Frank,

> The difference between traditional programming and AI is that, in
> traditional programming approach, we manually set all parameters
> and actions by imaging all possibilities. In AI, the machine “learns”
> parameters by finding the patterns in datasets (numerical data,
> images). Here the parameters refer to the coefficients or weights
> which determine a “model”. A Model is for us to make predictions
> after it has been well trained with datasets.

Thanks for your instructional efforts, but you may want to read a bit
more about the relationship between AI and machine learning. Patrick
Winston's AI text would be a good place to start:

https://www.amazon.com/Artificial-Intelligence-3rd-Winston/dp/0201533774

This has chapters on learning but also on many other aspects of AI.
You may even want to read my 1995 textbook on machine learning,
which covers more paradigms than you seem to know about:

https://www.amazon.com/Elements-Machine-Learning-Morgan-Kaufmann/dp/1558603018

> It is common for non-AI professionals to think anything with automatic
> functions (such as automations, robots) is AI. Right now, we have
> formed many branches in AI such as Supervised Learning, Unsupervised
> Learning, Reinforcement Learning, but the core concept is always
> machine learning. Without machine learning for various models, this
> is NO concepts of AI. We need to make sure what we are doing.

I assure you that I'm not confusing traditional programming with AI,
but also that not all AI is machine learning.

I helped launch the field of machine learning in the 1980s and I served
as first editor of the journal Machine Learning. I think you may want
to read more about the history of the field.

Regards,

Pat Langley
http://www.isle.org/~langley/
http://www.isle.org/~langley/cv.pdf
https://scholar.google.com/citations?user=5JcRw-sAAAAJ