For example, no matter many books you read about riding a bike, you still need to actually get on a bike and do some practice before you can ride it. The reading can certainly help, at least in theory, but, in practice, is not necessary and may even hurt (if it makes certain processes that need to be unconscious held too strongly in consciousness, due to the linguistic model presented in the book).
This is why LLMs being so strongly tied to natural language is still an important limitation (even it is clearly less limiting than most expected).
In practice it would make heavy use of RL, as humans do.
You know transformers can do math, right?
You're asking someone to answer this question in a text forum. This is not quite the gotcha you think it is.
The distinction between "knowing" and "putting into language" is a rich source of epistemological debate going back to Plato and is still widely regarded to represent a particularly difficult philosophical conundrum. I don't see how you can make this claim with so much certainty.
Riding a bike is, broadly, learning to co-ordinate your muscles in response to visual data from your surroundings and signals from your vestibular and tactile systems that give you data about your movement, orientation, speed, and control. As LLMs only output tokens that represent text, by definition they can NEVER learn to ride a bike.
Even ignoring that glaring definitional issue, an LLM also can't learn to ride a bike from books written by humans to humans, because an LLM could only operate through a machine using e.g. pistons and gears to manipulate the pedals. That system would be controlled by physics and mechanisms different from humans, and not have the same sensory information, so almost no human-written information about (human) bike-riding would be useful or relevant for this machine to learn how to bike. It'd just have to do reinforcement learning with some appropriate rewards and punishments for balance, speed, and falling.
And if we could embody AI in a sensory system so similar to the human sensory system that it becomes plausible text on bike-riding might actually be useful to the AI, it might also be that, for exactly the same reasons, the AI learns just as well to ride just by hopping on the thing, and that the textual content is as useless to it as it is for us.
Thinking this is an obvious gotcha (or the later comment that anyone thinking otherwise is going to have egg on their face) is just embarrassing. Much more of a wordcel problem than I would have expected on HN.
Can claim the same as your brain alone can’t control the bike because can’t access muscle tool.
You need to compare similar stuff
No, it literally cannot. See the reasons I give elsewhere here: https://news.ycombinator.com/item?id=46948266.
A more general AI trained with reinforcement learning and a novel architecture could surely learn to ride a bike, but an LLM simply cannot.
Related: Have you seen nvidea with their simulated 3d env. That might not be called llm but it’s not very far away from what our llm actually do right now. It’s just a naming difference
An LLM which makes a tool call to a function called `ride_bike`, where that function is a different sort of model with a different set of feedback mechanisms than those available to the LLM, is NOT the same thing at all. The LLM hasn't "learned" to ride the bike. The best you can say is that the LLM has learned that the bike can be ridden, and that it has a way of asking some other entity to ride on its behalf.
Now, could you develop such a model and make it available to an LLM? Sure, probably. But that's not an LLM. Moreover, it involves you, a human, making novel inroads on a different sort of AI/robotics problem. It simply is not possible to accomplish with an LLM.
And modern transformers definitely underperform models with built in priors (e.g. CNN) when they don't have massive amounts of data. Nevermind that LLMs simply can't at all handle all sorts of data types https://news.ycombinator.com/item?id=46948612.
Just another example of an HN commentator making statements about something they don't have any actual basic understanding of. Try reading some actual papers instead of the usual blog posts and marketing spam from frontier AI companies, you might learn something important.
Oh, so you mean, it would be in a harness of some sort that lets it connect to sensors that tell it things about its position, speed, balance and etc? Well, yes, but then it isn't an LLM anymore, because it has more than language to model things!
Not sure if it’s great just plain text, but would be better if could understand the position internally somehow.
In particular, sensor data doesn't have the same semantics or structure at all as language (it is continuous data and should not be tokenized; it will be multi-channel, i.e. have multiple streams, whereas text is single-channel; outputs need to be multi-channel as well, and realtime, so it is unclear if the LLM recursive approach can work at all or is appropriate). The lack of contextuality / interdependency, both within and between these streams might even mean that e.g. self-attention is not that helpful and just computationally wasteful here. E.g. what was said thousands of tokens ago can be completely relevant and change the meaning of tokens being generated now, but any bike sensor data from more than about a second or so ago is completely irrelevant to all future needed outputs.
Sure, maybe a transformer might still do well processing this data, but an LLM literally can't. It would require significant architectural changes just to be able to accept the inputs and make the outputs.
