Superhuman Adaptable Intelligence Challenges the Idea of AGI
Jul 30, 2026
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.
Interactive Visualization: Superhuman Adaptable Intelligence Challenges the Idea of AGI
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
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