aathanor

I’m not a coder, but I’ve been working extensively on the philosophical aspects of AI. Many technical people are influenced by an algorithmic view of intelligence, primarily because this aligns with programming and the general understanding of reasoning. However, pattern recognition, which is fundamental to LLMs, is not algorithmic. Consider this: a LLM constructs a virtual textual world where landscapes and objects are represented as text, and words are the building blocks of these features. It’s a vast 700+D mathematical space, but visualizing it as a virtual reality environment can help us comprehend its workings. When you provide a prompt, you essentially direct the LLM’s attention to a specific region within this space, where an immense number of sentences exist in various shapes and forms (textual shapes). All potential answers generated by the LLM are contained within this immediate landscape, centered around your prompt’s position. They are all readily available to the LLM at once.

There are certain methods (I would describe them as less algorithmic and more akin to selection criteria or boundaries) that enable the LLM to identify a coherent sequence of sentences as a feature closer to your prompt within this landscape. These methods involve some level of noise (temperature) and other factors. As a result, the LLM generates your text answer. There’s no reasoning involved; it’s simply searching for patterns that align with your prompt. (It’s not at all based on statistics and probabilities; it’s an entirely different process, more akin to instantly recognizing an apple, not by analyzing its features or comparing it to a statistical construct of “apple.”)

When you request a mathematical result, the LLM doesn’t engage in reasoning. It simply navigates to the point in its model’s hyperspace where your prompt takes it and explores the surrounding area. Given the extensive amount of training text, it will immediately match your problem formulation with similar formulations, providing an answer that appears to mimic reasoning solely because the existing landscape around your prompt facilitates this.

A LLM operates more like a virtual reality environment for the entire body of human-created text. It doesn’t navigate the space independently; it merely renders what exists in different locations within it. If we were to label this as reasoning, it’s no more than reasoning by analogy or imitation. People are right to suspect LLMs do not reason, but I think the reason (pun intended) for that is not that they simply do some sort of statistical analysis. This "stochastic parrots" paradigm supported by Chomsky is actually blocking our understanding of LLMs. I also think that seeing them as formidable VR engines for textual knowledge clarifies why they are not the path to AGI. (There is also the embodiment problem which is not solvable by adding sensors and actuators, as people think, but for a different reason)