I know this post was chronologically first, but since I read them out of order my reaction was “wow, this post is sure using some of the notions from the Waluigi Effect mega-post, but for humans instead of chatbots”! In particular, they’re both pointing at the notion that an agent (human or AI chatbot) can be in something like a superposition between good actor and bad actor unlike the naive two-tone picture of morality one often gets from children’s books.
Marcello(Marcello Herreshoff)
Karma: 900
I interpreted OP as saying that KataGo, despite being a super-human Go player, came up with a flawed approximation to the natural abstraction that two eyed groups are alive which was inaccurate in some situations (and that’s how it can be exploited by building a small living group that ends up appearing dead from its perspective).
One of my pet journalism peeves is the “as” (or sometimes “while”) construction, which I often see in titles or first sentences of articles. It looks like “<event A was happening> as <event B was happening>”. You can fact check the events and it’ll turn out they happened, but the phrasing comes with this super annoying nudge-nudge-wink-wink-implication that the two events totally have direct causal connection. Unfortunately, you can’t pin this on the journalist because they didn’t actually say it.
This sort of thing happens a lot. To give just a couple example templates, articles like “as <political thing happened>, markets rallied” or “<stock> falls as <CEO did something>” are often trying to pull this.
I broadly agree. Though I would add that those things could still be (positive motivation) wants afterwards, which one pursues without needing them. I’m not advocating for asceticism.
Also, while I agree that you get more happiness by having fewer negative motives, being run by positive motives is not 100% happiness. One can still experience disappointment if one wants access to Netflix, and it’s down for maintenance one day. However, disappointment is still both more hedonic than fear and promotes a more measured reaction to the situation.
Are you trying to say that it should work similarly to a desensitization therapy? But then, there might exist the reversed mode, where you get attached to things even more, as you meditate on why are they good to have. Which of these modes dominates is not clear to me.
I think you make a good point. I feel I was gesturing at something at something real when I wrote down the comparison notion, but didn’t express it quite right. Here’s how I would express it now:
The key thing I failed to point out in the post is that just visualizing a good thing you have or what’s nice about it is not the same as being grateful for it. Gratitude includes an acknowledgement. When you thank an acquaintance for, say, having given you helpful advice, you’re acknowledging that they didn’t necessarily have to go out of their way to do that. Even if you’re grateful for something a specific person didn’t give you, and you don’t believe in a god, the same feeling of acknowledgment is present. I suspect this acknowledgement is what pushes things out of the need-set.
And indeed, as you point out, just meditating on why something is good to have might increase attachment (or it might not, the model doesn’t make a claim about which effect would be stronger).
I don’t think I get this. Doesn’t this apply to any positive thing in life? (e.g. why single out the gratitude practise?)
I expect most positive things would indeed help somewhat, but that gratitude practice would help more. If someone lost a pet, giving them some ice cream may help. However, as long as their mind is still making the comparison to the world where their pet is still alive, the help may be limited. That said, to the extent that they manage to feel grateful for the ice cream, it seems to me as though their internal focus has shifted in a meaningful way, away from grasping at the world where their pet is still alive and towards the real world.
1. Yes, I agree with the synopsis (though expanded need-sets are not the only reason people are more anxious in the modern world).
2. Ah. Perhaps my language in the post wasn’t as clear as it could have been. When I said:
More specifically, your need-set is the collection of things that have to seem true for you to feel either OK or better.
I was thinking of the needs as already being about what seems true about future states of the world, not just present states. For example, your need for drinking water is about being able to get water when thirsty at a whole bunch of future times.
If this is true then a larger need-set would lead to more negative motivation due to there being more ways for something we think we need to be taken away from us.
Yes, exactly.
Your seemingly target-less skill-building motive isn’t necessarily irrational or non-awesome. My steel-man is that you’re in a hibernation period, in which you’re waiting for the best opportunity of some sort (romantic, or business, or career, or other) to show up so you can execute on it. Picking a goal to focus on really hard now might well be the wrong thing to do; you might miss a golden opportunity if your nose is at the grindstone. In such a situation a good strategy would, in fact, be to spend some time cultivating skills, and some time in existential confusion (which is what I think not knowing which broad opportunities you want to pursue feels like from the inside).
The other point I’d like to make is that I expect building specific skills actually is a way to increase general problem solving ability; they’re not at odds. It’s not that super specific skills are extremely likely to be useful directly, but that the act of constructing a skill is itself trainable and a significant part of general problem solving ability for sufficiently large problems. Also, there’s lots of cross-fertilization of analogies between skills; skills aren’t quite as discrete as you’re thinking.
“Aspiring Rationalist” Considered Harmful
The “aspiring” in “aspiring rationalist” seems like superfluous humility at best. Calling yourself a “rationalist” never implied perfection in the first place. It’s just like how calling yourself a “guitarist” doesn’t mean you think you’re Jimi Hendrix. I think this analogy is a good one, because rationality is a human art, just like playing the guitar.
I suppose one might object that the word “rational” denotes a perfect standard, unlike playing the guitar. However, we don’t hesitate to call someone an “idealist” or a “perfectionist” when they’re putting in a serious effort to conform to an ideal or strive towards perfection, so I think this objection is weak. The “-ist” suffix already means that you’re a person trying to do the thing, with all the shortcomings that entails.
