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Originally Posted by CBeisbol
I would be one of those people.
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Yes, I figured, but like I said the overwhelming majority of people would be against it, and if it were an opt-in type situation, very few would opt in, especially after it started producing the typically horrific results that neural network AIs tend to produce in the early stages. Especially when the final "payoff" is... weird, inexplicable crap (see below).
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What about just doing some simple tests? Hard-coding some basic guidelines (to prevent the above) and then looking at the short and long term outcomes of doing this move, or not doing this move?
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Neural networks are not AFAIK really good at looking at short and long term outcomes like this. I'm grossly oversimplifying this, I'm sure - I don't program AI - but the basics of a neural network is that you give the AI a set of data, you allow it to fire up, say, 1000 different versions of itself to make a series of discrete decisions - ideally the same decisions, which would make it that much harder if you set it up for people to play against, and if you tried to just sim the game against the current AI all it would be doing is figuring out how to exploit said AI - even using that as a basis for a 2nd or 3rd generation to play against players would probably come with some wonky results that you'd have to take a generation or more to untrain the AI.
It's not very easy to get one's head around. In fact, kind of the point of neural networks / machine learning is that you're not supposed to get your head around how exactly it comes to a given conclusion at all - a neural network is basically a "black box" where you put data in, you watch the outcomes of the data, you tell it what the best outcomes it came up with are so that it can use those for its next generation of power, and so on and so forth.
I'm not seeing how that would be anything close to a stand-in for the current AI. At best, it'd suck for a while and then finally figure out that the ideal way of doing things is the way GMs have been doing them all along, which strikes me as very unlikely. At worst, it'd come up with very alien methodologies. And through it all, all the dev team would be able to say to the "oh my god what did you do to the AI it was bad before but it's terrible now and still sucks!!!" crowd is, "be patient and also maybe whatever it's doing to your game is the real optimal way baseball GMing should be done".
The other, bigger issue is that OOTP would need to bring in a dev who knows how to work with neural networks / machine learning. That's a pretty specialized skill and people who are good at it make a crap-ton of money. It's at the point now to where even if one of the current devs were to train themselves up in machine learning to the point of confidence, they could just go to like Google or somewhere similar and make scads more than they ever could at OOTPDev. Maybe in 10 or 20 years when the market has died down they can reconsider.