LessWrong 2.0 Reader

Aw man, this is so exciting! There’s something really important to me about rationalist virtues having a home in the world. I’m not sure if what I’m imagining is what you’re proposing, exactly, but I think most anything in this vicinity would feel like a huge world upgrade to me.

Apparently I have a lot of thoughts about this. Here are some of them, not sure how applicable they are to this project in particular. I think you can consider this to be my hopes for what such a thing might be like, which I suspect shares some overlap.

It has felt to me for a few years now like something important is dying. I think it stems from the seeming inevitability of what’s before us—the speed of AI progress, our own death, the death of perhaps everything—that looms, shadow-like. And it’s scary to me, and sad, because “inevitability” is a close cousin of “defeat,” and I fear the two inch closer all the time.   

It’s a fatalism that creeps in slow, but settles thick. And it lurks, I think, in the emotional tenor of doom that resides beneath nominally probabilistic estimates of our survival. Lurks as well, although much more plainly, within AI labs: AGI is coming whether we want it to or not, pausing is impossible, the invisible hand holds the reins, or as Claude recently explained to me, “the cat is already out of the bag.” And I think this is sometimes intentional—we are supposed to think about labs in terms of the overwhelming incentives, more than we are supposed to think about them as composed of agents with real choice, because that dispossesses them of responsibility, and dispossesses us of the ability to change them.

There is a similar kind of fatalism that often attaches to the idea of the efficient marketplace—that what is desired has already been done, that if one sits back and lets the machine unfold it will arrive at all the correct conclusions itself. There is no room, in that story, for genuinely novel ideas or progress, all forward movement is the result of incremental accretions on existing structures. This sentiment looms in academia as well—that there is nothing fundamental or new left to uncover, that all low hanging fruit has been plucked. Academic aims rarely push for all that could be—progress is instead judged relatively, the slow inching away from what already is. 

And I worry this mentality is increasingly entrenching itself within AI safety, too. That we are moving away from the sort of ambitious science that I think we need to achieve the world that glows—the sort that aims at absolute progress—and instead moving closer to an incremental machine. After all, MIRI tried and failed to develop agent foundations so maybe we can say, “case closed?” Maybe “solving alignment” was never the right frame in the first place. Maybe it always was that we needed to do the slow inching away from the known, the work that just so happens not to challenge existing social structures. There seems to me, in other words, to be a consensus closing in: new theoretical insights are unlikely to emerge, let alone to have any real impact on engineering. And unlikelier, still, to happen in time. 

I find all of this fatalism terribly confused. Not only because it has, I think, caused people to increasingly depart from the theoretical work which I believe is necessary to reach the world that glows, but because it robs us of our agency. The closer one inches towards inevitability, the further one inches away from the human spirit having any causal effect in the world. What we believe is irrelevant, what is good and right is irrelevant; the grooves have been worn, the structures erected—all that’s left is for the world to follow course. We cannot simply ask people to do what’s right, because they apparently can’t. We cannot succeed at stopping what is wrong, because the incentives are too strong to be opposed. All we can do, it seems, is to meld with the structure itself, making minor adjustments on the margin.  

And there’s a feeling I get, sometimes, when I look at all of this, as if a tidal wave were about to engulf me. The machine has a life of its own; the world is moved by forces outside of my control. And it scares me, and I feel small. But then I remember that it’s wrong. 

There was a real death, I think, that happened when MIRI leadership gave up on solving alignment, but we haven’t yet held the funeral. I think people carry that—the shadow of the fear, unnamed but tangible: that we might be racing towards our inevitable death, that there might not be much hope, that the grooves have been worn, the structures erected, and all that’s left is to give ourselves away as we watch it all unravel. It’s not a particularly inspiring vision, and in my opinion, not a particularly correct one. The future is built out of our choices; they matter, they are real. Not because it would be nice to believe it, but because it is macroscopically true. If one glances at history, it’s obvious that ideas are powerful, that people are powerful. The incentives do not dictate everything, the status quo is never the status quo for very long. The future is still ours to decide. And it’s our responsibility to do so with integrity. 

I have a sense that this spirit has been slipping, with MIRI leadership largely admitting defeat, with CFAR mostly leaving the scene, with AI labs looming increasingly large within the culture and the discourse. I don’t want it to. I want someone to hold the torch of rationality and all its virtues, to stay anchored on what is true and good amidst a landscape of rapidly changing power dynamics, to fight for what’s right with integrity, to hold a positive vision for humanity. I want a space for deep inquiry and intellectual rigor, for aiming at absolute progress, for trying to solve the god damn problem. I think Lightcone has a good shot at doing a fantastic job of bringing something like this to life, and I’m very exited to see what comes of this!  

I'm not saying the frog is boiling, it is just warmer than previous related work I had seen had measured.

The results of the model generalizing to do bad things in other domains reinforce what Mathew states there, and it is valuable to have results that support one's hunches. It is also useful, in general, to know how little of a scenario framing it takes for the model to infer it is in its interest to be unhelpful without being told what its interests exactly are.

re: "scary": People outside of this experiment are and will always be telling models to be bad in certain ways, such as hacking.  Hacking LLMs are a thing that exists and will continue to exist.  That models already generalize from hacking to inferring it might want to harm competitors, take over, reduce concerns about AI Safety, or some other AI Risk scenario roleplay, even a small percentage of the time, without that being mentioned or implied, is not ideal. 

