> Connect this to a robot that has a real time camera feed. Have it constantly generate potential future continuations of the feed that it's getting -- maybe more than one. You have an autonomous robot building a real time model of the world around it and predicting the future. Give it some error correction based on well each prediction models the actual outcome and I think you're _really_ close to AGI.
In theory, yes. The problem is we've had AGI many times before, in theory. For example, Q learning, feed the state of any game or system through a neural network, have it predict possible future rewards, iteratively improve the accuracy of the reward predictions, and boom, eventually you arrive at the optimal behavior for any system. We've know this since... the 70's maybe? I don't know how far Q-learning goes back.
I like to do experiments with reinforcement learning and it's always exciting to think "once I turn this thing on, it's going to work well and find lots of neat solutions to the problem", and the thing is, it's true, that might happen, but usually it doesn't. Usually I see some signs of learning, but it fails to come up with anything spectacular.
I keep watching for a strong AI in a video game like Civilization as a sign that AI can solve problems in a highly complex system while also being practical enough that game creators are able to implement it in a practical way. Yes, maybe, maybe, a team with experts could solve Civilization as a research project, but that's far from being practical. Do you think we'll be able to show an AI a video of people playing Civilization and have the video predict the best moves before the AI in the game is able to predict the best moves?
Tbh I don't think an AI for Civ would that impressive, my experience is that most of time you can get away with making locally optimal decisions I.e growing your economy and fighting weaker states. The problem with current civ AI is that their economies can be often structured nonsensically, but optimized economies is usually just the matter of stacking bonuses together into specialized production zones, which can be solved via conventional algorithms.
The problem with game AI is that they "cheat". They don't play like a human. The civ AI straight up gets extra resources, AlphaStar in SC2 performed inhuman feats like giving commands in two different areas of the map simultaneously or spiking actions per minute to inhuman levels briefly. But even with all of that the AI still eventually loses. And then they start losing consistently as players play more against them.
Why? Because AI doesn't learn on the fly. The AI does things a certain way and beating it becomes a puzzle game. It doesn't feel like playing against a human opponent (although AlphaStar in SC2 probably came pretty close).
Learning on the fly is probably the biggest thing that (game) AI is lacking in. I'm not sure there's an easy solution for it.
Maybe, but a lot of people would like better AIs in strategy video games, it only adds to the frustration when people say "it wouldn't be that impressive". It's like saying "that would be easy... but it's not going to happen." (And I'm not focused on Civilization, it's just a well known example, I'd like to see a strong AI in any similar strategy game.)
I think it might be harder than StarCraft or Dota. Civilization is all about slow decision making (no APM advantages for the AI), and all the decisions are quite different, and you have to make them in a competitive environment where an opponent can raid and capture your cities.
I’ve been dying for someone to make a Civilization AI.
It might not be too crazy of an idea - would love to see a model fine-tuned on sequences of moves.
The biggest limitation of video game AI currently is not theory, but hardware. Once home compute doubles a few more times, we’ll all be running GPT-4 locally and a competent Civilization AI starts to look realistic.
I am 100% certain that the training of such an AI will result in winning a game without ever building a single city* and 1,000 other exploits before being nerfbatted enough to play a 'real' game.
(That doesn't mean I don't want to see the ridiculousness it comes up with!)
I knew it, I knew it! It would be a Spiffing Brit video.
That guy is a genius at finding exploits in computer games. I don't know how he does it, I think you need to play a fair bit of each game before you find these little corners of the ruleset.
If you train the model purely based on win rate, sure. Fortunately, we can efficiently use RLHF to train a model to play in a human-like way and give entertaining matches.
Maybe, maybe not. The stochastic, black-box nature of the current wave of ML systems gives me a gut feeling that using them like this is more of a Monkey's Paw wish granter than useful tool without a lot of refinement first. Time will tell!
I think it's also a matter of "shape". Like, GPT4 solves one "shape" of problem, given tokens, predict the next token. That's all it does, that's the only problem it has to solve.
A Civilization AI would have many problem "shapes". What do I research? Where do I build my city, what buildings do I build, how do I move my units, what units do I build, what improvements do I build, when do I declare war, what trade deals do I accept, etc, etc. Each of those is fundamentally different, and you can maybe come up with a scheme to make them all into the same "shape", but then that ends up being harder to train. I would be interested to see a good solution to this problem.
> I’ve been dying for someone to make a Civilization AI.
Would love to see someone to make an AI that can predict our economy, perhaps by modeling all the actors that participate in the economy using AI agents.
>> Usually I see some signs of learning, but it fails to come up with anything spectacular.
And even if it succeeds, it fails again as soon as you change the environment because RL doesn't generalise. At all. It's kind of shocking to be honest.
In theory, yes. The problem is we've had AGI many times before, in theory. For example, Q learning, feed the state of any game or system through a neural network, have it predict possible future rewards, iteratively improve the accuracy of the reward predictions, and boom, eventually you arrive at the optimal behavior for any system. We've know this since... the 70's maybe? I don't know how far Q-learning goes back.
I like to do experiments with reinforcement learning and it's always exciting to think "once I turn this thing on, it's going to work well and find lots of neat solutions to the problem", and the thing is, it's true, that might happen, but usually it doesn't. Usually I see some signs of learning, but it fails to come up with anything spectacular.
I keep watching for a strong AI in a video game like Civilization as a sign that AI can solve problems in a highly complex system while also being practical enough that game creators are able to implement it in a practical way. Yes, maybe, maybe, a team with experts could solve Civilization as a research project, but that's far from being practical. Do you think we'll be able to show an AI a video of people playing Civilization and have the video predict the best moves before the AI in the game is able to predict the best moves?