Alexander Gietelink Oldenziel

(...) the term technical is a red flag for me, as it is many times used not for the routine business of implementing ideas but for the parts, ideas and all, which are just hard to understand and many times contain the main novelties.
                                                                                                           - Saharon Shelah

 

As a true-born Dutchman I endorse  Crocker's rules.

For my most of my writing see my short-forms (new shortform, old shortform)

Twitter: @FellowHominid

Personal website: https://sites.google.com/view/afdago/home

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Yes sorry Eli, I meant to write out a more fully fleshed out response but unfortunately it got stuck in drafts.

The tl;dr is that I feel this perspective is singling out Sam Altman as some uniquely machiavellian actor in a way I find naive /misleading and ultimately maybe unhelpful. 

I think in general im skeptical of the intense focus on individuals & individual tech companies that LW/EA has develloped recently. Frankly, it feels more rooted in savannah-brained tribalism & human interest than a evenkeeled analysis of what factors are actually important, neglected and tractable. 

The idea I associate with scalable oversight is weaker models overseeing stronger models (probably) combined with safety-by-debate.  Is that the same or different from " recursive techniques for reward generation" ?

Currently, this general class of ideas seems to me the most promising avenue for achieving alignment for vastly superhuman AI (' superintelligence' )..

I want to be able to describe agents that do not have (vNM, geometric, other) rational preferences because of incompleteness or inconsistency but self-modify to become so. 

Eg. In vNM utility theory there is a fairly natural weakening one can do which is ask for a vNM-style representation theorem after dropping transitivity.

[ Incidentally, there is some interesting math here having to do with conservative vs nonconservative vector fields and potentials theory all the way to hodge theory. ]

does JB support this ? 

Im confused since in vNM we start with a preference order over probability distributions. But in JB irs over propositions?

Is there a benchmark in which SAEs clearly, definitely outperform standard techniques?

this seems concerning. Can somebody ELI5 what's going on here?

Mmmmm

Inconsistent and incomplete preferences are necessary for descriptive agent foundations. 

In vNM preference theory an inconsistent preference can be described as cyclic preferences that can be moneypumped. 

How to see this in JB ?

Is Tesla currently overvalued ?

P/e ratio is 188. The ceo has made himself deeply unpopular with many potential customers. Latest sales figures don't look good. Chinese competitors sell more total cars and seem to have caught up in terms of tech.

Happy to see this. 

I have some very basic questions:

How can I see inconsistent preferences within the Jeffrey Bolker framework? What about incomplete preferences ?

Is there any relation you can smimagube with imprecise probability / infraprobability, i.e. knightian uncertainty ?

Answer by Alexander Gietelink Oldenziel92

I'm actually curious about a related problem. 

One of the big surprises of the deep learning revolution has been the universality of gradient descent optimization. 

How large is the class of optimization problems that we can transform into a gradient descent problem of some kind? My suspicision is that it's a very large class; perhaps there is even a general way to transform any problem into a gradient descent optimization problem? 

The natural thing that comes to mind is to consider gradient descent of Langrangian energy functionals in (optimal) control theory. 

Can somebody ELI5 how much I should update on the recent SAE = dead salmon news?

On priors I would expect the SAE bear news to be overblown. 50% of mechinterp is SAEs - a priori, it seems unlikely to me that so many talented people went astray. But I'm an outsider and curious about alternate views. 

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