It seems that those two (actually three or four) ideas are parallel and not always compatible.
[please forgive my grammar]
1. There is noisy computers which can work despite or because some unreliable part. Neural netwroks are quite ok with it for example, so some people speculate that it will be possible to build specialized noisy circuits for specific networks.
2. There is stochastic computing, in which complicated numerical functions represented as probability density distributions (?)
3. And then there is probabalistic computing, when state randomly updated in accordance with some "temprature".
4. And finally there is randomized algoritms, which are closer to classical computer science but with some stream of input. Howver, people like Avi Wigderson who succesfully removed the "random" parts of those algoritms.
Plus there is funny things with non-associativity of floating-point numbers which can lead to non-determinism when the order of execution (summation for example) is arbitary, which can lead to funny results. But because neural netwroks are robust to noise to some degree, it will still work.
And the stuff which done by Avi Wigderson requires that computers work in determinstic way (except of that random stream), so it will not be very compatible with unreliable noisy computations.
However, it seems that stochastic, probabalistic and noisy computations could be combined.
Just for pedantic remark, brown dwarfs is not stars, but they are substellar objects and formed in the similar fation (and this is why question about edge case of very large exoplanted vs very small brown dwarf is interestring)https://en.wikipedia.org/wiki/Substellar_object definition relies on sustained hydrogen fusion
Personally, one solution is to subsrcibe for Nebula + CurisoityStream bundle to support creators (bought 2 subscriptions for me and friend) https://curiositystream.com/?coupon=nebula and use something like PeerTube for the rest of the stuff.
Youtube have great catalogue of the lectures and other educational material, so it is still kinda irreplaceable
If my understanding is correct, because Maturana was relatively strict about using autopoiesis only for biological systems with material and energy flow.
for example, Logtiech G502:
" Dual-Mode Hyper-Fast Scroll Wheel
Unlock the scroll wheel for hyper-fast continuous scrolling to spin quickly through long pages, or lock it down for single click precision scrolling. The weighty, metal wheel delivers confident, smooth and satisfying control for either mode. "
This is more to that!
60 is https://en.wikipedia.org/wiki/Superior_highly_composite_numb... which means that lot of fractional numbers can be represnted in nice way.
And because one year is around 360 days and 12 (another superior highly composite number) is roughly correspond to number of lunar months in the year, people used those to based their calendar and angular measurments around that.
And 60 is very close to 64, so it could be nicely represented in binary... (10 is kinda akward, too big for 8 and too small for 16)
Hm, I want to say that this lyrics pretty much exists.
Photo of person can be faked, but faked song or text is still (but maybe arbitary bad and non-original) as real and true as "real" song or text
If i remember correctly, this idea was in G.E.B.
(sorry for bad english)
Sorry for bad english, and maybe I am deeply wrong, but:
In extreme cases, it even not "photo" as some information about photons recieved by some optic system with noise reduction afterwards. Not, it just pictures, based on recognised faces, objects and stars. And I don't know why, but I feel panifully bad about it. It is not approximation of world-how-it-is, but some expectation about world-how-people-want-it. It can recognise constellation based on few stars, and will draw nice picture of great stary sky, but will delete starlink sattelite, meteora or supernova as some unexpected noise.
[please forgive my grammar]
1. There is noisy computers which can work despite or because some unreliable part. Neural netwroks are quite ok with it for example, so some people speculate that it will be possible to build specialized noisy circuits for specific networks. 2. There is stochastic computing, in which complicated numerical functions represented as probability density distributions (?) 3. And then there is probabalistic computing, when state randomly updated in accordance with some "temprature". 4. And finally there is randomized algoritms, which are closer to classical computer science but with some stream of input. Howver, people like Avi Wigderson who succesfully removed the "random" parts of those algoritms.
Plus there is funny things with non-associativity of floating-point numbers which can lead to non-determinism when the order of execution (summation for example) is arbitary, which can lead to funny results. But because neural netwroks are robust to noise to some degree, it will still work.
And the stuff which done by Avi Wigderson requires that computers work in determinstic way (except of that random stream), so it will not be very compatible with unreliable noisy computations. However, it seems that stochastic, probabalistic and noisy computations could be combined.