Four Mistakes to Avoid While Learning AI*zshBwujRdE_ATfFv

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Starting with the theory

“Don’t start with definitions and theory. Instead, start by connecting the subject with the results you want.”

— Jason Brownlee, PhD, Machine Learning Mastery

If you’ve read some of my articles, you may have noticed I love theoretical AI. It has a philosophical touch to it that amazes me. I like to ponder about the future of the field and what it’ll mean for us. How will AGI shape our lives? Will we create it without us even noticing? Will it be dangerous or peaceful?

I also like to read about the historical foundations. How were the cognitive sciences and computer science separated at birth in the mid-20th century? How did symbolic AI fall from grace to let neural networks step in, reaching eventually the hegemonic status they enjoy today? Why has AI suffered from winter periods?

And I also like to know where things come from. What is a convolution? How did transformers, based solely on attention, overthrow other paradigms in the last few years? How is it possible that adding parameters makes models qualitatively more powerful?

All these questions attract me like a moth to a light bulb. Yet, when I started learning AI and took Andrew Ng’s and Geoff Hinton’s courses back in 2017, I knew the philosophical, historical, and theoretical sides of AI wouldn’t get me a job. I wanted to work on a real-life project. I wanted to get my hands dirty. I wanted to get the know-how knowledge of actually building something from scratch. Books couldn’t teach me that. Stories and deep reflections couldn’t teach me that. Practical knowledge and useful skills are what I needed to keep the project from falling apart.

But the path isn’t equal for everyone. There are two situations in which starting with theory would make sense. First, if you’re studying computer science at the university you’ll probably learn the AI’s bases even if you don’t want to. But because you’re still at university — and the potential success of a project isn’t resting on your shoulders — it’s probably worth it to take advantage of it. Theory and history aren’t the most urgent aspects of AI, but they’re for sure an edge when competing with other people.

The second situation is if you’re headed to academia. Scholars and researchers know the pillars of the field they study. They don’t simply use AI to build a company or a project; they create those algorithms. They are the ones who know why transformers are getting more success than other models. They know why symbolic AI didn’t work. They know because they created it. And they also created deep learning. Every great breakthrough in AI came from scholars that knew the theory behind the practice.


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