In this post, I proclaim/endorse forum participation (aka commenting) as a productive research strategy that I've managed to stumble upon, and recommend it to others (at least to try). Note that this is different from saying that forum/blog posts are a good way for a research community to communicate. It's about individually doing better as researchers.
I have heard rumours that an AI Safety documentary is being made. Separate to this, a good friend of mine is also seriously considering making one, but he isn't "in" AI Safety. If you know who this first group is and can put me in touch with them, it might be worth getting across each others plans.
Summary: The post describes a method that allows us to use an untrustworthy optimizer to find satisficing outputs.
Acknowledgements: Thanks to Benjamin Kolb (@benjaminko), Jobst Heitzig (@Jobst Heitzig) and Thomas Kehrenberg (@Thomas Kehrenberg) for many helpful comments.
Imagine you have black-box access to a powerful but untrustworthy optimizing system, the Oracle. What do I mean by "powerful but untrustworthy"? I mean that, when you give an objective function as input to the Oracle, it will output an element that has an impressively low[1] value of . But sadly, you don't have any guarantee that it will output the optimal element and e.g. not one that's also chosen for a different purpose (which might be dangerous for many reasons, e.g. instrumental convergence).
What questions can you safely ask the Oracle? Can you use it to...
I gave "changing canon randomly" in the comment you are replying to. Is this how you propose limiting the hostile AIs ability to inject subtle hostile plans? Or similarly, "design the columns for this building. Oh they must all be roman arches." Would be a similar example.
[This is part of a series I’m writing on how to convince a person that AI risk is worth paying attention to.]
tl;dr: People’s default reaction to politics is not taking them seriously. They could center their entire personality on their political beliefs, and still not take them seriously. To get them to take you seriously, the quickest way is to make your words as unpolitical-seeming as possible.
I’m a high school student in France. Politics in France are interesting because they’re in a confusing superposition. One second, you'll have bourgeois intellectuals sipping red wine from their Paris apartment writing essays with dubious sexual innuendos on the deep-running dynamics of power. The next, 400 farmers will vaguely agree with the sentiment and dump 20 tons of horse manure in downtown...
More French stories: So, at some point, the French decided what kind of political climate they wanted. What actions would reflect on their cause well? Dumping manure onto the city center using tractors? Sure! Lining up a hundred stationary taxi cabs in every main artery of the city? You bet! What about burning down the city hall's door, which is a work of art older than the United States? Mais évidemment!
"Politics" evokes all that in the mind of your average Frenchman. No, not sensible strategies that get your goals done, but the first shiny thing the prot...
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This article was written by Sonia Joseph, in collaboration with Neel Nanda, and incubated in Blake Richards’s lab at Mila and in the MATS community. Thank you to the Prisma core contributors, including Praneet Suresh, Rob Graham, and Yash Vadi.
Full acknowledgements of contributors are at the end. I am grateful to my collaborators for their guidance and feedback.
Thanks for your comment. Some follow-up thoughts, especially regarding your second point:
There currently seems to be this implicit zeitgeist in the mech interp community that other modalities will simply be an extension or subcase of language. For example, a previous poster made the analogy about studying vision mech interp’s usefulness compared to mech interp’s: “Fusion power plants will need to be built in many countries, and it's increasing clear that fusion power plant construction can't only study building fusion power in the US.” The implicit assumpt...
Lots of people already know about Scott Alexander/ACX/SSC, but I think that crossposting to LW is unusually valuable in this particular case, since lots of people were waiting for a big schelling-point overview of the 15-hour Rootclaim Lab Leak debate, and unlike LW, ACX's comment section is a massive vote-less swamp that lags the entire page and gives everyone equal status.
It remains unclear whether commenting there is worth your time if you think you have something worth saying, since there's no sorting, only sifting, implying that it attracts small numbers of sifters instead of large numbers of people who expect sorting.
Here are the first 11 paragraphs:
...Saar Wilf is an ex-Israeli entrepreneur. Since 2016, he’s been developing a new form of reasoning, meant to transcend normal human bias.