Current edge models literally can't do what I said, because they are single-channel and either recursively applied transformers, or most-likely Vision Transformers (setting aside generative image / video models, which are generally diffusion-based). The architectures are all wrong for handling certain kinds of data, tokenization is deeply harmful when dealing with continuous data (destroys relative meaning of magnitudes), and self-attention can be hugely inexpensive and encourage over-fitting when the problem is such that long-term dependencies are not relevant.
Try actually reading some papers and building some models using e.g. PyTorch. You can't understand these things by metaphor and analogy.
This is like saying that no matter how much you know theoretically about a foreign language you still need to train your brain to talk it. It has little to do with the reality of that language or the correctness of your model of it, but rather with the need to train realtime circuits to do some work.
Let me try some variations: "no matter how many books you read about ancient history, you need to have lived there before you can reasonably talk about it". "No matter how many books you have read about quantum mechanics, you need to be a particle..."
To the contrary, this is purely speculative and almost certainly wrong, riding a bike is co-ordinating the realtime circuits in the right way, and language and a linguistic model fundamentally cannot get you there.
There are plenty of other domains like this, where semantic reasoning (e.g. unquantified syllogistic reasoning) just doesn't get you anywhere useful. I gave an example from cooking later in this thread.
You are falling IMO into exactly the trap of the linguistic reductionist, thinking that language is the be-all and end-all of cognition. Talk to e.g. actual mathematicians, and they will generally tell you they may broadly recruit visualization, imagined tactile and proprioceptive senses, and hard-to-vocalize "intuition". One has to claim this is all epiphenomenal, or that e.g. all unconscious thought is secretly using language, to think that all modeling is fundamentally linguistic (or more broadly, token manipulation). This is not a particularly credible or plausible claim given the ubiquity of cognition across animals or from direct human experiences, so the linguistic boundedness of LLMs is very important and relevant.
> You are falling IMO into exactly the trap of the linguistic reductionist, thinking that language is the be-all and end-all of cognition.
I'm not saying that at all. I am saying that any (sufficiently long, varied) coherent speech needs a world model, so if something produces coherent speech, there must be a world model behind. We can agree that the model is lacking as much as the language productions are incoherent: which is very little, these days.
This is circular, because you are assuming their world-model of biking can be expressed in language. It can't!
EDIT: There are plenty of skilled experts, artists and etc. that clearly and obviously have complex world models that let them produce best-in-the-world outputs, but who can't express very precisely how they do this. I would never claim such people have no world model or understanding of what they do. Perhaps we have a semantic / definitional issue here?
Ok. So I think I get it. For me, producing coherent discourse about things requires a world model, because you can't just make up coherent relationships between objects and actions long enough if you don't understand what their properties are and how they relate to each other.
You, on the other hand, claim that there are infinite firsthand sensory experiences (maybe we can call them qualia?) that fall in between the cracks of language and are rarely communicated (though we use for that a wealth of metaphors and synesthesia) and can only be understood by those who have experienced them firsthand.
I can agree with that if that's what you mean, but at the same time I'm not sure they constitute such a big part of our thought and communication. For example, we are discussing about reality in this thread and yet there are no necessary references to first hand experiences. Any time we talk about history, physics, space, maths, philosophy, we're basically juggling concepts in our heads with zero direct experience of them.
Well, not infinite, but, yes! I am indeed claiming much world models are patterns and associations between qualia, and that only some qualia are essentially representable as or look like linguistic tokens (specifically, the sounds of those tokens being pronounced, or their visual shapes if e.g. math symbols). E.g. I am claiming that the way one learns to e.g. cook, or "do theoretical math" may be more about forming associations between those non-linguistic qualia than, say, obviously, doing philosophy is.
> I'm not sure they constitute such a big part of our thought and communication
The communication part is mostly tautological again, but, yes, it remains very much an open question in cognitive science just how exactly thought works. A lot of mathematicians claim to lean heavily on visualization and/or tactile and kinaesthetic modeling for their intuitions (and most deep math is driven by intuition first), but also a lot of mathematicians can produce similar works and disagree about how they think about it intuitively. And we are seeing some progress from e.g. Aristotle using LEAN to generate math proofs in a strictly tokenized / symbolic way, but it remains to be seen if this will ever produce anything truly impressive to mathematicians. So it is really hard to know what actually matters for general human cognition.
I think introspection makes it clear there are a LOT of domains where it is obvious the core knowledge is not mostly linguistic. This is easiest to argue for embodied domains and skills (e.g. anything that requires direct physical interaction with the world), and it is areas like these (e.g. self-driving vehicle AI) where LLMs will be (most likely) least useful in isolation, IMO.
So you can't build an AI model that simulates riding a bike? I'm not stating a LLM model, I'm just saying the kind of AI simulation we've been building virtual worlds with for decades.
