Читать книгу Intelligent Connectivity - Abdulrahman Yarali - Страница 31
2.1.2 Machine Learning as a Precursor to AI
ОглавлениеThe learning algorithms discussed in the section above constitute the overall topic or subject of machine learning. This involves the study and innovation of both the algorithms and statistical frameworks where essential and critical tasks can be ensured without a specific pattern, depending upon how inference and adaptation should work. Essentially speaking, machine learning operations are viewed as being a “subset” of AI in which the algorithms implemented could effectively create a separate mathematical model through the full realization of the available data, but without the presence of a specific embedded task that has been constantly defined (Andrieu et al. 2003). At present, machine learning is being used. There is a definitive closeness detected for the technology in computational statistics, which can be immensely beneficial to everyone involved. There are many forms of learning made possible by this strain of technologies. However, AI is tied with that specific active learning event, which works based on choosing the exact variables to work upon selectively at the beginning itself (Arel, Rose, and Karnowski 2010). As a result of this, there is a significant decrease in costs accrued in terms of time and output. Therefore, machine learning holds a prominent position, which could be why fully‐fledged AI technologies could be developed in many ways. It is essential to “proverbially” go down to a far deeper extent than what one can imagine (West 2016). This is the overall effect of what machine learning could achieve concerning the technology of AI, and reflects far greater possibilities, all of which would be rendered quite possible even if the need for knowledge goes deeper than what one might imagine.