hummingbird | Deus Ex Machina

Can AI Approach Meaning? — Graph Neural Networks: A Technology Between Continuous Approximation and the Discontinuity of Meaning —

In the previous article, I explained a three-layer structure for describing AI decisions: Ontology DSL (...

2026.03.14

ICT技術:ICT Technology人工知能:Artificial Intelligence

How Should AI Decisions Be Described? — The Decision Representation Structure Required by the Decision Trace Model —

In previous articles, we have described AI systems as factories that mass-produce decisions. AI is not merely software...

2026.03.13

ICT技術:ICT Technology人工知能:Artificial Intelligence

Decision Trace Model and Ledger — Why AI Systems Need “Immutable History” —

When designing AI systems, many companies typically think in the following way: Build a model Create an ...

2026.03.12

ICT技術:ICT Technology人工知能:Artificial Intelligence

AI Quality Engineering — Why the Quality Engineering Becomes Important Again in the Age of AI —

When people talk about AI, most discussions tend to focus on topics such as: Model accuracy Data volume ...

2026.03.11

ICT技術:ICT Technology人工知能:Artificial Intelligence

AI Factory Model — AI Will Become a Manufacturing Industry, Not Just Software

AI Factory Model — AI Is Becoming a Manufacturing Industry When people talk about AI, most imagine the same thing. AI ...

2026.03.10

ICT技術:ICT Technology人工知能:Artificial Intelligence

Boundary Design: The Seven Boundaries That Safely Stop AI — What AI systems need is not “capability,” but “stopping conditions” —

When designing AI systems,most discussions focus on a single question: What can AI do? However, in real systems,there ...

2026.03.09

ICT技術:ICT Technology人工知能:Artificial Intelligence

Yield and Boundaries — Why Semiconductor Factories and AI System Design Are Surprisingly Similar

In semiconductor manufacturing,there is one metric that matters more than anything else. That metric is Yield. Yield ...

2026.03.08

ICT技術:ICT Technology人工知能:Artificial Intelligence

Five Design Patterns for Successful AI Customer Support — The Key Is Not the Model, but Boundary Design —

Many companies are introducing AI customer support. However, the outcomes are clearly divided into two groups. In some...

2026.03.07

ICT技術:ICT Technology人工知能:Artificial Intelligence

Can Meta-Learning Learn “Types”? — The Structural Relationship Between Meta-Learning and Ontology Design —

Meta-learning is often described as “learning how to learn.” It enables adaptation with very little data.It allows mod...

2026.03.06

ICT技術:ICT Technology人工知能:Artificial Intelligence

AI Approximates the World, but It Cannot Approximate Meaning — Continuous Approximation and the Discontinuity of Meaning: Why Meaning Remains in Logic

AI Approximates the World Well AI approximates the world remarkably well.With astonishing precision and smoothness, it ...

2026.03.05

ICT技術:ICT Technology人工知能:Artificial Intelligence

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Can AI Approach Meaning? — Graph Neural Networks: A Technology Between Continuous Approximation and the Discontinuity of Meaning —

How Should AI Decisions Be Described? — The Decision Representation Structure Required by the Decision Trace Model —

Decision Trace Model and Ledger — Why AI Systems Need “Immutable History” —

AI Quality Engineering — Why the Quality Engineering Becomes Important Again in the Age of AI —

AI Factory Model — AI Will Become a Manufacturing Industry, Not Just Software

Boundary Design: The Seven Boundaries That Safely Stop AI — What AI systems need is not “capability,” but “stopping conditions” —

Yield and Boundaries — Why Semiconductor Factories and AI System Design Are Surprisingly Similar

Five Design Patterns for Successful AI Customer Support — The Key Is Not the Model, but Boundary Design —

Can Meta-Learning Learn “Types”? — The Structural Relationship Between Meta-Learning and Ontology Design —

AI Approximates the World, but It Cannot Approximate Meaning — Continuous Approximation and the Discontinuity of Meaning: Why Meaning Remains in Logic

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