The Machine Learning Journey

Original article was published by Viren Luke Radhakrishnan on Artificial Intelligence on Medium


My Journey With ML — Part IV/IV

The Machine Learning Journey

Next steps, and why the process can become frustrating sometimes

I think many people have missed the point of Computer Science in general. We live in a world where young minds are made to believe that careers in tech are lucrative because of the availability of many well-paying, stable jobs in this field. When these same individuals enter the job market (after having studied either outdated or irrelevant content at University, that they may have been forced to join in the first place), they are met with a rude shock; they aren’t equipped with industry-ready knowledge, and must then spend their own time, money, and energy on arming themselves for the real-world. Now I know that things have been changing in the recent past. Not everyone is a victim of this conditioning. However, I would like to take the time to address these concerns and explain how AI is different and making a difference, not only to our careers but also to our quality of life.

I would also like to take this opportunity to talk about what I find lacking in this space and some ideas on how they can be improved. Lastly, I would like to recount some of the challenges I’ve faced over the last few months and how I’ve been able to overcome them.

The Real Problem

When you think of tech, what comes to your mind? Are you filled with passion, excitement, and wonder, to be living in this science-fiction-like world? Does the thought of being a creator of the future excite and inspire you? Or are you filled with dread, thinking about sitting in front of your lifeless monitor for another day, working a mundane job you don’t like, for a salary that is manageable at best and meager at worst? Either way of thinking wouldn’t be incorrect in and of themselves. However, what I find lacking lies not in the system itself but in the expectations it creates.

One of the most significant flaws I observe in our society is the segregation of careers into tech and non-tech categories. Many people believe that a tech job involves hours of mindless, motionless coding, and work in non-tech entails adventurous and exciting tasks that don’t pay well. And for a long time, this may have been the case. Today, however, I believe that the line between the two worlds is fading away and that the future is one where all jobs involve some degree of both kinds of tasks.

I spoke about this in Part I of this series; you can apply AI to just about any field and entirely revolutionize the way things have traditionally been done. This means that you don’t have to give up on your hobbies/passions/side-hustle in favor of a tech career; you can enhance them with tech. This also means that the number of opportunities is virtually infinite! What’s more, learning the required skills has never been easier; you don’t need an expensive degree to be competent; a few weeks of intense study on Coursera will suffice (as you may have realized if you read the other parts of this series).

I think it’s time that we, as a society, move out of the rigid mindset that defines success in terms of money. Instead, focus on success as a measure of applying oneself to finding new and innovative ways of doing old and mundane things. This, I believe, is what the world urgently needs and will, in my opinion, lead to a far greater generation of wealth in the long run.

Be Whacky, But Ethical

So how can you look for ways to be innovative? Be whacky! Think of things that sound absurd but are technically possible. Want to build a rover? Attach tiny cameras to dogs so that you can generate a large volume of motion data! (without harming the dogs, of course). Are you a pianist? Create a bot that plays alongside you! Maybe you’re a runner; create an app that helps you progressively increase your distance and better your timing. You will notice that the possibilities are endless with a little thought, no matter who you are.

Build something that people will use. I can’t stress enough how valuable this is. An example from my life- I liked being a Mobile App Developer when I became certified in Android App Development, but I loved being an App Developer when I built an official app for my University that thousands of people use. If you’re like me, you may find yourself attracted more to the impact than to the technology, and that’s okay.

Ethics is an important issue when it comes to AI. Please always ensure that your ideas take data privacy and ethics into consideration right from the get-go. Google has some great resources on this.

Next Steps

So you’ve just finished going through all the courses in the other parts of this series. You feel knowledgeable but still not completely confident. Here are some of the next steps you can take:

  • Look for a relevant job or internship.
  • Work on a personal project/enhance your side-hustle.
  • Contribute to an Open Source project.
  • Participate in an online competition; Kaggle is excellent for this.
  • Look for more advanced, niche courses that interest you (for example, AI at the Edge)
  • Search for Universities that offer advanced courses that challenge you.
  • Become a writer and post about your journey with ML online…

The Biggest Challenge

Every other day, something new is discovered. New research papers are continuously pushing the boundaries of what AI can do. What might be the hottest thing in the industry today may become deprecated tomorrow. You could lose touch with the subject for a year and not understand anything once you return.

This is a legitimate challenge as an AI developer. You can never learn enough. You can never stop learning. This may have been possible in traditional software programming, but not here. And this can pose a real challenge, especially if your mind is set against learning new things. Before getting into AI, make sure that you’re comfortable with this fact. The bright side is that online education is usually extremely affordable.

It’s A Bit Frustrating Sometimes

As the title suggests, the journey isn’t always comfortable. As mentioned in Part I, there are too many resources available, with too little guidance on picking the best ones (though I hope I have significantly guided you so far). Installation of software can sometimes be a headache. You learn something today, only to have to relearn it in 6 months. Proper documentation on many of the latest tools can be scarce, and you will have to fix broken things yourself. Sometimes your code doesn’t work for the silliest of reasons, and you have to spend an entire night fixing it (only to realize that you missed a bracket somewhere).

Traditional industry-based careers are still evolving. Many top companies require job applicants to have “5+ years of experience” with a tool that has only been out for 3–4 years (speaking from personal experience); this makes it all the more challenging for freshers to break into the industry. University curriculums are still evolving, and teachers are themselves having to learn all these concepts from scratch.

That said, does this mean it isn’t worth the effort getting started? No! As an AI practitioner, you will be at the pinnacle of modern technology, the torchbearer of innovation, and a change leader. The initial challenges can be quickly overcome, and when the learning process stops being daunting and starts becoming enjoyable, you won’t be able to stop (I’d give it six months to a year). As the job markets open up and the world moves towards AI, there will be big money in this space.

The day is fast approaching, where not having (at least a rudimentary) knowledge in AI won’t be an option. By starting now, you’ll be a step ahead in this revolution. Learn now when it’s a choice, for soon, it won’t be. 🙂