Rumors of AGI's arrival have been greatly exaggerated (garymarcus.substack.com)

Recent claims suggesting the arrival of artificial general intelligence (AGI) have been deemed misleading by experts, including researchers Gary Marcus, Walter Quattrociocchi, and Valerio Capraro. They argue that while large language models (LLMs) display impressive performance on specific benchmarks, this does not equate to genuine general intelligence. The authors emphasize that AGI should be defined by robust, flexible competence across various tasks and environments, not merely by superior benchmark scores, which can often be manipulated or do not reflect real-world applications. The significance of this discourse lies in the conceptual clarity it brings to the AI/ML community. It highlights the divergence between statistical performance and true intelligence, arguing that current AI systems, despite their advancements, still lack essential qualities like adaptability, goal-directed behavior, and reliable generalization across novel situations. By reiterating the original definitions of AGI, the authors call for a re-examination of how AI's capabilities are assessed, urging a focus on the underlying mechanisms rather than just observable behaviors, which often mask deeper discrepancies in cognitive processes between machines and humans.