We know that they do not reason because we know the algorithm behind the curtain. The model is generating the next token via model weights and some randomness. That’s all. It not reasoning. Sometimes it has an appearance of reasoning, but not if you know how it works. It doesn’t matter that the model manufacturer marketing department slaps a “Reasoning!” sticker on the side of the model. It’s not actually doing that. As an analogy, sometimes a stage magician in Las Vegas makes it seem that he’s making a woman disappear and a tiger appear in her place, but we all know that’s not what is really happening; It’s just a clever trick.
Well, could you define what reasoning actually means? What would an AI need to do to be considered capable of reasoning? What is the core difference between what we do that is considered reasoning verse what AI currently does that is not considered reasoning?
To be clear, I am not making a statement as to whether AI reasons or not. Its just slippery to say something isn't or can't do X when we can't really define X. Perhaps if we can put it down as an outcome rather than an, in my opinion, currently impossible to accurately define characteristic of a thing.
In many examples, LLMs betray the fact that they are not reasoning, because when provided with problems that can be solved with the ability to reason, they fail.
Even in this discussion someone provided an example of coming up with board game rules. LLMs found all board game rules valid, because they looked and sounded like board game rules. Even when they were not.
In short, You can learn a subject, you can make a mental model of it, you can play with it, and you can rotate or infer new things about it.
LLMs are more analogous to actors, who have learnt a stupendous amount of lines, and know how those lines work.
They are, by definition, models of language.
IF you want a better version - GENAI needs to be able to generate working voxels of hands and 3D objects just from images.
I don’t believe the board game rules example. I think this would be a piece of cake for an llm. I’m happy to be proven wrong here if you share an example.
>We know that they do not reason because we know the algorithm behind the curtain.
In other words, we didn't put the "reasoning algorithm" in LLMs therefore they do not reason. But what is this reasoning algorithm that is a necessary condition for reasoning and how do you know LLMs parameters didn't converge on it in the process of pre-training?
Model parameters are weights, not algorithms. The LLM algorithm is (relatively) fixed: generate the next token according to the existing context, the model weights, and some randomization. That’s it. There is no more algorithm than that. The training parameters can shift the probabilities for predicting a token given the context, but there’s no more to it than that. There is no “reasoning algorithm” in the weights to converge to.
This overly reductive description of LLMs misses the forest for the trees. LLMs are circuit builders, the converged parameters pick out specific paths through the network that define programs. In other words, LLMs are differentiable computers[1]. Analogous to how a CPU is configured by the program state to execute arbitrary programs, the parameters of a converged LLM configure the high level matmul sequences towards a wide range of information dynamics.
Statistics has little relevance to LLM operation. The statistics of the training corpus imparts constraints on the converged circuit dynamics, but otherwise has no representation internally to the LLM.
what if eventually the physical mechanism for human consciousness becomes fully understood? does understanding that process mean we are no longer intelligent?
How do you disprove it?