Fixing hardware failures in software is literally how quantum computing is supposed to work, and it’s clearly not a silly idea.
Generally speaking, there’s a lot of appeal to intuition here, but I don’t find it convincing. This isn’t good for Tokyo property prices? Well maybe, but how good of a heuristic is that when Mechagodzilla is on its way regardless.
There are IMO in-distribution ways of successfully destroying much of the computing overhang. It’s not easy by any means, but on a scale where “the Mossad pulling off Stuxnet” is 0 and “build self replicating nanobots” is 10, I think it’s is closer to a 1.5.
Indeed, there is nothing irrational (in an epistemic way) about having hyperbolic time preference. However, this means that a classical decision algorithm is not conducive to achieving long term goals.
One way around this problem is to use TDT, another way is to modify your preferences to be geometric.
A geometric time preference is a bit like a moral preference… it’s a para-preference. Not something you want in the first place, but something you benefit from wanting when interacting with other agents (including your future self).
In some corporate structure, you may want to avoid over performing at a job interview. Your manager wants to hire someone who is competent, but no so competent that he will replace him.
For get an older scenario, imagine you’re a hunter in a tribe and there’s an alpha leader. You want to be perceived as a good hunter so that you’ll get a larger share of resources but not so good that you threaten his power.
It then pays to signal that you do not intend to challenge the authority. One way to do it is to have poor self-esteem, by attributing your successes to luck.
I think you got your math wrong
If you get 20 out of 30 questions wrong, you are break even, therefore the probability of losing points by guessing is
Sum( (i 30), i = 21..30) / 2^30 ~ 2.14% > 1%
#e/ac
In general yes, here no. My impression from reading LW is that many people suffer from a great deal of analysis paralysis and are taking too few chances, especially given that the default isn’t looking great.
There is such a thing as doing a dumb thing because it feels like doing something (e.g. let’s make AI Open!) but this ain’t it. The consequences of this project are not going to be huge (talking to people) but you might get a nice little gradient read as to how helpful it is and iterate from there.
It seems to me that a narrative is generally a maximum likelihood explanation behind an event. If you observe two weird events, an explanation that links them is more likely than an explanation that doesn’t. That’s why causality is such a great explanation mechanism. I don’t think making narratives is a bug. The bug is discarding the rest of the probability distribution… we are bad are remembering complex multimodal distributions.
Sometimes, a narrative will even add unnecessary details and it looks like a paradox (the explanation would be more likely without the details). However, the explanation without the detail would be a zone while the explanation with the detail is a point. If we try to remember modes, it makes perfect sense to add the details.
Coming from here, I don’t really understand the advice to
“In other words, concentrate your probability mass”
It seems that concentrating the probability mass would reinforce the belief in the most likely explanation which is often a narrative.
It should be possible to ask content owners for permission and get pretty far with that.
AFAIK what character.ai does is fine tuning, with their own language models, which aren’t at parity with ChatGPT. Using a better language model will yield better answers but, MUCH MORE IMPORTANTLY, what I’m suggesting is NOT fine tuning.
What I’m suggesting gives you an answer that’s closer to a summary of relevant bits of LW, Arbital, etc. The failure mode is much more likely to be that the answer is irrelevant or off the mark than it being at odds with prevalent viewpoints on this platform.
Think more interpolating over an FAQ, and less reproducing someone’s cognition.
In addition
There aren’t that many actors in the lead.
Simple but key insights in AI (e.g doing backprop, using sensible weight initialisation) have been missed for decades.
If the right tail for the time to AGI by a single group can be long and there aren’t that many groups, convincing one group to slow down / paying more attention to safety can have big effects.
How big of an effect? Years doesn’t seem off the table. Eliezer suggests 6 months dismissively. But add a couple years here and a couple years there, and pretty soon you’re talking about the possibility of real progress. It’s obviously of little use if no research towards alignment is attempted in that period of course, but it’s not nothing.
On this page, the cumulative refers to the probability of obtaining at most p successes. You want to run it with 30 and 9 which gives you the right answer, 2.14%
Or you could put in 30 and 20 which gives you the complement.
What is lower than 1% is the probability of getting 8 or less right answers.
I find that going to the original paper generally does the trick. When an idea is new, the author will spell the details more carefully.
SilasBarta mentions difficulty with Boltzmann machines, Ackley et al.’s article is actually quite detailed, including a proof of the learning algorithm http://tinyurl.com/q4azfl
Popular with Silicon Valley VCs 16 years later: just maximize the rate of entropy creation🤦🏻♂️
We have a winner! laserfiche’s entry is the best (and only, but that doesn’t mean it’s not good quality) submission, and they win $5K.
Code and demo will be posted soon.
According to wikipedia, the definition of EDT is
Evidential decision theory is a school of thought within decision theory according to which the best action is the one which, conditional on your having chosen it, gives you the best expectations for the outcome.
This is not the same as “being a randomly chosen member of a group of people...” and I’ve explained why. The information about group membership is contained in the filtration.
This case is handled in the previous sentence. If this is your actual decision, and your actual decision is the product of a decision algorithm, then your decision algorithm is not EDT.
To put it another way, is your decision to chew gum determined by EDT our by your genes? Pick one.
A human driver does not make 100 “life or death decisions” per mile. They make many more decisions, most of which can easily be corrected, if wrong, by another decision.
The statistic is misleading though in that it includes people who text, drunk drivers, tired drivers. The performance of a well rested human driver that’s paying attention to the road is much, much higher than that. And that’s really the bar that matters for self driving car, you don’t want a car that is doing better than the average driver who—hey you never know—could be a drunk.