>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.
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.
>That's reinforcement learning and it's even more "telling the computer what to do" than teaching it with examples.
No one tells the computer what to do. They just let it do it's thing, and give it a reward when it succeeds.
>Not all AI has a reward function. Specific algorithms do. "All" AI?
Fine, all general AI. Like game playing etc. Minimax isn't general, and it does require a precise "value function" to tell it how valuable each state is. Classification also isn't general, but it also requires precise loss function.
Sure they do. Say you have a machine learning algorithm, that can learn a task
from examples, and let's notate it like so:
y = f(x)
Where y is the trained system, f the learning function and x the training
examples.
The "x", the training examples, is what tells the computer what to learn,
therefore, what to do once it's trained. If you change the x, the learner can do
a different y. Therefore, you're telling the computer what to do.
In fact, once you train a computer for a different y, it may or may not be
really good at it, but it certainly can't do the old y anymore. Which is what I
mean by "machine learning can't lead to AGI". Because machine learning
algorithms are really bad at generalising from one domain to another, and the ability to do so
is necessary for general intelligence.
Edit: note that the above has nothing to do with supervised vs unsupervised etc. The point is that you train the algorithm on examples, and that necessarily removes any possibility of autonomy.
>> Fine, all general AI. Like game playing etc.
I'm still not clear what you're saying; game-playing AI is not an instance of
general AI. Do you mean "general game-playing AI"? That too doesn't always
necessarily have a reward function. If I remember correctly for instance, Deep
Blue did not use reinforcement learning and Watson certainly does not (I got access to the
Watson papers, so I could double-check if you doubt this).
Btw, every game-playing AI requires a precise evaluation function. The
difference with machine-learned game-playing AI is that this evaluation function
is sometimes learned by the learner, rather than hard-coded by the programmer.
The thing about neural networks is they can generalize from one domain to another. We don' have a million different algorithms, one for recognizing cars, and another for recognizing dogs, etc. They learn features that both have in common.
>The "x", the training examples, is what tells the computer what to learn, therefore, what to do once it's trained. If you change the x, the learner can do a different y. Therefore, you're telling the computer what to do.
But with RL, a computer can discover it's own training examples from experience. They don't need to be given to it.
>I'm still not clear what you're saying; game-playing AI is not an instance of general AI.
But it is! The distinction between the real world and a game is arbitrary. If an algorithm can learn to play a random video game, you can just as easily plug it into a robot and let it play "real life". The world is more complicated, of course, but not qualitatively different.
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.