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So ... what is Machine Learning?
It is definitely not this. This is not a true Machine Learning.

My definition is: Machine Learning is when a system has the ability to formulate 'sensible' questions and then include the answers into asking more questions thus growing its knowledge base and improving its ability to provide answers.

The definition is closer to that of Arthur Samuel than to the one given by Tom M. Mitchell - which seems to be the one used in this lecture.

Everything we humans learn starts with a question. The first step in the scientific method is the question, and then comes the hypothesis.

That is why I say, let focus on building systems capable of asking questions instead of systems that provide answers.

Knowledge is like a coral reef - the answers are the dead part of the reef and the questions are the live layer of the reef. Eventually the questions become answers and the knowledge grows as does the coral reef.

Even toddlers ask questions with the tone of their voice before they can formulate any words/answers.

I think the phrase 'Machine Learning' got hijacked from its true meaning. The knowledge domain we propagate today as 'machine learning' is not what will make the machines 'learn' in the true sense of the word 'learn'.

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