From: ' on behalf of Itamar Arel To: Ben Goertzel Cc: Jeffrey Epstein <[email protected]> Subject: Re: Distinguishing Cats from Dogs Date: Sat, 10 Oct 2009 15:19:48 +0000 I apologize for joining this thread a bit late this morning. I concur with Ben: the key point is that my system learns purely by viewing examples, even if they represent complex, high-dimensional patterns. There is absolutely no fine-tuning or hand-crafting of features, which is how one envisions the brain works. It is also what makes it scale and serve as an engine for AGI. As for Poggio's work, I believe that our system can be viewed as a working, scalable version of Poggio's (and others') concepts, particularly in the context of having a hierarchical inference system that comprises of identical cortical circuits. I appreciate the elegance in that architecture, specially since it proves to work well. - Itamar On Sat, Oct 10, 2009 at 10:27 AM, Ben Goertzel < > wrote: Hi, OK, I'll try to keep this reply "as simple as possible but no simpler ... To compare Itamar's emotion recognition to Poggio's, you would need to run both systems on the same corpus of videos, divided into the same set of categories, and then calculate the precision and recall of both systems. If they're not tested on the same corpus and the same set of categories, you can't make a rigorous comparison. I don't know if they've been tested on the same corpus or not. But, then you also have to look at how much hand-tuning of the feature extractors was done. If one system achieves better precision/recall figures than the other based on hand-tuning of feature extractors, this is nice for practical applications but not helpful for AGI. A more AGI-relevant test would be to test the two systems, without hand-tuning, on some categorization problem for which neither system was hand-configured: for instance, try them both on classifying different species of parrot, without telling Itamar or Tomaso in advance