>>>>>>>>>> Matt Mahoney [mailto:[EMAIL PROTECTED] wrote
> eat(Food f) > eat(Food f, List<SideDish> l) > eat (Food f, List<Tool> l) > eat (Food f, List<People> l) > ... This type of knowledge representation has been tried and it leads to a morass of rules and no intuition on how children learn grammar. We do not know how many grammar rules there are, but it probably exceeds the number of words in our vocabulary, given how long it takes to learn. <<<<<<<<<<<<<<< As I said, my intention is not to find a set of O-O like rules to create AGI. The fact that early approaches failed to build AGI by a set of similar rules does not prove, that AGI cannot consist of such rules. For example, there were also approaches to create AI by biological inspired neural networks with some minor success but there was not the real breakthrough too. So this does not prove anything but that the problem of AGI is not so easy to solve. The brain is still a black box regarding many phenomenon. We can analyze our own conscious thoughts and our communication which is nothing else than sending ideas and thoughts from one brain to the other brain via natural language. I am convinced, that the structure and contents of our language is not independent of the internal representation of knowledge. And from language we must conclude that there are O-O like models in the brain because the semantics is O-O. There might be millions of classes and relationships. And surely every day or night, the brain refactores parts of its model. The roadmap to AGI will probably be top-down and not bottom-up. The bottom-up approach is used by biological evolution. Creating AGI by software engineering means that we first must know where we want to go and then how to go there. Human language and conscious thoughts suggests that AGI must be able to represent the world O-O like at the top-level. So this ability is the answer for the question where we want to go. Again, this does not mean that we must find all the classes and objects. But we must find an algorithm that generates O-O like models of its environment based on its perceptions and some bias where the need for the bias can be proven from reasons of performance. We can expect that the top-level architecture of AGI is the easiest part in an AGI project, because the contents of our own consciousness gives us some hints (but not all) how our own world representation works at the top-level. And this is O-O in my opinion. There is also a phenomenon of associations between patterns (classes). But this is just a question of retrieving information and attention to relevant parts of the O-O model and is no contradiction to the existence of the O-O paradigm. When we go to lower levels, it is clear that difficulties arise. The reason is that we have no possibility for conscious introspection of the low levels in our brain. Science gives us hints mainly for the lowest levels (chemistry, physics...). So the medium layers of AGI will be the most difficult layers. By the way this is also often the case in normal software. In the medium layers there will be base functionalities and the framework for the top-level. ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=101455710-f059c4 Powered by Listbox: http://www.listbox.com
