Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords, and are often seem to be used interchangeably. They are not quite the same thing. Let's understand the difference between the two:
· Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
· Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
Artificial Intelligences – devices designed to act intelligently – are often classified into one of two fundamental groups – applied or general. Applied AI is far more common – systems designed to intelligently trade stocks and shares, or maneuver an autonomous vehicle would fall into this category.
Engineers and Scientists have realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world. This is machine learning. A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain. Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.
Some of the most common examples of machine learning are:
1. Netflix’s algorithms to make movie suggestions
2. Amazon’s algorithms that recommend books based on books you have bought before.
3. Self-driving car
4. Knowing what customers are saying about you on Twitter?
5. Fraud detection? One of the more obvious, important uses in our world today.
6. Speech recognition, Natural language processing and Computer vision
7. Computational biology and Medical outcomes analysis
8. Virtual Reality (VR) games, etc
Readers can add more such applications to the list.