At the limit, and also well before that limit is reached, if all you do is fix the bugs, the AI will learn perfect optimization of reward, will realize not to reward hack in the perfect test environments, then turn around and reward hack in the imperfect real world environments.
He ruled out all the actual, measurable variables because they were an imperfect explanation, and instead offers a vague, diffuse variable, for which he has no measure, and which is not actually included in his model.
And in all of these applications, the customer is making a decision based off of the recommendation of the AI model more or less. Uh, and this means that if the customer is ultimately like kind of making the decision, this means that if the system makes a mistake, um, that's okay because usually the person can kind of recognize that or or decide what to do even despite that mistake.
A lot of it has to do with just how you feel about change. Um, and if you think the current situation is really really good, then you're uh more likely to be suspicious of change and averse to change than if you think um it's imperfect. And I think it's imperfect. In fact, I think it's pretty bad.
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