3 Takeaways from my Journey as 1st Employee of an AI Startup

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The importance of a strong theoretical background

There was a recent debate in Towards Data Science about the role of having deep mathematical knowledge in machine learning. GreekDataGuy — formerly known as Chris The Data Guy — wrote a piece titled “You Don’t Need Math For Machine Learning.” Soon afterward, Sarem Seitz replied defending the opposite view: “You DO need math for Machine Learning.”

GDG claimed math is overrated in machine learning. Coding and knowing how to handle data is more important because libraries can do the “heavy lifting for you.” He’s a proponent of a top-down approach: Learn by getting your hands dirty first. Sarem Seitz, in contrast, argued that a strong mathematical and theoretical background can give you a broader toolbox to face unexpected issues. You understand your models. And you can debug faster or easily “spot violations of the theory.”

I fall on the side of Sarem here. Because I was heavily trained in math and physics, I managed to find engineering solutions to problems that had nothing to do with data or AI — problems that I had to solve regardless. Still, if you asked me if I think math is equally important for all tech positions at all companies in AI, my answer is “no.” As I showed in the previous section, working at a startup isn’t the same as working at a large, traditional company. The degree to which math — or other types of theoretical knowledge— is useful varies with the degree of specificity of your job. I found it extremely helpful to know math — and engineering — because I was facing a broader set of challenges and issues than most people do.

Here’s a true story. When I joined the company, they were trying to use a set of bracelets to translate signs to words. The idea was to map a set of hand/arm movements to a “sign class” using variables such as angular velocity, trajectory, and muscle pressure. After a few weeks of working on it, I realized there was a fundamental mistake in the way we were doing the measurements. The bracelets weren’t recording a crucial variable. Had I been a pure programmer — or even a data scientist — I would have never even looked for that problem. I was told to use data from the bracelets, why would I doubt if they were working correctly? How could I have solved, or even understood, such a problem without some engineering competence?

I don’t think a situation like this is common in a bigger company, though. Hardware engineers would handle questions like this one. However, nowadays many tech companies follow the startup model. You don’t know what you will find tomorrow. You don’t know what tools you’ll need to find a solution. Actually, you may not even know if the question you’re being asked is the right question. In these cases, a strong theoretical, mathematical, and engineering background could save the day.

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