Superhuman Adaptable Intelligence Challenges the Idea of AGI

This episode examines a paper arguing that AGI, as conventionally defined ("an AI that can do everything a human can do"), is an incoherent target because humans themselves aren't generally intelligent. Drawing on Legg and Hutter's Universal Intelligence, the No Free Lunch theorem, and Moravec's Paradox, the discussion uses examples like Magnus Carlsen losing to any mid-range chess engine and bats' echolocation outperforming human spatial senses to show that human cognition is a narrow, evolution-tuned specialization rather than a template for general intelligence. The hosts also cover the pushback from Demis Hassabis and Elon Musk, who argue the brain is Turing-complete and thus general in principle, and weigh that against the paper's counter that finite time, memory, and attention make "in principle" claims practically meaningless. Along the way, the conversation contrasts this framework with Narayanan and Kapoor's "AI as normal technology" view and debates why pinning down a rigorous definition of AGI actually matters for regulation and safety commitments, not just academic pedantry. Listeners interested in how loose terminology shapes AI policy and hype cycles will find the paper's proposed two-axis map of AGI definitions a useful lens for cutting through the discourse. Sources: 1. AI Must Embrace Specialization via Superhuman Adaptable Intelligence — Judah Goldfeder, Philippe Wyder, Yann LeCun, Ravid Shwartz Ziv, 2026 http://arxiv.org/abs/2602.23643 2. On the Measure of Intelligence — François Chollet, 2019 https://scholar.google.com/scholar?q=On+the+Measure+of+Intelligence 3. Levels of AGI: Operationalizing Progress on the Path to AGI — Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, Shane Legg (Google DeepMind), 2023 https://scholar.google.com/scholar?q=Levels+of+AGI%3A+Operationalizing+Progress+on+the+Path+to+AGI 4. A Path Towards Autonomous Machine Intelligence — Yann LeCun, 2022 https://scholar.google.com/scholar?q=A+Path+Towards+Autonomous+Machine+Intelligence 5. Sparks of Artificial General Intelligence: Early Experiments with GPT-4 — Sébastien Bubeck et al. (Microsoft Research), 2023 https://scholar.google.com/scholar?q=Sparks+of+Artificial+General+Intelligence%3A+Early+Experiments+with+GPT-4 6. A Generalist Agent (Gato) — Reed, Zolna, Parisotto, Colmenarejo, Novikov, Barth-Maron, Gimenez, Sulsky, Kay, Springenberg, Eccles, Bruce, Razavi, Edwards, Heess, Chen, Hadsell, Vinyals, Bordbar, de Freitas, 2022 https://scholar.google.com/scholar?q=A+Generalist+Agent+%28Gato%29 7. Emergent Abilities of Large Language Models — Wei, Tay, Bommasani, Raffel, Zoph, Borgeaud, Yogatama, Bosma, Zhou, Metzler, Chi, Hashimoto, Vinyals, Liang, Dean, Fedus, 2022 https://scholar.google.com/scholar?q=Emergent+Abilities+of+Large+Language+Models 8. ARC-AGI-2 and the ARC Prize — Chollet, Knoop, et al. (ARC Prize Foundation), 2024 https://scholar.google.com/scholar?q=ARC-AGI-2+and+the+ARC+Prize 9. On the Opportunities and Risks of Foundation Models — Bommasani, Hudson, Adeli, et al. (Stanford CRFM, ~100 authors), 2021 https://scholar.google.com/scholar?q=On+the+Opportunities+and+Risks+of+Foundation+Models 10. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning — DeepSeek-AI, 2025 https://scholar.google.com/scholar?q=DeepSeek-R1%3A+Incentivizing+Reasoning+Capability+in+LLMs+via+Reinforcement+Learning Interactive Visualization: Superhuman Adaptable Intelligence Challenges the Idea of AGI