When you say you use a kanban-style system, does that just refer to the fact that there are columns that you drag items between, or does it specifically mean that you also make use of an ‘in progress’ column?
If so, do you have one for each ‘todo’ column, or what?
And do you have a column for the ‘capture’ aspect of GTD, or do you do something else for that?
Are you interested in these debates in order to help form your own views, or convince others?
I feel like debates are inferior to reading people’s writings for the former purpose, and for the latter they deal collateral damage by making the public conversation more adversarial.
I keep reading the title as Attention: SAEs Scale to GPT-2 Small.
Thanks for the heads up.
I think what I was thinking of is that words can have arbitrary consequences and be arbitrarily high cost.
In the apologising case, making the right social API call might be an action of genuine significance. E.g. it might mean taking the hit on lowering onlookers’ opinion of my judgement, where if I’d argued instead that the person I wronged was talking nonsense I might have got away with preserving it.
John’s post is about how you can gain respect for apologising, but it does have often have costs too, and I think the respect is partly for being willing to pay them.
Words are a type of action, and I guess apologising and then immediately moving on to defending yourself is not the sort of action which signals sincerity.
Explaining my downvote:
This comment contains ~5 negative statements about the post and the poster without explaining what it is that the commentor disagrees with.
As such it seems to disparage without moving the conversation forward, and is not the sort of comment I’d like to see on LessWrong.
The second footnote seems to be accidentally duplicated as the intro. Kinda works though.
“Not invoking the right social API call” feels like a clarifying way to think about a specific conversational pattern that I’ve noticed that often leads to a person (e.g. me) feeling like they’re virtuosly giving up ground, but not getting any credit for it.
It goes something like:
Alice: You were wrong to do X and Y.
Bob: I admit that I was wrong to do X and I’m sorry about it, but I think Y is unfair.
discussion continues about Y and Alice seems not to register Bob’s apology
It seems like maybe bundling in your apology for X with a protest against Y just doesn’t invoke the right API call. I’m not entirely sure what the simplest fix is, but it might just be swapping the order of the protest and the apology.
Is it true that scaling laws are independent of architecture? I don’t know much about scaling laws but that seems surely wrong to me.
e.g. how does RNN scaling compare to transformer scaling
Your example of a strong syllogism (‘if A, then B. A is true, therefore B is true’) isn’t one.
It’s instead of the form ‘If A, then B. A is false, therefore B is false’, which is not logically valid (and also not a Jaynesian weak syllogism).
If Fisher lived to 100 he would have become a Bayesian
Fisher died at the age of 72
———————————————————————————————————
Fisher died a Frequentist
You could swap the conclusion with the second premise and weaken the new conclusion to ‘Fisher died before 100’, or change the premise to ‘Unless Fisher lived to a 100 he would not become a Bayesian’.
Augmenting humans to do better alignment research seems like a pretty different proposal to building artificial alignment researchers.
The former is about making (presumed-aligned) humans more intelligent, which is a biology problem, while the latter is about making (presumed-intelligent) AIs aligned, which is a computer science problem.
I don’t think that that’s the view of whoever wrote the paragraph you’re quoting, but at this point we’re doing exegesis
Hm, I think that paragraph is talking about the problem of getting an AI to care about a specific particular thing of your choosing (here diamond-maximising), not any arbitrary particular thing at all with no control over what it is. The MIRI-esque view thinks the former is hard and the latter happens inevitably.
Right, makes complete sense in the case of LLM-based agents, I guess I was just thinking about much more directly goal-trained agents.
I like the distinction but I don’t think either aimability or goalcraft will catch on as Serious People words. I’m less confident about aimability (doesn’t have a ring to it) but very confident about goalcraft (too Germanic, reminiscent of fantasy fiction).
Is words-which-won’t-be-co-opted what you’re going for (a la notkilleveryoneism), or should we brainstorm words-which-could-plausibly-catch on?
Perhaps, or perhaps not? I might be able to design a gun which shoots bullets in random directions (not on random walks), without being able to choose the direction.
Maybe we can back up a bit, and you could give some intuition for why you expect goals to go on random walks at all?
My default picture is that goals walk around during training and perhaps during a reflective process, and then stabilise somewhere.
I think that’s a reasonable point (but fairly orthogonal to the previous commenter’s one)
A gun which is not easily aimable doesn’t shoot bullets on random walks.
Or in less metaphorical language, the worry is that mostly that it’s hard to give the AI the specific goal you want to give it, not so much that it’s hard to make it have any goal at all. I think people generally expect that naively training an AGI without thinking about alignment will get you a goal-directed system, it just might not have the goal you want it to.
Sounds like the propensity interpretation of probability.
Thanks. Do you use this system for reading list(s) too?