When I read a sentence, the words flow together as if they were always meant to be that way â each one a tidy packet of meaning. But the moment a machine touches that same sentence, it gets carved up. Sometimes into whole words, sometimes into slivers like âun-happi-ness,â and occasionally down to single letters. …
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The Philosophy of Meaning and Why It Matters for AI
When we ask what something means, we rarely settle for a dictionary definition. Human meaning is always something more—an interplay of intention, context, and the entire life of the person asking. Aiko Murakami has spent years watching technical systems stumble over exactly this layer of significance, and the hitch isn’t just about data or computing …
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Why Large Language Models Do Not Understand What They Say
When a text-generation system produces the sentence “The cat sat on the mat,” it has performed a remarkable statistical feat. It has selected each word from thousands of candidates, arranged them according to patterns absorbed from billions of text samples, and delivered a grammatically correct string. What it has not done is form any conception …
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Why Large Text Generators Do Not Understand What They Produce
When a large-scale text generator produces a coherent paragraph about quantum mechanics or composes a passable sonnet, it is tempting to attribute understanding to the system. The output looks like understanding. It carries the contours of comprehension, the rhythm of thought. But appearances, as philosophers from Descartes to Dennett have reminded us, can be profoundly …
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Claude 3.7 Sonnet’s Extended Thinking Mode Is Reshaping How We Build Agentic Systems
The Problem We’ve Been Living With For the past eighteen months, I’ve watched teams struggle with the same architectural tension: you want your AI agents to reason deeply about hard problems, but you also need responses that don’t arrive next Tuesday. The workaround has been messy. Chain-of-thought prompting works, but it’s fragile. You’re essentially asking …
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