In the rapidly evolving landscape of artificial intelligence, a new wave of research is challenging our understanding of AI's cognitive capabilities, particularly in the realm of large language models like ChatGPT. A recent paper from a research team at Arizona State University, published on the preprint platform arXiv, sheds light on a potential misconception about these models. The study suggests that contrary to popular belief, these AI models do not engage in genuine thought or reasoning processes; instead, they are merely identifying correlations within data.

The researchers argue that while AI models may generate a sequence of seemingly logical intermediate steps before providing an answer, this does not equate to reasoning. They caution against anthropomorphizing AI models, warning that such a perspective can lead to a misunderstanding of their true mechanisms. The "thinking" of large models, they assert, is rooted in calculating correlations between data points rather than grasping causal relationships.

To substantiate their claim, the researchers reference reasoning models such as DeepSeek R1, which, despite excelling in certain tasks, do not demonstrate human-like cognitive abilities. The study concludes that there is no actual reasoning process present in AI outputs, and viewing AI-generated intermediate inputs as a reasoning process can lead to a misleading confidence in their problem-solving capabilities.

This research serves as a reminder of the need for caution in an era increasingly reliant on AI. As our comprehension of the capabilities of large models deepens, future AI research may pivot towards more interpretable directions, aiding users in gaining a clearer understanding of the actual workings of AI.