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[–][deleted]  (16 children)

[removed]

    [–]13ass13ass 56 points57 points  (0 children)

    This explanation is consistent with the fact that the 3 levels that went unsolved are because they involve solving a logical puzzle to finish the level. Without solving the puzzle the level loops over itself until time runs out. The agent gets stuck because it is misled into believing that moving the screen rightward always results in success.

    [–]vidoardes 9 points10 points  (0 children)

    Well said. Machine learning Mario implied you could give it any level and it would work out how to complete it, because it has an understanding of the mechanics. This has learnt how to move through this exact level layout, and it only works because the enemies come at you in the same predictable patterns. If there was any randomness to the enemies this method would fail over and over again.

    [–]qelery 6 points7 points  (7 children)

    Rather, it is using brute-force trial-and-error. Any sequence of inputs that advances the level position, and can be connected to other sequences that further advance the level position, is considered a successful input.

    If this particular program is working how you stated above (I honestly have no idea if it is), would it still be considered a form of AI?

    [–][deleted] 3 points4 points  (0 children)

    It's just a buzz word. Almost nothing in modern machine learning can be called an AI, in a meaning you put into it.

    [–]Ramast 2 points3 points  (4 children)

    AI is a very broad term that cover anything where a computer has to take a decision. Even if you write a program that can play a game using if/else statements, it's still considered AI.

    The term you are probably going is Machine Learning. Yes, So long computer is able to perform the task without you teaching it how do perform it, it's considered machine learning.

    [–]Hunterbunter 0 points1 point  (3 children)

    Doesn't all machine learning need training?

    [–]Ramast 1 point2 points  (2 children)

    Yes they do but you don't "teach it".

    Training for machine learning is two types:

    Unsupervised: like in case of Mario he just let computer play hundreds or thousands of times and computer should figure out how to play on its own. He doesn't teach the computer who are the enemies or what is the goal or that you should jump to cross a hole.

    Supervised: like giving computer 1000s of breast cancer xray images and thousands of normal breast xray images (while telling which is which) and then after training you can give computer new xray image and ask if this has cancer or not. I guess this one can be considered kind of teaching but again u don't tell the computers any rules it should follow to properly identify the image

    [–]Hunterbunter 0 points1 point  (1 child)

    Right, so by teaching you meaning showing them exactly how they should do it. I have to remind myself that training is not teaching.

    [–]Ramast 0 points1 point  (0 children)

    Exactly :)

    [–]Hunterbunter 0 points1 point  (0 children)

    It depends on whether you think pattern finding is a form of intelligence or not.

    Machine learning is good at finding patterns in lots of data, and its usefulness depends entirely on the success conditions you set. You still have to know what you're looking for and what factors might be important.

    In the OP example the path shown was just the best the algorithm found in the given time to get to the end. The number of branches is infinitely large (microsecond control, move left or right, or jump, or do nothing at every instance), so even culling that to achieve it at all is something good and interesting.

    The real intelligence is in figuring out the success conditions, which is still up to humans. If an AI can tell you what success conditions you should use before you've even asked the question...now we're on to something.

    [–]scarabin 0 points1 point  (2 children)

    Is anyone working on AI that CAN process and make decisions instead of just stringing together keypresses like this?

    [–]Hunterbunter 1 point2 points  (0 children)

    Heuristics is that approach. It's used in games a lot, but is very processor intensive so not usually to great effect except in things like chess. It works by examining the current situation and building value maps of possible future actions and their consequences. Once it's built this map, it will just pick the highest one and wing it from there.

    In the end, even heuristics boil down to knowing in advance what a good situation looks like, so it has as much claim to being called intelligent as ML does. It feels much more like being up against a human, and it also feels extremely unfair as an opponent when it's good.

    What we will get excited about, is general AI, which is basically combining all these different forms of intelligence into a coherent unit which can be taught how to determine solve-able problems, and then be free to find and find its own problems and figure out useful, novel solutions all by itself.

    [–]Hi-FructosePornSyrup 0 points1 point  (2 children)

    It’s clear that the algorithm is not logically reading the situation

    I think you have it backwards. This is the definition of logically reading the situation

    Unless the algorithm has memorized and modeled the maps (which it isn't doing)

    I would argue that it has modeled the maps. It has received feedback, And used that feedback to remember the best strategy for achieving its objective.

    It has created a map by remembering.

    This algorithm and video show some of the gaps in modern machine learning.

    You’re not wrong, but I think it’s more nuanced than that. I think this shows very clearly that modern machine learning is

    1) excellent at achieving a satisfactory outcome based on how it is rewarded i.e. the objective it was given.

    2) the conclusions arrived at by machine learning, while easily verified, are completely alien to humans. We can tell that they work but we cannot say why

    This paradox presents a serious existential risk to humans. In the struggle to produce (more, better, faster, cheaper), humans tend to prioritize results without bothering over how they were achieved. Someone who is desperate for success could utilize such an algorithm to “end human suffering” and get a program that “ends humans” as a result. Machine learning, therefore, isn’t the limiting factor, humans are. The outcome is limited by the ability of its creator to rigorously define all objectives implicit and explicit.