Scientists choose experiments to maximize information gain. There is no
reason that machine learning algorithms couldn't do this, but often they
don't.
Heh. I would say that scientists attempt to do this and machine learning
algorithms should do it.
So where is the difference other than in the quality of implementation (i.e.
"other than who performs it, of course").
----- Original Message -----
From: "Matt Mahoney" <[EMAIL PROTECTED]>
To: <agi@v2.listbox.com>
Sent: Saturday, October 25, 2008 1:41 PM
Subject: Re: [agi] If your AGI can't learn to play chess it is no AGI
--- On Sat, 10/25/08, Mark Waser <[EMAIL PROTECTED]> wrote:
> AIXI says that a perfect solution is not computable. However, a very
> general principle of both scientific research and machine learning is
> to
> favor simple hypotheses over complex ones. AIXI justifies these
> practices
> in a formal way. It also says we can stop looking for a universal
> solution, which I think is important. It justifies our current ad-hoc
> approach to problem solving -- we have no choice.
Excellent. Thank you. Another good point to be pinned
(since a number of people frequently go around and around on it).
Is there anything else that it tells us that is useful and
not a distraction?
The fact that Occam's Razor works in the real world suggests that the
physics of the universe is computable. Otherwise AIXI would not apply.
- - - - - - - - - - - - - -
Also, since you invoked the two in the same sentence as if
they were different things . . . .
What is the distinction between scientific research and machine learning
(other than who performs it, of course). Or, re-phrased, what is the
difference between a machine doing scientific research and
a machine that is simply learning?
<I'd love to hear everybody chiming in on that last question>
Scientists choose experiments to maximize information gain. There is no
reason that machine learning algorithms couldn't do this, but often they
don't.
-- Matt Mahoney, [EMAIL PROTECTED]
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agi
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