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I think you are confusing utility functions with intelligence. All AIs need utility functions. An AI without a utility function would just do nothing. It would

I think you are confusing utility functions with intelligence. All AIs need utility functions. An AI without a utility function would just do nothing. It would have no reason to beat Atari games, because it wouldn't get any reward for doing so. Even humans have utility functions. For example, we get rewards for having sex, or eating food, or just making social relationships with other humans. Or we have negative reinforcement from pain, and getting hurt, or getting rejected socially. You can come up with more complicated utility functions. Like instead of beating the game, it's goal could be to explore as much of the game as possible. To discover novel things in the game. Kind of like a sense of boredom or novelty that humans have. But in the end it's still just a utility function, it doesn't change how the algorithm itself works to achieve it. AGI is entirely agnostic to the utility function.

YeGoblynQueenne 11y ago
>> I think you are confusing utility functions with intelligence. No, what I'm really saying is that you can't have an autonomous agent that needs to be told what to do all the time. In machine learning, we train algorithms by giving them examples of what we want them to learn, so basically we tell them what to learn. And if we want them to learn something new, we have to train them again, on new data. Well, that's not conducive to autonomous or "general" intelligence. There may be any number of tasks that your "general" AI will need to perform competently at. What's it gonna do? Come back to you and cry every time it fails at something? So then you have a perpetual child AI that will never stand on its own two feet as an adult, because there's always something new for it to learn. Happy little AI, for sure, but not very useful and not very "general". Except for a general nuisance, maybe. Edit: I'm saying that machine learning can't possibly lead to general AI, because it's crap at learning useful things on its own.
- Houshalter 11y ago
  Machine learning doesn't "need to be told what to do all the time". No one told alphaGo what strategies were the best. It figured that out on it's own, by playing against itself. There is also unsupervised and semi-supervised learning, which can take advantage of unlabelled data. Even supervised learning can work really well on weakly labelled data. E.g. taking pictures from the internet and using the words that occur next to them as labels. As opposed to hiring a person to manually label all of them. I don't know what situation you are imagining that would make the AI "come back and cry". You will need to give an example.
  - YeGoblynQueenne 11y ago
    >> Machine learning doesn't "need to be told what to do all the time". No one told alphaGo what strategies were the best. Of course they did. They trained it with examples of Go games and they also programmed it with a reward function that led it to select the winning games. Otherwise, it wouldn't have learned anything useful. >> There is also unsupervised and semi-supervised learning, which can take advantage of unlabelled data. Sure, but unsupervised learning is useless for learning specific behaviours. You use it for feature discovery and data exploration. As to semi-supervised learning, it's "semi" supervised: it learns its own features, then you train it with labels so that it learns a mapping from those features it discovered to the classes you want it to output. >> I don't know what situation you are imagining that would make the AI "come back and cry" That was an instance of humour [1]. [1] https://en.wikipedia.org/wiki/Humour https://en.wikipedia.org/wiki/Humour
    - Houshalter 11y ago
      >Of course they did. They trained it with examples of Go games and they also programmed it with a reward function that led it to select the winning games. Otherwise, it wouldn't have learned anything useful. Yes, but it doesn't need to be trained with examples of Go games. It helps a lot, but it isn't 100% necessary. It can learn to play entirely through self play. The atari games were entirely self play. As for having a reward function for winning games, of course that is necessary. Without a reward function, any AI would cease to function. That's true even of humans. All agents need reward functions. See my original comment. >That was an instance of humour Yes I know what humour is lel. I asked you for a specific example where you think this would matter. Where your kind of AI would do better than a reinforcement learning AI.
      - YeGoblynQueenne 11y ago
        >> The atari games were entirely self play. That's reinforcement learning and it's even more "telling the computer what to do" than teaching it with examples. Because you're actually telling it what to do to get a reward. >> Without a reward function, any AI would cease to function. I can't understand this comment, which you made before. Not all AI has a reward function. Specific algorithms do. "All" AI? Do you mean all game-playing AI? Even that's stretching it, I don't remember minimax being described in terms of rewards say, and I certainly haven't heard any of about a dozen classifiers I've studied and a bunch of other systems of all sorts (not just machine learning) being described in terms of rewards either. Unless you mean "reward function" as the flip side of a cost function? I suppose you could argue that- but could you please clarify? >> your kind of AI Here, there's clearly some misunderstanding because even if I have a "my kind" of AI, I didn't say anything like that. I'm sorry if I didn't make that clear. I'm not trying to push some specific kind of AI, though of course I have my preferences. I'm saying that machine learning can't lead to AGI, because of reasons I detailed above.

