AI research is dead, long live AI (kylrth.com)

A recent discourse in the AI community has emerged, catalyzed by Mike Cook’s perspective on the evolving landscape of AI research. As Cook notes, the field has morphed significantly, with many recent studies focusing on developing new agent pipelines using large language models (LLMs) to perform human tasks, thereby driving the perception that artificial general intelligence has arrived. This shift has led to a decline in innovative research, with a staggering portion of AI papers revolving around leveraging these LLMs rather than pioneering new knowledge or methodologies. For the AI/ML community, this evolution raises critical concerns about the future of research and development. Areas like natural language processing, once rich with diverse subdisciplines, have seen their foundations overshadowed by a reliance on a few dominant models, potentially stifling exploration of other meaningful avenues. As researchers adapt to a reality where LLMs are viewed as oracles of knowledge, the task of genuine inquiry has transformed into a challenge of interpretation and utilization, prompting an urgent need to reconsider what constitutes substantive progress in AI research.