So, now that you agree that we can build AI models of simulations, what are those AI models doing. Are they using a binary language that can be summarized?
Calling everything "language" is not some gotcha, the middle "L" in LLM means natural language. Binary code is not "language" in this sense, and these terms matter. Robotics AIs are not LLMs, they are just AI.
Any series of self consistent encoded signals can be language. You could feed an LLM wireless signals if until it learned how to connect to your wifi if you wanted to. Just assign tokens. You're acting like words are something different than encoded information. It's the interconnectivity between those bits of data that matters.
This works against your argument. Someone who can ride a bike clearly knows how to ride a bike, that they cannot express it in tokenized form speaks to the limited scope ofof written word in representing embodiment.
Nvidia is out building LLM driven models that work with robot models that simulate robot actions. World simulation was a huge part of AI before LLMs became a thing. With a tight coupling between LLMs and robot models we've seen an explosion in robot capabilities in the last few years.
You know what robots communicate with their actuators and sensors with. Oh yes, binary data. We quite commonly call that words. When you have a set of actions that simulate riding a bicycle in virtual space that can be summarized and described. Who knows if humans can actually read/understand what the model spits out, but that doesn't mean it's invalid.
It would be more precise to say that complex world modeling is not done with LLMs, or that LLMs only supplement those world models. Robotics models are AI, calling them LLMs is incorrect (though they may use them internally in places).
The middle "L" in LLM refers to natural language. Calling everything language and words is not some gotcha, and sensor data is nothing like natural language. There are multiple streams / channels, where language is single-stream; sensor data is continuous and must not be tokenized; there are not long-term dependencies within and across streams in the same way that there are in language (tokens thousands of tokens back are often relevant, but sensor data from more than about a second ago is always irrelevant if we are talking about riding a bike), making self-attention expensive and less obviously useful; outputs are multi-channel and must be continuous and realtime, and it isn't even clear the recursive approach of LLMs could work here.
Another good example of world models informed by work in robotics is V-JEPA 2.
My point is merely that the knowledge of how to ride a bike is not necessarily expressible in natural language such that reading a "how to" is sufficient to ride on your first try. It takes falling over a few times to learn how to control your body+bike hybrid.
Once you understand that, you realize that the human brain has an internal model of almost everything it is interacting with and replicating human level performance requires the entire human brain, not just isolated parts of it. The reason for this is that since we take our brains for granted, we use even the complicated and hard to replicate parts of the brain for tasks that appear seemingly trivial.
When I take out the trash, organic waste needs to be thrown into the trash bin without the plastic bag. I need to untie the trash bag, pinch it from the other side and then shake it until the bag is empty. You might say big deal, but when you have tea bags or potato peels inside, they get caught on the bag handles and get stuck. You now need to shake the bag in very particular ways to dislodge the waste. Doing this with a humanoid robot is basically impossible, because you would need to model every scrap of waste inside the plastic bag. The much smarter way is to make the situation robot friendly by having the robot carry the organic waste inside a portable plastic bin without handles.
Every single time I travel somewhere new, whatever research I did, whatever reviews or blogs I read or whatever videos I watched become totally meaningless the moment I get there. Because that sliver of knowledge is simply nothing compared to the reality of the place.
Everything you read is through the interpretation of another person. Certainly someone who read a lot of books about ancient history can talk about it - but let's not pretend they have any idea what it was actually like to live there.
For instance, say you are a woman with a lookalike celebrity, someone who is a very close match in hair colour, facial structure, skin tone and body proportions. You would like to browse outfits worn by other celebrities (presumably put together by professional stylists) that look exactly like her. You ask an LLM to list celebrities that look like celebrity X, to then look up outfit inspiration.
No matter how long the list, no matter how detailed the prompt in the features that must be matched, no matter how many rounds you do, the results will be completely unusable, because broad language dominates more specific language in the corpus.
The LLM cannot adequately model these facets, because language is in practice too imprecise, as currently used by people.
To dissect just one such facet, the LLM response will list dozens of people who may share a broad category (red hair), with complete disregard to the exact shade of red, whether or not the hair is dyed and whether or not it is indeed natural hair or a wig.
The number of listicles clustering these actresses together as redheads will dominate anything with more specific qualifiers, like ’strawberry blonde’ (which in general counts as red hair), ’undyed hair’ (which in fact tends to increase the proportion of dyed hair results, because that’s how linguistic vector similarity works sometimes) and ’natural’ (which again seems to translate into ’the most natural looking unnatural’, because that’s how language tends to be used).