Understanding AI for Dummies
- September 12, 2019
Machine learning, a subset of AI, is an area that deals with familiarizing the machine with scenarios and predict its outcome by giving it multiple events to see the same event (sometimes with minor changes) so that the machine will be able to grab patterns that are common between them and make predictions in the future.
It might be easier to cite an example for a better understanding.
Imagine the shape of a dog. There are some features that make us know that the wagging tail, the lopsided ears, the hanging out tongues, etc. To make sure a Machine recognizes a dog, numerous images are fed into the machine and it tries to recognize the face of a dog. So, after coming across many examples, it gets successful in recognizing patterns.
Now, if that same machine across a cat, it will not be able to recognize it. That is because it never came across an event where the images of cats could be processed. So, it would seem very new to the machine. Similarly, if a machine is accustomed to playing and predict the outcomes of a baseball match, suddenly giving it a hockey stick will leave the machine confounded. That’s the only limitation with Machine Learning.