YeGoblynQueenne 11y ago

>> I think you are confusing utility functions with intelligence. No, what I'm really saying is that you can't have an autonomous agent that needs to be told what to do all the time. In machine learning, we train algorithms by giving them examples of what we want them to learn, so basically we tell them what to learn. And if we want them to learn something new, we have to train them again, on new data. Well, that's not conducive to autonomous or "general" intelligence. There may be any number of tasks that your "general" AI will need to perform competently at. What's it gonna do? Come back to you and cry every time it fails at something? So then you have a perpetual child AI that will never stand on its own two feet as an adult, because there's always something new for it to learn. Happy little AI, for sure, but not very useful and not very "general". Except for a general nuisance, maybe. Edit: I'm saying that machine learning can't possibly lead to general AI, because it's crap at learning useful things on its own.

Houshalter 11y ago
Machine learning doesn't "need to be told what to do all the time". No one told alphaGo what strategies were the best. It figured that out on it's own, by playing against itself. There is also unsupervised and semi-supervised learning, which can take advantage of unlabelled data. Even supervised learning can work really well on weakly labelled data. E.g. taking pictures from the internet and using the words that occur next to them as labels. As opposed to hiring a person to manually label all of them. I don't know what situation you are imagining that would make the AI "come back and cry". You will need to give an example.
- YeGoblynQueenne 11y ago
  >> Machine learning doesn't "need to be told what to do all the time". No one told alphaGo what strategies were the best. Of course they did. They trained it with examples of Go games and they also programmed it with a reward function that led it to select the winning games. Otherwise, it wouldn't have learned anything useful. >> There is also unsupervised and semi-supervised learning, which can take advantage of unlabelled data. Sure, but unsupervised learning is useless for learning specific behaviours. You use it for feature discovery and data exploration. As to semi-supervised learning, it's "semi" supervised: it learns its own features, then you train it with labels so that it learns a mapping from those features it discovered to the classes you want it to output. >> I don't know what situation you are imagining that would make the AI "come back and cry" That was an instance of humour [1]. [1] https://en.wikipedia.org/wiki/Humour https://en.wikipedia.org/wiki/Humour
  - Houshalter 11y ago
    >Of course they did. They trained it with examples of Go games and they also programmed it with a reward function that led it to select the winning games. Otherwise, it wouldn't have learned anything useful. Yes, but it doesn't need to be trained with examples of Go games. It helps a lot, but it isn't 100% necessary. It can learn to play entirely through self play. The atari games were entirely self play. As for having a reward function for winning games, of course that is necessary. Without a reward function, any AI would cease to function. That's true even of humans. All agents need reward functions. See my original comment. >That was an instance of humour Yes I know what humour is lel. I asked you for a specific example where you think this would matter. Where your kind of AI would do better than a reinforcement learning AI.
    - YeGoblynQueenne 11y ago
      >> The atari games were entirely self play. That's reinforcement learning and it's even more "telling the computer what to do" than teaching it with examples. Because you're actually telling it what to do to get a reward. >> Without a reward function, any AI would cease to function. I can't understand this comment, which you made before. Not all AI has a reward function. Specific algorithms do. "All" AI? Do you mean all game-playing AI? Even that's stretching it, I don't remember minimax being described in terms of rewards say, and I certainly haven't heard any of about a dozen classifiers I've studied and a bunch of other systems of all sorts (not just machine learning) being described in terms of rewards either. Unless you mean "reward function" as the flip side of a cost function? I suppose you could argue that- but could you please clarify? >> your kind of AI Here, there's clearly some misunderstanding because even if I have a "my kind" of AI, I didn't say anything like that. I'm sorry if I didn't make that clear. I'm not trying to push some specific kind of AI, though of course I have my preferences. I'm saying that machine learning can't lead to AGI, because of reasons I detailed above.