Billions of cell derived from Henrietta Lacks agree with you.

That environment is you.

Just like human atoms have been repurposed to make other things.

sure, this whole discussion is ultimately subjective. maybe the Chinese room itself is actually sentient. my question is, why are we arguing about it? who benefits from the idea that these systems are conscious?

> Do you think a fat pig is beautiful? Like a hairy fat pig that snorts and rolls in the mud… is this animal so beautiful to you that you would want to make love to this animal?

I don't want to make love to the night sky, so that last bit is completely irrelevant to the question of beauty. As for whether a pig is beautiful, sure, in its own way. I think they're nice animals and there is something beautiful in seeing them enjoy their little lives.

> Of course not! Because pigs are intrinsically and universally ugly...

It would seem not.

> Is this for real?

Frankly, I think you should be the one answering that question. You’re comparing appreciating looking at the sky to bestiality. Then you follow it up with another barrage of wrong assumptions about what I think and can or cannot articulate. None of that has anything to do with the argument. I didn’t even touch on LLMs, my point was squarely about the human experience. Please don’t assume things you know nothing about regarding other people. The HN guidelines ask you to not engage in bad faith and to steel man the other person’s argument.

We think it's beautiful because it's like a background that we don't have to think about. If that background were hostile, we'd have to think and we would not think it looks beautiful.

The idea I keep coming back to is that as far as we know it took roughly 100k-1M years for anatomically modern humans to evolve language, abstract thinking, information systems, etc. (equivalent to LLMs), but it took 100M-1B years to evolve from the first multi-celled organisms to anatomically modern humans.

In other words, human level embodiment (internal modelling of the real world and ability to navigate it) is likely at least 1000x harder than modelling human language and abstract knowledge.

And to build further on what you are saying, the way LLMs are trained and then used, they seem a bit more like DNA than the human brain in terms of how the "learning" is being done. An instance of an LLM is like a copy of DNA trained on a play of many generations of experience.

So it seems there are at least four things not yet worked out re AI reaching human level "AGI":

1) The number of weights (synapses) and parameters (neurons) needs to grow by orders of magnitude

2) We need new analogs that mimic the brains diversity of cell types and communication modes

3) We need to solve the embodiment problem, which is far from trivial and not fully understood

4) We need efficient ways for the system to continuously learn (an analog for neuroplasticity)

It may be that these are mutually reinforcing, in that solving #1 and #2 makes a lot of progress towards #3 and #4. I also suspect that #4 is economical, in that if the cost to train a GPT-5 level model was 1,000,000 cheaper, then maybe everyone could have one that's continuously learning (and diverging), rather than everyone sharing the same training run that's static once complete.

All of this to say I still consider LLMs "intelligent", just a different kind and less complex intelligence than humans.

>A huge limitation of LLMs is that they have no ability to dynamically engage with the world.

They can ask for input, they can choose URLs to access and interpret results in both situations. Whilst very limited, that is engagement.

Think about someone with physical impairments, like Hawking (the now dead theoretical physicist) had. You could have similar impairments from birth and still, I conjecture, be analytically one of the greatest minds of a generation.

If you were locked in a room {a non-Chinese room!}, with your physical needs met, but could speak with anyone around the World, and of course use the internet, whilst you'd have limits to your enjoyment of life I don't think you'd be limited in the capabilities of your mind. You'd have limited understanding of social aspects to life (and physical aspects - touch, pain), but perhaps no more than some of us already do.

> A huge limitation of LLMs is that they have no ability to dynamically engage with the world.

A pure LLM is static and can’t learn, but give an agent a read-write data store and suddenly it can actually learn things-give it a markdown file of “learnings”, prompt it to consider updating the file at the end of each interaction, then load it into the context at the start of the next… (and that’s a really basic implementation of the idea, there are much more complex versions of the same thing)

I think we are talking past each other a bit, probably because we have been exposed to different sets of information on a very complicated and diverse topic.

Have you ever explored the visual simulations of what goes on inside a cell or in protein interactions?

For example what happens inside a cell leading up to mitosis?

https://m.youtube.com/user/RCSBProteinDataBank

Is a pretty cool resource, I recommend the shorter videos of the visual simulations.

