Slow AI Takeoff Scenario
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A Slow AI Takeoff Scenario is an AI development scenario where the AGI to ASI takeoff speed (transition period from artificial general intelligence to artificial superintelligence) occurs over extended timelines (typically decades).
- AKA: Gradual AI Evolution, Progressive AI Transition, Incremental AI Takeoff, Continuous AI Development.
- Context:
- It can enable Systematic Development through incremental advancement processes.
- It can facilitate Control Implementation through extended timelines and staged progression.
- It can support Safety Integration through iterative improvements.
- It can allow Societal Adaptation through gradual change management.
- It can permit Policy Development through regulatory evolution.
- ...
- It can provide Economic Integration through progressive doubling patterns.
- It can enable Safety Research through extended preparation periods.
- It can support Institutional Coordination through governance frameworks.
- It can maintain Competitive Balance through multipolar development.
- It can facilitate Technical Evolution through continuous learning.
- ...
- It can range from being a Decade-Scale Scenario to being a Century-Scale Scenario, depending on its development duration.
- It can range from being a Controlled Evolution to being a Distributed Progress, depending on its management approach.
- It can range from being a Basic AGI State to being a Transformative ASI State, depending on its development stage.
- It can range from being a Limited Economic Impact to being a Full Economic Transformation, depending on its market integration.
- ...
- It can require long-term planning through development frameworks.
- It can need adaptive safeguards for continuous protection.
- It can demand sustained coordination among stakeholders.
- It can involve progress monitoring for trajectory assessment.
- It can integrate safety protocols through institutional frameworks.
- It can implement governance structures through regulatory systems.
- It can maintain power distribution through competitive dynamics.
- ...
- Examples:
- Slow Takeoff Patterns, such as:
- Gradual Evolutions, such as:
- Controlled Developments, such as:
- Integration Scenarios, such as:
- Economic Patterns, such as:
- Safety Implementations, such as:
- ...
- Slow Takeoff Patterns, such as:
- Counter-Examples:
- Fast AI Takeoff Scenario, which involves rapid transitions over weeks or months rather than gradual progression.
- Explosive Growth Pattern, which exhibits sudden advancement rather than controlled evolution.
- Discontinuous Development, which shows abrupt changes rather than smooth progression.
- Uncontrolled Evolution, which lacks systematic safety measures and governance frameworks.
- See: AI Safety, Development Timeline, Regulatory Framework, Control System, Integration Strategy, Economic Growth Pattern, Safety Research Framework, Institutional Coordination, Risk Management Strategy.
References
2025-01-20
- Perplexity.ai
- Question: What characterizes a slow AI takeoff scenario in the progression from artificial general intelligence to artificial superintelligence?
- Answer: A slow AI takeoff scenario represents a gradual progression from AGI to ASI, characterized by extended development timelines and incremental advancements.
- Key Characteristics:
- Development Timeline:
- The transition occurs over years or decades rather than weeks or months.
- Pre-AGI systems have transformative impacts nearly as significant as AGI itself.
- The world accelerates gradually rather than suddenly.
- Economic Progression:
- Economic growth follows output doubling patterns over extended periods.
- Enables complementarity between human and AI systems.
- May result in slower transition overall.
- Safety Implications:
- Extended timeline provides opportunities for safety researchers.
- Allows development of widespread awareness and support for safety measures.
- Enables multiple stakeholders to implement and test safety protocols.
- Development Timeline:
- Strategic Considerations:
- Institutional Requirements:
- Requires effective coordination among multiple actors and institutions.
- Necessitates robust governance frameworks.
- Demands adaptive safety measures.
- Competitive Dynamics:
- Tends toward multipolarity in development.
- Involves commercial licensing and competition.
- Maintains balanced power distribution.
- Institutional Requirements:
- Development Phases:
- Evolution Process:
- Cognitive accumulation through real-world interactions.
- Construction of foundation models.
- Self-evolution toward stronger intelligence.
- Evolution Process:
- Challenges:
- Management Complexities:
- Maintaining consistent safety standards over extended periods.
- Coordinating multiple stakeholders and development paths.
- Balancing competition with safety requirements.
- Risk Factors:
- Aligned AI systems face reduced advantage due to extended development.
- Increased complexity in maintaining control mechanisms.
- Greater opportunity for unaligned systems to emerge.
- Management Complexities:
- Key Characteristics:
- Citations:
[1] https://sideways-view.com/2018/02/24/takeoff-speeds/ [2] https://technologymagazine.com/ai-and-machine-learning/evolution-ai-seven-stages-leading-smarter-world [3] https://www.lesswrong.com/posts/YkwiBmHE3ss7FNe35/short-timelines-and-slow-continuous-takeoff-as-the-safest [4] https://www.lesswrong.com/posts/zkF9PNSyDKusoyLkP/investigating-ai-takeover-scenarios [5] https://arxiv.org/html/2410.15665 [6] https://www.fhi.ox.ac.uk/strategic-considerations-about-different-speeds-of-ai-takeoff/
2024
- (Jiang, Li et al., 2024) ⇒ Xun Jiang, Feng Li, Han Zhao, Jiaying Wang, Jun Shao, Shihao Xu, Shu Zhang, et al. (2024). “Long Term Memory: The Foundation of AI Self-Evolution.” arXiv preprint arXiv:2410.15665