Trying to get into alignment. Have a low bar for reaching out!
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Sodium
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One thing not mentioned here (and I think should be talked about more) is that the naturally occurring genetic distribution is very unequal in a moral sense. A more egalitarian society would put a stop to Eugenics Performed by a Blind, Idiot God.
Have your doctor ever asked about if you have a family history of [illness]? For so many diseases, if your parents have it, you’re more likely to have it, and your kids are more likely to have it. These illnesses plague families for generations.
I have a higher than average chance of getting hypertension. Without technology, so will my future kids. With gene editing, we can just stop that, once and for all. A just world is a world where no child is born predetermined to endure avoidable illness simply because of ancestral bad luck.
I’m not sure if the rationalists did anything they shouldn’t do re: Ziz. Going forward though, I think epistemic learned helplessness/memetic immune systems should be among the first things to introduce to newcomers to the site/community. Being wary that some ideas are, in a sense, out to get you, is a central part of how I process information.
Not exactly sure how to implement that recommendation though. You also don’t want people to use it as a fully general counterargument to anything they don’t like.
Ranting a bit here, but it just feels like the collection of rationalist thought is so complex and, even with the various attempts at organizing everything. Thinking well is hard, and involves many concepts, and we haven’t figured it all out yet! It’s kind of sad to see journalists trying to understand the rationalist community and TDT.
Another thing that comes to mind is the FAIR site (formerly mormon apologetics), where members of the latter day saints church tries to correct various misconceptions people have about the church.[1] There’s a ton of writing on there, and provides an example of how people have tried to, uh, improve their PR through writing stuff online to clear up misconceptions.
And did it work? I suspect it probably had a small positive effect. I know very little about this, but my hunch would be that the popularity of mormons comes from having lots of them everywhere in society, and people get to meet them and realize that those people are pretty nice.
(See also Scott Alexander’s book review on The Secrets to Our Success)Why are people so bad at reasoning? For the same reason they’re so bad at letting poisonous spiders walk all over their face without freaking out. Both “skills” are really bad ideas, most of the people who tried them died in the process, so evolution removed those genes from the population, and successful cultures stigmatized them enough to give people an internalized fear of even trying.
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They also provide various evidence for their faith. The one I find particularly funny concerns whether Joseph Smith could have written the book of mormon. It states that Smith (1) had limited education (2) was not a writer and that (3) the book of mormon was very long and had 258k words.
This calls to mind a certain other author, with limited formal education, little fiction writing experience, non-mainstream sexual preferences, and also wrote a very long book (660k words!) that reached many people in the world who ended up finding him very convincing…
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You link a comment by clicking the timestamp next to the username (which, now that I say it, does seem quite unintuitive… Maybe it should also be possible via the three dots on the right side).
While this post didn’t yield a comprehensive theory of how fact finding works in neural networks, it’s filled with small experimental results that I find useful for building out my own intuitions around neural network computation.
I think that’s speaks to how well these experiments are scoped out that even a set of not-globally-coherent findings yield useful information.
So I think the first claim here is wrong.
Let’s start with one of those insights that are as obvious as they are easy to forget: if you want to master something, you should study the highest achievements of your field. If you want to learn writing, read great writers, etc.
If you want to master something, you should do things that causally/counter factually increase your ability (in the order of most to least cost-effective). You should adopt interventions that actually make you better compared to the case that you haven’t done them.
Any intervention could have different treatment effects on some people versus others. In other words, maybe spending a lot of time around other adults worked for those children, but it might not work for your child. Just like how penicillin helps in people who don’t have an allergic reaction to them.
With that out of the way though, I thought this is a super cool post and is one of those lesswrong posts that I remember after reading it once. I think a huge part of the value is just opening up the space of possibilities we can imagine for children.
In other words, I think the post detailed a set of interventions that potentially have a positive treatment effect and seem worthy to try. Absent this post, I might not have came up with these interventions myself (or, more likely, I would have to go through the trouble of doing the research the author did). Thanks for sharing these stories!
Perhaps I am missing something, but I do not understand the value of this post. Obviously you can beat something much smarter than you if you have more affordances than it does.
FWIW, I have read some of the discourse on the AI Boxing game. In contrast, I think those posts are valuable. They illustrate that even with very little affordances a much more intelligent entity can win against you, which is not super intuitive especially in the boxed context.
So the obvious question is, how does differences in affordances lead to differences in winning (i.e., when does brain beat brawn)? That’s a good question to ask, but I think that’s intuitive to everyone already? Like that’s what people allude to when they ask “why can’t you just unplug the AI.”
However, experiment conducted here itself is flawed for the reasons other commenters have mentioned already (i.e., you would not beat LeelaQueenOdds, which is rated 2630 FIDE in blitz). Furthermore, I’m struggling to see how you could learn anything in a chess context that would generalize to AI alignment. If you want to understand how affordances interact with wining you should research AI control.
Anyways I notice that I am confused and welcome attempts to change my views.
