AGI-to-ASI Transition Duration

An AGI-to-ASI Transition Duration is a computing system development duration if a AGI to ASI transition period (between artificial general intelligence system and artificial superintelligent systems).

• AKA: AI Takeoff Speed, Intelligence Transition Rate, AGI ASI Development Timeline.
• Context:
  • It can often require Continuous Monitoring through System Assessments.
  • It can often need International Coordination through Global Frameworks.
  • It can often involve Stakeholder Management through Collaborative Processes.
  • It can often demand Real-Time Response through System Alerts.
  • ...
  • It can range from being a Fast AI System Takeoff to being a Slow AI System Takeoff, depending on its Development Duration.
  • ...
  • It can influence AI Safety Strategy through Development Pace considerations.
  • It can affect AI Risk Assessment through Timeline Projections and Preparation Requirements.
  • It can shape AI Preparedness Planning through Resource Allocation and Strategy Timelines.
  • It can inform AI Policy Development through Regulatory Requirements and Governance Timelines.
  • It can determine Safety Implementation Windows through Development Phases.
  • It can guide Resource Distribution through System Requirements.
  • It can impact AI Safety Frameworks through Implementation Timelines.
  • It can influence AI Alignment Strategys through Development Windows.
  • It can affect System Resource Allocations through Preparation Requirements.
  • It can require Emergency Protocols through Crisis Managements.
  • It can integrate with Global Monitoring Systems for Progress Tracking.
  • ...
• Examples:
  • Fast AI Transition Scenarios, such as:
    • Recursive Self-Improvement Transitions, such as:
      • Rapid Capability Enhancement for System Optimization.
      • Exponential Growth Pattern for Quick Evolution.
    • Intelligence Explosion Scenarios, such as:
      • Sudden Breakthrough Event for Critical Advancement.
      • Cascade Effect Pattern for Rapid Transformation.
  • Slow AI Transition Scenarios, such as:
    • Gradual Development Patterns, such as:
      • Incremental Advancement Process for Steady Growth.
      • Controlled Evolution Path for Managed Progress.
    • Distributed Progress Patterns, such as:
      • Collaborative Development Process for Shared Advancement.
      • Coordinated Evolution Path for Collective Progress.
  • ...
• Counter-Examples:
  • AI Capability Growth Rate, which measures Performance Improvement rather than System Transition Speed.
  • AI Development Timeline, which tracks Overall Progress rather than specific Transition Periods.
  • AI Evolution Pattern, which focuses on General Advancement rather than AGI ASI Transition.
  • Machine Learning Progress, which involves Narrow AI Enhancement rather than General Intelligence Transition.
  • Technical Capability Scaling, which concerns System Scaling rather than Intelligence Transition.
• See: Fast AI Takeoff Pattern, Slow AI Takeoff Pattern, AGI Development Process, ASI Transition Phase, AI Safety Framework, Global Coordination System, Risk Management Protocol, Emergency Response Framework.

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References

2025-01-20

• Perplexity.ai
  • Question: What is the AGI to ASI Transition Speed and how does it vary? Please explain its key characteristics, different transition scenarios, and their strategic implications. Include information about risk assessment and safety considerations.
  • Answer: The AGI to ASI Transition Speed represents a critical period in artificial intelligence development that could range from minutes to decades. This concept fundamentally shapes how we approach AI safety, governance, and preparation strategies.
    • Key Characteristics:
      • Speed Classifications:
        • Fast takeoff scenarios predict transitions occurring within minutes to months.
        • Medium takeoff scenarios estimate transitions taking months to years.
        • Slow takeoff scenarios project transitions spanning years to decades.
      • Influencing Factors:
        • Computational resources and hardware limitations.
        • Recursive self-improvement capabilities.
        • Regulatory and safety frameworks.
        • Global coordination and governance structures.
    • Fast Takeoff Scenarios:
      • Characteristics:
        • Rapid recursive self-improvement cycles.
        • Exponential capability growth.
        • Limited human intervention opportunity.
      • Probability Estimates:
        • 1-second transition: ~5% likelihood.
        • 5-minute transition: ~60-70% likelihood.
        • 15-minute transition: ~100% likelihood under specific conditions.
    • Slow Takeoff Scenarios:
      • Advantages:
        • Greater opportunity for safety implementation.
        • Better alignment verification.
        • More time for governance adaptation.
      • Challenges:
        • Potential competitive pressures.
        • Coordination difficulties.
        • Resource allocation complexity.
    • Strategic Implications:
      • Safety Considerations:
        • Fast takeoffs require pre-built safety measures.
        • Slow takeoffs allow iterative safety development.
        • Both scenarios need robust monitoring systems.
      • Governance Requirements:
        • International coordination frameworks.
        • Regulatory adaptation mechanisms.
        • Continuous audit capabilities.
    • Risk Assessment:
      • Critical Factors:
        • Development trajectory predictability.
        • Safety measure implementation windows.
        • Resource availability and distribution.
      • Mitigation Strategies:
        • Comprehensive safety protocols.
        • International cooperation frameworks.
        • Continuous monitoring systems.
  • Citations:

