You and I can't learn to ride a bike by reading thousands of books about cycling and Newtonian physics, but a robot driven by an LLM-like process certainly can.
In practice it would make heavy use of RL, as humans do.
In practice it would make heavy use of RL, as humans do.
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.
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.