Position: Stop treating ‘AGI’ as the north-star goal of AI research
Authors: Borhane Blili-Hamelin, Christopher Graziul, Leif Hancox-Li, Hananel Hazan, El-Mahdi El-Mhamdi, Avijit Ghosh, Katherine A Heller, Jacob Metcalf, Fabricio Murai, Eryk Salvaggio, Andrew J Smart, Todd Snider, Mariame Tighanimine, Talia Ringer, Margaret Mitchell, Shiri Dori-Hacohen
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Collaboration | 1AI Risk and Vulnerability Alliance, NY, USA 2University of Chicago, IL, USA 3Vijil, CA, USA 4Tufts University, MA, USA 5Calicarpa, Switzerland 6Ecole Polytechnique, France 7Hugging Face, NY, USA 8University of Connecticut, CT, USA 9Google, CA, USA 10Data & Society Research Institute, NY, USA 11Worcester Polytechnic Institute, MA, USA 12Rochester Institute of Technology, NY, USA 13Eberhard Karls Universit at T ubingen, T ubingen, Germany 14Conservatoire national des arts et m etiers, Lise-CNRS, France 15University of Illinois Urbana-Champaign, IL, USA. |
| Pseudocode | No | The paper is a position paper that presents arguments and recommendations, and does not describe a specific algorithm or procedure that would require pseudocode. |
| Open Source Code | No | The paper is a position paper presenting arguments and recommendations. It does not describe any specific methodology or implementation for which source code would be provided by the authors. |
| Open Datasets | No | The paper is a position paper that primarily focuses on theoretical and conceptual arguments regarding AI research goals. It does not present new empirical research that would involve the use or release of specific datasets by the authors. |
| Dataset Splits | No | As the paper is a position paper and does not conduct original empirical experiments with datasets, there is no information regarding dataset splits. |
| Hardware Specification | No | The paper is a position paper discussing conceptual arguments and recommendations for AI research. It does not report on any experiments conducted by the authors that would require hardware specifications. |
| Software Dependencies | No | The paper is a position paper focused on conceptual arguments about AI research goals, not on implementing or evaluating a specific system. Therefore, no software dependencies are provided. |
| Experiment Setup | No | The paper is a position paper that discusses conceptual arguments and recommendations for AI research. It does not describe any empirical experiments or their setup. |