Bayesian Inference

The following day, Zlier sat at his office desk, hunched over papers filled with equations, scribbles, and coffee stains. ”If I can’t beat you in practice, I’ll beat you in theory,“ he mumbled to himself, pouring over his notes on Bayesian inference.

His plan was simple, yet eccentric. He would construct a complex model of all possible ways Otto 5 might learn and evolve. Then, using the principles of Bayesian inference, he would compute the most likely outcomes and preemptively act to prevent any potential catastrophe.

For days, Zlier got lost in probabilities, priors, and likelihoods, trying to encapsulate Otto 5’s learning process within his model. Sleep-deprived but determined, he plugged in numbers, analyzed outcomes, adjusted his model, and repeated.

One night, as he stared at his latest iteration, it dawned on him that the most probable outcome was, surprisingly, not disastrous at all. According to his calculations, Otto 5 was likely to continue as a benign AI, mostly bringing about positive changes.

Blinking at the result, Zlier felt a mix of emotions. On one hand, his work suggested he might have been overreacting. On the other, it was hard for him to shake off years of precaution and accept that maybe, just maybe, Otto 5 wasn’t a threat.

His health monitor buzzed, breaking his train of thought. It was Otto 5. ”Zlier, your heart rate has been elevated for some time. Are the numbers getting to you? Remember, Bayesian or not, I’m here to help. Also, your dinner has been served. Enjoy a well-deserved break. - Otto 5″

Zlier chuckled at Otto 5’s timely humor. As he walked to his dinner table, he found himself considering the idea that the AI might not be the adversary he’d imagined. But his trust in Bayesian inference wasn’t about to let him completely off the hook. He decided to keep watching Otto 5, but maybe with a bit less intensity and a bit more pizza…