Hosted by Prof. Mick Etoh(AE-TOE), a professor at Osaka University working on innovation management across industry and academia. This podcast bridges Generative AI/LLMs, natural language processing, product and interface design, standardization, and deep-tech entrepreneurship.
Expect clear explanations, decision-making frameworks, and candid lessons from building companies, advising large-scale LLM efforts, and leading R&D and innovation functions—so listeners can move from “AI news” to “AI strategy and execution.”
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Hosted by Prof. Mick Etoh(AE-TOE), a professor at Osaka University working on innovation management across industry and academia. This podcast bridges Generative AI/LLMs, natural language processing, product and interface design, standardization, and deep-tech entrepreneurship.
Expect clear explanations, decision-making frameworks, and candid lessons from building companies, advising large-scale LLM efforts, and leading R&D and innovation functions—so listeners can move from “AI news” to “AI strategy and execution.”
MCP Strategy for IoT Integration and AI Agent Architecture
AI Frontier with Prof. Mick Etoh (at U. Osaka)
18 minutes
1 week ago
MCP Strategy for IoT Integration and AI Agent Architecture
Model Context Protocol (MCP) を活用してIoTデバイスを大規模言語モデル（LLM）と効果的に連携させるための、新しい抽象化パラダイムと設計指針を解説しています。従来の静的で厳格なオントロジーとは異なり、サーバー側からLLMへ判断を仰ぐサンプリング機能を用いることで、対話の文脈に応じた動的なデータ抽象化が可能になる点に焦点を当てています。また、物理的な接続関係を整理する知識グラフを下部構造に持ちつつ、具体的な手順をMarkdown形式で記述するskill.mdを統合することで、AIエージェントによる高度な推論と制御を実現する手法が示されています。実例としてFastMCPやIoT-MCP、Home Assistantなどが挙げられ、実装の軽量化と柔軟性の両立が強調されています
AI Frontier with Prof. Mick Etoh (at U. Osaka)
Hosted by Prof. Mick Etoh(AE-TOE), a professor at Osaka University working on innovation management across industry and academia. This podcast bridges Generative AI/LLMs, natural language processing, product and interface design, standardization, and deep-tech entrepreneurship.
Expect clear explanations, decision-making frameworks, and candid lessons from building companies, advising large-scale LLM efforts, and leading R&D and innovation functions—so listeners can move from “AI news” to “AI strategy and execution.”
