This is the reply after asking a scientist to endorse putting a draft of an article into a more fitting category on the arXiv, which is a mere preprint archive (not a journal or anything like that) where I already put almost 20 articles, and the endorsement would be anonymous, so this established scientist fears that the enforcers at the PC arXiv may notify his or her University - this is the state of free speech today, the reality of the scientific community:
I am truly sorry about certain things that I wrote. For example, I have previously written about "mass incarceration" of blacks and guns in the US. I have done so also with the feeling that such would give me more traction with "liberal" readers - yes, I often cannot stop myself from being an asshole - I am truly sorry about this and hang my head in shame!
We already must deal with computers too much rather than too little, and there is already lots of advanced computing done also for example in materials science and nanotechnology, for example molecular dynamics (MD) and Monte Carlo simulations. The molecular biologist’s programs for predicting protein folding can also count as nanotechnology. Nevertheless, all of our previous articles* concluded that we need more computing, and several mentioned statistics. This would sound predictable if coming from a statistical physicist with a background in computing, advertising his skills. However, we mean a more efficient computing rather than simply more.
The (Pre-)Neanderthals were the first, you see:
When optimizing in multi-dimensional parameter spaces, local maximums are not as much of a problem as being misguided by maximums that are constrained on a lower dimensional subspace. Therefore, so called ‘walk-in’ methods are necessary. They must explore all directions of the high dimensional space. Apart from such details, we are more interested in complexity as such in order to allow complex reactions and properties/behaviors in the first place (before optimizing), and to further research how proxy-measures of complexity compare to performance.