What Is The Difference Between Machine Learning And Artificial Intelligence

Colloquially, 'artificial intelligence' is the intelligence exhibited by machines or computer software. When machines reason, learn and solve problems in the same way as intelligent humans do, it is referred to as artificial intelligence. AI is accomplished by studying how human brains think, decide and function, in diverse work domains. The outcomes of this study is used to develop highly intelligent computer programs or robots. Machine Learning, on the other hand, is a subset of AI. It is a type of AI which looks for data to perform a task without being explicitly programmed.

AI is a broader concept and it has been around for quite long now. The idea behind developing AI was to automate day-to-day services in a wide range of sectors such as healthcare, banking, manufacturing, and more. AI programs are meant to provide answers to general questions. It is programmed to parse data from huge volumes of information, make new modifications by putting together independent data sets and deliver answers as efficiently as possible.

AI is classified into two groups, namely 'applied' and 'general'. Manoeuvring an autonomous car would fall in the category of Applied AI. Whereas, generalised AI performs tasks which are less common. This is the area which gave rise to Machine Learning. In other words, ML is a branch of AI. ML uses the technology of AI, but in an advanced manner. ML does not need hand-coding software routines for it to function. It is 'trained' to use large amounts of data to accomplish a specific task. 

Researchers and chatbot developers realised that when computers are exposed to new data, they can adapt to those modifications and produce reliable results from previous computations, without being programmed to specific tasks over and over again. Thus, ML was born. It is a sub-field of AI and is gaining fresh momentum.