Announcement 1: I, the organizer, will be 5-10min late. Announcement 2: apparently there’s some music thing happening at the amphitheater! I’ll set up somewhere northeast of the amphitheater when I get there, and post more precise coordinates when I have.
Optimization Process
Karma: 574
$10 bounty for anybody coming / passing through Capitol Hill: pick up a blind would-be attendee outside the Zeek’s Pizza by 19th and Mercer. DM me your contact information, and I’ll put you in touch, and I’ll pay you on your joint arrival.
Update: the library is unexpectedly closed due to staffing issues. The event is now at Fuel Coffee, one block south and across the street.
If the chance of rain is dissuading you: fear not, there’s a newly constructed roof over the amphitheater!
Hey, folks! PSA: looks like there’s a 50% chance of rain today. Plan A is for it to not rain; plan B is to meet in the rain.
See you soon, I hope!
You win both of the bounties I precommitted to!
Lovely! Yeah, that rhymes and scans well enough for me!
Here are my experiments; they’re pretty good, but I don’t count them as “reliably” scanning. So I think I’m gonna count this one as a win!
(I haven’t tried testing my chess prediction yet, but here it is on ASCII-art mazes.)
I found this lens very interesting!
Upon reflection, though, I begin to be skeptical that “selection” is any different from “reward.”
Consider the description of model-training:To motivate this, let’s view the above process not from the vantage point of the overall training loop but from the perspective of the model itself. For the purposes of demonstration, let’s assume the model is a conscious and coherent entity. From it’s perspective, the above process looks like:
Waking up with no memories in an environment.
Taking a bunch of actions.
Suddenly falling unconscious.
Waking up with no memories in an environment.
Taking a bunch of actions.
and so on.....
The model never “sees” the reward. Each time it wakes up in an environment, its cognition has been altered slightly such that it is more likely to take certain actions than it was before.
What distinguishes this from how my brain works? The above is pretty much exactly what happens to my brain every millisecond:
It wakes up in an environment, with no memories[1]; just a raw causal process mapping inputs to outputs.
It receives some inputs, and produces some outputs.
It’s replaced with a new version—almost identical to the old version, but with some synapse weights and activation states tweaked via simple, local operations.
It wakes up in an environment...
and so on...
Why say that I “see” reward, but the model doesn’t?
- ^
Is it cheating to say this? I don’t think so. Both I and GPT-3 saw the sentence “Paris is the capital of France” in the past; both of us had our synapse weights tweaked as a result; and now both of us can tell you the capital of France. If we’re saying that the model doesn’t “have memories,” then, I propose, neither do I.
I was trying to say that the move used to justify the coin flip is the same move that is rejected in other contexts
Ah, that’s the crucial bit I was missing! Thanks for spelling it out.
Reflectively stable agents are updateless. When they make an observation, they do not limit their caring as though all the possible worlds where their observation differs do not exist.
This is very surprising to me! Perhaps I misunderstand what you mean by “caring,” but: an agent who’s made one observation is utterly unable[1] to interact with the other possible-worlds where the observation differed; and it seems crazy[1] to choose your actions based on something they can’t affect; and “not choosing my actions based on X” is how I would define “not caring about X.”
- ^
Aside from “my decisions might be logically-correlated with decisions that agents in those worlds make (e.g. clone-prisoner’s-dilemma),” or “I am locked into certain decisions that a CDT agent would call suboptimal, because of a precommitment I made (e.g. Newcomb)” or other fancy decision-theoretic stuff. But that doesn’t seem relevant to Eliezer’s lever-coin-flip scenario you link to?
- ^
Ben Garfinkel: no bounty, sorry! It’s definitely arguing in a “capabilities research isn’t bad” direction, but it’s very specific and kind of in the weeds.
Barak & Edelman: I have very mixed feelings about this one, but… yeah, I think it’s bounty-worthy.
Kaj Sotala: solid. Bounty!
Drexler: Bounty!
Olah: hrrm, no bounty, I think: it argues that a particular sort of AI research is good, but seems to concede the point that pure capabilities research is bad. (“Doesn’t [interpretability improvement] speed up capabilities? Yes, it probably does—and Chris agrees that there’s a negative component to that—but he’s willing to bet that the positives outweigh the negatives.”)
Yeah, if you have a good enough mental index to pick out the relevant stuff, I’d happily take up to 3 new bounty-candidate links, even though I’ve mostly closed submissions! No pressure, though!
Thanks for the links!
Ben Garfinkel: sure, I’ll pay out for this!
Katja Grace: good stuff, but previously claimed by Lao Mein.
Scott Aaronson: I read this as a statement of conclusions, rather than an argument.
I paid a bounty for the Shard Theory link, but this particular comment… doesn’t do it for me. It’s not that I think it’s ill-reasoned, but it doesn’t trigger my “well-reasoned argument” sensor—it’s too… speculative? Something about it just misses me, in a way that I’m having trouble identifying. Sorry!
Yeah, I’ll pay a bounty for that!
Thanks for the collection! I wouldn’t be surprised if it links to something that tickles my sense of “high-status monkey presenting a cogent argument that AI progress is good,” but didn’t see any on a quick skim, and there are too many links to follow all of them; so, no bounty, sorry!
Respectable Person: check. Arguing against AI doomerism: check. Me subsequently thinking, “yeah, that seemed reasonable”: no check, so no bounty. Sorry!
It seems weaselly to refuse a bounty based on that very subjective criterion, so, to keep myself honest, I’ll post my reasoning publicly. His arguments are, roughly:
Intelligence is situational / human brains can’t pilot octopus bodies.
(“Smarter than a smallpox virus” is as meaningful as “smarter than a human”—and look what happened there.)
Environment affects how intelligent a given human ends up. ”...an AI with a superhuman brain, dropped into a human body in our modern world, would likely not develop greater capabilities than a smart contemporary human.”
(That’s not a relevant scenario, though! How about an AI merely as smart as I am, which can teleport through the internet, save/load snapshots of itself, and replicate endlessly as long as each instance can afford to keep a g4ad.16xlarge EC2 instance running?)
Human civilization is vastly more capable than individual humans. “When a scientist makes a breakthrough, the thought processes they are running in their brain are just a small part of the equation… Their own individual cognitive work may not be much more significant to the whole process than the work of a single transistor on a chip.”
(This argument does not distinguish between “ability to design self-replicating nanomachinery” and “ability to produce beautiful digital art.”)
Intelligences can’t design better intelligences. “This is a purely empirical statement: out of billions of human brains that have come and gone, none has done so. Clearly, the intelligence of a single human, over a single lifetime, cannot design intelligence, or else, over billions of trials, it would have already occurred.”
(This argument does not distinguish between “ability to design intelligence” and “ability to design weapons that can level cities”; neither had ever happened, until one did.)
Things have coalesced near the amphitheater. When the music kicks off again, we’ll go northeast to… approximately here. 47.6309473, −122.3165802 JMJM+99F Seattle, Washington