Gear-level models are expensive - often prohibitively expensive. Black-box approaches are usually much cheaper and faster. But black-box approaches rarely generalize - they're subject to Goodhart, need to be rebuilt when conditions change, don't identify unknown unknowns, and are hard to build on top of. Gears-level models, on the other hand, offer permanent, generalizable knowledge which can be applied to many problems in the future, even if conditions shift.
Honestly, maybe further controversial opinion, but this [30 million for a board seat at what would become the lead co. for AGI, with a novel structure for nonprofit control that could work?] still doesn't feel like necessarily as bad a decision now as others are making it out to be?
The thing that killed all value of this deal was losing the board seat(s?), and I at least haven't seen much discussion of this as a mistake.
I'm just surprised so little prioritization was given to keeping this board seat, it was probably one of the most important assets of the ...
Oh good point– I think my original phrasing was too broad. I didn't mean to suggest that there were no high-quality policy discussions on LW, moreso meant to claim that the proportion/frequency of policy content is relatively limited. I've edited to reflect a more precise claim:
...The vast majority of high-quality content on LessWrong is about technical stuff, and it's pretty rare to see high-quality policy discussions on LW these days (Zvi's coverage of various bills would be a notable exception). Partially as a result of this, some "serious policy people" d
The forum has been very much focused on AI safety for some time now, thought I'd post something different for a change. Privilege.
Here I define Privilege as an advantage over others that is invisible to the beholder. This may not be the only definition, or the central definition, or not how you see it, but that's the definition I use for the purposes of this post. I also do not mean it in the culture-war sense as a way to undercut others as in "check your privilege". My point is that we all have some privileges [we are not aware of], and also that nearly each one has a flip side.
In some way this is the inverse of The Lens That Does Not See Its Flaws: The...
What are the advantages of noticing all of this?
Ilya Sutskever and Jan Leike have resigned. They led OpenAI's alignment work. Superalignment will now be led by John Schulman, it seems. Jakub Pachocki replaced Sutskever as Chief Scientist.
Reasons are unclear (as usual when safety people leave OpenAI).
The NYT piece (archive) and others I've seen don't really have details.
OpenAI announced Sutskever's departure in a blogpost.
Sutskever and Leike confirmed their departures in tweets.
Updates:
Friday May 17:
Leike tweets, including:
...I have been disagreeing with OpenAI leadership about the company's core priorities for quite some time, until we finally reached a breaking point.
I believe much more of our bandwidth should be spent getting ready for the next generations of models, on security, monitoring, preparedness, safety, adversarial robustness, (super)alignment, confidentiality, societal impact, and related topics.
These problems are quite hard to get right,
This interview was terrifying to me (and I think to Dwarkesh as well), Schulman continually demonstrates that he hasn't really thought about the AGI future scenarios in that much depth and sort of handwaves away any talk of future dangers.
Right off the bat he acknowledges that they reasonably expect AGI in 1-5 years or so, and even though Dwarkesh pushes him he doesn't present any more detailed plan for safety than "Oh we'll need to be careful and cooperate with the other companies...I guess..."
We start with an LLM trained on 50T tokens of real data, however capable it ends up being, and ask how to reach the same level of capability with synthetic data. If it takes more than 50T tokens of synthetic data, then it was less valuable per token than real data.
But at the same time, 500T tokens of synthetic data might train an LLM more capable than if trained on the 50T tokens of real data for 10 epochs. In that case, synthetic data helps with scaling capabilities beyond what real data enables, even though it's still less valuable per token.
With Go, we ...
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...
Thanks :) Uh, good question. Making some good links? Have you done much nondual practice? I highly recommend Loch Kelly :)
Man, I wish that was my experience. I feel like I’m constantly asking GPT4o a question, getting a weird or bad response. Then switching to 4 to finish the job.
Thanks to Taylor Smith for doing some copy-editing this.
In this article, I tell some anecdotes and present some evidence in the form of research artifacts about how easy it is for me to work hard when I have collaborators. If you are in a hurry I recommend skipping to the research artifact section.
During AI Safety Camp (AISC) 2024, I was working with somebody on how to use binary search to approximate a hull that would contain a set of points, only to knock a glass off of my table. It splintered into a thousand pieces all over my floor.
A normal person might stop and remove all the glass splinters. I just spent 10 seconds picking up some of the largest pieces and then decided...
Thanks!