"Mathematicians have tried many different possibilities and have yet to find a single one that works. But they also haven't been able to prove that such a box doesn't exist, so the hunt is on for a perfect cuboid." apparently, something wrong here.
It reminds me the other day I read a 2012 NLP PhD paper where there is no contact information(email, etc) all over. Frustrating. And recent news about a new genetic editing technology claimed by a seemingly 'nobody' unrecognized in bioinformatics community before is causing great controversy that the research could not be replicated, lacking critical detail about process and data. When asked why he does not want to disclose who indeed replicated, the author said it would let them down. I just don't understand the reasoning. All about social pshycology in science?
Don't you remember that crowd-sourced stock predication website that publish the anonymized data and let crowd build model without knowing which specific stock/portfolio is used?
The trend is microservice with polyglot environment where developers can use their favorite language for the right task. I can see Java, Python, Go, Nodejs all having their place in back end. Especially with PaaS buildpack, docker container, devops, it is less and less significant to choose one particular language as primary back end language.
Isn't this approach similar to or used in DeepMind's AlphaGo where the policy network is corresponding to high-level representational knowledge(kind of expert system in traditional AI), and the value network is corresponding to the decision making part(reinforcement learning)?
The other day I saw a post on social media that how he explained spaces from 0-dimension to 10-dimention in simple drawings. At the end, he claims that 10-dimentional space is just a dot, and there is no higher dimensional space more than 10-dimention any more. And he justifies his claims based on the string theory. At that time immediately I thought about the billions of parameter space in deep neural network and fractal spaces such as Mandelbrot Set. How could one simulate the universe based on only 3D space-time lattice?
Maybe you are heavily on huge algorithmic work. In practice, most algorithms are polynomial-time, and we are looking for under-polynomial-time alternative - log(n). For exponential-time, we are just looking for an approximate alternative.
I always thought "NP-complete/NP-hard" metaphor only meaningful in theoretical work. In practice, it does not make much sense in guiding what/how the real world problem is to be solved.
I guess the difference with the simulated annealing approach would be that this approach(and the simple picking-the-most-similar-column approach) assumes you know the particular shredding method in advance, whereas the simulated annealing does not have this requirement. For example, does this still work out for shredding diagonally in some unknown degree?
It is baffling that machine translation on human language is approaching human accuracy but it is still far behind on programming language, given that it is supposed to be more closed/matching to metal/machine.