{"id":1297,"date":"2026-08-29T10:00:11","date_gmt":"2026-08-29T00:00:11","guid":{"rendered":"https:\/\/www.reefwing.com.au\/?p=1297"},"modified":"2026-08-29T10:00:12","modified_gmt":"2026-08-29T00:00:12","slug":"large-language-monkeys-why-noise-yields-no-knowledge","status":"publish","type":"post","link":"https:\/\/www.reefwing.com.au\/?p=1297","title":{"rendered":"Large Language Monkeys: Why Noise Yields No Knowledge"},"content":{"rendered":"\n
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There is an old claim that a truly random source contains all knowledge: give monkeys enough time at typewriters and Shakespeare falls out. In 2024 two Sydney mathematicians did the arithmetic and found the universe ends first. But the idea has a modern tail. We now have language models that can spot meaningful text instantly, so why not let randomness generate and an LLM extract? This episode works through why that fails, and why the failure is precise: in a random stream, the address of any text costs as many bits as the text itself. Along the way: Borges’ Library of Babel, a website that actually built it, DeepMind systems that made the generate-and-filter idea work by cheating in exactly the right way, and what your brain does with noise that an LLM cannot.<\/p>\n\n\n\n

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