LessWrong 2.0 Reader

Learning math fundamentals from a textbook, rather than via one's own sense of where the densest confusions are, is sort of an oxymoron. If you want to be rigorous, you should do anything but defer to consensus.

And from a socioepistemological perspective: if you want math fundamentals to be rigorous, you'd encourage people to try to come up with their own fundamentals before they einstellung on what's been written before. If the fundamentals are robust, they're likely to rediscover it; if they aren't, there's a chance they can revolutionize the field.

It depends on overall probability distibution. Previously Eliezer thought something like that p(doom|trying to solve alignment) = 50% and p(doom|trying to solve AI ban without alignment) = 99% an then updated to p(doom|trying to solve alignment) = 99% and p(doom|trying to solve AI ban without alignment) = 95%, which makes solving AI ban even if pretty much doomed but worthwhile. But if you are, say, Alex Turner, you could start with the same probabilities, but update towards p(doom|trying to solve alignment) = 10%, which makes publishing papers on steering vectors very reasonable.

The other reasons:

1. I expect majority of policy people to be on EA forum, maybe I am wrong;
2. Kat Woods has large twitter thread about how posting on Twitter is much more useful than posting on LW/AF/EAF in terms of public outreach. 

Replied in PM.

Very happy to see a concrete outcome from these suggestions!

Of potential interest: Michael Levin seemed to define the boundaries of multicellular organisms by whether or not they shared an EM field, and Bernardo Kastrup in the same discussion seemed to define the boundaries by whether or not they shared metabolism.

I think this post might suffer from the lack of distinction between karma and agreement/disagreement on the level of posts. I don't think it deserves negative karma, but with this range of topics, it is certain to elicit a lot of disagreement.

Of course, one meta-issue is the diversity of opinion, both in the AI community and in the AI existential safety community.

The diversity of opinion in the AI community is huge, but it is somewhat obfuscated by "money, compute, and SOTA success" effects, which tend to create an artificial impression of consensus when one looks from the outside. But people often move from leading orgs to pursue less standard approaches, in particular, because large orgs are often not so friendly to those non-standard approaches.

The diversity of opinion in the AI existential safety community is at least as big (and is probably even larger, which is natural given that the field is much younger, with its progress being much less certain), but, in addition to that, the diversity is less obfuscated, because it does not have anything resembling the Transformer-based LLM highly successful center around which people can consolidate.

I doubt that the diversity of opinion in the AI existential safety community is likely to decrease, and I doubt that such a decrease would be desirable.

Another meta-issue is how much we should agree on the super-importance of compute. On this meta-issue, the consensus in the AI community and in the AI existential safety community is very strong (and in the case of the AI existential safety community, the reason for this consensus is that compute is, at least, a lever one could plausibly hope to regulate).

But is it actually that unquestionable? Even with Microsoft backing OpenAI, Google should have always been ahead of OpenAI, if it were just the matter of raw compute.

The Llama-3-70B training run is only in millions of GPU hours, so the cost of training can't much exceed 10 million dollars, and it is a model roughly equivalent to early GPT-4 in its power.

I think that non-standard architectural and algorithmic breakthroughs can easily make smaller players competitive, especially as inertia of adherence to "what has been proven before" will inhibit the largest players.

Then, finally, there is all this focus of conversations around "AGI", both in the AI community and in the AI existential safety community.

But for the purpose of existential safety we should not focus on "AGI" (whatever that might be). We should focus on a much more narrow ability of AI systems to accelerate AI research and development.

Here we are very close. E.g. John Schulman in his latest podcast with Dwarkesh said

Even in one or two years, we'll find that the models can do a lot more involved tasks than they can do now. For example, you could imagine having the models carry out a whole coding project instead of it giving you one suggestion on how to write a function. You could imagine the model taking high-level instructions on what to code and going out on its own, writing any files, and testing it, and looking at the output. It might even iterate on that a bit. So just much more complex tasks.

OK, so we are likely to have that (I don't think he is over-optimistic here), and the models are already very capable of discussing AI research papers and exhibit good comprehension of those papers (that's one of my main use cases for LLMs: to help me understand an AI research paper better and faster). And they will get better at that as well.

This combination of the coming ability of LLMs to do end-to-end software projects on their own and the increasing competence of LLMs in their comprehension of AI research sounds like a good reason to anticipate rapidly intensifying phenomenon of AI systems accelerating AI research and development faster and faster in a very near future. Hence the anticipation of very short timelines by many people (although this is still a minority view, even in the AI existential safety circles).

For which parts do you feel cringe?

I think point 2 is plausible but doesn't super support the idea that it would eliminate the biosphere; if it cared a little, it could be fairly cheap to take some actions to preserve at least a version of it (including humans), even if starlifting the sun.

Point 1 is the argument which I most see as supporting the thesis that misaligned AI would eliminate humanity and the biosphere. And then I'm not sure how robust it is (it seems premised partly on translating our evolved intuitions about discount rates over to imagining the scenario from the perspective of the AI system).

On the angle of demonstrating that you can learn the material and the skills and generally proving your math mettle: Can you study the books, do a sampling of the problems in the back of each chapter until you think you've mastered it, and then take the tests directly, without being signed up for a class?  Maybe find old exams, perhaps from other institutions (surely someone somewhere has published an exam on each subject)?  Or, for that matter, print out copies of old Putnam contests, set a timer, and see how well you do?

As someone who never entered college in the first place, I consider it a prosocial thing to make college degrees less correlated with competence.  Don't add to the tragedy of that commons!

(Boring meta note) Since this is a post, not a comment, agreement karma votes and regular karma votes are conflated.