How can you determine the best AI algorithms career path for your skills?

Interest is a powerful psychological state. It makes us feel energetic and excited, fully engaged and focused. Because of the vastness and complexity of the AI field, finding a niche that aligns with one's interests is very important, below are some reasons: 1. Delving into a specific niche allows individuals to focus their learning efforts on a defined set of topics. This focused approach enables deeper understanding, and faster skill acquisition. 2. When individuals work in an area that genuinely interests them, they are more likely to feel motivated and enthusiastic about their work. This motivation can fuel their efforts, sustain their commitment, and enable them to persevere through challenges or setbacks.

Don't ignore the Fundamentals. The basics serve as the building blocks upon which more advanced concepts are constructed. However, without a solid understanding of foundational principles, it becomes challenging to grasp and effectively apply complex ideas and techniques in AI. Understanding mathematical and statistical concepts has helped me in my journey. I can communicate within interdisciplinary teams and with stakeholders. It also helps me to collaborate with my team members when working on AI projects.

Having a portfolio is a very good way to demonstrate skills, expertise, and experience in AI. It provides an avenue to showcase projects you've worked on, your thought process, and the algorithms you've implemented, as well as solutions you've developed, thereby providing potential employers or collaborators with concrete evidence of your capabilities. Another importance is networking. Networking is essential for career advancement and opportunities in AI. By actively participating in AI communities, attending conferences, and/or collaborating on projects with peers, you expand your professional network and connect with like-minded individuals, potential mentors, employers, or collaborators.

In my experience, best algorithms are the well tailored ones to the situation. What works in the pure research environment need not work in the real life scenario with all those external factors pitching in. In a customer facing industry, the dimensions vary from financial sensitivity to sentimentality. In a B2B scenario, the dimensions are the groups of individual customers. Financial sensitivity is at a different level. Algorithm should be able to capture it.

While niching down on one specific AI algorithm makes you a specialist in that field and increases your chance of finding a position in that field, keep in mind that it also sets you up for failure in a field that is changing by the minute. You have to be as much a generalist as you are a specialist in one field because you don't want to panic once the paradigm changes completely. Keep an eye on the whole picture, and just enjoy what you like to learn about.