In the spirit of exploring the world of Machine Learning and A.I., I decided to write my reflection on Ryan Shapiro's EOTO presentation and talk about what I learned.
How did this come to be the machine learning and A.I. we know of today? Back in the mid-1900s, software was developing in minor ways to create big futures for computers. It started out as being used as a scoring program for games of checkers, next it developed image recognition, and later was used for mapping routes. In the late 1960s, multilayering technology allowed for future neural network research.
To begin, there is a clear difference between machine learning and artificial intelligence. Machine learning is HOW a computer develops its intelligence, while A.I. uses software to think like a human and perform tasks without the help of humans. It is almost like different parts of a brain; machine learning is the process while A.I. is the performance.
Machine learning is a software program that uses the momentum of algorithms and statistics to motivate its understanding of data. In order for it to be considered true machine learning, there is no human involvement in the process. The idea as a whole is to train computers to think and function the way humans would without the assistance of a person to interfere.
What is the process of machine learning?
First, the computer Identifies relevant data sets and prepares them for analysis. Then, Chooses the type of machine learning algorithm to use. Next, it builds an analytical model based on the chosen algorithm. It further trains the model on test data sets, revising it as needed. Lastly, it runs the model to generate scores and other findings.
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