This is what I think is the next evolution of these models. Our brains are made up of many different types of neurones all interspersed with local regions made up of specific types. From my understanding most approaches to tensors don't integrate these different neuronal models at the node level; it's usually by feeding several disparate models data and combining an end result. Being able to reshape the underlying model and have varying tensor types that can migrate or have a lifetime seems exciting to me.

Strongly agree with this. When we were further from AGI, many people imagined that there is a single concept of AGI that would be obvious when we reached it. But now, we're close enough to AGI for most people to realize that we don't know where it is. Most people agree we're at least moving more towards it than away form it, but nobody knows where it is, and we're still too focused on finding it than making useful things.

Incorrect. Vertebrate animal brains update their neural connections when interacting with the environment. LLMs don't do that. Their model weights are frozen for every release.

Maybe panpsychism is true and the machine actually does have a soul, because all machines have souls, even your lawnmower. But possibly the soul of a machine running a frontier AI is a bit closer to a human soul than your lawnmower’s soul is.

Maybe the soul is not as mysterios as we think it is?

What is the scientific definition of intelligence? I assume that is it is comprehensive, internally consistent, and that it fits all of the things that are obviously intelligent and excludes the things which are obviously not intelligent. Of course being scientific I assume it is also falsifiable.

Just because it's not independent and autonomous does not mean it could not be intelligent.

If existing humans minds could be stopped/started without damage, copied perfectly, and had their memory state modified at-will would that make us not intelligent?

It sounds like you are saying the only difference is that human stimulus streams don't shut on and off?

If you were put into a medically induced coma, you probably shouldn't be consider intelligent either.

>Humans think even when not being prompted by other humans

That's more of an implementation detail. Humans take constant sensory input and have some sort of way to re-introduce input later (e.g. remember something).

Both could be added (even trivially) to LLMs.

And it's not at all clear human thought is contant. It just appears so in our naive intuition (same how we see a movie as moving, not as 24 static frames per second). It's a discontinuous mechanism though (propagation time, etc), and this has been shown (e.g. EEG/MEG show the brain sample sensory input in a periodic pattern, stimuly with small time difference are lost - as if there is a blind-window regarding perception, etc).

>and in some cases can learn new things by having intuition make a concept clear or by performing thought experiments or by combining memories of old facts and new facts across disciplines

Unless we define intuition in a way that excludes LLM style mechanisms a priori, whose to say LLMs don't do all those things as well, even if in a simpler way?

They've been shown to combine stuff across disciplines, and also to develop concepts not directly on their training set.

And "performing thought experiments" is not that different than the reasoning steps and backtracking LLMs also already do.

Not saying LLMs are on parity with human thinking/consciousness. Just that it's not clear that they're doing more or less the same even at reduced capacity and with a different architecture and runtime setup.

The environment is constantly prompting you. That ad you see of Coca Cola is prompting you to do something. That hunger feeling is prompting “you” to find food. That memory that makes you miss someone is another prompt to find that someone - or to avoid.

Sometimes the prompt is outside your body other times is inside.

>Roughly, actual intelligence needs to maintain a world model in its internal representation

And how's that not like stored information (memories) and weighted links between each and/or between groups of them?

> Roughly, actual intelligence needs to maintain a world model in its internal representation

This is GOFAI metaphor-based development, which never once produced anything useful. They just sat around saying things like "people have world models" and then decided if they programmed something and called it a "world model" they'd get intelligence, it didn't work out, but then they still just went around claiming people have "world models" as if they hadn't just made it up.

An alternative thesis "people do things that worked the last time they did them" explains both language and action planning better; eg you don't form a model of the contents of your garbage in order to take it to the dumpster.

https://www.cambridge.org/core/books/abs/computation-and-hum...

Please check an example #2 here: https://github.com/PicoTrex/Awesome-Nano-Banana-images/blob/...

It is not "language alone" anymore. LLMs are multimodal nowadays, and it's still just the beginning.

And keep in mind that these results are produced by a cheap, small and fast model.

> What I mean is that the current generation of LLMs don’t understand how concepts relate to one another.

They must be able to do this implicitly; otherwise why are their answers related to the questions you ask them, instead of being completely offtopic?

https://phillipi.github.io/prh/

A consequence of this is that you can steal a black box model by sampling enough answers from its API because you can reconstruct the original model distribution.

The definition of 'Markov chain' is very wide. If you adhere to a materialist worldview, you are a Markov chain. [Or maybe the universe viewed as a whole is a Markov chain.]

> Which is why they’re so bad at maths for instance.

I don't think LLMs currently are intelligent. But please show a GPT-5 chat where it gets any math problem wrong, that most "intelligent" people would get right.

You and Chomsky are probably the last 2 persons on earth to believe that.

I completely agree. LLMs only do call and response. Without the call there is no response.

>For one thing, I have internal state that continues to exist when I'm not responding to text input

Do you? Or do you just have memory and are run on a short loop?

Yep. Although the average human also considered proficiency in mathematics to be indicative of intelligence until we invented the pocket calculator, so maybe we're just not smart enough to define what intelligence is.

Not really, we saw this decades ago: https://en.wikipedia.org/w/index.php?title=ELIZA_effect

But they're experiencing their training data, their pseudo-randomness source, and your prompts?

Like, to put it in perspective. Suppose you're training a multimodal model. Training data on the terabyte scale. Training time on the weeks scale. Let's be optimistic and assume 10 TB in just a week: that is 16.5 MB/s of avg throughput.

Compare this to the human experience. VR headsets are aiming for what these days, 4K@120 per eye? 12 GB/s at SDR, and that's just vision.

We're so far from "realtime" with that optimistic 16.5 MB/s, it's not even funny. Of course the experiencing and understanding that results from this will be vastly different. It's a borderline miracle it's any human-aligned. Well, if we ignore lossy compression and aggressive image and video resizing, that is.

(and I include humans in this) experience reality.

A fellow named Plato had some interesting thoughts on that subject that you might want to look into.