AI: A Guide for Thinking Humans

In May 2024, OpenAI released GPT-4o (omni), which, they wrote, “can reason across audio, vision, and text in real time.”  And last month they released the GPT-o1 model, which they claim performs “complex reasoning”, and which achieves record accuracy on many “reasoning-heavy” benchmarks.

But others have questioned the extent to which LLMs (or even enhanced models such as GPT-4o and o1) solve problems by reasoning abstractly, or whether their success is due, at least in part, to matching reasoning patterns memorized from their training data, which limits their ability to solve problems that differ too much from what has been seen in training.

In a previous post on LLM reasoning, I asked why it matters whether LLMs are performing “actual reasoning” versus behavior that just looks like reasoning:

Why does this matter? If robust general-purpose reasoning abilities have emerged in LLMs, this bolsters the claim that such systems are an important step on the way to trustworthy general intelligence.  On the other hand, if LLMs rely primarily on memorization and pattern-matching rather than true reasoning, then they will not be generalizable—we can’t trust them to perform well on ‘out of distribution’ tasks, those that are not sufficiently similar to tasks they’ve seen in the training data.

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