The Turing Machine, and the Dawn of AI

There is a disconnect between what the public sees, what industry promotes, and what practitioners in academia and the workforce know to be true, especially when there’s significant money to be made from sweeping claims about a hot-button topic. AI has reached that point. This post is an attempt to ground the conversation in what these systems are, what they are not, and where the underlying ideas actually come from.

Let’s get something out of the way: “AI” is not new to computer scientists. The ambition to build machines that can perform tasks we associate with intelligence is baked into the history of the field. So let’s back up. Imagine a problem so operationally important, and so computationally intense, that even brilliant analysts can’t solve it reliably by hand. You might get the right answer occasionally, but you can’t scale it, you can’t trust it, and you can’t do it fast enough to matter.

That was the situation Allied cryptanalysts faced at Bletchley Park in the early 1940s, confronting industrial-scale encryption, most famously the German Enigma. The point wasn’t that no human could ever get lucky; it was that the problem demanded mechanized search, automation, and systems that could do the tedious work at speeds humans simply couldn’t match.

If you want to draw a line from that era to today, this is a reasonable place to start: not as “AI” in the modern marketing sense, but as a historical precursor to automated reasoning, machines built to systematically explore a space of possibilities faster and more consistently than a person ever could.

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