LessWrong 2.0 Reader

My p(doom) was low when I was predicting the yudkowsky model was ridiculous, due to machine learning knowledge. Now that we have AGI [LW · GW] of the kind I was expecting, we have more people working on figuring out what the risks really are, and the previous concern of the only way to intelligence being RL seems to be only a small reassurance because RL agents who act in the real world is in fact scary.

Possible bias, that when famous and rich people kill themselves, everyone is discussing it, but when poor people kill themselves, no one notices?

Also, I wonder what technically counts as "suicide"? Is drinking yourself to death, or a "suicide by cop", or just generally overly risky behavior included? I assume not. And these seem to me like methods a poor person would choose, while the rich one would prefer a "cleaner" solution, such as a bullet or pills. So the reported suicide rates are probably skewed towards the legible, and the self-caused death rate of the poor could be much higher.

Please don't write comments all in boldface. It feels like you're trying to get people to pay more attention to your comment than to others, and it actually makes your comment a little harder to read as well as making the whole thread uglier.

I asked ChatGPT 

Have there been any great discoveries made by someone who wasn't particularly smart? (i.e. average or below)

and it's difficult to get examples out of it. Even with additional drilling down and accusing it of being not inclusive of people with cognitive impairments, most of its examples are either pretty smart anyway, savants or only from poor backgrounds. The only ones I could verify that fit are:

• Richard Jones accidentally created the Slinky
• Frank Epperson, as a child, Epperson invented the popsicle
• George Crum inadvertently invented potato chips

I asked ChatGPT (in a separate chat) to estimate the IQ of all the inventors is listed and it is clearly biased to estimate them high, precisely because of their inventions. It is difficult to estimate the IQ of people retroactively. There is also selection and availability bias.

e/acc is not a coherent philosophy and treating it as one means you are fighting shadows.

Landian accelerationism at least is somewhat coherent. "e/acc" is a bundle of memes that support the self-interest of the people supporting and propagating it, both financially (VC money, dreams of making it big) and socially (the non-Beff e/acc vibe is one of optimism and hope and to do things -- to engage with the object level -- instead of just trying to steer social reality). A more charitable interpretation is that the philosophical roots of "e/acc" are founded upon a frustration with how bad things are, and a desire to improve things by yourself. This is a sentiment I share and empathize with.

I find the term "techno-optimism" to be a more accurate description of the latter, and perhaps "Beff Jezos philosophy" a more accurate description of what you have in your mind. And "e/acc" to mainly describe the community and its coordinated movements at steering the world towards outcomes that the people within the community perceive as benefiting them.

I think I might create a new post using information from this post [AF · GW] which covers the new AI alignment landscape.

I use GreaterWrong as my front-end to interface with LessWrong, AlignmentForum, and the EA Forum. It is significantly less distracting and also doesn't make my ~decade old laptop scream in agony when multiple LW tabs are open on my browser.

Thanks for this post. This is generally how I feel as well, but my (exaggerated) model of the AI aligment community would immediately attack me by saying "if you don't find AI scary, you either don't understand the arguments on AI safety or you don't know how advanced AI has gotten". In my opinion, a few years ago we were concerned about recursively self improving AIs, and that seemed genuinely plausible and scary. But somehow, they didn't really happen (or haven't happened yet) despite people trying all sorts of ways [LW · GW] to make it happen. And instead of a intelligence explosion, what we got was an extremely predictable improvement trend which was a function of only two things i.e. data + compute. This made me qualitatively update my p(doom) downwards, and I was genuinely surprised that many people went the other way instead, updating upwards as LLMs got better. 

General meta-problem of such discussions is that direct counterargument to "LLMs are safe" is to tell how to make LLM unsafe, and it's not a good practice.

Daniel Filan: But I would’ve guessed that there wouldn’t be a significant complexity difference between the frequencies. I guess there’s a complexity difference in how many frequencies you use.

Vikrant Varma: Yes. That’s one of the differences: how many you use and their relative strength and so on. Yeah, I’m not really sure. I think this is a question we pick out as a thing we would like to see future work on.

My pet hypothesis here is that (a) by default, the network uses whichever frequencies were highest at initialization (for which there is significant circumstantial evidence) and (b) the amount of interference differs significantly based on which frequencies you use (which in turn changes the quality of the logits holding parameter norm fixed, and thus changes efficiency).

In principle this can be tested by randomly sampling frequency sets, simulating the level of interference you'd get, using that to estimate the efficiency + critical dataset size for that grokking circuit. This gives you a predicted distribution over critical dataset sizes, which you could compare against the actual distribution.

Tbc there are other hypotheses too, e.g. perhaps different frequency sets are easier / harder to implement by the neural network architecture.