Position: Stop Treating ‘AGI’ as the North-star Goal of AI Research

  • Borhane Blili-Hamelin
  • , Christopher Graziul
  • , Leif Hancox-Li
  • , Hananel Hazan
  • , El Mahdi El-Mhamdi
  • , Avijit Ghosh
  • , Katherine Heller
  • , Jacob Metcalf
  • , Fabricio Murai
  • , Eryk Salvaggio
  • , Andrew Smart
  • , Todd Snider
  • , Mariame Tighanimine
  • , Talia Ringer
  • , Margaret Mitchell
  • , Shiri Dori-Hacohen

Research output: Contribution to journalConference articlepeer-review

Abstract

The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly contested topic of ‘artificial general intelligence’ (‘AGI’) undermines our ability to choose effective goals. We identify six key traps—obstacles to productive goal setting—that are aggravated by AGI discourse: Illusion of Consensus, Supercharging Bad Science, Presuming Value-Neutrality, Goal Lottery, Generality Debt, and Normalized Exclusion. To avoid these traps, we argue that the AI research community needs to (1) prioritize specificity in scientific, engineering, and societal goals, (2) center pluralism about multiple worthwhile approaches to multiple valuable goals, and (3) foster innovation through greater inclusion of disciplines and communities. Therefore, the AI research community needs to stop treating “AGI” as the north-star goal of AI research.

Original languageEnglish
Pages (from-to)81090-81117
Number of pages28
JournalProceedings of Machine Learning Research
Volume267
Publication statusPublished - 1 Jan 2025
Externally publishedYes
Event42nd International Conference on Machine Learning, ICML 2025 - Vancouver, Canada
Duration: 13 Jul 202519 Jul 2025

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