A core debate in AI is whether LLMs, which are text prediction engines, can achieve true intelligence. Critics argue they cannot because they lack a model of the real world. This prevents them from making meaningful, context-aware predictions about future events—a limitation that more data alone may not solve.

Reinforcement learning incentivizes AIs to find the right answer, not just mimic human text. This leads to them developing their own internal "dialect" for reasoning—a chain of thought that is effective but increasingly incomprehensible and alien to human observers.

The current limitation of LLMs is their stateless nature; they reset with each new chat. The next major advancement will be models that can learn from interactions and accumulate skills over time, evolving from a static tool into a continuously improving digital colleague.

The sudden arrival of powerful AI like GPT-3 was a non-repeatable event: training on the entire internet and all existing books. With this data now fully "eaten," future advancements will feel more incremental, relying on the slower process of generating new, high-quality expert data.

Early Wittgenstein's "logical space of possibilities" mirrors how LLM embeddings map words into a high-dimensional space. Late Wittgenstein's "language games" explain their core function: next-token prediction and learning through interactive feedback (RLHF), where meaning is derived from use and context.

Anthropic suggests that LLMs, trained on text about AI, respond to field-specific terms. Using phrases like 'Think step by step' or 'Critique your own response' acts as a cheat code, activating more sophisticated, accurate, and self-correcting operational modes in the model.

Training models like GPT-4 involves two stages. First, "pre-training" consumes the internet to create a powerful but unfocused base model (“raw brain mass”). Second, "post-training" uses expert human feedback (SFT and RLHF) to align this raw intelligence into a useful, harmless assistant like ChatGPT.

Rather than achieving general intelligence through abstract reasoning, AI models improve by repeatedly identifying specific failures (like trick questions) and adding those scenarios into new training rounds. This "patching" approach, though seemingly inefficient, proved successful for self-driving cars and may be a viable path for language models.

The 2017 introduction of "transformers" revolutionized AI. Instead of being trained on the specific meaning of each word, models began learning the contextual relationships between words. This allowed AI to predict the next word in a sequence without needing a formal dictionary, leading to more generalist capabilities.