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Gunnar_Zarncke
Karma: 10,716
The spoilered map is no longer rendering, but a direct link is here:
As to the “why are there two mechanisms that do about the same thing?” I guess this part of the answer:
So if you’re an animal at constant risk of having your behavior hijacked by parasites, what do you do?
First, you make your biological signaling cascades more complicated. You have multiple redundant systems controlling every part of behavior, and have them interact in ways too complicated for any attacker to figure out.
and with actual revenue.
“It’s not just the number one or two companies—the whole batch is growing 10% week on week,”
YC makes all startups in the batch report KPIs even from before being accepted into the batch, If you participate in their Startup School, you are asked to track and report weekly numbers, such as number of users.
Paul Graham posts unlabeled charts from YC startups every now and then, so I assume the aggregate of all of these is what Garry Tan is refering to. Unfortunately, it is not possible to reproduce his analysis. But we should see the effect with the next round of exits. They should happen faster or at higher valuations compared to previous batches.
Relevant study:
Cindy Meston and Penny Frohlich (2003) investigated how residual physiological arousal from a roller‑coaster ride affects perceptions of attractiveness. Participants at an amusement park either just finished or were about to begin a ride. They then rated the attractiveness and dating desirability of an opposite‑gender target photograph.
Those exiting the ride rated the photographed person as significantly more attractive and more desirable for dating than those entering, but only when riding with a non‑romantic partner. The fear‑induced arousal from the ride could get misattributed to attractiveness when the actual source (the ride) isn’t consciously linked to the arousal.
https://labs.la.utexas.edu/mestonlab/files/2016/05/excitation-transfer.pdf
I don’t like that the written transcripts of the videos don’t read as well as written posts would. Or at least that’s what I think. They contain a lot more fluff, which is more tolerable when speaking, but less so in writing.
Paul Graham discusses that good thinking requires good writing and vice versa.
someone who never writes has no fully formed ideas about anything nontrivial.
I’m saying thinking well is a necessary condition for writing really well, not a sufficient one.
You have written a lot, but maybe what you notice in your video transcripts is that one of the effects of writing is missing. I don’t think it has to be literal writing. Generalizing Paul Graham, I think that for clear thinking you need to put ideas in a form that forces precision and makes them grow into more. The grow into more seems to be clearly the case with the engagement and the posts here. But video often doesn’t have the precision—maybe that’s what shows in the fluff?
I can relate to the feeling. Whenever something I posted got downvoted without comment, I wondered about the reasons. Without comment, what can the poster learn from the downvotes? It feels like being sent away. Which it might. But that’s how a community maintains its standards—for better or worse. I think you point out the ”...or worse.” I think it is a risk maybe worth taking. The alternative is Well-Kept Gardens Die By Pacifism.
Yeah, Yudkowsky also writes:
Surprisingly correct, considering the wince I had at the starting frame.
I agree. But it is not sooo easy to do. Not with image generation anyway. Maybe someone wants to try?
Good post.
Related post: Alignment versus AI Alignment
You can add another user as co-editor and see if they run into the same problem. For example, you can ask the moderators via the intercom to try to edit the post.
Young adults’ personalities are changing...
As I wrote in response to an apparently previous version of that post that was deleted:
I roll to disbelieve. Big five character traits are usually stable esp. in adulthood. If this is a true trend, it might be caused by younger people with different profiles entering the sample. But I guess it is sampling demography drift or younger people interpreting differently.
I disbelieve that the effect is as pronounced as it appears in the graphs. Lots of measures changing by 15%p. It doesn’t seem to be in line with prior research on Big Five trait stability.
If we assume that conscientiousness stays the same for the cohort (normally it would go up a bit), that would still mean that the 10 years of youth added to the 16-39-ers would start at more than 15%p*2.5 = 45%p lower than previous cohorts. I don’t buy that.
Would be nice if you could make the six parts an LW Sequence.
Predictions
How confident are you in each of these predictions? The way they are worded sounds pretty confident (80%?).
Filtering is effective at making models safer.
A team at EleutherAI, UK AISI, and Oxford University asked:
Can we prevent LLMs from learning unsafe technical capabilities (such as biorisk) by filtering out enough of the relevant pretraining data before we begin training a model? Even a fully jailbroken model is unlikely to be helpful if it is deeply ignorant of dangerous knowledge.
They find that data filtering is significantly more tamper-resistant than current safeguards without impacting general capability. It doesn’t provide against use of in-context knowledge.
Sure, but that is expensive. Why would more than one team need to do it?
Hm. It turns out it wouldn’t be soo expensive. ChatGPT estimated at least 12K$.
[Question] Is there a safe version of the common crawl?
Would you let us know how much money/credits you spent on it overall, and separately, how many hours on your laptop, and how much RAM?
My first thought was that many startups kind of start as coops—at least in the limiting case of just founders. Maybe the reason we don’t see more coops can be explained why not more startups stay that way:
External capital need.
For some reason each round of hires gets less and less shares.
Different agents sense and store different information bits from the environment and affect different property bits of the environment. Even if two agents have the same capability (number of bits controlled), the facets they may actually control may be very different. Only at high level of capability, where more and more bits are controlled overall, do bitsets overlap more and more and capabilities converge—instrumental convergence.
Related to that: You have much fewer variables under consideration that you can even have standard names for. A remnant of this effect can be seen in typical Fortan programs.