The learning problem

I would like to write about artificial intelligence, since, having failed to achieve a sufficient academic grade and done a lot of reading that I was unable to synthesize, I will document my own learning process as a cognitive one, hoping that this could be developed to an algorithm where parallel processors and quanta chips are able to emulate real multi-processing and neo-thinking.
I am one who learns by copying basically and I do not consider myself a very sharp person, my thinking skills might have been reduced through lack of practice, and, my memory is lost as I age.
Is common sense all that common? My belief is that this is possible, nonetheless there are still a lot of barriers are scientists for reasons of being as human as we are can manage to develop so much in a career or a lifetime, therefore whilst there appears to be a lot of theory and programming resources available, there is hugely under-utilized potential. Is it just a money problem?
Further reading
1. The learning curve, as learning varies with experience.

Studying emotions...

I have considered myself a database architect throughout most of my career, however, if you ask my friendly dog he would probably bark back that I am a mixed breed. One of the most important elements in data integrity and information quality is written in books, and, as my attention is distracted from standard textbooks that I still follow, creativity seems more interesting to engage in as I ask myself questions that make me feel like I am thinking out of the box. Effectively nobody understands me, therefore, my ideas must be really jumbled out of a jigsaw puzzle.
Having been inspired by recent studies - considered inethical by the press, I came up with the basic variables leading to analyzing emotions as the basis for artificial intelligence. One might use EEG tests, as equipment and then gather the data and crunch it to a research project where sample data would be the basis for a set of training data that could be used for knowledge discovery, initially based on a score that is multiplied by a weighting to measure the impact of emotional bias that would be gathered through medical equipment, and, the simplest of formulas would look like this:
Θ = 1 x 0.99 = 0.99 or 99%
NOTE: Θ is a fraction of 1 and 0.99 represents the emotional bias weighting fraction, a measure to gauge the statistical error when these factors are aggregated and processed through signal processing has to be analyzed and this analysis will then be developed to a software prototype, following this basic percentage driven score, which falls short of mathematical analyses.

Further reading
1. MTIQ Publications.
2. DHS Information Quality Standards
3. Whitehouse Information Policy
4. DSDM Consortium