To pursue a career in AI, the most relevant educational paths are electrical/computer engineering and computer science, as these fields provide essential knowledge about machine behavior and programming. While neuroscience and philosophy can contribute to understanding AI concepts, they are less directly applicable to employment opportunities. The discussion highlights the importance of interdisciplinary knowledge, incorporating statistics, linguistics, and applied mathematics, particularly in machine learning, data mining, and computer vision. Graduate degrees are often necessary for advanced roles in AI research, especially in areas that handle large datasets. Key subjects to focus on include calculus, discrete math, statistics, and algorithms, along with any available AI-specific courses. Understanding domain-specific knowledge is also crucial, as different applications may require specialized techniques and methodologies.