> I agree with you that our current pace of progress toward a really
> smart OpenCog system is disappointingly slow.  As noted before, my
> hope/aim is that, once we have achieved a sufficient level of
> functionality, demonstration of the system will suffice to bring in
> dramatically more funding, which then will accelerate progress.

Specifically what I'm aiming for is interesting general-intelligence behavior
in animated agents and (assuming a planned collaboration with Hanson
Robotics goes well)
humanoid robots...

I understand you are pumped about quantitative benchmarking....  My
own view is that the focus on precision and recall stats in machine
learning has had both positive and negative aspects....  I don't yet
see how to make useful, rigorous, quantitative incremental progress
benchmarks for early-stage AGI...

I note that the recent excitement about deep learning is based on
qualitative results like

http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html?pagewanted=all&_r=0

rather than on quantitative benchmark results improvements...

I think that if we succeed in creating an OpenCog agent that gives a
suitable qualitative impression of general intelligence, based on a
foundation that any expert who looks into the details can see is based
on sophisticated learning, then this will be enough to motivate a
burst of attention and $$ for OpenCog and for AGI in general...

It's true that getting to the point of such a qualitatively exciting
demonstration is taking longer than I've hoped....  But ultimately,
whether my task duration estimations are off by a small integer
multiple or not is not the main point.  The main point is whether the
core AGI design can work or not...

--- Ben G


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