> If you buy into this line of thinking then it quickly becomes apparent that LLM's are more or less a dead end when it comes to AGI. Simulating isnt emulating... an LLM is as likely to become intelligent as a forecast is to control the weather
Up until this point, I agree.
This puts humans on too high a pedestal: LLMs aren't magic, and we're not magic either.
(There's other reasons for me to think Transformers aren't the answer, but not this kind of reasoning).
Even if from a technical perspective you're right, I think people need to be careful with the "x is not special" talk. It is a put down and it's how things like human and animal rights get obliterated and how the environment gets ruined.
"Trees aren't special", "Dolphins aren't special", "Koala's suck, let's put a mine here instead", "Pigs don't have emotions or are dumb, so it's fine to factory farm" etc.
I don't get the argument. I don't think something being magic will stop humans from exploiting it. At the end of the day intelligent people are great at coming up with excuses as to why they should do something bad. "Just chop that one tree down, its in the wrong place anyway" "Just kill that one dolphin, its old anyway" when taken together these add up to bad outcomes we dislike. Much better to discourage / fine / ban all tree chopping and dolphin killing and let select professionals remove sick trees and dolphins.
Indeed. But I said "X is not magic", rather than "X is not special" — until we have an answer to the hard problem of consciousness (or agree which of the 40 definitions of the word "consciousness" we're using when discussing if an AI has it), we can't possibly determine if an LLM has it or not.
(My gut feeling says "LLMs are not conscious", but my gut has had a lot of false beliefs over the years as well as correct ones, so I give it a corresponding level of trust).
Fair enough then. I sort of use the terms interchangeably in this context.
When you think about it, a bird is “magic” in the sense there is a whole universe and eco system to give that bird the platform for existence. A real living bird isn’t just a concept.
So sometimes I wonder if we just say we’re insignificant because it’s a simpler way to think. It makes the idea of death and loss easier to bear.
If I tell myself I’m just a spec of dust and that I’m bit special, it can be quite comforting.
Conceptually we understand things about how birds work but the fact there is a blob of millions or billions of cells functioning to produce a bird, which can fly, completely autonomously is quite peculiar and there is a type of magic or wonder to it all which makes me think birds are both special and magic if you think differently about existence and not just the intellectual concept of a bird.
My gut feeling is that consciousness isn’t as deep and mysterious as people think it is. It’s possible that consciousness is an inevitable result of putting a sufficiently intelligent mind into a body and, as a result, the mind can’t help but weave a story about itself that connects events together.
Similarly with other properties of intelligence and the brain that we like to think are mysterious and deep.
The weather isn’t magic either. It’s produced by physical mechanisms. But everyone would probably agree that a model simulating some rough aggregate of those mechanisms isn’t “weather” itself.
On the other hand. Take that weather model and render its output into a stereoscopic 3D world with photorealistic particle systems and whatever. To someone wearing a Vision Pro or similar high-def VR headset, the model is now “the weather” in the system their senses occupy. It’s missing a lot of actual sensory cues — the rain isn’t wet, the wind won’t chill your skin, and so on. But it’s close enough for some convincing applications. A caveman with no experience with technology would undoubtedly believe himself transported into a different world with real weather.
LLMs are a bit like that now. Their simulation abilities took such a sudden leap, we’re like cavemen wearing headsets.
The only way I can model what you're trying to say, is if I assume you think "the mind" is a separate kind of substance, and not merely information processing that just happens to be implemented on biological electrochemistry in our skulls.
A (philosophical) dualist can easily say that no computation is ever intelligent. I don't think this can ever be said by a (philosophical) materialist.
We pretty much are compared to present-day neural architectures. How many simulated neurons and synapses are in the largest architectures, and how do those numbers compare to humans?
Unknown for the actual largest due to secrecy; 1% for the largest public models… but also organic ones are definitely a bit different from digital ones, and the jury is still out if those differences matter and if so by how much.
The comparison would therefore be with a mid-sized rodent, horse, or raven rather than a human.
(But even that's misleading, because the LLM doesn't have to use tokens to represent "contract left supracoracoideus" and "lay egg").
Edit: also, I've not heard much suggestion that anyone knows how certain genes do things like giving humans the inherent capability to recognise and create smiles or other similar reflexes, so we don't really know how much of our brains a pre-trained by evolution; furthermore, I think organic life is more sample-efficient for learning things than any AI so far.
Tokens aren't a necessary differentiator here. There is no fundamental technical reason why tokenization is used, it just has certain practical advantages. And the distinction almost disappears when we look at multimodal transformers, which process images, audio, and video broken apart into sequences of blocks of binary data.
There's no reason for any specific tokenisation, but the Transformer always has some tokenisation.
Tokens are allowed to be blocks of pixels, for example. No reason we couldn't have a token be a specific muscle or sensory nerve.
What I'm saying is that Large Language Models don't have a body, so no nerves and muscles to have to be represented within them; conversely, organic life does have those things and thus organic brains must spend some of their complexity on those things.
This means they have the possibility to equal us for language even with no capacity for vision, walking, tying shoelaces, or playing catch.
The attention mechanism is in practice implemented using three linear layers. The matrix multiplication to average the output and to implement the masking is the only non-neuronal part of that computation, but it can be seen as an activation function.
Usually, linear perceptrons and ReLUs or GeLUs are used. Due to the enormous compute requirements to evaluate models of interesting size, other types of neuronal networks and activation functions have received very little attention (pun intended) so far.
Using ReLU instead of sigmoid is a significant departure with regards to how closely it models actual neurons.
Using non fully connected layers is as well. Our brains likely aren’t fully connected, but the connections that matter are made stronger through living life and learning.
If you squint, it’s kind of like training a dense series of linear layers, but that’s not what we’re doing anymore (for the better)
Comparing NNs to organic brains is an apples to oranges comparison, is what I’m saying.
Lack of adaption is mainly a feature, we choose not to train them in real-time and instead make available fixed models with repeatable behaviour. We could, if we wanted to, update the model weights continuously in response to feedback.
I think the biggest difference is that they need far more examples than we need, to learn anything.
Up until this point, I agree.
This puts humans on too high a pedestal: LLMs aren't magic, and we're not magic either.
(There's other reasons for me to think Transformers aren't the answer, but not this kind of reasoning).