From: Vladimir Nesov (firstname.lastname@example.org)
Date: Sat Feb 28 2009 - 19:39:34 MST
On Sun, Mar 1, 2009 at 5:14 AM, William Pearson <email@example.com> wrote:
> On 01/03/2009, Vladimir Nesov <firstname.lastname@example.org> wrote:
>> I won't look at it so optimistically. Machine learning is actually
>> moving towards AGI, they just mostly don't realize it. ;-) As the
>> inference algorithms and representations become more powerful, at one
>> point they may produce something dangerous. And it's not 100 people,
>> it's a mainstream effort.
> I'm not quite sure it is moving straight towards AGI, I think it is
> veering somewhat to the left ;-). Still useful movement but not on
> My criticism of it is that it still deals with fixed hypothesis
> spaces, AFAIK. To get AGI you need to go to systems that redefine
> their own hypothesis spaces.* Or have you seen examples of ML systems
> that do this or are moving towards it?
You only say that because you see "non-fixed hypothesis spaces" as a
way to make machine learning sufficiently effective. So do they. There
are lots of problems for which the currently available methods are
insufficiently expressive, and people are searching for better
methods. As the methods they've got become more powerful, they'll
realize the potential; there is no stopping it.
-- Vladimir Nesov http://causalityrelay.wordpress.com/
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