AGI Is Here?
12 hours ago
- #Scaffolding
- #AI Progress
- #AGI
- The ambiguity in defining AGI makes measuring AI progress challenging.
- Various definitions of AGI include passing the Turing Test, demonstrating creativity, developing new skills, solving unfamiliar tasks, general capability across domains, surpassing human intelligence, outperforming humans economically, and independently solving complex problems without human oversight.
- AGI is already here, achieved through the combination of AI models and scaffolding (orchestration tools and abilities around models).
- Key scaffolding components include tool calling (enabling models to affect the world beyond language), MCP (Model Context Protocol for standardized integrations), Claude Code (providing general-purpose utilities like web search and file management), and OpenClaw (allowing continuous, proactive agent operation).
- This system as a whole can create its own tools and skills, manage context and memory, integrate with real-world services, and adaptively solve problems.
- All proposed AGI definitions are met: modern AI can fool humans in tests, demonstrate creativity-like outputs, develop new skills (e.g., through Claude Code), solve unfamiliar tasks via generalization and tools, operate across multiple domains, outperform humans in specific areas, enhance economic productivity, and solve problems independently (e.g., with OpenClaw).
- While model improvements optimize efficiency, scaffolding is crucial for enabling AGI capabilities, especially after a baseline intelligence threshold was crossed with models like GPT-4.