This category of perspective is critical to the point I was making. Another might be the meaning / definition of complexity, which I don't think is well understood yet and might be the crux. For me to say "the difference between life and what we call machines is just complexity" would require the same understanding of "complexity" to have shared meaning.

I'm not exactly sure what complexity is, and I'm not sure anyone does yet, but the closest I feel I've come is maybe integrated information theory, and some loose concept of functional information density.

So while it probably seemed like I was making a shallow case at a surface level, I was actually trying to convey that when one digs into science at all levels of abstraction, the differences between life and machines seem to fall more on a spectrum.

I’ve seen incredible sunsets while stressed depressed and worse. Are you saying sunsets cannot be experienced as beautiful on their own?

Now that's an interesting point of view.

Involving blind people would be an interesting experiment.

Anyway, until the sixties the ability to play a game of chess was seen as intelligence, and until about 2-3 years ago the "turing test" was considered the main yardstick (even though apparently some people talked to eliza at the time like an actual human being). I wonder what the new one is, and how often it will be moved again.

Claude 4.5

Q) Do you think the night sky is beautiful

A) I find the night sky genuinely captivating. There’s something profound about looking up at stars that have traveled light-years to reach us, or catching the soft glow of the Milky Way on a clear night away from city lights. The vastness it reveals is humbling. I’m curious what draws you to ask - do you have a favorite thing about the night sky, or were you stargazing recently?

>> Meanwhile, I just asked ChatGPT "Do you think the night sky is beautiful?" And it responded "Yes, I do..." and went on to explain why while describing senses its incapable of experiencing.

> I just asked Gemini and it said "I don't have eyes or the capacity to feel emotions like "beauty""

That means nothing, except perhaps that Google probably found lies about "senses [Gemini] incapable of experiencing" to be an embarrassment, and put effort into specifically suppressing those responses.

None of your examples refute the direct evidence of internal world model building which has been demonstrated (for example: https://adamkarvonen.github.io/machine_learning/2024/01/03/c... ).

Instead you have retreated to qualia like "well" and "sucks hard".

> hallucinating

Literally every human memory. They may seem tangible to you, but they're all in your head. The result of neurons behaving in ways which have directly inspired ML algorithms for nearly a century.

Further, history is rife with examples of humans learning from books and other written words. And also of humans thinking themselves special and unique in ways we are not.

> When using Claude Code or codex to write Swift code, I need to be very careful to provide all the APIs that are relevant in context (or let it web search), or garbage will be the result.

Yep. And humans often need to reference the documentation to get details right as well.

> Those big changes always happens because someone presented a simpler model that explains stuff enough we can build stuff on it.

Again, no it doesn't. It didn't with industrial steelmaking, which was ad hoc and lucky. It isn't with AI, which no one actually understands.

I was referring not to individual learning ability but to natural selection and evolutionary pressure, which IMO is easy to describe as a band-aid patch that takes a generation or more to apply.

That doesn't resolve the question.

  > Not sure where you imagine my inconsistency is.

  >> Let's take a step back. At what point is it me making the toast and not the toaster? Is it because I have to press the level? We can automate that. Is it because I have to put by bread in? We can automate that. Is it because I have to have the desire to have toast and initiate the chain of events? How do you measure that?

Maybe I've (and others?) misunderstood your claim from the get-go? You seem to have implied that LLMs understand chess, physics, programming, etc because of their performance. Yet now it seems your claim is that the LLM and I are doing those things together. If your claim is that a LLM understands programming the same way a toaster understands how to make toast, then we probably aren't disagreeing.

But if your claim is that a LLM understands programming because it can produce programs that yield a correct output to test cases, then what's the difference from the toaster? I put the prompts in and pushed the button to make it toast.

I'm not sure why you imagine the inconsistency is so difficult to see.

Just like a toaster with the lever jammed down, yes!

I mean, that was the question I was asking... If it wasn't clear, my answer is no.

It’s only responsible for the toasting part. The bread machine makes the bread.

How do you get bread? Don't tell me you got it at the market. That's just paying someone else to get it for you.

I would say it understands if given many variations of a problem statement, it always gives correct answer without fail. I have this complicated mirror question that only Deepseek and qwen3-max got right every time, still they only answered it correctly about a dozen times, so we're left with high probability, I guess.

