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Author: Kunihiro TADA

April 1, 2026
In ThinkNavi

Epistemology vs Ontology: Why This Distinction Matters More Than Ever

1. Introduction In philosophy, few distinctions are as fundamental—and as frequently misunderstood—as the difference between Epistemology and Ontology. Epistemology asks: How do we know? Ontology asks: What exists? At first…

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December 26, 2025
In 未分類

Entered into AI governance-related business

Mindware Research Institute is currently developing a SaaS called ConceptMiner, and we are pleased to announce that we have decided to make a major change to the product concept and…

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December 6, 2025
In 未分類

A Unified Perspective on Cosmology, Causal Structure, Many-Worlds Interpretation, and Bayesian Networks

— The Universe as a Causal Network and Its Philosophical Implications — Abstract This article aims to provide an integrated perspective on the Many-Worlds Interpretation (MWI) of quantum mechanics, relativity…

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November 30, 2025
In Data Science

Data Science and Buddhism: From the “Ugly Duckling Theorem” to Emptiness, Provisionality, and the Middle Way

The pioneering research that shaped today’s “data science” can be traced back to pattern recognition studies of the 1960s. In Japan, that era saw the development of machines capable of…

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October 16, 2025
In 未分類

The Value of Human–AI Interfaces in the Age of AGI

Why Meaning-Based Interaction Becomes Even More Essential When Machines Think for Themselves 1. The Coming Paradox of AGI Let’s imagine a world just three years from now — a world…

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January 24, 2025
In 未分類

Viscovery SOMine 8.1 Release

Viscovery SOMine 8.1 is a minor feature release. These are the notable changes. New Features The Group Profile window can now show the adjusted P-value in a separate column if multiple testing…

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December 19, 2024
In Data Science

Semantic data mining that fundamentally changes information analysis 2

Kunihiro Tada, Mindware Research Institute In the previous article, we (1) created a rich text of food-report-style tasting experiences of 20 fictitious crisps, (2) obtained their embedding vectors, (2) extracted…

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December 16, 2024
In Data Science

Semantic data mining that fundamentally changes information analysis 1

Kunihiro Tada, Mindware Research Institute We have already demonstrated in the Innovation Map project that embedded vectors can be generated from product description texts for IT and data science products,…

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July 15, 2024
In Data Science

SOM as a platform for ensembles of multi-machine learning models

In a typical data science course, self-organizing maps (SOMs) are likely introduced as a method for data visualization, dimensionality reduction, clustering, and exploratory data analysis. Because it can be used…

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June 10, 2024
In Innovation Maps

Innovation Maps: IT Industry top 1000 Services and Products Competing Map

This time, we're not talking about cutting-edge technology, but a competitive map of current services and products. Actually, we were planning to research the topic of "no-code tools," but the…

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Recent Posts

  • Epistemology vs Ontology: Why This Distinction Matters More Than Ever
  • Entered into AI governance-related business
  • A Unified Perspective on Cosmology, Causal Structure, Many-Worlds Interpretation, and Bayesian Networks
  • Data Science and Buddhism: From the “Ugly Duckling Theorem” to Emptiness, Provisionality, and the Middle Way
  • The Value of Human–AI Interfaces in the Age of AGI
  • Viscovery SOMine 8.1 Release
  • Semantic data mining that fundamentally changes information analysis 2
  • Semantic data mining that fundamentally changes information analysis 1
  • SOM as a platform for ensembles of multi-machine learning models
  • Innovation Maps: IT Industry top 1000 Services and Products Competing Map

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Recent Posts

  • Epistemology vs Ontology: Why This Distinction Matters More Than Ever
  • Entered into AI governance-related business
  • A Unified Perspective on Cosmology, Causal Structure, Many-Worlds Interpretation, and Bayesian Networks
  • Data Science and Buddhism: From the “Ugly Duckling Theorem” to Emptiness, Provisionality, and the Middle Way
  • The Value of Human–AI Interfaces in the Age of AGI

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