[1] https://mindboundlabs.substack.com/p/from-agi-to-asi-in-minutes-exploring
[2] https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-2.html
[3] https://www.lesswrong.com/posts/JWJnFvmB8ugi7bHQM/a-solution-for-agi-asi-safety
[4] https://sideways-view.com/2018/02/24/takeoff-speeds/
[5] https://www.lesswrong.com/posts/xxMYFKLqiBJZRNoPj/ai-governance-across-slow-fast-takeoff-and-easy-hard
[6] https://yoshuabengio.org/2024/10/30/implications-of-artificial-general-intelligence-on-national-and-international-security/
[7] https://www.un.org/techenvoy/sites/www.un.org.techenvoy/files/GDC-submission_the-millennium-project.pdf
[8] https://www.vladsnewsletter.com/p/ai-agi-asi
[9] https://kanerika.com/blogs/artificial-superintelligence/
[10] https://www.alignmentforum.org/posts/cxgtQXnH2uDGBJJGa/redefining-fast-takeoff
[11] https://www.alignmentforum.org/posts/wTKjRFeSjKLDSWyww/possible-takeaways-from-the-coronavirus-pandemic-for-slow-ai
[12] https://ai.gopubby.com/towards-artificial-general-intelligence-agi-and-what-is-in-store-for-us-0c86a0c93768?gi=7a3b1ec1fc3a
[13] https://wwbmmm.github.io/asi-safety-solution/en/main.html
[14] https://www.reddit.com/r/singularity/comments/14s3c2c/which_are_your_valid_points_for_slow_transition/
[15] https://www.youtube.com/watch?v=7FLY-DZuUSo
[16] https://www.alignmentforum.org/posts/eGihD5jnD6LFzgDZA/agi-safety-from-first-principles-control
[17] https://youevolve.net/ani-agi-and-asi-what-do-they-mean/
[18] https://www.leewayhertz.com/artificial-general-intelligence/
[19] https://en.wikipedia.org/wiki/Existential_risk_from_artificial_general_intelligence
[20] https://yoshuabengio.org/2024/10/30/implications-of-artificial-general-intelligence-on-national-and-international-security/
[21] https://www.linkedin.com/posts/matteocastiello_these-are-the-six-stages-of-agi-as-defined-activity-7191363865543110658-pkut
[22] https://arxiv.org/html/2401.07836v3
[23] https://www.linkedin.com/pulse/criteria-agiasi-arrival-gradual-emergent-process-over-doug-hohulin-qvwnc
[24] https://www.youtube.com/watch?v=bwDNqwKSE8Y
[25] https://www.un.org/techenvoy/sites/www.un.org.techenvoy/files/GDC-submission_the-millennium-project.pdf
[26] https://x.com/IntuitMachine/status/1855204199211970721
[27] https://forum.effectivealtruism.org/posts/AkBtFH36XEMG6kgYj/ai-safety-101-agi
[28] https://www.linkedin.com/pulse/3-types-artificial-intelligence-ani-agi-asi-potential-s-m-aminul-z4icf
[29] https://blockbuster.thoughtleader.school/p/convergence-effect-why-artificial
[30] https://www.greaterwrong.com/posts/n2bQB3mnBniRfwkFy/a-slow-takeoff-might-still-look-fast
[31] https://www.reddit.com/r/singularity/comments/wuw17l/why_there_wont_be_a_fast_takeoff_according_to/
[32] https://www.alignmentforum.org/posts/xxMYFKLqiBJZRNoPj/ai-governance-across-slow-fast-takeoff-and-easy-hard
[33] https://www.lesswrong.com/posts/qYzqDtoQaZ3eDDyxa/distinguishing-ai-takeover-scenarios
[34] https://www.fhi.ox.ac.uk/strategic-considerations-about-different-speeds-of-ai-takeoff/
[35] https://www.lesswrong.com/posts/CH9mkk6BqASf3uztv/counting-agis
[36] https://www.youtube.com/watch?v=qk3bQrSfUzs
[37] https://forum.effectivealtruism.org/posts/2uGShxLsXWGExJYNL/digital-minds-takeoff-scenarios
[38] https://www.lesswrong.com/posts/hRohhttbtpY3SHmmD/takeoff-speeds-have-a-huge-effect-on-what-it-means-to-work-1
[39] https://www.forbes.com/sites/lanceeliot/2024/12/10/sneaky-shiftiness-on-the-boundaries-between-ai-versus-agi-and-ultimately-ai-superintelligence/
[40] https://www.lesswrong.com/posts/Da8gSHjyrJnnXK5xK/economic-post-asi-transition
[41] https://www.ibm.com/think/topics/ai-safety
[42] https://forum.effectivealtruism.org/posts/aKvcNc2DbgAvXHtcw/rolling-thresholds-for-agi-scaling-regulation
[43] https://www.linkedin.com/pulse/from-agi-asi-openais-vision-future-artificial-abe-karar-xm8ef
[44] https://www.reddit.com/r/singularity/comments/1exveqw/updated_agiasi_predictions/
[45] https://openai.com/index/planning-for-agi-and-beyond/
[46] https://yoshuabengio.org/2024/07/09/reasoning-through-arguments-against-taking-ai-safety-seriously/
[47] https://www.avenga.com/magazine/integrating-ai-for-smarter-risk-assessment/
[48] https://aitglobalinc.com/what-is-ai-agi/ 

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