Choosing the Right AI Program: A Guide for Aspiring Students
Written on
Choosing the right degree or program in Artificial Intelligence (AI) at a university isn't as simple as it may seem. The decision is more complex than merely selecting to study AI, particularly if your goal is to become a professional in the field within a few years.
The above image illustrates various keywords and subjects found in AI course titles for MSc, PhD, Diplomas, and BSc degrees across universities in the UK. The courses I reviewed had even more keywords within their individual modules.
Embarking on a career in AI through academia isn't necessarily straightforward. If you neglect to carefully consider your course choices, you may end up in a data-centric program when your true interest lies in the hardware aspects of AI—perhaps better suited for a course in Robotics or Mechanics.
In this article, I present a practical framework that offers a flexible approach to selecting an AI-related course at most higher education institutions. This framework can be used personally or shared with budding AI enthusiasts.
What, Why, How, and Where
How does one decide which AI program to pursue at a university? There isn’t a one-size-fits-all answer. A practical method for selecting AI courses involves applying various reasoning techniques that take into account your ambitions (what and why), skills (how), and surroundings (where).
The "what" and "why" pertain to both internal and external motivations driving your pursuit of specific goals. The "what" helps ensure you are not merely chasing a vague objective; it clarifies what you aim to achieve in a 30 to 50-year career in AI, including milestone goals.
The "how" requires introspection, offering insights into your strengths and weaknesses. The "where" examines the external factors influencing your choices.
Let’s delve into the reasoning frameworks.
Three Steps to Guide Your AI Course Selection
- Work Backwards (Inductive Reasoning)
- Think Ahead (Deductive Reasoning)
- Assess Your Skills and Interests (Practical Reasoning)
This article emphasizes inductive reasoning.
Working Backwards: Inductive Reasoning
Working backwards involves visualizing yourself in a position where you've achieved a specific goal. This process is followed by outlining the thoughtful steps needed to progress from one objective to the next.
To illustrate, think of a role model in the AI field whose position you aspire to reach. For this exercise, let’s consider James Manyika, the Senior Vice President of Technology and Society at Google (Alphabet).
James has effectively leveraged his technical academic background to create significant value for clients worldwide through strategic leadership. Recently, he was recognized in Time's 100 most influential people in AI.
In a field that values technical expertise, early recognition of leadership skills can help you distinguish yourself among peers. James exemplifies how technical knowledge can guide businesses and governmental strategies in adopting AI technology. He was appointed Vice-Chair for the Global Development Council by ex-President Barack Obama and serves on the boards of organizations such as Khan Academy and The Aspen Institute.
Let’s analyze the academic subjects that could have prepared James for his current position. I identified three main areas: 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 CS covers software architecture and database design. Knowledge in these areas equips individuals to plan and develop technological strategies for various organizations—acting as generalists who can navigate the evolving AI landscape.
I also included Robotics due to its importance in the AI industry. AI often requires synergy between hardware and software, so understanding how edge devices operate or the role of semiconductors can be advantageous for future leaders in AI.
Upon reviewing James's LinkedIn profile, I noted his Bachelor of Science in Electrical Engineering from the University of Zimbabwe aligns with the hardware aspect of AI. He also holds an MSc in Mathematics and Computer Science and a Doctorate in AI and Robotics, showcasing a comprehensive understanding of both software and hardware within the AI field.
Reflection
Reflecting on my own academic and career path, I initially prioritized software. I chose Software Engineering for my Bachelor's degree due to the critical role of software in technology.
Took A Masters In Machine Learning And I Was (VERY) Unprepared
An exploration of my transition from full-time employment to a Master's student of Artificial Intelligence.
towardsdatascience.com
While my MSc focused on Computer Vision, Deep Learning, and Machine Learning, I justified this choice due to the growing importance of these technologies in mobile applications. Currently, I'm developing MiniPT, a mobile app that leverages computer vision to provide virtual personal training.
