Schleppy AGI (www.seriousanimals.com)

Dwarkesh Patel's recent essay posits that modern AI systems are still years away from achieving Artificial General Intelligence (AGI), primarily due to their lack of true continual learning. He argues that while current models excel in specific tasks through tailored training via Reinforcement Learning with Verifiable Rewards (RLVR), they fail to generalize across the diverse and shifting requirements of real-world jobs. This is rooted in their inability to learn continuously like humans, leading to issues such as catastrophic forgetting, which limits their effective memory and overall adaptability. The essay highlights ongoing debates within the AI community concerning timelines for achieving AGI, with some experts, like Dario Amodei, optimistic about significant advancements as soon as 2026. In contrast, others express skepticism, citing the need for breakthroughs in memory and general learning capabilities. Notably, advancements in hardware, such as Nvidia's massive compute capabilities, may facilitate revolutionary improvements in training AI models. While current systems demonstrate potential in specific applications, the quest for a true, human-like intelligence remains fraught with challenges, and the field may need more than just incremental improvements to cross the threshold into genuine AGI.