On 10/04/2013 15:42, Matt Mahoney wrote:
Anyway, my suggestion is:
1. Devise specific tests and measurement criteria, for example
precision and recall on ImageNet, or reading text.
2. Estimate the computation required and decide if the goal is feasible.
3. Test the algorithm and publish results.
It seems that the last result produced by OpenCog (other than
commercial projects that nobody knows about) is intelligent game
characters in a simulated world several years ago. This tests none of
the hard problems in AI like language, vision, art, or robotics. All
of the work has been on software development, and none on basic
research. How do you know you are on the right path without ever doing
tests or experiments along the way?
Practically any form of software development involves lots of testing.
However, looking at:
http://multiverseaccordingtoben.blogspot.com/2011/06/why-is-evaluating-partial-progress.html
...certainly suggests that Ben has some rather odd ideas about testing.
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