Apple says generative AI cannot think like a human - research paper pours cold water on reasoning models
https://www.tomshardware.com/tech-industry/artificial-intelligence/apple-says-generative-ai-cannot-think-like-a-human-research-paper-pours-cold-water-on-reasoning-models
Apple researchers discovered that LRMs perform differently depending on problem complexity. On simple tasks, standard LLMs, without explicit reasoning mechanisms, were more accurate and efficient and delivered better results with fewer compute resources. However, as problem complexity increased to a moderate level, models equipped with structured reasoning, like Chain-of-Thought prompting, gained the advantage and outperformed their non-reasoning counterparts. When the complexity grew further, both types of models failed completely: their accuracy dropped to zero regardless of the available compute resources. (Keep in mind that the the Claude 3.7 Sonnet Thinking and DeepSeek-R1 LRMs have limitations when it comes to their training.)
This is upset LANL managers who have an insane zeal for AI in hopes of getting rid of the scientists.
2 comments:
There was a recent report from some LANL fellows about the AI. I heard management was very disappointed in the report in that it says pretty much what the paper from Apple said. Also LANL management is very big into the idea that LANL will like the next open AI place or something and AI will do the scientific work.
On an 14 hour flight I sat next to a college student who bought Wi-Fi to have Claude summarizes research papers into an essay which he then feeds into an “AI detection” website. He repeats this process with Claude over and over until the output clears the website’s detection.
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