[dataviz1000]

Claude is more like a Probabilistic Turing Machine. [0] It's correctness isn't deterministic but rather a distribution. It is predictable.

There are lots of places in computer science that determinism isn't necessarily the best.

- UDP video calls let packets vanish or arrive corrupted, because waiting for retransmissions would freeze the picture.

- Stochastic gradient descent picks a random mini-batch of training data and treats it as the whole dataset, because computing the true gradient on every step would make training infeasible.

- Speculative execution in modern CPUs guesses which branch a program will take and rolls back when wrong, because waiting for the real answer would leave half the silicon idle.

What we want is something like a Las Vegas algorithm, a probabilistic machine with a cheap verifier.

[0] https://en.wikipedia.org/wiki/Probabilistic_Turing_machine

[skydhash]

Determinism isn’t important, but there are usually threshold of acceptable performance in all off those cases. Machine Learning was all about improving accuracy even when you know that there’s probably an error somewhere. But the current LLM hypers refuses to acknowledge that the answer to a prompt may be wrong (even though the providers have a warning line about it).

Instead of assessing the risk and provide corrective methods, all they do is pushing to use it more and more everywhere.