>> No one tells the computer what to do.
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.