When is it appropriate to go for machine learning systems?

Is machine learning applicable to all scenarios? When exactly should we have the machine learn rather than directly programming the machine with instructions to carry out the task?

Machine learning systems are not knowledge-based systems. In knowledge-based systems, we can directly use the knowledge to codify all possible rules to infer a solution. We go for machine learning when such codification of instructions is not straightforward. Machine learning programs will be more applicable in the following scenarios:

• Very complex tasks that are difficult to program: There are regular tasks humans perform, such as speaking, driving, seeing and recognizing things, tasting, and classifying things by looking at them, which seem so simple to us. But, we do not know how our brains are wired or programmed or what rules need to be defined to perform all this seamlessly, for which we could create a program to replicate these actions. It is possible through machine learning to perform some of them, not to the extent that humans do, but machine learning has great potential here.
• Very complex tasks that deal with a huge volume of data: There are tasks that include analyzing huge volumes of data and finding hidden patterns, or coming up with new correlations in the data, that are not humanly possible. Machine learning is helpful for tasks for which we do not humanly know the steps to arrive at a solution and which are so complex in nature due to the various solution possibilities that it is not humanly possible to determine solutions.
• Adapting to changes in environment and data: A program hardcoded with a set of instructions cannot adapt itself to the changing environment and is not capable of scaling up to new environments. Both of these can be achieved using machine learning programs.