Artificial Intelligence, Machine Learning, Statistics, and Data Mining
Few day before, I saw an interesting question on stats.stackexchange.com, after spending few minutes of readings all the answers on the stack I felt I should also write my version here assuming what I would have answered if I really had too.
Question: stats.stackexchange.com : What is the difference between Artificial Intelligence, Machine Learning, Statistics, and Data Mining? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches? What exactly do they have in common and where do they differ? If there is some kind of hierarchy between them, what would it be?
I assume the author of the question is trying to get a clear picture by understanding the line of separation that distinguish each field from the other. So, here is my take to explain this in a more simplified way that I ever could do.
Machine learning is a science that involves development of self-learning algorithms. These algorithms are very generic in nature and it can be applied to various problems in any domain. Data mining is a practice of applying algorithms (mostly Machine learning algorithms) to solve a problem. Statistics is a study of how to collect, organises, analyse, and interpret numerical information from data. Statistics can slip into two taxonomy called Descriptive statistics and Inferential statistics. Descriptive statistics involves method of organising, summering and picturing information from data. Inferential statistics invokes method of using information from sample to draw conclusion about the population.
Machine learning uses statistics (mostly inferential statistics) to develop self-learning algorithms. Data mining uses statistics (mostly Descriptive statistics) on results obtained from algorithms, it used to solve the problem. Data mining as a field emerged to solve problems in the miscellaneous domain (particularity in business), acquired different techniques and practices that are used in different field of studies.
In 1960 practitioners who solved problems (mostly business problems) used term Data fishing to call the work they do. In 1989 Gregory Piatetsky Shapiro used term knowledge Discovery in the Database (KDD). In 1990 a company used term Data mining with the trademark to represent their work. Today data mining and KDD are used interchangeably.
Artificial Intelligence is a science to develop a system or software to mimic human to respond and behave in a circumference. As field with extremely broad scope, AI has defined its goal into multiple chunks. Later each chuck has become a separate field of study to solve its problem.
Here is a major list of AI goal (a.k.a. AI problems)
2. Knowledge representation
3. Automated planning and scheduling
4. Machine learning
5. Natural language processing
6. Computer vision
8. General intelligence, or strong AI
As mentioned in the list, Machine learning is a field emerged from one the AI goal to help machine or software to learn on its own to solve problems it can come across. Natural language processing is another such field emerged from AI goal to help machine to communicate with real human. Computer vision is a field emerged from AI goal to identify and distinguish objects that the machine could see. Robotics is a field emerged from AI goal to give a physical appearance for a machine to do physical actions.
Is some kind of hierarchy between them, what would it be?
One way of representing the hierarchical relationship between these science and study can be drawn from historical facts when they have emerged. Origin of science and study.
Statistics – 1749
Artificial Intelligence - 1940
Machine leaning – 1946
Data mining – 1980
History of statistics is believed to be started around 1749 to represent information. Practitioners use statistics to represent the economic status of states and to represent the material resource put on the military use. Later usage of statistics was leveraged to include data analysis and organization.
History of Artificial Intelligence happened to be existing has two types namely classic and modern. Classical Artificial Intelligence can be seen in ancient time stories and writings. However Modern AI emerged in 1940 when describing the idea of mimicking human like machine.
In 1946, Origin of Machine leaning emerged as branch of Artificial Intelligence with purpose to solve the goal of making machines to learning itself without programming/ hardwiring explicitly.
Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches?
It would be appropriate to say they (Statistics, Artificial Intelligence and Machine Leaning) are highly inter dependent field that they can't survive alone without leading help from others. It is also good to see these 3 fields a one globe field instead of 3 diffident subjects. As with this perception as one globe field these three fields have contributed their excellence in solving common goal. As a result, the solution as such where applicable in many different domains where the core problem is same under the hood.
This is time data mining come into picture, it took the solution obtained from the globe field and applied it to different domains (business, military, medicine, space) to solve problems that has the same nature under the hood. This is also the time where data mining expanded its popularity.
I Hope my explanation has everything that need to answer the authors question and I believed it would have definitely helped anyone who is trying to understand the sweet spot of each field and how they are related. If you got anything to say or share about the article, then please let me know your thoughts in the comment section.