Lecture Notes: Predictions for AGI and Superintelligence by 2027

Introduction to Leopold Ashen Brener

• Former OpenAI employee, fired for leaking internal documents.
• Detailed predictions on how companies will achieve AGI (Artificial General Intelligence).
• Emphasized importance of situational awareness regarding AGI.
• Urged viewers to watch his explanatory video and read his comprehensive document on AGI stages.

Key Insights and Predictions

General Overview

• Current narrative shift from billion to trillion dollar compute clusters signifies the start of the AGI race.
• By 2025-2026, machines predicted to outpace college graduates.
• Superintelligence expected by the end of the decade.
• National security forces will increase significantly during this period.
• Few hundred people (mostly in San Francisco AI Labs) currently have situational awareness.

AGI Timeline

• GPT-4 to AGI: Achievable by 2027.
  • GPT-2 to GPT-4: Jump from preschooler to smart high schooler abilities in 4 years.
  • Expecting a similar qualitative jump by 2027.
  • Chart illustrates growth in effective compute from GPT-2 to GPT-4.

Expected Developments

• 2024-2028 Growth: Predicted more acceleration in AI growth based on historical compute scale-ups.
• Automated AI Research: By 2027-2028, likely to have automated AI research engineers.
  • Recursive self-improvement will speed up superintelligence development.

Analysis of Trends and Metrics

Model Improvements and Predictions

• Continuous Improvements: Jump from GPT-2 (preschooler) to GPT-4 (smart high schooler).
• Math Benchmark: Accuracy in mathematical problem-solving increased dramatically from GPT-3 (5%) to Gemini 1.5 Pro (90%).
• Algorithmic Efficiencies: Massive improvements seen, e.g., 1000x efficiency improvement in math benchmark over 2 years.
  • Pricing to attain specific benchmarks (e.g., 50%) significantly lowered.

Influencing Factors in AI Progress

• Compute and Algorithmic Gains: Expected continued growth in both compute scale and algorithmic efficiencies.
• Unhobbled Models: Enhanced abilities through various scaffolding and tools.
• Context Length Expansion: From 2k to 1 million context lengths enabling better performance.
• Post-training Enhancements: Improvements after base training making significant positive impacts.

Charts and Graphs

• Key visual representations showing growth trends and predictions for AI capabilities, compute, algorithmic efficiencies.

Security Concerns and Strategic Implications

Security Threats and Espionage

• Current Security Gaps: Lack of robust security confers significant risks related to AGI development.
  • Major AI labs need to elevate security measures to prevent espionage (e.g., from CCP).
• Immediate Action Needed: Failure today could have irreversible impacts, jeopardizing U.S. advantage in AGI race.

Control over Superintelligence

• Alignment Problem: Ensuring AI behaves in expected manner remains unsolved.
  • Need mechanisms to ensure AI systems are safe and aligned with human values.
• Potential for Dictatorship: Superintelligent systems could be exploited by authoritarian regimes to consolidate power indefinitely.
  • Highlights need for freedom, democracy, and vigilant oversight.

Future of Security in AI Development

• Enhanced Security Measures: OpenAI’s response emphasizing secure training architectures.
• Shifts in AI Landscape: Transition expected where AI becomes closely guarded like national secrets (comparable to classified military technology).

Conclusion

Future Outlook

• Accelerated AI Research: Expect dramatic improvements in AI capabilities by 2027 due to recursive self-improvement.
• Security and Alignment: Critical importance of securing AI systems and ensuring proper alignment to prevent misuse.
• Decisive Period: Next 5-10 years are crucial for determining the trajectory of AGI development and its safe implementation.

References and Further Learning

• Encourage revisiting the transcript for more comprehensive insights.