> 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 thought.

[mubou]

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.

[gwd]

> LLMs don't approximate human thought, though. ...Please, I'm begging you, go read some papers and watch some videos about machine learning and how LLMs actually work.

I know how LLMs work; so let me beg you in return, listen to me for a second.

You have a theoretical-only argument: LLMs do text prediction, and therefore it is not possible for them to actually think. And since it's not possible for them to actually think, you don't need to consider any other evidence.

I'm telling you, there's a flaw in your argument: In actuality, the best way to do text prediction is to think. An LLM that could actually think would be able to do text prediction better than an LLM that can't actually think; and the better an LLM is able to approximate human thought, the better its predictions will be. The fact that they're predicting text in no way proves that there's no thinking going on.

Now, that doesn't prove that LLMs actually are thinking; but it does mean that they might be thinking. And so you should think about how you would know if they're actually thinking or not.

[Sohcahtoa82]

> it will be something that is not an LLM

I think it will be very similar in architecture.

Artificial neural networks already are approximating how neurons in a brain work, it's just at a scale that's several orders of magnitude smaller.

Our limiting factor for reaching brain-like intelligence via ANN is probably more of a hardware limitation. We would need over 100 TB to store the weights for the neurons, not to mention the ridiculous amount of compute to run it.

[codedokode]

> not to mention the ridiculous amount of compute to run it.

How does the brain computes the weights then? Or maybe your assumption than brain is equivalent to a mathematical NN is wrong?

[yahoozoo]

How much compute do you think the human brain uses? They're training these LLMs with (hundreds of) thousands of GPUs.

[handfuloflight]

Isn't language expressed thought?

[fwip]

Language can be a (lossy) serialization of thought, yes. But language is not thought, nor inherently produced by thought. Most people agree that a process randomly producing grammatically correct sentences is not thinking.

[wongarsu]

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

[robotresearcher]

The thermostat analogy, and equivalents, are age-old.

[dleeftink]

I'd say the opposite also applies: to the extent LLMs have an internal language, we understand very little of it.