Redirect Left wrote:
Redirect Left wrote:
@Redirect Left: Fixing or improving an existing AI would be more useful I think. We have lots of AIs, but very few maintainers. That also gives you a head start, as you have to do less ground-work.
Well, i'll have a look around, and if I find one that users seem to particularly enjoy that I can add to or resolve a glitch with (it'll take a while to learn AI code anyway) I'll see if it allows derivitive works / outside corrections, improvements & additions.
Unfortunately I had to quickly give up on this idea, as i found the way AIs are coded to be very unituitive and hard to figure out exactly.
Yes, they are, but at the same time they are not.
The main reason why you find the code unintuitive and hard to read is because you didn't write it. Code follows the logic of its author, and everybody has a slightly different logic. It takes time and effort to figure out what logic the code has and to make that your own. In my experience, if you stick with studying it, you'll start to see patterns at some point, and then slowly code starts to make sense.
Of course this also goes in the other direction. Code that you or I write looks unintuitive and hard to understand to most others. It's not bad code, it's just how it works.
I hope in the future, the method of implementing AI is improved to be a lot easier to do. Building a fully working and decent AI that builds proper networks and is very profitable seems to be more effort than the pleasure you'd get out of it at the end, at least from what I saw.
I hope too that it gets easier, but I don't expect that to happen in my lifetime tbh unless you throw a lot of CPU power at it (ie deep learning and such). Writing a good AI is just a hard problem.
Ever tried to find a computation that understands when a network is "proper and very profitable"?
For most AI authors, I think the end-result is less relevant, but the journey or path to the end is. The pleasure of learning new things, and finding how to fix that problem in a nice way.