An algorithm that could 'predict' bits in arbitrary sequences which it
had never been exposed to can hardly be called a 'learning algorithm'.
You are confusing a class of 'innate' algorithms that could be used as
a utilities for a learning program. If the infeasible idealization was
feasible one would suppose that it would be a very powerful tool.
There are some similarities between those kinds of algorithms (if any
of my memories of your previous discussions are intact) and various
Bayesian algorithms (which could be used in a range of learning
situations) that can produce some sophisticated result that could
model the learning that took place. for instance if the algorithm
produced a number of interrelated Bayesian Networks that modeled what
was learned that would be called a 'general learning algorithm'. (Not
only Bayesian Networks of course, but that is an example.)
Jim Bromer


On Sun, Nov 23, 2014 at 9:35 PM, Matt Mahoney via AGI <[email protected]> wrote:
> On Sun, Nov 23, 2014 at 8:25 PM, Alan Grimes <[email protected]> wrote:
>> An AGI is a **LEARNING SYSTEM**. Learning is it's sine qua non. If it
>> can't learn, then it isn't an AGI, once you are at that point, all you
>> need to do is connect a few network cables and walk away.
>
> I don't understand why you haven't created an AGI yet, if it's so simple.
>
> Oh wait, it's not. Are you familiar with Legg's proof that there is no
> such thing as a simple, general purpose learning algorithm? Powerful
> learners are necessarily complex. http://arxiv.org/abs/cs/0606070
>
> In case you don't want to read the paper, here is a simple proof.
> Suppose you have a small program with Kolmogorov complexity n that
> learns to predict bits in arbitrary sequences. Then I can create a
> sequence with Kolmogorov complexity slightly greater than n that your
> program can't predict. My program simulates your program and outputs
> the opposite of whatever your program predicts.
>
> --
> -- Matt Mahoney, [email protected]
>
>
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