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The provided sources offer an extensive analysis of the Artificial General Intelligence (AGI) timeline debate, beginning with a comprehensive definition of AGI itself, differentiating it from narrow and superintelligence, and outlining core capabilities like generalization and common sense. It explores various architectural pathways to AGI, such as symbolic and connectionist approaches, highlighting how differing definitions influence timeline predictions. The text then maps the spectrum of predictions, noting a recent trend toward shorter timelines driven by industry leaders and large language model advancements, contrasted with the more cautious estimates from broader academic surveys and historical AI hype cycles. It presents arguments for a near-term horizon, emphasizing scaling laws and the potential for recursive self-improvement, while also detailing reasons for a distant horizon, citing fundamental limitations of current deep learning and unsolved problems. Finally, the sources examine the evolving science of AGI evaluation, moving beyond the outdated Turing Test to new benchmarks like ARC-AGI and Humanity's Last Exam, to assess current AI performance and identify key signposts for future progress, reflecting a deeper scientific conflict over the nature of intelligence.