His
"i ain't reading all that
with probability p i'm happy for u tho
and with probability 1-p sorry that happened"
Hi, I’d like to share my paper that proposes a novel approach for building white box neural networks.
The paper introduces semantic features as a general technique for controlled dimensionality reduction, somewhat reminiscent of Hinton’s capsules and the idea of “inverse rendering”. In short, semantic features aim to capture the core characteristic of any semantic entity - having many possible states but being at exactly one state at a time. This results in regularization that is strong enough to make the PoC neural network inherently interpretable and also robust to adversarial attacks - despite no form of adversarial training! The paper may be viewed as a manifesto for a novel white-box approach to deep learning.
As an independent researcher I’d be grateful for your feedback!
These are interesting considerations! I haven't put much thought on this yet but I have some preliminary ideas.
Semantic features are intended to capture meaning-preserving variations of structures. In that sense the "next word" problem seems ill-posed as some permutations of words preserve meaning; in reality its a hardly natural problem also from the human perspective.
The question I'd ask here is "what are the basic semantic building blocks of text for us humans?" and then try to model these blocks using the machinery of semantic features, i.e. model the ...
About 15 years ago, I read Malcolm Gladwell's Outliers. He profiled Chris Langan, an extremely high-IQ person, claiming that he had only mediocre accomplishments despite his high IQ. Chris Langan's theory of everything, the Cognitive Theoretic Model of the Universe, was mentioned. I considered that it might be worth checking out someday.
Well, someday has happened, and I looked into CTMU, prompted by Alex Zhu (who also paid me for reviewing the work). The main CTMU paper is "The Cognitive-Theoretic Model of the Universe: A New Kind of Reality Theory".
CTMU has a high-IQ mystique about it: if you don't get it, maybe it's because your IQ is too low. The paper itself is dense with insights, especially the first part. It uses quite a lot of nonstandard terminology (partially...
Let us start with a (non-quantum) logical coinflip - say, look at the heretofore-unknown-to-us-personally 256th binary digit of pi, where the choice of binary digit is itself intended not to be random.
If the result of this logical coinflip is 1 (aka "heads"), we'll create 18 of you in green rooms and 2 of you in red rooms, and if the result is "tails" (0), we'll create 2 of you in green rooms and 18 of you in red rooms.
After going to sleep at the start of the experiment, you wake up in a green room.
With what degree of credence do you believe - what is your posterior probability - that the logical coin came up "heads"?
There are exactly two tenable answers that I can see, "50%" and...
“You generalise probability, when anthropics are involved, to probability-2, and say a number defined by probability-2; so I’ll suggest to you a reward structure that rewards agents that say probability-1 numbers. Huh, if you still say the probability-2 number, you lose”.
This reads to me like, “You say there’s 70% chance no one will be around that falling tree to hear it, so you’re 70% sure there won’t be any sound. But I want to bet sound is much more likely; we can get measure the sound waves, and I’m 95% sure our equipment will register the sound. Wanna bet?”
This is my personal opinion, and in particular, does not represent anything like a MIRI consensus; I've gotten push-back from almost everyone I've spoken with about this, although in most cases I believe I eventually convinced them of the narrow terminological point I'm making.
In the AI x-risk community, I think there is a tendency to ask people to estimate "time to AGI" when what is meant is really something more like "time to doom" (or, better, point-of-no-return). For about a year, I've been answering this question "zero" when asked.
This strikes some people as absurd or at best misleading. I disagree.
The term "Artificial General Intelligence" (AGI) was coined in the early 00s, to contrast with the prevalent paradigm of Narrow AI. I was getting my undergraduate computer science...
I very much agree with this. You're not the only one! I've been thinking for a while that actually, AGI is here (by all previous definitions of AGI).
Furthermore, I want to suggest that the people who are saying we don't yet have AGI will in fact never be satisfied by what an AI does. The reason is this: An AI will never ever act like a human. By the time its ability to do basic human things like speak and drive are up to human standards (already happened), its abilities in other areas, like playing computer games and calculating, will far exceed ours...