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Keep the Future Human: Why and How We Should Close the Gates to AGI and Superintelligence, and What We Should Build Instead

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arxiv 2311.09452 v4 pith:HBAYC6JC submitted 2023-11-15 cs.CY

classification cs.CY
keywords shouldhumanwhatcalledcapabilitiesfuturegatesgeneral-purpose
verification ladder T0 review T1 audit T2 compute T3 formal
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Dramatic advances in artificial intelligence over the past decade (for narrow-purpose AI) and the last several years (for general-purpose AI) have transformed AI from a niche academic field to the core business strategy of many of the world's largest companies, with hundreds of billions of dollars in annual investment in the techniques and technologies for advancing AI's capabilities. We now come to a critical juncture. As the capabilities of new AI systems begin to match and exceed those of humans across many cognitive domains, humanity must decide: how far do we go, and in what direction? This essay argues that we should keep the future human by closing the "gates" to smarter-than-human, autonomous, general-purpose AI -- sometimes called "AGI" -- and especially to the highly-superhuman version sometimes called "superintelligence." Instead, we should focus on powerful, trustworthy AI tools that can empower individuals and transformatively improve human societies' abilities to do what they do best.

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Cited by 3 Pith papers

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

  1. How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements

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  2. Compute Requirements for Algorithmic Innovation in Frontier AI Models

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Estimated development compute for 36 LLM pretraining innovations shows half would remain possible under GPT-2-level or 8-H100 compute caps.

  3. Technical Requirements for Halting Dangerous AI Activities

    cs.AI 2025-07 conditional novelty 5.0 of 10

    A taxonomy of compute-centric technical interventions, graded by readiness and mapped to five AI governance plans, argues that halting dangerous AI requires substantial control over AI compute.

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