Yes
I think the alignment stress testing team should probably think about AI welfare more than they currently do, both because (1) it could be morally relevant and (2) it could be alignment-relevant. Not sure if anything concrete that would come out of that process, but I’m getting the vibe that this is not thought about enough.
since it’s near-impossible to identify which specific heuristic the model is using (there can always be a slightly more complex, more general heuristic of which your chosen heuristic is a special case).
I’m putting some of my faith in low-rank decompositions of bilinear MLPs but I’ll let you know if I make any real progress with it :)
This sounds like a plausible story for how (successful) prosaic interpretability can help us in the short to medium term! I would say though, I think more applied mech interp work could supplement prosaic interpretability’s theories. For example, the reversal curse seems mostly explained by what little we know about how neural networks do factual recall. Theory on computation in superposition help explain why linear probes can recover arbitrary XORs of features.
Reading through your post gave me a chance to reflect on why I am currently interested in mech interp. Here’s a few points where I think we differ:
I am really excited about fundamental, low-level questions. If they let me I’d want to do interpretability on every single forward pass and learn the mechanism of every token prediction.
Similar to above, but I can’t help but feel like I can’t ever truly be “at ease” with AIs unless we can understand them at a level deeper than what you’ve sketched out above.
I have some vague hypothesis about what a better paradigm for mech interp could be. It’s probably wrong, but at least I should try look into it more.
I’m also bullish on automated interpretability conditional on some more theoretical advances.
Best of luck with your future research!
Pr(Ai)2R is at least partially funded by Good Ventures/OpenPhil
I think the actual answer is: the AI isn’t smart enough and trips up a lot.
But I haven’t seen a detailed write up anywhere that talks about why the AI trips up and what are the types of places where it trips up. It feels like all of the existing evals work optimize for legibility/reproducibility/being clearly defined. As a result, it’s not measuring the one thing that I’m really interested in: why don’t we have AI agents replacing workers. I suspect that some startup’s internal doc on “why does our agent not work yet” would be super interesting to read and track over time.
I read this post in full back in February. It’s very comprehensive. Thanks again to Zvi for compiling all of these.
To this day, it’s infuriating that we don’t have any explanation whatsoever from Microsoft/OpenAI on what went wrong with Bing Chat. Bing clearly did a bunch of actions its creators did not want. Why? Bing Chat would be a great model organism of misalignment. I’d be especially eager to run interpretability experiments on it.
The whole Bing chat fiasco is also gave me the impetus to look deeper into AI safety (although I think absent Bing, I would’ve came around to it eventually).
When this paper came out, I don’t think the results were very surprising to people who were paying attention to AI progress. However, it’s important to the “obvious” research and demos to share with the wider world, and I think Apollo did a good job with their paper.
TL; DR: This post gives a good summary of how models can get smarter over time, but while they are superhuman at some tasks, they can still suck at others (see the chart with Naive Scenario v. Actual performance). This is a central dynamic in the development of machine intelligence and deserves more attention. Would love to hear other’s thoughts on this—I just realized that it needed one more positive vote to end up in the official review.
In other words, current machine intelligence and human intelligence are compliments, and human + AI will be more productive than human-only or AI-only organizations (conditional on the same amount of resources).
The post sparked a ton of follow up questions for me, for example:Will machine intelligence and human intelligence continue to be compliments? Is there some evaluation we can design that tells us the degree to which machine intelligence and human intelligence are compliments?
Would there always be some tasks where the AIs will trip up? Why?
Which skills will future AIs become superhuman at first, and how could we leverage that for safety research?
When we look at AI progress, does it look like the AI steadily getting better at all tasks, or that it suddenly gets better at one or another, as opposed to across the board? How would we even split up “tasks” in a way that’s meaningful?
I’ve wanted to do a deep dive into this for a while now and keep putting it off.
I think many others have made the point about an uneven machine intelligence frontier (at least when referenced with the frontiers of human intelligence), but this is the first time I saw it so succinctly presented. I think this post warrents to be in the review, and if so it’ll be a great motivator for me to write up my thoughts on the questions above!
OpenAI released another set of emails here. I haven’t looked through them in detail but it seems that they contain some that are not already in this post.
Any event next week?
Yeah my view is that as long as our features/intermediate variables form human understandable circuits, it doesn’t matter how “atomic” they are.
Almost certainly not original idea: Given the increasing fine-tuning access to models (see also the recent reinforcement fine tuning thing from OpenAI), see if fine tuning on goal directed agent tasks for a while leads to the types of scheming seen in the paper. You could maybe just fine tune on the model’s own actions when successfully solving SWE-Bench problems or something.
(I think some of the Redwood folks might have already done something similar but haven’t published it yet?)
If people outside of labs are interested in doing this, I think it’ll be cool to look for cases of scheming in evals like The Agent Company, where they have an agent act as a remote worker for a company. They ask the agent to complete a wide range of tasks (e.g., helping with recruiting, messaging coworkers, writing code).
You could imagine building on top of their eval and adding morally ambiguous tasks, or just look through the existing transcripts to see if there’s anything interesting there (the paper mentions that models would sometimes “deceive” itself into thinking that it’s completed a task (see pg. 13). Not sure how interesting this is, but I’d love to see if someone could find out).