METR releases a report, Evaluating frontier AI R&D capabilities of language model agents against human experts: https://metr.org/blog/2024-11-22-evaluating-r-d-capabilities-of-llms/
Daniel Kokotajlo and Eli Lifland both feel that one should update towards shorter timelines remaining until the start of rapid acceleration via AIs doing AI research based on this report:
the meetup page says 7:30pm, but actually the building asks people to leave by 9pm
Gwern was on Dwarkesh yesterday: https://www.dwarkeshpatel.com/p/gwern-branwen
We recorded this conversation in person. In order to protect Gwern’s anonymity, we created this avatar. This isn’t his voice. This isn’t his face. But these are his words.
Thanks, that's very useful.
If one decides to use galantamine, is it known if one should take it right before bedtime, or anytime during the preceding day, or in some other fashion?
I think it's a good idea to include links to the originals:
https://arxiv.org/abs/2408.08152 - "DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search"
Scott Alexander wrote a very interesting post covering the details of the political fight around SB 1047 a few days ago: https://www.astralcodexten.com/p/sb-1047-our-side-of-the-story
I've learned a lot of things new to me reading it (which is remarkable given how much material related to SB 1047 I have seen before)
the potential of focusing on chemotherapy treatment timing
More concretely (this is someone's else old idea), what I think is still not done is the following. Chemo kills dividing cells, this is why the rapidly renewing tissues and cell populations are particularly vulnerable.
If one wants to spare one of those cell types (say, a particular population of immune cells), one should take the typical period of its renewal, and use that as a period of chemo sessions (time between chemo sessions, a "resonance" of sorts between that and the period of the cell population renewal for the selected cell type). Then one should expect to spare most of that population (and might potentially be able to use higher doses for better effect, if the spared population is the most critical one; this does need some precision, not a typical today's "relaxed logistics" approach where a few days this or that way in the schedule is nothing to worry about).
I don't know if that ever progressed beyond the initial idea...
(That's just one example, of course, there is a lot of things which can be considered and, perhaps, tried.)
This depends on many things (one's skills, one's circumstances, one's preferences and inclinations (the efficiency of one's contributions greatly depends on one's preferences and inclinations)).
I have stage 4 cancer, so statistically, my time may be more limited than most. I’m a PhD student in Computer Science with a strong background in math (Masters).
In your case, there are several strong arguments for you to focus on research efforts which can improve your chances of curing it (or, at least, of being able to maintain the situation for a long time), and a couple of (medium strength?) arguments against this choice.
For:
If you succeed, you'll have more time to make impact (and so if your chance of success is not too small, this will contribute to your ability to maximize your overall impact, statistically speaking).
Of course, any success here will imply a lot of publicly valuable impact (there are plenty of people in a similar position health-wise, and they badly need progress to occur ASAP).
The rapid development of applied AI models (both general purpose models and biology-specific models) creates new opportunities to datamine and juxtapose a variety of potentially relevant information and to uncover new connections which might lead to effective solutions. Our tools progress so fast that people are slow to adapt their thinking and methods to that progress. So new people with fresh outlook have reasonable shots (of course, they should aim for collaborations). In this sense, your PhD CS studies and your strong math is very helpful (a lot of the relevant models are dynamic systems, timing of interventions is typically not managed correctly as far as I know (there are plenty of ways to be nice to particularly vulnerable tissues by timing the chemo right and thus being able to make it more effective, but this is not a part of the standard-of-care yet as far as I know), and so on).
You are likely to be strongly motivated and to be able to maintain strong motivation. At the same time you'll know that it is the result that counts here, not the effort, and so you will be likely to try your best to approach this in a smart way, not in a brute force effort way.
Possibly against:
The psychological implications of working on your own life-and-death problem are non-trivial. One might choose to embrace them or to avoid them.
Focusing on "one's own problem" might be compatible or not very compatible with this viewpoint you once expressed: https://www.lesswrong.com/posts/KFWZg6EbCuisGcJAo/immortality-or-death-by-agi-1?commentId=QYDvovQZevDmGtfXY
(Of course, there are plenty of other interesting things one can do with this background (PhD CS studies and strong math). For example, one might decide to disregard the health situation and to dive into technical aspects of AI development and AI existential safety issues, especially if one's estimate of AI timelines yields really short timelines.)
Thanks for the references.
Yes, the first two of those do mention co-occurring anxiety in the title.
The third study suggests a possibility that it might just work as an effective anti-depressant as well. (I hope there will be further studies like that; yes, this might be a sufficient reason to try it for depression, even if one does not have anxiety. It might work, but it's clearly not a common knowledge yet.)
Indeed