Those next tokens sure are unlikely despite this.

Like, this is just so silly. Come with me for a little thought experiment.

Picture in front of you a Kibana dashboard. It displays, say, latencies.

Now apply some statistic functions. Let's find the time ranges where we had outlier tail latencies, for example.

You pin down some patterns, cool. But this is post-incident. We want to alert on-incident.

So you begin writing some rules. And then some more rules. And then even more rules. All of a sudden you captured the entire logic of the program emitting these metrics, along with the surrounding dependencies'.

See where I'm going with this? To do sufficiently well at prediction, you'll need to model the entire constitution of the thing you're trying to predict, along with the stuff going through it. Locally, all predictions will be unlikely. But globally, you'll be right.

But if you do that, you quite literally "understand" and simulate the entire thing. That's the whole point, and this is why "just predicting the next token", "stochastic parrot" and other anti-AI dogwhistles are so flagrantly asinine. They imply some sort of rudimentary Markov process, or at best some sort of dozen or so variable statistics research paper type prediction. It's a laughable proposition, given the quite literally trillions of parameters actually in use, and all the research that has already went into identifying countless semantically interpretable latent spaces and activation patterns.

> Most of the hate I’ve seen have been for the people and companies involved with AI not the technology itself.

Anecdotes are fun! Visit any Reddit thread where AI is brought up and watch that ratio shift very rapidly. The hate and cope train is incessant there.