The Department of Commerce is announcing a new policy on AI Accountability today, with an event at 12pm EST, April 11th. The linked page also includes a link to the new policy. There is an opportunity to leave comments on the policy until June 10th.
Epistemic status - could use more rigor. Thomas Kuhn's 1962 book "The Structure of Scientific Revolutions" (SSR) is a foundational work in the sociology and history of science. SSR makes several important claims: 1. Scientific disciplines (e.g. astrophysics) undergo a cycle of paradigmatic 'normal science' and non-paradigmatic 'revolutionary science'. 2....
There are, but what does having a length below 10^90 have to do with the solomonoff prior? There's no upper bound on the length of programs.
Yes, you are missing something.
Any DEADCODE that can be added to a 1kb program can also be added to a 2kb program. The net effect is a wash, and you will end up with a ratio over priors
Thirder here (with acknowledgement that the real answer is to taboo 'probability' and figure out why we actually care)
The subjective indistinguishability of the two Tails wakeups is not a counterargument - it's part of the basic premise of the problem. If the two wakeups were distinguishable, being a halfer would be the right answer (for the first wakeup).
Your simplified example/analogies really depend on that fact of distinguishability. Since you didn't specify whether or not you have it in your examples, it would change the payoff structure.
I'll also note you are being a little loose with your notion of 'payoff'. You are calculating the payoff for the entire experiment, whereas I define the... (read more)
Thanks for sharing that study. It looks like your team is already well-versed in this subject!
You wouldn't want something that's too hard to extract, but I think restricting yourself to a single encoder layer is too conservative - LLMs don't have to be able to fully extract the information from a layer in a single step.
I'd be curious to see how much closer a two-layer encoder would get to the ITO results.
:Here's my longer reply.
I'm extremely excited by the work in SAEs and their potential for interpretability, however I think there is a subtle misalignment in the SAE architecture and loss function, and the actual desired objective function.
The SAE loss function is:
, where is the -Norm.
or
I would argue that, however, what you are actually trying to solve is the sparse coding problem:
where, importantly, the inner optimization is solved separately (including at runtime).
Since is an overcomplete basis, finding that minimizes the inner loop (also known as basis pursuit denoising[1] ) is a notoriously challenging problem, one which a single-layer encoder is underpowered to compute. The SAE's encoder thus introduces a significant error , which means that you are actual loss... (read more)
This is great work. My recommendation: add a term in your loss function that penalizes features with high cosine similarity.
I think there is a strong theoretical underpinning for the results you are seeing.
I might try to reach out directly - some of my own academic work is directly relevant here.
This is one of those cases where it might be useful to list out all the pros and cons of taking the 8 courses in question, and then thinking hard about which benefits could be achieved by other means.
Key benefits of taking a course (vs. Independent study) beyond the signaling effect might include:
But these depend on the courses and your personality. The precommitment might be unnecessary due to your personal work habits, the curation might be misaligned with what you are interested in learning, and the other students or TAs may not have useful insights that you can't figure out in your own.
Hope that helps.
Instead of demanding orthogonal representations, just have them obey the restricted isometry property.
Basically, instead of requiring , we just require .
This would allow a polynomial number of sparse shards while still allowing full recovery.
I think the success or failure of this model really depends on the nature and number of the factions. If interfactional competition gets too zero-sum (this might help us, but it helps them more, so we'll oppose it) then this just turns into stasis.
During ordinary times, vetocracy might be tolerable, but it will slowly degrade state capacity. During a crisis it can be fatal.
Even in America, we only see this factional veto in play in a subset of scenarios - legislation under divided government. Plenty of action at the executive level or in state governments don't have to worry about this.
The Department of Commerce is announcing a new policy on AI Accountability today, with an event at 12pm EST, April 11th.
The linked page also includes a link to the new policy.
There is an opportunity to leave comments on the policy until June 10th.
Epistemic status - could use more rigor.
Thomas Kuhn's 1962 book "The Structure of Scientific Revolutions" (SSR) is a foundational work in the sociology and history of science.
SSR makes several important claims:
Is this a failure of inner or outer alignment?
[epistemic status - I'm new to thinking about anthropics, but I don't see any obvious flaws]
If a tree falls on sleeping beauty famously claims to have dissolved the Sleeping Beauty problem - that SB's correct answer just depended on what the reward structure for her answers, and that her actual credance didn't matter.
Several lesswrongers seem unsatisfied with that answer - understandably, given a longstanding commitment to epistemics and Bayesianism!
I would argue that ata did some key work in answering the problem from a purely epistemic perspective.
Recall the question SB is to be asked upon waking:
... (read more)Each interview consists of one question, “What is your credence now
I'm having trouble understanding your suggestion, especially the second paragraph. Could you spell it out a bit more?