More people should consider dropping out of high school, particularly if they:
In most places, once you reach an age younger than the typical age of graduation you are not legally obligated to attend school. Many continue because it's normal, but some brief analysis could reveal that graduating is not worth the investment for you.
Some common objections I heard:
Why finish?
The mistake in this objection is thinking there was a single reason I wanted to leave school. I was increasing my free time, not making a bet on a particular technology.
In some cases this is true. You might be surprised if you demonstrate long term commitment and the ability to get financial support though.
Leaving high school is not the right decision for everyone, but many students won't even consider it. At least make the option available to yourself.
This should be an equivalent problem, yes.
Yes, your description of my hypothetical is correct. I think it's plausible that approximating things that happened in the past is computationally easier than breaking some encryption, especially if the information about the past is valuable even if it's noisy. I strongly doubt my hypothetical will materialize, but I think it is an interesting problem regardless.
My concern with approaches like the one you suggest is that they're restricted to small parts of the universe, so with enough data it might be possible to fill in the gaps.
Present cryptography becomes redundant when the past can be approximated. Simulating the universe at an earlier point and running it forward to extract information before it's encrypted is a basic, but difficult way to do this. For some information this approximation could even be fuzzy, and still cause damage if public. How can you protect information when your adversary can simulate the past?
The information must never exist as plaintext in the past. A bad way to do this is to make the information future-contingent. Perhaps it could be acausally inserted into the past by future agents, but probably you would not be able to act on future-contingent information in useful ways. A better method is to run many homomorphically encrypted instances of a random function that might output programs that do computations that yield sensitive information (e.g, an uploaded human). You would then give a plaintext description of the random function, including a proof that it probably output a program doing computations likely adversaries would want. This incentivizes the adversary to not destroy the program output by the random function, because it may not be worth the cost of destruction and replacement with something that is certainly doing better computations.
This method satisfies the following desiderata:
1. The adversary does not know the output of the encrypted random function, or the outputs of the program the random function output
2. There is an incentive to not destroy the program output by the random function
One problem with this is that your advserary might be superintelligent, and prove the assumptions that made your encryption appear strong to be incorrect. To avoid this, you could base your cryptography on something other than computational hardness.
My first thought was to necessitate computations that would make an adversary incur massive negative utility to verify the output of a random function. It's hard to predict what an adversary's preferences might be in advance, so the punishment for verifying the output of the random function would need to be generically bad, such as forcing the adversary to expend massive amounts of computation on useless problems. This idea is bad for obvious reasons, and will probably end up making the same or equally bad assumptions about the inseparability of the punishment and verification.
We are recruiting people interested in using Rallypoint in any way. The form has an optional question for what you hope to get out of using Rallypoint. Even if you don't plan on contributing to bounties or claiming them and just want to see how others use Rallypoint we are still interested in your feedback.
Yes. If the feedback from the beta is that people find Rallypoint useful we will do a public release and development will continue. I want to focus on getting the core bounty infrastructure very refined before adding many extra features. Likely said infrastructure would be easily adapted to more typical crowdfunding and a few other applications, but that is lower on the priority list than getting bounties right.
I don't understand the distinction you draw between free agents and agents without freedom.
If I build an expected utility maximizer with a preference for the presence of some physical quantity, that surely is not a free agent. If I build some agent with the capacity to modify a program which is responsible for its conversion from states of the world to scalar utility values, I assume you would consider that a free agent.
I am reminded of E.T. Jaynes' position on the notion of 'randomization', which I will summarize as "a term to describe a process we consider too hard to model, which we then consider a 'thing' because we named it."
How is this agent any more free than the expected utility maximizer, other than for the reason that I can't conveniently extrapolate the outcome of its modification of its utility function?
It seems to me that this only shifts the problem from "how do we find a safe utility function to maximize" to "how do we find a process by which a safe utility function is learned", and I would argue the consideration of the latter is already a mainstream position in alignment.
If I have missed a key distinguishing property, I would be very interested to know.
I believe you misinterpreted the quote from disturbance. They were implying that they would bring about AGI at the moment before their brain would be unsalvageable by AGI such that they could be repaired, assumedly in expectation of immortality.
I also don't think the perspective that we would likely fail as a civilization without AGI is common on LessWrong. I would guess that most of us would expect a smooth-ish transition to The Glorious Future in worlds where we coordinate around [as in don't build] AI. In my opinion the post is good even without this claim however.
models that are too incompetent to think through deceptive alignment are surely not deceptively aligned.
Is this true? In Thoughts On (Solving) Deep Deception, Jozdien gives the following example that suggests otherwise to me:
Back in 2000, a computer scientist named Charles Ofria was studying the evolution of simulated organisms. He wanted to limit their replication rate, so he programmed the simulation to pause after each mutation, measure the mutant’s replication rate in an isolated test environment, and delete the mutant if it replicated faster than its parent.
Eventually, organisms evolved to recognize and pause their replication in the test environment and resume normal replication when released. Ofria tried solving this by making it harder to detect the test environment, but the organisms simply started to randomly accelerate their replication so as to slip through the test environment some of the time.
I don't see why a model would need to be cognitively able to process its own alignment for its alignment to be improper, and I think this assumption is quite key to the main claims of the post.
I dropped out one month ago. I don't know anyone else who has dropped out. My comment recommends students consider dropping out on the grounds that it seemed like the right decision for me, but it took me a while to realize this was even a choice.
So far my experience has been pleasant. I am ~twice as productive. The total time available to me is ~2.5-3x as much as I had prior. The excess time lets me get a healthy amount of sleep and play videogames without sacrificing my most productive hours. I would make the decision again, and earlier if I could.