I have been busy traveling, going between the extremes of sunny Rome and chilly Minneapolis.
Last week, I attended an EUCognition meeting on ‘social cognition’ – with many talks addressing the general area of language learning and its use in communication between cognitive agents. The speakers included Stevan Harnad, Luc Steels, Michael Arbib and Jordan Pollack. So, quite apart from the location in Venice, the event was highly informative. I was particularly intrigued by Arbib’s description of constructive grammars and the issue of how new content (e.g., a new counting system) makes its way into language and turns into efficient communication on that subject. I wonder if this can also help explain some questions I brought up in earlier posts, about how dynamically dexterous behaviours are acquired, stored and improved.
This week, I am attending a workshop on the topic of protein folding – at the Institute for Mathematics and its Applications, in Minneapolis. Despite appearances to the contrary, there are many common threads between the problems of modeling and computing about robotic and protein motions. For instance, there were a couple of speakers who mentioned the use of machine learning tools that would be familiar to a roboticist – use of manifold learning algorithms to infer good ‘low-dimensional representations’ that help speed-up computation, use of a database to induce empirical energy landscapes, etc. However, the thing that strikes me most about this area is the immense complexity and intricate hierarchy of the system under study – many complex engineering systems seem relatively simple in comparison to the actual complexity of biological systems. Lots more to be done!