> For the complete documentation index, see [llms.txt](https://book.mlinpractice.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://book.mlinpractice.com/data-science-machine-learning-ai.md).

# Data Science, Machine Learning, AI?

The terms data science, machine learning, and artificial intelligence are sometimes used pretty interchangeably. What really is the difference? Here is how I see it.

**Artificial Intelligence (AI)** is a field of research that tries to create intelligent machines. It is almost as long as the idea of computers. Alan Turing is one of the pioneers to think about the topic and proposing the [Turing test](https://en.wikipedia.org/wiki/Turing_test) as a way to define AI.

**Machine Learning (ML)** is an area of research that has been around since at least the 1980s, that aims at creating programs that can automatically learn from data (instead of a human having to specify and write the program explicitly). ML is one possible approach to getting AI, but it is not the only one. In fact, early attempts at creating AI used a more classical software approach, for example by using logic and symbolic reasoning.

**Data Science** originated in the late 2000s, famously being clarified by [Drew Conway's Venn diagram](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) as a new kind of job profile for people who are good at business, statistics and ML, and programming.

So given all that I'd say that data scientists can use ML (but don't have to) to create AIs (but could also work on other kinds of "smart" solutions).

In recent years people have started to use AI when they want to imply that the methods are somehow more powerful than just using statistics, but in fact I think most of what we call AI today is still a far way off.


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