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arxiv: 2502.03689 · v4 · pith:BGQZBHI7 · submitted 2025-02-06 · cs.CY

Stop treating `AGI' as the north-star goal of AI research

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classification cs.CY
keywords researchgoalgoalscommunityargueengineeringmultipleneeds
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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 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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    cs.CL 2026-05 unverdicted novelty 6.0

    Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.

  2. Instructions Shape Production of Language, not Processing

    cs.CL 2026-05 unverdicted novelty 5.0

    Instructions primarily shape the production stage of language models rather than the processing stage, with task-specific information and causal effects stronger in output tokens than input tokens.