What if LLMs are mostly crystallized intelligence? (www.lesswrong.com)

Recent discussions among AI researchers suggest that Large Language Models (LLMs) exhibit primarily crystallized intelligence—the ability to leverage learned patterns from training data—while their fluid intelligence, which encompasses reasoning and problem-solving capabilities, remains significantly weaker than that of humans. This distinction is critical as it implies that while LLMs can excel at certain tasks, such as the SAT, they lack the versatile general reasoning services humans naturally possess. With projections of slowed advancements in AI due to potential limitations in data availability, this raises concerns about a slowdown in overall AI progress, prompting companies to focus more on specialized data generation to enhance LLM capabilities. The implications of this research are twofold. First, as LLMs continue to demonstrate strengths in technical tasks but struggle with fluid reasoning, they may help prolong AI safety concerns by preventing rapid advancements towards more autonomous capabilities. However, researchers caution that should new paradigms enabling more efficient learning emerge, the stability of current safety models may be threatened. Ultimately, while LLMs are making strides in AI research and development, the interplay between crystallized and fluid intelligence remains a critical area to explore, with the potential for sudden leaps in capabilities that the AI community must prepare for.