Artificial intelligence in general is a very wide topic. It includes various aspects of machine learning as well as reasoning, knowledge representation and many more. The Wikipedia page for Artificial Intelligence has a high-level overview of the various sub-fields of AI.
So to narrow this question down, I would focus my reply on machine learning.
First of all, you need to know about software engineering and programming in general because all practical aspects of ML and AI involve writing code.
Also to be able to read books and papers and to understand the theory behind machine learning you will need to have some knowledge of math. In particular, the following topics would be very useful: calculus, linear algebra, probability theory and statistics, combinatorics.
Then you should read some basic overviews of machine learning. “Pattern Recognition and Machine Learning” by Chris Bishop is a good book to get started. Also take a look at Ian Goodfellow’s answer about his top three favorite books about ML: Which are the top 3 books that you would recommend in Machine Learning?
Books give you an overview of theory, but it’s also important to implement and play with some practical machine learning systems. Udacity and Coursera usually have high quality courses with practical exercises. I found the following two after a quick search: ND Summary and Machine Learning | Coursera (Disclaimer: I haven’t done any of these two specific courses, but I’ve done similar courses in the past from both Udacity and Coursera.)
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