An exploration of AI thought process visualization using text embeddings and t-SNE plotting, specifically analyzing how the Deepseek model processes questions. The analysis reveals distinct phases in AI reasoning, including search, thinking, and concluding stages, demonstrated through various philosophical and practical prompts.
An exploration of the challenges in determining causal relationships between variables through academic research, highlighting how most observational studies must be discarded due to methodological issues. The analysis reveals that even the most rigorous studies often produce conflicting or ambiguous results, with randomized trials being the most reliable despite their limitations.
A study comparing human and AI analysis of data patterns revealed that Large Language Models (LLMs) struggle to identify obvious visual patterns during exploratory data analysis, despite being proficient at creating visualizations and analyzing quantitative metrics. This limitation suggests both advantages in avoiding confirmation bias and potential drawbacks in hypothesis generation, highlighting important considerations for integrating LLMs into scientific workflows.