I have been thinking about this for years, probably two decades. The answer to your question or the definition, I am sure you know, is rather difficult. I don't think it is impossible, but there's a risk of diving into a deep dark pit of philosophical thought going back to at least the ancient Greeks.

And, if we did go through that exercise, I doubt we can come out of it with a canonical definition of understanding.

I was really excited about LLM's as they surfaced and developed. I fully embraced the technology and have been using it extensively with full top-tier subscriptions to most services. My conclusion so far: If you want to destroy your business, adopt LLM's with gusto.

I know that's a statement that goes way against the train ride we are on this very moment. That's not to say LLM's are not useful. They are. Very much so. The problem is...well...they don't understand. And here I am, back in a circular argument.

I can define understanding with the "I know it when I see it" meme. And, frankly, it does apply. Yet, that's not a definition. We've all experienced that stare when talking to someone who does not have sufficient depth of understanding in a topic. Some of us have experienced people running teams who should not be in that position because they don't have a clue, they don't understand enough of it to be effective at what they do.

And yet, I still have not defined "understanding".

Well, it's hard. And I am not a philosopher, I am an engineer working in robotics, AI and applications to real time video processing.

I have written about my experiments using LLM coding tools (I refuse to call them AI, they are NOT intelligent; yes, need to define that as well).

In that context, lack of understanding is clearly evident when an LLM utterly destroys your codebase by adding dozens of irrelevant and unnecessary tests, randomly changes variable names as you navigate the development workflow, adds modules like a drunken high school coder and takes you down tangents that would make for great comedy if I were a tech comedian.

LLMs do not understand. They are fancy --and quite useful-- auto-complete engines and that's about it. Other than that, buyer beware.

The experiments I ran, some of them spanning three months of LLM-collaborative coding at various levels --from very hands-on to "let Jesus drive the car"-- conclusively demonstrated (at least to me) that:

1- No company should allow anyone to use LLMs unless they have enough domain expertise to be able to fully evaluate the output. And you should require that they fully evaluate and verify the work product before using it for anything; email, code, marketing, etc.

2- No company should trust anything coming out of an LLM, not one bit. Because, well, they don't understand. I recently tried to use the United Airlines LLM agent to change a flight. It was a combination of tragic and hilarious. Now, I know what's going on. I cannot possibly imagine the wild rides this thing is taking non-techies on every day. It's shit. It does not understand. It' isn't isolated to United Airlines, it's everywhere LLMs are being used. The potential for great damage is always there.

3- They can be great for summarization tasks. For example, you have have them help you dive deep into 300 page AMD/Xilinx FPGA datasheet or application note and help you get mentally situated. They can be great at helping you find prior art for patents. Yet, still, because they are mindless parrots, you should not trust any of it.

4- Nobody should give LLMs great access to a non-trivial codebase. This is almost guaranteed to cause destruction and hidden future effects. In my experiments I have experienced an LLM breaking unrelated code that worked just fine --in some cases fully erasing the code without telling you. Ten commits later you discover that your network stack doesn't work or isn't even there. Or, you might discover that the stack is there but the LLM changed class, variable or method names, maybe even data structures. It's a mindless parrot.

I could go on.

One response to this could be "Well, idiot, you need better prompts!". That, of course, assumes that part of my experimentation did not include testing prompts of varying complexity and length. I found that for some tasks, you get better results by explaining what you want and then asking the LLM to write a prompt to get that result. You check that prompt, modify if necessary and, from my experience, you are likely to get better results.

Of course, the reply to "you need better prompts" is easy: If the LLM understood, prompt quality would not be a problem at all and pages-long prompts would not be necessary. I should not have to specify that existing class, variable and method names should not be modified. Or that interfaces should be protected. Or that data structures need not be modified without reason and unless approved by me. Etc.

It reminds me of a project I was given when I was a young engineer barely out of university. My boss, the VP of Engineering where I worked, needed me to design a custom device. Think of it as a specialized high speed data router with multiple sources, destinations and a software layer to control it all. I had to design the electronics, circuit boards, mechanical and write all the software. The project had a budget of nearly a million dollars.

