This post is a not a so secret analogy for the AI Alignment problem. Via a fictional dialog, Eliezer explores and counters common questions to the Rocket Alignment Problem as approached by the Mathematics of Intentional Rocketry Institute.
MIRI researchers will tell you they're worried that "right now, nobody can tell you how to point your rocket’s nose such that it goes to the moon, nor indeed any prespecified celestial destination."
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My best guess is that people in these categories were ones that were high in some other trait, e.g. patience, which allowed them to collect datasets or make careful experiments for quite a while, thus enabling others to make great discoveries.
I'm thinking for example of Tycho Brahe, who is best known for 15 years of careful astronomical observation & data collection. Similar for Gregor Mendel's 7-year-long experiments on peas. Same for Dmitry Belayev and fox domestication. Of course I don't know their cognitive scores, but those don't seem like a bott...
Classic type of argument-gone-wrong (also IMO a way autistic 'hyperliteralism' or 'over-concreteness' can look in practice, though I expect that isn't always what's behind it): Ashton makes a meta-level point X based on Birch's meta point Y about object-level subject matter Z. Ashton thinks the topic of conversation is Y and Z is only relevant as the jumping-off point that sparked it, while Birch wanted to discuss Z and sees X as only relevant insofar as it pertains to Z. Birch explains that X is incorrect with respect to Z; Ashton, frustrated, reiterates ...
Perhaps half of the value of misaligned AI control is from acausal trade and half from the AI itself being valuable.
Why do you think these values are positive? I've been pointing out, and I see that Daniel Kokotajlo also pointed out in 2018 that these values could well be negative. I'm very uncertain but my own best guess is that the expected value of misaligned AI controlling the universe is negative, in part because I put some weight on suffering-focused ethics.
This is a linkpost for our paper Explaining grokking through circuit efficiency, which provides a general theory explaining when and why grokking (aka delayed generalisation) occurs, and makes several interesting and novel predictions which we experimentally confirm (introduction copied below). You might also enjoy our explainer on X/Twitter.
Abstract
One of the most surprising puzzles in neural network generalisation is grokking: a network with perfect training accuracy but poor generalisation will, upon further training, transition to perfect generalisation. We propose that grokking occurs when the task admits a generalising solution and a memorising solution, where the generalising solution is slower to learn but more efficient, producing larger logits with the same parameter norm. We hypothesise that memorising circuits become more inefficient with larger training datasets while generalising circuits do...
My speculation for Omni-Grok in particular is that in settings like MNIST you already have two of the ingredients for grokking (that there are both memorising and generalising solutions, and that the generalising solution is more efficient), and then having large parameter norms at initialisation provides the third ingredient (generalising solutions are learned more slowly), for some reason I still don't know.
Higher weight norm means lower effective learning rate with Adam, no? In that paper they used a constant learning rate across weight norms, but Adam ...
If you use ublock (or adblock, or adguard, or anything else that uses EasyList syntax), you can add a custom rule
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which will remove the reaction section underneath comments and the highlights corresponding to those reactions.
The former of these you can also do through the element picker.
Post for a somewhat more general audience than the modal LessWrong reader, but gets at my actual thoughts on the topic.
In 2018 OpenAI defeated the world champions of Dota 2, a major esports game. This was hot on the heels of DeepMind’s AlphaGo performance against Lee Sedol in 2016, achieving superhuman Go performance way before anyone thought that might happen. AI benchmarks were being cleared at a pace which felt breathtaking at the time, papers were proudly published, and ML tools like Tensorflow (released in 2015) were coming online. To people already interested in AI, it was an exciting era. To everyone else, the world was unchanged.
Now Saturday Night Live sketches use sober discussions of AI risk as the backdrop for their actual jokes, there are hundreds...
I agree with RL agents being misaligned by default, even more so for the non-imitation-learned ones. I mean, even LLMs trained on human-generated data are misaligned by default, regardless of what definition of 'alignment' is being used. But even with misalignment by default, I'm just less convinced that their capabilities would grow fast enough to be able to cause an existential catastrophe in the near-term, if we use LLM capability improvement trends as a reference.
The history of science has tons of examples of the same thing being discovered multiple time independently; wikipedia has a whole list of examples here. If your goal in studying the history of science is to extract the predictable/overdetermined component of humanity's trajectory, then it makes sense to focus on such examples.
But if your goal is to achieve high counterfactual impact in your own research, then you should probably draw inspiration from the opposite: "singular" discoveries, i.e. discoveries which nobody else was anywhere close to figuring out. After all, if someone else would have figured it out shortly after anyways, then the discovery probably wasn't very counterfactually impactful.
Alas, nobody seems to have made a list of highly counterfactual scientific discoveries, to complement wikipedia's list of multiple discoveries.
To...
Bowler's comment on Wallace is that his theory was not worked out to the extent that Darwin's was, and besides I recall that he was a theistic evolutionist. Even with Wallace, there was still a plethora of non-Darwinian evolutionary theories before and after Darwin, and without the force of Darwin's version, it's not likely or necessary that Darwinism wins out.
...But Wallace’s version of the theory was not the same as Darwin’s, and he had very different ideas about its implications. And since Wallace conceived his theory in 1858, any equivalent to
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1Le coût d’opportunité des délais en développement technologique
Par Nick Bostrom
Abstract: Grâce à des technologies avancées, on pourrait maintenir une très grande quantité de personnes menant des vies heureuses dans la région accessible de l’univers. Chaque année où la colonisation de l’univers ne se déroule pas représente un coût d’opportunité; des vies qui valent d’êtres vécues ne peuvent être réalisées. D’après des estimations plausibles, ce coût est extrêmement élevé. Mais la leçon pour les utilitaristes n’est pas qu’il faut maximiser la cadence du développement technologique, mais sa sécurité. Autrement dit, il faut maximiser la probabilité que la colonisation se déroule.
Le rythme de perte de vies potentielles
En ce moment, des soleils illuminent et réchauffent des pièces vides et des trous noirs absorbent une portion de l’énergie inutilisée du cosmos. Chaque minute,...
Is this a translation of Bostrom's article? If yes, could you please make this more explicit (maybe as a first paragraph in the text, in English), and include a link to the original?
