Is It Too Late to Become an AI Researcher at Age 23?

Introduction

Many people feel that starting their journey in academia, particularly in the field of Artificial Intelligence (AI), at age 23 is too late. But is it really? In this article, we will explore the feasibility and potential challenges of becoming an AI researcher after a later start in education. We will also delve into the experiences of those who have taken a more unconventional academic path and provide insights to help you make an informed decision.

Is 23 Too Late to Start?

Myth vs. Reality

It is often said that you are too old to start your education at 23, but this belief is more a product of societal norms than empirical evidence. In reality, several individuals have successfully navigated this path and have made significant contributions to the field of AI. For example, some have taken a detour into industry roles before returning to academia to pursue further studies, and have achieved great success.

Take, for instance, a researcher who spent several years working in the industry before completing an MSc, and later a PhD, to ultimately become a renowned machine learning and computer vision expert and professor. This example demonstrates that it is far from too late to start your academic journey at 23.

Challenges of a Later Start

Financial Pressures

One significant challenge of starting an academic journey later in life is the financial burden. If you are already in the workforce and need to pay for your studies, this can be quite daunting. For instance, someone who attempted to switch fields and get a BS en route to a PhD found it difficult to commit the time needed due to work obligations. Consequently, their path toward graduate studies was more challenging.

However, if you have a way to fund your education, such as financial assistance or sponsorships, then a few years delay may not hinder your academic career. The key is to find a sustainable financial model that allows you to balance work and studies effectively.

Academic and Personal Considerations

The Cost of Delay

Starting your academic journey later in life may also come with time-related challenges. Your peak learning abilities might be more optimal at a higher level of education, such as at the MSc or even PhD level. This could make it more challenging to sustain the learning intensity required for research work. However, as mentioned earlier, individuals with unique gifts can still maintain their learning speed through consistent practice.

Another significant factor to consider is the personal and professional commitments that arise during your 20s and early 30s. Balancing the demands of a research career with growing a family, managing mortgages, and pursuing career trajectories can be challenging. Spending long hours at the keyboard might put you at a disadvantage in terms of family and personal life.

Conclusion

While it is not too late to become an AI researcher at 23, it is essential to weigh the potential challenges and sacrifices. Each individual's circumstances are unique, and what works for one might not work for another. The key is to make a well-informed decision based on your personal goals, financial stability, and academic pursuits.

Ultimately, the decision to start your journey in academia at 23 or any other age is subjective. While there may be challenges, there are also numerous success stories that demonstrate it is entirely possible to succeed in the field of AI with a later start.