Thinking about a top-level post on FOMO and research taste

• Fear of missing out defined as inability to execute on a project cuz there's a cooler project if you pivot
• but it also gestures at more of a strict negative, where you think your project sucks before you finish it, so you never execute
• was discussing this with a friend: "yeah I mean lesswrong is pretty egregious cuz it sorta promotes this idea of research taste as the ability to tear things down, which can be done armchair"
• I've developed strategies to beat this FOMO and gain more depth and detail with projects (too recent to see returns yet, but getting there) but I also suspect it was nutritious of me to develop discernment about what projects are valuable or not valuable for various threat models and theories of change (in such a way that being a phd student off of lesswrong wouldn't have been as good in crucial ways, tho way better in other ways).
  • but I think the point is you have to turn off this discernment sometimes, unless you want to specialize in telling people why their plans won't work, which I'm more dubious on the value of than I used to be

Idk maybe this shortform is most of the value of the top level post

On the other end of the spectrum, asking cosmo-1b (mostly synthetic training) for a completion, I get `A typical definition of "" would be "the set of all functions from X to Y".`

Using sliding electrical contacts for power is fine for current high-speed trains, but it doesn't work as well above 200 m/s.

I expect if you average over more contrast pairs, like in CAA (https://arxiv.org/abs/2312.06681), more of the spurious features in steering vectors are cancelled out leading to higher quality vectors and greater sparsity in the dictionary feature domain. Did you find this?

Unclear why this is supposed to be a scary result.

"If prompting a model to do something bad generalizes to it being bad in other domains, this is also evidence for the idea that prompting a model to do something good will generalize to it doing good in other domains" - Matthew Barnett

I'll also note that if you want to show up anywhere in the world and get good takes from people on the "how aliens might build AGI" question, Constellation might currently be the best bet (especially if you're interested in decision-relevant questions about this).

I struggled with the notation on the figures; this comment tries to clarify a few points for anyone else who may be confused by it.

• There are three main diagrams to pay attention to in order to understand what's going on here:
  • The Z1R Process (this is a straightforward Hidden Markov Model diagram, look them up if it's unclear).
  • The Z1R Mixed-State Presentation, representing the belief states of a model as it learns the underlying structure.
  • The Z1R Mixed-State Simplex. Importantly, unlike the other two this is a graph and spatial placement is meaningful.
• It's better to ignore the numeric labels on the green nodes of the Mixed-State Presentation, at least until you're clear about the rest. These labels are not uniquely determined, so the relationship between the subscripts can be very confusing. Just treat them as arbitrarily labeled distinct nodes whose only importance is the arrows leading in and out of them. Once you understand the rest you can go back and understand the subscripts if you want^[1].
• However, it's important to note that the blue nodes are isomorphic to the Z1R Process diagram (n_101 = SR, n_11 = S0, n_00 = S1. Once the model has entered the correct blue node, it will thereafter be properly synchronized to the model. The green nodes are transient belief states that the model passes through on its way to fully learning the model.
• On the Mixed-State Simplex: I found the position on the diagram quite confusing at first. The important thing to remember is that the three corners represent certainty that the underlying process is in the equivalent state (eg the top corner is n_00 = S1). So for example if you look at n_1, the model is confident that the underlying process is definitely not in n_11 (S0), since it's as far as possible from that corner. And the model believes that the process is more likely to be in n_101 (SR) than in n_00 (S1). Note how this corresponds to the arrows leaving n_00 & their probabilities in the Mixed-State Presentation (67% chance of transitioning to n_101, 33% chance of transitioning to n_00).
• Some more detail on n_0 if this isn't clear after the previous paragraph: 
  • If we're in n_0, we've just seen a 0 (per the mixed-state presentation).
  • That means that there's a 2/3 chance we're currently in S1, and a 1/3 chance we're currently in S0. And, of course, a 0 chance that we're currently in SR.
  • Therefore the point on which n_0 should lie should be maximally far from the SR corner (n_101), and closer to the S1 corner (n_00) than to the S0 corner (n_11). Which is what we in fact see.

@Adam Shai [LW · GW] please correct me if I got any of that wrong!

1. ^{^}

   Here's the details if you still want them after you've understood the rest. Each node label represents some path that could be taken to that node (& not to other nodes), but there can be multiple such paths. For example, n_11 could also be labeled as n_010, because those are both sequences that could have left us in that state. So as we take some path through the Mixed-State Presentation, we build up a path. If we start at n_s and follow the 1 path, we get to n_1. If we then follow the 0 path, we reach n_10. If we then follow the 0 path, the next node could be called n_100, reflecting the path we've taken. But in fact any path that ends with 00 will reach that node, so it's just labeled n_00. So initially it seems as though we can just append the symbol emitted by whichever path we take, but often there's some step where that breaks down and you get what initially seems like a totally random different label.

(I work out of Constellation and am closely connected to the org in a bunch of ways)

I think you're right that most people at Constellation aren't going to seriously and carefully engage with the aliens-building-AGI question, but I think describing it as a difference in culture is missing the biggest factor leading to the difference: most of the people who work at Constellation are employed to do something other than the classic FHI activity of "self-directed research on any topic", so obviously aren't as inclined to engage deeply with it.

I think there also is a cultural difference, but my guess is that it's smaller than the effect from difference in typical jobs.