4 karma · joined September 12, 2022
The problem is that they forgot the 1 against all adversarial nature of internet security. A trillion dollar empire set against one way of securing assets is in essence a trillion dollar bounty on breaking your security regime. And what was found? That the system was complex enough that most security "hacks" were mostly social engineering with a sprinkling of insecure gateways and passwords. Neither the users of wallets or the engineers of exchanges put in enough time and effort to secure their holdings in relation to the dollar values they held. Wild swings in the stability of coin values created arbitrage opportunities which made some users exceedingly rich, prompting more dumb money to enter the system. That's a bug, not a feature.
I still think there's value for crypto to evade censorship and manipulation of populations via legislation of the money supply. But that requires a whole slew of low level techs to maintain and verify transactions. At some level that may make it as onerous and bureaucratic as our current financial systems.
I just hope that this doesn't cause financial contagion in the traditional sectors.
Here's a link to the latest draft. I don't know that it will ever be good enough to be an academic paper, but maybe someone will find it entertaining.
Always looking for any substantive commentary. I'd be incredibly interested to know if anyone thought there might be a way to model A^ using a neural network that satisfied the conditions in the paper.
https://www.dropbox.com/s/uveu8eiq2snsojs/Sep12Noon.pdf?dl=0
I hadn't know about these journals though so that's a definite plus. I'll keep them in mind when looking where to submit. I should also mention my aim isn't to publish per se, but to know whether I'm right. I don't know if publishing the results exclusively (if they are significant) would be ethical.
Thanks for the detailed reply!
As to the literature for machine learning, I currently do not understand the state of the art. My understanding of the models is that they are systems of neural networks with varying degrees of complexity, but in essence are still neural networks (without A^ as described). I would need to read more on the literature or talk to someone who has as it's a complicated field. One issue is that I don't know that there is any one right model or system of neural networks that has been shown to work - some apparently do and some don't and it's been more of an experimental approach. So who to cite? Navigating the literature may be difficult.
Mostly I just want someone to read my paper because the ideas are rather startling to me, but I don't know if this has been done before. If it's original work I think there are important reasons to have it published so everyone can read it at once, which is explained in the paper. But I may be completely off base.
To the extent that the lack of citation invalidates the conclusions of the paper? I don't know. I think that Korzybski's attitude towards maps, and several other areas where academics have been building on incorrect ideas (such as the theory of interest or that this would necessitate a Marxian dialectic in opposition), mean that original research need be done without necessarily building off of recent research.
Again, super ambitious paper. I could be entirely wrong.
https://www.dropbox.com/s/d4yznf0v2e71ksw/Sep11EarlyMorning....