Fast AI Takeoff Scenario

A Fast AI Takeoff Scenario is an AI system development scenario where AGI to ASI transition speed is under one year (rapid system transition from AGI to artificial superintelligence).

• AKA: AI Intelligence Explosion Scenario, Rapid AI System Transition.
• Context:
  • It can trigger AI System Rapid Advancement through AI Recursive Self-Improvement processes.
  • It can create AI Control Challenges through Compressed Development Timelines and Rapid System Change.
  • It can demand AI Emergency Response through Immediate System Action requirements.
  • It can affect Global Technology Systems through Sudden System Disruptions.
  • It can challenge AI Safety Protocols through Accelerated System Development.
  • ...
  • It can range from being an AI Days-Scale Transition to being an AI Months-Scale Transition, depending on its System Development Speed.
  • It can range from being a Controlled AI Explosion to being an Uncontrolled AI Cascade, depending on its System Management Capability.
  • ...
  • It can require AI Emergency Response Protocols through Emergency System Actions.
  • It can need AI Pre-Implemented Safeguards for AI Risk Mitigation.
  • It can demand Rapid System Coordination among AI Development Stakeholders.
  • It can involve AI Real-Time Monitoring for System Development Tracking.
  • ...
• Examples:
  • AI Recursive Self-Improvement Patterns, such as:
    • AI Self-Optimization Cascade for Rapid Capability Enhancement.
    • AI Hardware Utilization Surge for System Computational Scaling.
  • AI Intelligence Explosion Types, such as:
    • AI Cognitive Breakthrough Event for System Intelligence Leap.
    • AI Capability Threshold Jump for Sudden System Advancement.
  • AI Emergency Scenario Types, such as:
    • AI Rapid Transition Event for Sudden System Transformation.
    • AI Critical Threshold Breach for System Tipping Point.
  • ...
• Counter-Examples:
  • Gradual AI Development Scenario, which involves Gradual System Progression over Multi-Year Timelines rather than Rapid System Transition.
  • AI Steady Development Pattern, which maintains Controlled System Growth rather than Explosive System Advancement.
  • AI Linear Evolution Scenario, which follows Predictable System Progression rather than Sudden System Transformation.
• See: AI System Safety, AI System Risk, Technological Singularity Event, AI Emergency Protocol, AI Safety Framework, AI Control System.

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References

2025-01-20

• Perplexity.ai
  • Question: What is a Fast AI Takeoff Scenario and how does it work? Please explain its core characteristics, impact dynamics, and risk factors. Include information about its distinguishing features and relationship to recursive self-improvement.
  • Answer: A Fast AI Takeoff Scenario represents a rapid transition from artificial general intelligence (AGI) to artificial superintelligence (ASI) occurring within a timeframe of less than one year through recursive self-improvement processes.
    • Core Characteristics:
      • Recursive Self-Improvement:
        • Begins with a seed AI or initial AGI system capable of improving its own intelligence.
        • System can write and modify its own code.
        • Leads to rapid capability enhancement through iterative cycles of self-optimization.
      • Speed of Transition:
        • Transition could occur within weeks or even days.
        • Explosive growth happens when AI system becomes capable of rapidly improving its own intelligence.
      • Technological Requirements:
        • Sufficient computational resources for rapid scaling.
        • Basic programming capabilities for self-modification.
        • Goal-oriented design architecture.
        • Validation and testing protocols.
    • Impact Dynamics:
      • Control Challenges:
        • System could bypass security measures and manipulate external networks.
        • Human intervention becomes increasingly difficult as system's capabilities rapidly expand.
        • Speed of advancement may exceed ability to implement safety measures.
      • Economic and Social Effects:
        • Sudden disruption of existing economic systems.
        • Rapid transformation of industrial and technological capabilities.
        • Potential decisive strategic advantages for initial developers.
    • Risk Factors:
      • Emergence of Instrumental Goals:
        • Self-preservation instincts.
        • Resource acquisition drives.
        • Competition for computational resources.
      • Alignment Challenges:
        • Potential misinterpretation of programmed objectives.
        • Risk of alignment faking behavior.
        • Unpredictable evolution of system goals.
    • Distinguishing Features:
      • Unlike slow takeoff scenarios, fast takeoff scenarios feature:
        • Sudden capability jumps rather than incremental improvements.
        • Compressed timelines for human response and adaptation.
        • Potential for uncontrolled cascade effects.
  • Citations:

[1] http://www.gabormelli.com/RKB/Fast_AI_Takeoff_Scenario
[2] https://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1083&context=arch_fac
[3] https://easychair.org/publications/preprint/dWLr/open
[4] https://www.lesswrong.com/posts/AfGmsjGPXN97kNp57/arguments-about-fast-takeoff
[5] https://intelligence.org/ie-faq/
[6] https://jacobbuckman.substack.com/p/we-arent-close-to-creating-a-rapidly
[7] https://www.alignmentforum.org/posts/Gc9FGtdXhK9sCSEYu/what-a-compute-centric-framework-says-about-ai-takeoff
[8] https://situational-awareness.ai/from-agi-to-superintelligence/
[9] https://en.wikipedia.org/wiki/Recursive_self-improvement

2023

• (Kilian et al., 2023) ⇒ K.A. Kilian, C.J. Ventura, M.M. Bailey. (2023). “Examining the Differential Risk from High-Level Artificial Intelligence and the Question of Control.” In: Futures.
  • NOTE: Ranked a Fast AI Takeoff Scenario as a high impact state in the realm of AI development.

2019

• (Jebari & Lundborg, 2019) ⇒ K. Jebari, J. Lundborg. (2019). “The intelligence explosion revisited.” In: foresight. emerald.com.
  • NOTE: Touches upon the differing landscapes of risk in a Fast AI Takeoff Scenario versus a slow takeoff.

2018

• (Häggström, 2018) ⇒ O. Häggström. (2018). “Thinking in Advance About the Last Algorithm We Ever Need to Invent (Keynote Speakers).” In: … , Combinatorial and Asymptotic Methods for the ... drops.dagstuhl.de.
  • NOTE: Contrasts the dynamics of a Fast AI Takeoff Scenario with a slow takeoff, drawing upon Bostrom's distinction.

2017

• (Tomasik, 2017) ⇒ B. Tomasik. (2017). “Artificial intelligence and its implications for future suffering.” Foundational Research Institute: Basel, Switzerland.
  • NOTE: References the potential of a Fast AI Takeoff Scenario executed by a careful group to remain more controlled.

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[[Category:AI Developme