When I first studied AI, I realized something unexpected: I didn’t quite think like a great software developer. I found little joy in reshaping my thinking to fit the logic of programming languages.
But now, things have shifted dramatically.
Today’s large language models (LLMs) are powered by neural networks so intricate that we need entirely new tools and techniques just to understand how they reason. What’s fascinating—and a bit unsettling—is how these models approach problems in ways that are often very different from our own. Sometimes they’re less precise, like in this example of how an LLM computes 36 + 95 (see image).
It’s a reminder: while these systems can appear intelligent, we must stay vigilant about their outputs and decision-making processes.
And as quantum computing looms on the horizon, one can’t help but wonder: what new kinds of “thinking” will we need to decode next?
Source: https://www.anthropic.com/news/tracing-thoughts-language-model
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