| The most meaningful definition of intelligence is one that captures the essence/nature of human/animal intelligence, which is where the word originated. I think you can get to the core of it by considering the evolutionary benefit of intelligence - what beneficial behavioral capability has been optimized - which comes down to being able to utilize past experience to predict/plan future outcomes, rather than being locked into reactive behavior patterns like simpler animals. LLMs, trained to predict based on past "experience", might (perhaps charitably) be considered to exhibit some intelligence, but where they notably fail is in situations where better prediction (utilization of prior experience) requires a process more similar to search with backtracking than a linear application of rules derived from the training data - i.e. in the areas of reasoning and planning. You can try to put lipstick on the pig by adding RL-based post-training or wrapping the LLM in an agentic loop, trying to extract more value out of the training data and gain some semblance of reasoning, but at the end of the day it's still a pig - at heart just an expert system not a cognitive architecture. Another obvious limitation of LLMs is that they are just a repository of canned knowledge/rules, with no ability to learn from "runtime" experience, and therefore lacking the ability to learn to handle novel problems by experimentation and adaptation to failure. The limited intelligence of LLMs is firmly baked into their architecture - the transformer, being just as pass-thru model, as well as the way they are trained by SGD rather than an algorithm capable of continuous incremental learning. |