How to Select the Right AI Course at University
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Chapter 1: Understanding the Decision-Making Process
Selecting the right degree or program in Artificial Intelligence (AI) at a university is far from simple. If it were, this article would be unnecessary. There are numerous factors to consider, especially if your goal is to become a skilled AI professional in the future.
The word cloud below illustrates common keywords from AI-related course titles across various UK universities, encompassing MSc, PhD, Diplomas, and BSc programs. Each course I reviewed contained even more specific keywords within their modules.
Becoming an AI expert through academia isn't a straightforward journey. If you fail to give adequate consideration to your university course choices, you might end up in a program focused on data analysis when your true interest lies in hardware aspects of AI. In such a case, you might be better suited for a program in Robotics or Mechanics.
In this article, I will share a flexible framework to assist you in selecting an appropriate AI-related course at many institutions. This framework can be useful for you or for young individuals who aspire to enter the field of AI.
Section 1.1: The Decision-Making Framework
How do you decide which AI course to enroll in at university? There isn’t a definitive answer, but a pragmatic approach involves leveraging various reasoning methods that take into account your ambitions (what and why), your abilities (how), and your surroundings (where).
The 'what' and 'why' revolve around your internal and external motivations for pursuing a specific goal. Specifically, the 'what' process ensures that you are not merely pursuing an arbitrary goal but that you clearly understand what you want to achieve in a 30 to 50-year career in AI, along with interim milestones.
The 'how' encourages self-reflection, necessitating a clear understanding of your limitations and strengths. The 'where' considers how external influences affect your choices.
Let's delve into the reasoning frameworks.
Three-Step Reasoning to Select AI Courses
- Work Backwards (Inductive Reasoning)
- Think Ahead (Deductive Reasoning)
- Consider Your Interests and Skills (Practical Reasoning)
This article will focus on the first method: working backwards.
Section 1.2: Working Backwards: Inductive Reasoning
Inductive reasoning involves envisioning yourself in a position where you've achieved your desired outcome. From there, you can outline the steps and actions necessary to reach that goal.
Here's my approach: Identify a role model, someone currently in the AI field whose position you aspire to. For this exercise, let’s consider James Manyika, the Senior Vice President of Technology and Society at Google (Alphabet).
James has effectively utilized his strong technical background to create value for clients worldwide through strategic leadership and decision-making. Recently, he was recognized as one of Time's 100 most influential people in AI.
In a domain that prioritizes technical expertise, having early leadership qualities can set you apart from your peers. James’s role illustrates how technical skills can guide businesses and national strategies in AI adoption. He was appointed Vice-Chair for the Global Development Council by ex-President Barack Obama and serves on the boards of organizations like Khan Academy and The Aspen Institute.
Now, let’s work backwards from the ideal position James holds: What AI-related subjects and topics might have paved the way for his success?
After some reflection, I identified three primary areas of study: Artificial Intelligence, Computer Science (CS), and Robotics.
AI and CS encompass a wide range of topics. An AI course exposes students to deep learning, machine learning, automation, and data science, while Computer Science covers software architecture, database design, and software principles. By studying AI and CS, you gain essential knowledge about various subjects relevant to technology.
Robotics is also a vital area of focus, as hardware considerations play a significant role in the AI industry. AI requires a continuous interplay between hardware and software; thus, understanding how edge devices function or the influence of semiconductors can be advantageous for long-term leaders in the tech sector.
To see how my academic plan aligns with James’s, I took a look at his LinkedIn profile. He earned a Bachelor’s degree in Electrical Engineering from the University of Zimbabwe, which emphasizes the hardware components involved in AI.
James holds an MSc in Mathematics and Computer Science, along with a Doctorate in AI and Robotics. His academic journey reflects a holistic understanding of AI, encompassing both software and hardware aspects.
Reflection
Reflecting on my academic and career path, I initially focused on software. I chose Software Engineering for my Bachelor's degree due to the significance of software in technology. My MSc program, while not broadly covering AI, concentrated on Computer Vision, Deep Learning, and Machine Learning. I believed that as cameras become more powerful, understanding how to design and implement machine learning models for computer vision would be invaluable.
Currently, I am developing MiniPT, a computer vision-enabled mobile app that offers users a virtual personal trainer.
As I write this, AI research is increasingly focused on natural language processing (NLP). With the rise of large language models like BERT and GPT-3, it’s now more beneficial for aspiring learners to concentrate on NLP topics in their studies.
For the past two years, I have honed my technical skills as a Computer Vision Engineer at a startup in London. Additionally, I have written articles on Medium, taught courses on the O'Reilly platform, and given lectures at Imperial College Business School.
Inductive reasoning isn’t limited to academic choices; it can be applied in various areas, including finance and health.
Breaking It Down
This section outlines actionable steps to help you understand what AI-related topics to pursue in higher education.
- Define Your Endgame
- Visualize the AI practitioner you aim to become. Identify a role model in that position, using platforms like LinkedIn to guide your search.
- Analyze Their Path
- Examine your role model’s educational and professional decisions. Review their publications and contributions to understand the relevance of their chosen subjects.
- Identify Key Subjects
- Understand both overarching subjects and specific modules offered at your prospective institutions. These embedded topics shape your grasp of the overarching subject, influencing your engagement with AI.
- Specialization vs. Generalization
- Recognize the benefits of being either a specialist or a generalist in the AI field. Specialization may lead to higher pay, but competition for these roles can be fierce.
- Revisit Your Choices
- Your decisions aren't final. Adjust your academic trajectory based on current developments in AI. Flexibility can often lead to better opportunities.
- Gain Practical Experience
- Engage in internships or projects that expose you to real-world AI challenges. Early exposure to practical scenarios can give you a competitive edge.
- Choosing a Program/University
- When selecting a university, consider factors like faculty expertise, curriculum depth, resource availability, research opportunities, and cost.
Conclusion
Applying inductive reasoning to AI course selection, guided by your desired career outcome, can help you strategize and take actionable steps toward your goals. While success isn’t guaranteed, this thoughtful approach increases your chances of finding a suitable academic path in AI.
Remember, no path is set in stone. The AI field is constantly evolving, and continuous self-reflection, combined with an adaptable mindset, will help you stay aligned with industry dynamics.
In future articles, I’ll explore additional reasoning frameworks for selecting AI courses at higher education institutions. Until then, stay curious.