He brought me into his office and handed me a single sheet of paper with a top-level functional diagram. Inputs, outputs, interfaces. We had a half hour discussion about objectives and required timeline. He asked me if I could get it done. I said yet.

He checked in with me every three months or so. I never needed anything more than that single piece of paper and the short initial conversation because I understood what we needed, what he wanted, how that related to our other systems, available technology, my own capabilities and failings, available tools, etc. It took me a year to deliver. It worked out of the box.

You cannot do that with LLMs because they don't understand anything at all. They mimic what some might confuse for understanding, but they do not.

And, yet, once again, I have not defined the term. I think everyone reading this who has used LLMs to a non-trivial depth...well...understands what I mean.

Apologies if I ramble a bit here, this was typed in a bit of a hurry. Hopefully I answer some of your points.

First, regarding robotresearcher and simondota's comments, I am largely in agreement with what they say here. The "toaster" argument is a variant of the Chinese Room argument, and there is a standard rebuttal here. The toaster does not act independently of the human so it is not a closed system. The system as a whole, which includes the human, does understand toast. To me, this is different from the other examples you mention because the machine was not given a list of explicit instructions. (I'm no philosopher though so others can do a better job of explaining this). I don't feel that this is an argument for why LLMs "understand", but rather why the concept of "understanding" is irrelevant without an appropriate definition and context. Since we can't even agree on what constitutes understanding, it isn't productive to frame things in those terms. I guess that's where my maths background comes in, as I dislike the ambiguity of it all.

My "mostly junior" comment is partially in jest, but mostly comes from the fact that LLM and diffusion model research is a popular stream for moving into big tech. There are plenty of senior people in these fields too, but many reviewers in those fields are junior.

> I've also seen members of audiences to talks where people ask questions like mine ("are benchmarks sufficient to make such claims?") with responses of "we just care that it works."

This is a tremendous pain point to me more than I can convey here, but it's not unusual in computer science. Bad researchers will live and die on standard benchmarks. By the way, if you try to focus on another metric under the argument that the benchmarks are not wholly representative of a particular task, expect to get roasted by reviewers. Everyone knows it is easier to just do benchmark chasing.

> I also do not believe these people are less critical.

I think the fact that the "we just care that it works" argument is enough to get published is a good demonstration of what I'm talking about. If "more datasets" and "more scale" are the major types of criticisms that you are getting, then you are still working in a more fortunate field. And yes, I hate it as much as you do as it does favor the GPU rich, but they are at least potentially solvable. The easiest papers of mine to get through were methodological and often got these kinds of comments. Theory and SciML papers are an entirely different beast in my experience because you will rarely get reviewers that understand the material or care about its relevance. People in LLM research thought that the average NeurIPS score in the last round was a 5. Those in theory thought it was 4. These proportions feel reflected in the recent conferences. I have to really go looking for something outside the LLM mainstream, while there was a huge variety of work only a few years ago. Some of my colleagues have noticed this as well and have switched out of scientific work. This isn't unnatural or something to actively try to fix, as ML goes through these hype phases (in the 2000s, it was all kernels as I understand).

> approaches are narrow and still benchmark chasing > as a mathematician you think these systems create world models

When I say "world model", I'm not talking about outputs or what you can get through pure inference. Training models to perform next frame prediction and looking at inconsistencies in the output tells us little about the internal mechanism. I'm talking about appropriate representations in a multimodal model. When it reads a given frame, is it pulling apart features in a way that a human would? We've known for a long time that embeddings appropriately encode relationships between words and phrases. This is a model of the world as expressed through language. The same thing happens for images at scale as can be seen in interpretable ViT models. We know from the theory that for next frame prediction, better data and more scaling improves performance. I agree that isn't very interesting though.

> We are definitely abusing the terms "Out of Distribution" and "Zero shot".

Absolutely in agreement with everything you have said. These are not concepts that should be talked about in the context of "understanding", especially at scale.

> I think our scaling is just making the problem harder to evaluate.

Yes and no. It's clear that whatever approach we will use to gauge internal understanding needs to work at scale. Some methods only work with sufficient scale. But we know that completely black-box approaches don't work, because if they did, we could use them on humans and other animals.

