LessWrong 2.0 Reader

When you rephrase this to be about search engines

I think the main reason why we won't censor search to some abstract conception of "community values" is because users won't want to rent or purchase search services that are censor to such a broad target

It doesn't describe reality. Most of us consume search and recommendations that has been censored (e.g. removing porn, piracy, toxicity, racism, taboo politics) in a way that put's cultural values over our preferences.

So perhaps it won't be true for AI either. At least in the near term, the line between AI and search is a blurred line.

A before and after would be even better!

note that the Brier score at the bottom is a few percentage points lower than what's shown in the chart; the probability distributions GPT outputs differ a bit between runs despite a temperature of 0

It's now possible to get mostly deterministic outputs if you set the seed parameter to an integer of your choice, the other parameters are identical, and the model hasn't been updated.

A Theory of Usable Information Under Computational Constraints

We propose a new framework for reasoning about information in complex systems. Our foundation is based on a variational extension of Shannon's information theory that takes into account the modeling power and computational constraints of the observer. The resulting \emph{predictive V-information} encompasses mutual information and other notions of informativeness such as the coefficient of determination. Unlike Shannon's mutual information and in violation of the data processing inequality, V-information can be created through computation. This is consistent with deep neural networks extracting hierarchies of progressively more informative features in representation learning. Additionally, we show that by incorporating computational constraints, V-information can be reliably estimated from data even in high dimensions with PAC-style guarantees. Empirically, we demonstrate predictive V-information is more effective than mutual information for structure learning and fair representation learning.

h/t Simon Pepin Lehalleur

Organizational structure is an alignment mechanism. 

While I sympathize with the stated intentions, I just can't wrap my head around the naivety.  OpenAI corporate structure was a recipe for bad corporate governance. "We are the good guys here, the structure is needed to make others align with us."- an organization where ethical people can rule as benevolent dictators is the same mistake committed socialists made when they had power.  

If it was that easy, AI alignment would be solved by creating ethical AI committed to alignment and giving it as much power as possible. 

Altruists are normal humans. Nothing changes priorities faster than large sums of money.  Any mix of ideals and profit-making must be arranged in a way that concerns don't mix. People in charge of non-profits making life-changing money if the profit-making is a success can't work. 

Bad organizational structure  puts well meaning humans like Sutskever repeteatly into position where he must choose between wast sums of money  or his ethical commitments.  

What you are missing here is that S. mutants often lives in pockets between tooth an epithelium or between teeth with direct permanent contact to epithelium. Due to the geometry of these spaces access to saliva is very poor so metabolites can enrich to levels way beyond those you suggest here.

This mechanism is also a big problem with the pH study above.

hat's fantastic to hear! I am thrilled the information was helpful for me.

hat's fantastic to hear! I'm thrilled the information was helpful for me.

Great work! It's easy to overlook the importance of this kind of community infrastructure, but I suspect that it makes a significant difference.