When you are presented with a new concept, the first step is to "mechanically" learn it. At that point, you are able to solve only questions that closely match what you were taught.
The next step is to really understand the meaning of the concept, in a deeper sence. In school, this is usually achieved by providing excercises that are progressively harder and harder. Harder in this case means that the questions diverges more from the learnt material and more and more requires deeper understanding of the concept.
If you only go so far as learning the...
You touch into a topic that is all too common in the first half, and that is the problem of definitions. It is not unusual to find people having an argument over something, without first doing a clear definition of the question.
For example, what is intelligence, and what is conscience? There are lots of discussions about the possibility or impossibility to create artificial versions of these, without first having a common definition. Such an argument is almost a waste of time, except for the situation where it may lead to better understanding of definition...
I have two observations, one personal and one general:
Once, I tried to apply artificial neural nets on the task to evaluate positional situations in the game of Go. I did a very basic error, which was to train the net only on positive examples. The net quickly learned to give high scores for these, but then I tested on bad situations it still reported high scores. Maybe a little naive mistake, but you have to learn sometimes.
A very common example is testing of software. Usually, people pay much attention on testing the positive cases, and verifying that th... (read more)