LessWrong 2.0 Reader

I'm not saying it improves decision making. I'm saying it's an argument for improving our decision making in general, if mundane decisions we wouldn't normally think are all that important have much larger and long-lasting consequences. Each mundane decision affects a large number of lives that parts of me will experience, in addition to the effects on others.

My point was that only 3 is relevant. How it improves average decision making?

I've been at Sciences Po for a few months now. Do you have any general advice? I seem to have trouble taking the subjects seriously enough to any real effort in them, which you seem to point out as a failure mode you skirted. Asking as many people I can for this, as I'm going through a minor existential crisis. Thanks!

insofar as the simplest & best internal logical-induction market traders have strong beliefs on the subject, they may very well be picking up on something metaphysically fundamental. Its simply the simplest explanation consistent with the facts.

Theorem 4.6.2 in logical induction says that the "probability" of independent statements does not converge to $1$ or $0$, but to something in-between. So even if a mathematician says that some independent statement feels true (eg some objects are "really out there"), logical induction will tell him to feel uncertain about that.

Hey, i'm controlling the sparsity when I'm creating the batch of the data, so during that time, i sample according to the probability i'm assigning for that feature. 

re: features getting baked into the bias: yeah, that might be one of the intuitions we can develop but to me the interesting part is that that kind of behaviour didn't happen in any of the other cases when the importance was varying and just happened when the feature importance for all of them is equal. I don't have a concrete intuition on why that might be the case, still trying to think on it.  

One operationalization is splitting out positive and negative predictions/models in all three questions (or cost benefit etc).

I don't see #1 affecting decision making because it happens no matter what, and therefore shouldn't differ based on our own choices or values. I guess you could argue it implies an absurdly high discount rate if you see the resulting branches as sufficiently separate from one another, but if the resulting worlds are ones I care about, then the measure dilution is just the default baseline I start from in my reasoning. Unless there is some way we can or could meaningfully increase the multiplication rate in some sets of branches but not others? I don't think that's likely with any methods or tech I can foresee.

#2 seems like an argument for improving ourselves to be more mindful in our choices to be more coherent on average, and #3 an argument for improving our average decision making. The main difference I can think of for how measure affects things is maybe in which features of the outcome distribution/probabilities among choices I care about. 

I think there's a problem with the entire idea of terminal goals, and that AI alignment is difficult because of it.

"What terminal state does you want?" is off-putting because I specifically don't want a terminal state. Any goal I come up with has to be unachievable, or at least cover my entire life, otherwise I would just be answering "What needs to happen before you'd be okay with dying?"

An AI does not have a goal, but an utility function. Goals have terminal states, once you achieve them you're done, the program can shut down. An utility function goes on forever. But generally, wanting just one thing so badly that you'd sacrifice everything else for it.. Seems like a bad idea. Such a bad idea that no person has ever been able to define an utility function which wouldn't destroy the universe when fed to a sufficiently strong AI.

I don't wish to achieve a state, I want to remain in a state. There's actually a large space of states that I would be happy with, so it's a region that I try to stay within. The space of good states form a finite region, meaning that you'd have to stay within this region indefinitely, sustaining it. But something which optimizes seeks to head towards a "better state", it does not want to stagnate, but this is precisely what makes it unsustainable, and something unsustainable is finite, and something finite must eventually end, and something which optimizes towards an end is just racing to die. A human would likely realize this if they had enough power, but because life offers enough resistance, none of us ever win all our battles. The problem with AGIs is that they don't have this resistance.

The after-lives we have created so far are either sustainable or the wish to die. Escaping samsara means disappearing, heaven is eternal life (stagnation) and Valhalla is an infinite battlefield (a process which never ends). We wish for continuance. It's the journey which has value, not the goal. But I don't wish to journey faster.

It's not my field of expertise, so I have only vague impressions of what is going on, and I certainly wouldn't recommend anyone else use me as a source.

That makes sense. Are there any promising developments in the field of AI safety that make you think that we will be able to answer that question by the time we need to?