Source: Deep Learning on Medium
Expert system is a computer system that emulates the decision-making ability of a human expert
Expert system are also known as Rule Based Systems. How does a human being make a decision? They look at inputs (features) and then based on previous experience, they decide the output / result.
To develop computers to make decisions, we can represent the above data by converting green to 1, red to 0.
What is the semantics of decision making?
- Features (Inputs)
- Rules (Combines features into some rule base)
Now we can make computers predict by converting the rules to simple if and else statements
Some limitations of Expert Systems:
- Tedious when there is a lot of data
- The rules can be unknown (E.g: Symptoms for Ebola)
- The relationship between different features can be highly complex and hence the rules can be too difficult to figure out
- The rules may be inexpressible (E.g: Emotions)
In machine learning, we give some data and we ask the machine to find a model that best explains the relation between input and output.
Goal is finding a function that maps input to output by learning to find the correct set of parameters.
Why is ML very successful now? — 3D’s
- There is abundant data now.
- We have access to wide variety of algorithms and functions and different people have made their effort and code public.
- We now have fast and cheap cloud computing solutions.
The key difference between Expert Systems and ML is:
In Expert System we have a rule base. And in ML, we have a family of functions and algorithms that allow us to learn the parameters.
All images courtesy of PadhAI One Fourth Labs