How We Find Out Things is important. Intelligence is the ability to find things out about a situation or subject, and to apply what we have previously found out to a new or novel situations or subjects.
Machine Learning does this through methods of Computational Statistics. It is powerful, but we still need to be there applying our own Intelligence & Sense-making abilities to ensure the results are logical & consistent with what we would expect.
As Ramon y Cajal, Spanish biologist and father of modern neuroscience, noted in his excellent book of advice for novice scientific researchers (1999), the key stages of a scientific investigation are (1) observation, (2) experimentation, (3) working hypotheses, and (4) proof.
Applied to a machine learning problem, this would look like:
(1) observe or explore the situation and the data, noting anything striking about it – its features, outcomes, any odd datapoints, and so on, and any intuitions we may have about the data and the relationships or connections between the various features (independent variables) and outcomes (dependent variables);
(2) experiment with the data to uncover key descriptive statistics and trends, and to deepen our sense of understanding about the situation and dataset – strictly, much of this ‘experimentation’ takes place programmatically behind the scenes using SciKitLearn functions;
(3) develop a working hypothesis, I.e. our prediction model – again, this happens programmatically with SKLearn;
(4) evaluate and prove (or disprove) the working hypothesis – here the evaluation and validation steps are used to prove/disprove the accuracy of the predictive model generated
References:
Ramon y Cajal, Santiago (1999). Translated by Swanson, Neely, and Swanson, Larry W. Advice for a Young Investigator. MIT Press, Cambridge, MA.
(Note: this title is also available online from MIT CogNet* in PDF form here: http://cognet.mit.edu/book/advice-young-investigator)
*CogNet is a research tool from MIT Press for students of Neuro and Cognitive Sciences, including AI and Computational Modelling.