> The claims are we've created world models yet many of them are not self-consistent.

For this definition of world model, I see this the same way as how we used to have "language models" with poor memory. I conjecture this is more an issue of alignment than a lack of appropriate representations of internal features, but I could be totally wrong on this.

>> any human who ever lived > Is this falsifiable? Even restricting to those currently living? On what tests? In which way? Does the category of error matter?

Software reliably beats the best players that have ever played it in public, including Kasparov and Carlsen, the best players of my lifetime (to my limited knowledge). By analogy to the performance ratchet we see in the rest of sports and games, and we might reasonably assume that these dominant living players are the best the world has ever seen. That could be wrong. But my argument does not hang on this point, so asking about falsifiability here doesn't do any work. Of course it's not falsifiable.

Y'know what else is not falsifiable? "That AI doesn't understand what it's doing".

  > can you name one functional difference between an AI that understands, and one that merely behaves correctly in its domain of expertise?

No one seems to have changed their opinion about anything in the wake of AIs routinely passing the Turing Test. They are fooled by the chatbot passing as a human, and then ask about ducks instead. The most celebrated and seriously considered quacks like a duck argument has been won by the AIs and no-one cares.

By the way, the ornithologists' criteria for duck is probably genetic and not much to do with behavior. A dead duck is still a duck.

And because we know what a duck is, no-one is yelling at ducks that 'they don't really duck' and telling duck makers they need a revolution in duck making and they are doomed to failure if they don't listen.

Not so with 'understanding'.

  > "why should we care?"

I don't think anyone is saying that the calculator isn't a useful tool. But certainly we should push back when people are claiming it understands math and can replace all mathematicians.

  > If we're to persuade anyone, we'll need to articulate a case for why understanding matters

I'll say that a major motivating reason of why I went into physics in the first place is because I found that a deep understanding was a far more efficient way of learning how to do things. I started as an engineer and even went into engineering after my degree. Physics made me a better engineer, and I think a better engineer than had I stayed in engineering. Understanding gave me the ability to not just take building blocks and put them together, but to innovate. Being able to see things at a deeper level allowed me to come to solutions I otherwise could not have. Using math to describe things allowed me to iterate faster (just like how we use simulations). Understanding what the math meant allowed me to solve the problems where the equations no longer applied. It allowed me to know where the equations no longer applied. It told me how to find and derive new ones.

I often found that engineers took an approach of physical testing first, because "the math only gets you so far." But that was just a misunderstanding of how far their math took them. It could do more, just they hadn't been taught that. So maybe I had to take a few days working things out on pen and paper, but that was a cheaper and more robust solution than using the same time to test and iterate.

Understanding is a superpower. Problems can be solved without understanding. A mechanic can fix an engine without knowing how it works. But they will certainly be able to fix more problems if they do. The reason to understand is because we want things to work. The problem is, the world isn't so simple that every problem is the same or very similar to another. A calculator is a great tool. It'll solve calculations all day. Much faster than me, with higher accuracy, but it'll never come up with an equation on its own. That isn't to call it useless, but I need to know this if I want to get things done. The more I understand what my calculator can and can't do, the better I can use that tool.

Understanding things, and the pursuit to understand more is what has brought humans to where they are today. I do not understand why this is even such a point of contention. Maybe the pursuit of physics didn't build a computer, but it is without a doubt what laid the foundation. We never could have done this had we not thought to understand lightning. We would have never been able to tame it like we have. Understanding allows us to experiment with what we cannot touch. It does not mean a complete understanding nor does it mean perfection, but it is more than just knowledge.

  > 1. Make it clear to them that with "internal monologue" you do not mean an actual audible hallucination

And yes, this has been discussed in depth. It was like literally the first thing...

But no, they do not have conversations in their heads like I do. They do not use words as their medium. I have no doubt that their experience is different from mine.

  > 2. Ask them if they EVER have imagined themselves or others saying or asking anything

  > The cat actually proves my point.

  > Fun fact: whenever animals (apes, birds) have been taught language, they never ask questions (some claim they did, but if you dig in you'll see that the interpretation is extremely dubious).

Are you really claiming that something doesn't think if it's possible to fool it with simple tricks?

Seriously?