AI research is a broad field with many subfields, but generally AI researchers have a strong background in mathematics, computer science, and practical programming experience. Some subfields may include researchers that are highly specialized in another area (for example, computational neuroscience).

If you are interested in beginning to explore AI concepts and build foundational skills outside of higher education, there are many resources to choose from. Online courses from platforms like Coursera, edX, and Udacity can help you deepen your understanding of AI and machine learning. To gain hands-on experience, participate in AI competitions such as those hosted by Kaggle. Engage with the AI community by following researchers on social media, joining forums or mailing lists, and attending conferences or workshops. Play with open-source models and practice implementing small machine learning algorithms by hand.

However, most AI researchers have undergraduate, and even graduate school degrees or post-graduate studies. An undergraduate degree in math or computer science could provide a strong foundation for further study. Some foundational subjects include linear algebra, calculus, probability, and statistics, as well as practical experience programming and understanding algorithms and data structures. From there, graduate degrees are often done with various specializations and research areas.

Above all, don't be afraid to get started! Learning some python and experimenting with existing machine learning libraries would be a great start, and there are many free introductory courses, syllabi, and videos on the internet you can use to familiarize yourself with the concepts!
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