Nuance in all things. A dive into (Anti-) "AI" Myths (2025)
The article discusses the complexities and misconceptions surrounding the term 'AI', advocating for a more precise terminology like Machine Learning and transformers. It highlights the historical development of these technologies and their pervasive presence in everyday applications. The author acknowledges their own limitations in expertise while encouraging readers to critically evaluate the information presented about AI.
- ▪The term 'AI' is considered misleading, with a preference for terms like Machine Learning and transformers.
- ▪Transformers, introduced by Google, simulate neural networks to efficiently process and weigh data inputs.
- ▪The author admits to not being a trained data scientist but emphasizes the importance of critical evaluation of AI discussions.
Opening excerpt (first ~120 words) tap to expand
Nuance in all things. A dive into (Anti-) "AI" Myths Jun 4, 2025 • Oliver D. Reithmaier “AI” is weird, I get it. Somehow, it’s everywhere, and chances are you either hate it with a passion or love it and wish your entire life could make use of it. Sure, that’s your right. And I’m not saying you’re wrong. Indeed, I have used “AI” before and still think it’s an interesting tool when you want to get into a subject that is fairly technical and invites to hands-on learning. But this text is not about that. This text initially started as a rant about people complaining about “AI”, but has transformed to a piece about the basic properties of “AI”, why I think most people talking about “AI” are wrong in one aspect or another, and why I think this will ultimately crash.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at K4tana - Cutting Edge Research Blogs.
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