AI tools are becoming capable enough to pick off open Erdos problem (mathstodon.xyz)

Recent developments in AI suggest that advanced tools are increasingly capable of addressing open problems in mathematics, particularly those proposed by Paul Erdős. A new repository details how AI has contributed to our understanding of these problems, highlighting that while AI has not yet autonomously solved major mathematical challenges, its analytical capabilities show promise. The conversation emphasizes fostering collaboration between mathematicians and computer scientists to leverage AI's potential further, suggesting that a paradigm shift in systematic mathematics may be on the horizon. Key insights reveal that current language models (LLMs) could effectively "compress" existing mathematical knowledge, enabling them to deduce solutions based on previously solved problems. This compression is akin to mathematical deduction, indicating a nuanced interplay between memorization and prediction within these models. Moreover, the training of specialized AI systems appears to enable a transformation of implicit mathematical knowledge into localized, explicit information, suggesting that advanced tools might be unlocking previously obscured solutions embedded within older networks. This evolution signifies a crucial step towards harnessing AI's capabilities in tackling complex mathematical inquiries and reinforces the importance of interdisciplinary collaboration in advancing the field.