How to Begin a Career in Artificial Intelligence if You’re Coming from a Different Field

Many individuals find themselves changing their career paths over time as they realize they are not where they want to be. However, transitioning into a field as lucrative as Artificial Intelligence (AI) can be daunting. It requires dedication, hard work, and a willingness to learn a different set of skills. This guide aims to provide a roadmap for those interested in transitioning into AI even if they have a background in a non-computer science domain.

Step 1: Master the Basics of Programming

Your journey into the world of Artificial Intelligence begins with programming. While professional instructors can be helpful, there are many online resources available to self-learn. Here are some tools and languages you may want to familiarize yourself with:

Java: A versatile and widely-used language, particularly in enterprise environments.

Python: An easy-to-learn, powerful language that is increasingly popular, especially for beginners and research purposes.

PYTORCH: An open-source machine learning library built by Facebook’s AI Research lab, useful for deep learning experiments.

Java NIO, SLF4J, Parallax Scrolling, Java Cryptography, YAML, Python Data Science, Java i18n, GitLab, TestRail, VersionOne, DBUtils, Common CLI, Seaborn, Ansible, LOLCODE, current affairs 2018, Apache Commons Collections: A mix of libraries, frameworks, and tools that can help you gain a more comprehensive programming skill set.

While Python is a popular choice, especially for beginners, it’s not the only language you should consider. Java, for instance, is often used in enterprise environments, and having a strong understanding of different languages can be beneficial. Alternatively, 'LOLCODE' can be used for learning fundamental programming concepts in a different and entertaining manner.

Step 2: Strengthen Your Mathematical Foundation

Artificial Intelligence is not just about coding. It involves a deep understanding of algorithms, algorithms and data structures, and a strong mathematical foundation. The following subjects are crucial:

Calculus: Understanding derivatives and integrals is important for many machine learning algorithms.

Statistics: Knowledge of probability distributions, hypothesis testing, and regression analysis is essential for understanding your data and making informed predictions.

Discrete Mathematics: This includes topics such as logic, set theory, and graph theory, which are fundamental to many areas of computer science and AI.

These subjects are more than theoretical; they are the building blocks you need to understand the mechanisms that govern AI. If you are coming from a non-computer science background, it might be helpful to revisit these concepts in more detail.

Step 3: Dive into AI Literature

Finally, immerse yourself in the literature of Artificial Intelligence. Start reading research papers, textbooks, or any material that covers the field. Even if you don't fully understand everything right away, immersing yourself in the material will start connecting the dots over time. Here are some key areas to focus on:

AI Research Papers: Look for seminal works or recent publications in journals like IEEE Transactions on Neural Networks and Learning Systems, Journal of Machine Learning Research, or Artificial Intelligence.

AI Textbooks: Some key books include “Machine Learning: A Probabilistic Perspective” by Kevin P. Murphy, “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig, and “Pattern Recognition and Machine Learning” by Christopher M. Bishop.

Conclusion

Transitioning into Artificial Intelligence is a challenging but rewarding journey. By learning to program, building a strong mathematical foundation, and immersing yourself in the literature, you can begin to develop the skills necessary to succeed in this field. Best of luck on your new career path!