Furthermore, it appears harmful to add the “aspiring”. It creates dilution. Think of what it would mean for a group of people to call themselves “aspiring guitarists”. The trouble is, it also applies to the sort of person who daydreams about the adulation of playing for large audiences but never gets around to practicing. However, to honestly call yourself a “guitarist”, you would have to actually, y’know, play the guitar once in a while.
While I acknowledge I’m writing this many years too late, please consider dropping the phrase “aspiring rationalist” from your lexicon.
Well, I made another UserScript for the current site: https://github.com/mherreshoff/lw-antikibitzer
I agree with some of the other commenters that the term “decision theory” ought to be reserved for the overarching problem of which decision algorithm to use, and that the distinction you’re referring to ought to be called something like “adversarial” vs “non-adversarial” or “rival” vs “non-rival”. Nonetheless, I think this is an interesting handle for thinking about human psychology.
If we view these as two separate modes in humans, and presume that there’s some kind of subsystem that decides which mode to use, then false positives of that subsystem look like potential explanations for things like The Imp of the Perverse, or paranoia.
In the link post you’re referring to, what Scott actually says is:
I suspect this is true the way it’s commonly practiced and studied (“if you’re feeling down, listen to this mindfulness tape for five minutes a day!”), less true for more becoming-a-Buddhist-monk-level stuff.
Yes. Google docs does contain a lame version of the thing I’m pointing at. The right version is that the screen is split into N columns. Each column displays the children of the selection from the previous column (the selection could either be an entire post/comment or a span within the post/comment that the children are replies to.)
This is both a solution to inline comments and a tree-browser that lets you see just the ancestry of a comment at a glance with out having to manually collapse everything else.
Also: you replied to my comment and I didn’t see any notifications. I found your reply by scrolling around. That’s probably a bug.
If you want to encourage engagement, don’t hide the new comment box all the way down at the bottom of the page! Put another one right after the post (or give the post a reply button of the same sort the comments have.)
One UI for this I could imagine (for non-mobile wide-screen use) is to have the post and the comments appear in two columns with the post on the left and the comments on the right (Similar to the Mac OS X Finder’s column view.) Then when the user clicks on a comment the appropriate bit of the post would get highlighted.
In fact, I could see doing a similar thing for the individual comments themselves to create a view that would show the *ancestry* of a single comment, stretching left back to the post the conversation was originally about. This could save a fair amount of scrolling.
Agreed. Also, at some point Eigenkarma is going to need to include a recency bias so that the system can react quickly to a commenter going sour.
I agree with the spirit of this. That said, if the goal is to calculate a Karma score which fails to be fooled by a user posting a large amount of low quality content, it might be better to do something roughly: sum((P*x if x < 0 else max(0, x-T)) for x in post_and_comment_scores). Only comments that hit a certain bar should count at all. Here P is the penalty multiplier for creating bad content, and T is the threshold a comment score needs to meet to begin counting as good content. Of course, I also agree that it’s probably worth weighting upvotes and downvotes separately and normalizing by reads to calculate these per-(comment or post) scores.
From discussions I had with Sam, Scott, and Jack:
To solve the problem, it would suffice to find a reflexive domain with a retract onto .
This is because if you have a reflexive domain , that is, an with a continuous surjective map , and is a retract of , then there’s also a continuous surjective map .
Proof: If is a retract of then we have a retraction and a section with . Construct . To show that is a surjection consider an arbitrary . Thus, . Since is a surjection there must be some with . It follows that . Since was arbitrary, is also a surjection.
“Self-Reference and Fixed Points: A Discussion and an Extension of Lawvere’s Theorem” by Jorge Soto-Andrade and Francisco J. Varela seems like a potentially relevant result. In particular, they prove a converse Lawvere result in the category of posets (though they mention doing this for in an unsolved problem.) I’m currently reading through this and related papers with an eye to adapting their construction to (I think you can’t just use it straight-forwardly because even though you can build a reflexive domain with a retract to an arbitrary poset, the paper uses a different notion of continuity for posets.)
In Defense of the Shoggoth Analogy
In reply to: https://twitter.com/OwainEvans_UK/status/1636599127902662658
The explanations in the thread seem to me to be missing the middle or evading the heart of the problem. Zoomed out: an optimization target at level of personality. Zoomed in: a circuit diagram of layers. But those layers with billions of weights are pretty much Turing complete.
Unfortunately, I don’t think anyone has much idea how all those little learned computations are make up said personality. My suspicion is there isn’t going to be an *easy* way to explain what they’re doing. Of course, I’d be relieved to be wrong here!
This matters because the analogy in the thread between averaged faces and LLM outputs is broken in an important way. (Nearly) every picture of a face in the training data has a nose. When you look at the nose of an averaged face, it’s based very closely on the noses of all the faces that got averaged. However, despite the size of the training datasets for LLMs, the space of possible queries and topics of conversation is even vaster (it’s exponential in the prompt-window size, unlike the query space for the average faces which are just the size of the image).
As such, LLMs are forced to extrapolate hard. So, I’d expect that which particular generalizations they learned, hiding in those weights, to start to matter once users start poking them in unanticipated ways.
In short, if LLMs are like averaged faces, I think they’re faces that will readily fall apart into Shoggoths if someone looks at them from an unanticipated or uncommon angle.