As I write this, AI research is increasingly centered on natural language processing (NLP). With advancements like BERT and GPT-3, it’s now more beneficial for newcomers to delve into NLP topics academically.
In my first two years post-Master's, I honed my technical skills as a Computer Vision Engineer in a London startup, authored articles, and delivered lectures on Data Science at Imperial College Business School.
Working backwards is not limited to academia; it can be applied to various aspects of life, including finance and health.
Breaking It Down
This section synthesizes the previous content into actionable steps for understanding which AI-related topics to pursue in higher education.
Define Your Endgame
Knowing your destination is crucial. While the journey is valuable, having a clear goal motivates you to start. Visualize yourself as the AI professional you aspire to be.
- Identify a role model currently in that position. LinkedIn is an excellent resource for this.
Break Down Their Path
- Analyze your role model’s educational and professional decisions.
- Look for their publications, articles, or videos.
- Consider how their educational choices contributed to their current role.
Identify Overarching and Embedded Subjects
To grasp the educational path leading to your desired position, it’s essential to understand both the primary subjects and the specific modules covered at your chosen institution. These embedded topics shape your interest in the practical aspects of AI.
- Investigate the core subjects in your role model's background.
- Explore the module content of AI-related courses at your potential institutions.
Specialization vs Generalization
Both specialization and generalization have their merits in the AI field. Choosing early on can clarify which AI sector you wish to enter. While specialists may command high salaries, competition for such roles can be intense, and they may be less abundant.
- Understand the distinctions between specialist and generalist AI roles in your chosen domain.
Revisit Your Choices
Your decisions are flexible. Adjusting your academic trajectory based on interests is often possible, even within university settings.
For example, I initially enrolled in Computing Studies but switched to Software Engineering after recognizing the software's importance in the future. This transition was facilitated by a brief discussion with the department head.
- Regularly reassess your academic choices and modify them based on advancements in AI.
- Recognize that AI is cyclical; certain topics trend for years before being eclipsed by others.
Integrate Practical Experience
Engaging in applied AI scenarios allows you to confront real-world challenges faced by professionals in developing AI products. Early exposure to these issues can provide a competitive advantage.
I began my career with a year-long internship at General Electric as an IT Business Analyst, which evolved into software projects that optimized inventory management. This experience taught me crucial lessons about user-centric application development.
- Seek internships, apprenticeships, and research opportunities relevant to your role model’s background.
- As an AI student, engage in professional activities early, such as writing and teaching.
Choosing a Program/University
With a plethora of universities and programs available, it's essential to approach your selection thoughtfully. While I cannot specify which institution or program is best, I can offer considerations to help guide your choice.
Is attending a prestigious university essential for studying AI?
I attended the University of Surrey for my Master's degree, where I received a solid education in Computer Vision and Deep Learning. While the university has a decent reputation, it ranks #59 in Europe and #209 globally for AI studies.
You do not need to attend a top university to receive a quality education or launch a successful AI career. Many argue that alternatives like boot camps and online certifications can also provide valuable knowledge.
What should you look for when selecting an AI course?
- Faculty Expertise: Ensure that instructors are knowledgeable and up-to-date with recent developments in AI.
- Curriculum Depth: Choose a program that covers foundational topics relevant to your desired AI career.
- Resource Availability: Inquire about GPU access and computing resources at the university.
- Research Opportunities: Consider whether the program offers pathways to further academic pursuits like PhDs.
- Cost: Evaluate the program's fees against your financial situation and explore available funding options.
Conclusion
Applying inductive reasoning to the selection of AI courses, guided by your desired career outcome, enables you to formulate strategies and actionable steps toward achieving your goals.
While it doesn't guarantee success, this structured approach enhances your likelihood of making informed choices that align with your aspirations and circumstances.
Remember, no path is fixed. The AI landscape is continually evolving, so your academic roadmap should adapt accordingly.
In upcoming articles, I will discuss the other reasoning frameworks for selecting AI courses in higher education.
Until then, stay curious.