Editing Talk:2898: Orbital Argument

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Ok, so regarding [https://www.explainxkcd.com/wiki/index.php?title=2898:_Orbital_Argument&diff=336067&oldid=336066 this argument], the problem is removing the clarification that this is not subject to consensus 'averaging'. Two totally different opinions which cannot be averaged, merged, subsampled or intermingled. It is maybe useful to mention taking multiple weather predictions and generating the most supported trend, much as natural language processing algorithms, but here the two statements cannot be combined in simple numerical or tokenwise ways (that is the point). Yes, state that mid-point estimations are useful (I'm happy with such a statement, and preserved/enhanced it), but do not remove the salient issue that ''in this case'' it is not a useful process. It's beyond even [[2893: Sphere Tastiness]] illogic. '''Which is the joke'''.... [[Special:Contributions/172.71.242.55|172.71.242.55]] 18:51, 28 February 2024 (UTC)
 
Ok, so regarding [https://www.explainxkcd.com/wiki/index.php?title=2898:_Orbital_Argument&diff=336067&oldid=336066 this argument], the problem is removing the clarification that this is not subject to consensus 'averaging'. Two totally different opinions which cannot be averaged, merged, subsampled or intermingled. It is maybe useful to mention taking multiple weather predictions and generating the most supported trend, much as natural language processing algorithms, but here the two statements cannot be combined in simple numerical or tokenwise ways (that is the point). Yes, state that mid-point estimations are useful (I'm happy with such a statement, and preserved/enhanced it), but do not remove the salient issue that ''in this case'' it is not a useful process. It's beyond even [[2893: Sphere Tastiness]] illogic. '''Which is the joke'''.... [[Special:Contributions/172.71.242.55|172.71.242.55]] 18:51, 28 February 2024 (UTC)
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Re: expert averaging, it's non-intuitive to consider that different symbolic statements in text can be averaged, but with a neural predictive model (NN, human brain) this actually is possible. You *can* average the latent vector representations of two text inputs (x,x') in the (vectorized) latent space of a neural model (z + z'). Latent averaging is often used in ML as an empirical heuristic to improve performance (sentence embeddings, mixture-of-experts models), and can be hypothesized to operate via the mechanism of improving the efficiency  of a learned Bayesian circuit that performs abductive reasoning. Averaging is more obviously seen in the output token space, for example, you might symbolically average temperatures from two weather models (formalized as ensemble models, consensus models, etc.). So although it sounds weird at first glance, averaging experts in either latent or manifest space is often a good heuristic for a guess, and is rewarded as a result. Arguably, White Hat is using this algo and actually making a good guess here, although if he can't explain his thought process  symbolically (he's just doing it because it feels "nice"), his accuracy may come off as an "accident." Caveats: there are definitely conditions under which averaging experts can go awry in both latent and manifest spaces (false balance, non-expert data, partial observability, etc.), but this arguably isn't the case in either the orbital or wave-particle initial observations.
 
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[[Special:Contributions/108.162.242.37|108.162.242.37]] 01:21, 29 February 2024 (UTC)
 

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