John, Thanks for your reply to my questions about your project "Tommy" in your previous post. I'm very interested about the details but please forgive my relative freshness to this field (CS graduate heading to an AI master :) I'm particularly interested in the types of pattern mining you're planning to perform, the types of patterns you think are necessary to search for (spatial, temporal, causal, etc), and which search techniques you're going to use given time constraints. But I guess you will release more information as the project continues.
Mike Tintner wrote on May 16, 2007 3:13 PM:
Good example of how analogy/ blending works. But then you seem to be going back (in my terms) to the idea that analogy works by symbolic mappings.
As far as I can see, John is not referring to any particular symbolic implementation, but to analogy in general. You can go both ways: NARS and Copycat both find analogies, the former symbolically but the latter is somewhere between symbolic and connectionist.
The obvious term that I have been searching for in these discussions is MORPHING. The way analogy actually works, I suggest, both in your example and most of the time, is by the brain morphing one graphic/image into another or into a composite..
The brain does not use images for representation, except tiny patches in the very 'lowest' regions in the visual cortex. Representation is abstract, distributed. You could read "Seeing and Visualizing: It's Not What You Think" by Zenon W. Pylyshyn for a comprehensive synthesis of research and theory. Your idea that some kind of 'morphing' happens in the brain is not new. An interesting technique (imho) is Geoffrey Hinton's RBM (Restricted Boltzmann Machine) which is a form of generative neural network. After training on handwriting digits it can perform 'confabulation' which means that the network wanders between different consistent constraint states. This is results in interesting 'movies', which look like simplified versions of human imagination and dream. You should see them, it's fairly consistent with your view of 'morphing'. Despite the fairly limited amount of artificial neurons and the networks' generative nature, they perform very well (last time I checked, the best) on the MNIST handwritten digit database set. It is computionally expensive though. ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
