What Machines Don't Know (mail.cyberneticforests.com)

The piece argues that LLMs are best understood not as miniature minds but as extremely complex next-token predictors: they tokenize language into high-dimensional embeddings (vectors) and “slot” words by shifting activations through a vast parameterized vector space. Using the ball-maze metaphor, each token’s position is continuously renegotiated against others until the system finds a statistically plausible sequence; training uses backpropagation to tune those positions, and inference noise (temperature) loosens or tightens which slots tokens can occupy. Novel or surprising outputs are therefore serendipitous recombinations of learned token positions, not evidence of internal reasoning or imagination. This distinction matters for AI/ML practitioners and policymakers because equating LLM output with human thought risks overattributing agency, intent, or understanding to models. Technically, the essay underscores key mechanisms—tokenization, embeddings, transformer architectures, parameters, and temperature—while stressing that machine “grammar” is inseparable from token-placement rules. Implications include caution in interpreting model claims, limits on using LLMs as proxies for human cognition, and a reminder that improving architectures and metrics for genuine grounding, self-modeling, or world interaction remains crucial if we want systems that do more than produce plausible text.