homework to do: learn the vocabulary of neuro(-bio-)logy, provide reliable..rock-proof definitions

The recent posting on familiarity, recognition, creation of new neurons, their offshoot, self, and the brain causes me another set of things to do for a homework:

• Get my reliable definitions for the topics I am dealing with, here, online, publicly. Such as for neuron, axon, cell division, dendrite.
• Learn the proper vocabulary for the items I don't know by name, such as rank growth, what somebody is aware of, somewhat, knows that an item exists (if that's being called 'knowledge', that is rather too less discerning for my purposes), nerve cell core body and others.

And all that although the latest piece of homework is not yet finished

      
Updates: none so far

previous description of this blog

Today I change(d) the description of this blog. The prior one was:

The Model of Meaning is a knowledge representation approach that shall allow to skip the training phase of an artificial neural network. – I started research in this field of subject earlier, but I am very interested in collaboration with researchers of related fields like artificial intelligence, neurobiology etc. Software applications of findings of this project also would be very appreciated.

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Updates: 20070624: Tagged the posting. Updated the posting style (layout) to my current style, such as using blockquotes when appropriate, more precise word picks, better grammar.

Approach from Tagging

ontology

Ontology is a form of classification which may be thought of as "conceptual" relationships. Whereas in a taxonomy terms may be classified together as "fruit", in an ontology the conceptual relationship may be "fruit which are used in pies" or "growing fruit for sale". Ontological relationships are inherently self-describing. Ontologies are the backbone of the semantic web, as they provide multiple links to data and therefore can support search and insightful navigation. — Source

Problem, I see: The meaning of "fruit which are used in pies" is not accessible to the machine. The machine depends on this kind of "knowledge" given to it, cannot exceed the borders resulting from that kind of knowledge. For short: The machine is unable to determine by itself whether or not a given item belongs to a "fruit which are used in pies" relationship, if nowhere is stated that this is the case. Same for different kinds of thesaurus relationships ("broader term", "narrower term", etc.), which I originally criticised.


I avoided to touch the semantic web attempt, since I feared such a kind of approach. Now, when I had to look up taxonomy, classification, terminology, etc. to avoid to mess them up, I came across the above statement, that the semantic web in fact approaches this way. Sad.

In my opinion, a machine needs a way, to get competence to decide. Knowledge to make the decision on. Knowledge on knowledge. Knowledge on knowledge to get able to verify its own knowledge. As long as there are parts of the machine's knowledge the machine cannot access, it hasn't got a chance to get independent of external minds, e.g. humans.

I don't think that the best approach to achieve that goal is to stack more and more complexity/abstraction layers on the already not-working attempts. Instead, I think, it is necessary to determine a way to make most basic knowledge accessible to the machine, in a way that the machine doesn't have to depend on externally given knowledge. I think, that if that basic goal is achieved, more complex kinds of content/knowledge can be built atop.<<

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Updates: 20070624: Tagged the posting. Updated the posting style (layout) to my current style, such as using blockquotes when appropriate, more precise word picks, better grammar.

[Merged from (the now removed) ia: organizing notions:]

We can start to think now of all of scales of human knowledge and understanding. Thinking of libraries with all their cross-listed bibliographies, the concepts that we've learned in our lives, or just within physics. We can go further down in scale to the articles, the sentences, the words, the phonems. — Source

Let me see, how far your thoughts evolved upon this: How do you think a simple pair of "red" and "not-red" ist representable? Recursively it isn't. -- At least not that simple as you might expect.<<

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Updates: 20070624: Updated the posting style (layout) to my current style, such as using blockquotes when appropriate; and might have removed my workaround for backlinks blogger.com didn't support in earlier times. Now, backlings are there, therefore the bypass can be dropped.