> They're useless to you because you don't seem to understand how neural network or current AI tech works. In the current fashion, generalist AI like a game engine is very much impossible.
I have some understanding how that works. You seem to read me incorrectly and extend what I say to something different.
Again, and I repeat myself, I don't claim "generalist AI". I don't claim that we can have a machine learning algorithm that learns ANY game mechanics perfectly just by looking at limited amount game play. Obviously that's not true for any neural network including human brain.
What I do claim is that we can have a machine learning algorithm that can learn some interesting game mechanics just by looking at game play. That is what the researchers intended and got some interesting results.
> Game states are too large to be entirely seen even by a computer. We need pruning and MCTS for simple adversarial searches in games like chess, Go; What makes you think some magical AI can imitate a game engine?
Ok, you may have a different definition of "game state" than I do.
My definition (which I think is the correct one): The state of the variables in a game program that define the state of the game. The game state may include variables that contain the position of monsters, player's score etc. The game state can be used
Game state is NOT all the possible game states of a particular game. That's called game state space. I'm sure you know that, I'm just being pedantic of course.
Now let us consider the game of Chess, which is not the best example, but let's do it anyway. The game state in chess is basically the position of the chess pieces on a board. That is about 64 variables. It is conceivable that we could use a machine learning algorithm to learn the _rules_ of the game and how to render the current state of the chess game ("game engine") just by showing the algorithm valid input (player move, image of the game board before move) and output (image of the game board after move) of the game.
Can we learn the rules of chess perfectly? Well, that might be hard, I fully understand that. We should carefully select all the interesting chess game positions to make sure the machine learns everything correctly and we would never be sure.
But that is not what we're trying to do here! We're trying to use machine learning to get some useful and possibly creative game engines without manually defining the rules (programming). The idea is not to replicate something existing perfectly. That was never my claim.
BTW, although this is not exactly what we're discussing here, Matthew Lai has done some interesting research on this topic. [1]
"Instead of the conventional method of teaching computers how to play chess by giving them hardcoded rules, this project is an attempt to use machine learning to figure out how to play chess through self-play, and have them derive their own rules from the games."
> What makes you think some magical AI can imitate a game engine?
Your rhetorical question seems to assume that I've said something I haven't said. If you have an argument, state that in sentences.
Let me repeat. A neural network (or some other machine learning model) can learn to do similar things than a game engine does, namely take user input, maintain game state and render game state. Therefore this model can be considered as a game engine for a given game being modeled. That's the extent of my argument. Nothing more. I don't think we need any magic with this.
[1] https://arxiv.org/abs/1509.01549