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Kimi Founder Yang Zhilin on K2, Agentic LLMs, & AGI: The Beginning of Infinity | Scaling & Innovation Strategy
Key topics covered include:
• K2 Model Development: Yang Zhilin details the technical breakthroughs in K2, emphasizing the focus on Token Efficiency (getting more intelligence from the same amount of data) using non-Adam optimization techniques like the MOG optimizer.
• Agentic LLMs: The shift from "Brain in a Vat" models (pure reasoning) to Agentic LLMs that interact with the external environment through tools and multi-turn operations. This ability facilitates complex, long-running tasks through Test Time Scaling.
• The Path to AGI: AGI is described as a direction rather than a specific milestone, noting that in many domains, models already outperform 99% of humans.
• Innovation and Scaling: Discussion on the conceptual L1-L5 hierarchy (Chat, Reasoning, Agent, Innovation, Organization) and the critical need for using AI to train AI (Innovation, or L4) to solve the generalization challenges facing agents (L3).
• Philosophical Context: Insights drawn from the book "The Beginning of Infinity," underscoring that problems are unavoidable but solvable, and that AI serves as a powerful accelerator of human civilization.
Yang Zhilin also addresses Kimi's open-source strategy, the challenge of the data crunch in LLM scaling, and the evolving systems complexity required for truly universal models.
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By Neuralintel.orgKey topics covered include:
• K2 Model Development: Yang Zhilin details the technical breakthroughs in K2, emphasizing the focus on Token Efficiency (getting more intelligence from the same amount of data) using non-Adam optimization techniques like the MOG optimizer.
• Agentic LLMs: The shift from "Brain in a Vat" models (pure reasoning) to Agentic LLMs that interact with the external environment through tools and multi-turn operations. This ability facilitates complex, long-running tasks through Test Time Scaling.
• The Path to AGI: AGI is described as a direction rather than a specific milestone, noting that in many domains, models already outperform 99% of humans.
• Innovation and Scaling: Discussion on the conceptual L1-L5 hierarchy (Chat, Reasoning, Agent, Innovation, Organization) and the critical need for using AI to train AI (Innovation, or L4) to solve the generalization challenges facing agents (L3).
• Philosophical Context: Insights drawn from the book "The Beginning of Infinity," underscoring that problems are unavoidable but solvable, and that AI serves as a powerful accelerator of human civilization.
Yang Zhilin also addresses Kimi's open-source strategy, the challenge of the data crunch in LLM scaling, and the evolving systems complexity required for truly universal models.