I wonder if machine learning could be used to model the probability distributions at a greater capacity, and thus reduce backtracking.
One might also consider placing the algorithm in the context of a generative adversarial network (GAN) to adapt the tile probability modeling ML component towards a pattern that is less distinguishable from a real city.
Death metal is my go-to work music for when the fight with entropy feels like a losing battle. Especially as the rest of our capitalist society tells you to be happy all the time, death metal offers you the intuitive sense that you're not alone in the pain, and some solace in the fact that some people are brave enough to lament their difficulty out loud.
I think catharsis is really important and people should teach themselves to seek it the way they seek their professional goals, food and companionship.
Conventional processors will eventually run into physical limits of heat dissipation that will impede moore's law. Reversible computers are a way of circumventing this physical limitation because they dissipate less energy as heat.
When I think exabyte scale queries on a columnar datastore I think aggregations, but then I have this question: Why do we need to do exabyte scale queries in the first place? Wouldn't statistical inference via random sampling be faster and accurate enough?
(Granted, often times aggregations are happening after some filtering, at which point the relation being aggregated might be considerably smaller than exabyte scale.)
What differentiates LLVM IR from, say, JVM bytecode? I'm curious because there's a stalled out GNU project under GCC called GCJ that would compile JVM bytecode to native. I wonder if the issue became that statically linking in the JVM in the binary resulted in a lot of bloat, or something more intrinsic to the suitability of JVM bytecode as a platform-independent IR...
Don Libes' daughter went to my high school, and I was working as the "webmaster" for the high school newspaper's website at the time. He would ruthlessly criticize via email the product our self-taught and earnest 2010-era PHP web development. I am not a fan of Don Libes. For those interested, the website was/is: http://theblackandwhite.net/
Sure. I think what the OP was playing around with was whether we could generalize from, as you said, "expert knowledge of the problem[s] and good algorithms" so that AI could replace components in the incumbent programming paradigms. Agreed, when the end problem is well-defined and you know how "do" machine learning, the world is your oyster.
Why do we need to jump to ML-based programming again? I'm confident that as we build simpler interfaces and workflows for replacing the most modular components in our programs with AI, we'll begin to see which components are the next lowest-hanging fruit and what concrete ML problems need to be solved to model them.
We imagine a future where AI becomes a dominant paradigm for "writing" software -- I think that will be the case, but not in the way everyone suspects. I think 80% of the new value of software in the future will be derived from AI components, but that 80% of the production costs will still go into the structural glue code that supports the value-powerhouse models. Thus, most of the software written in the future will look similar to most of the code written today.
I also suspect that as the complexity of AI models increases, the structural code required to support these models will keep pace in complexity. For this reason, I see a future where the unit cost of high-quality software does not see any big drop, but where the value of this software continues to increase exponentially. A corollary to this is that valuable software will be no easier to program in the future than it is today.
One might also consider placing the algorithm in the context of a generative adversarial network (GAN) to adapt the tile probability modeling ML component towards a pattern that is less distinguishable from a real city.