The Four Fallacies of Modern AI (blog.apiad.net)

In “The Four Fallacies of Modern AI,” the author explores how deeply ingrained misconceptions continue to skew understanding and progress in AI development. Drawing on computer scientist Melanie Mitchell’s 2021 framework, the article highlights four key fallacies: the illusion of a smooth path from narrow AI to human-like general intelligence; the mistaken belief that tasks difficult for humans are equally hard for AI (Moravec’s Paradox); misleading anthropomorphic language that ascribes human traits like “understanding” to AI systems; and the myth that intelligence can exist independently of a physical body. These fallacies, the author argues, contribute to hype cycles, misplaced priorities, and a fragile, overly optimistic view of AI’s capabilities and trajectory. Technically, the article challenges the notion that current advances—such as large language models and multimodal agents—are straightforward steps toward AGI, emphasizing unresolved challenges like the commonsense knowledge gap and embodied cognition. It contrasts two philosophical paradigms shaping AI research: the Cognitive Paradigm, which sees intelligence as embodied and integrated with physical and social interaction, and the Computationalist Paradigm, which bets on scaling general methods via massive computation to eventually solve complex AI problems. This intellectual tension influences funding decisions, research focus, and public expectations, with significant societal impacts due to the economic and geopolitical hype cycles it fuels. The piece calls for a more nuanced, evidence-based engagement with AI’s real capabilities and limitations, urging the community to resist simplistic narratives to foster sustainable progress.