Sidd Sachdeva

Sidd Sachdeva

AI for social good

Reflection: Mastering the science and engineering of the self

3 minute read

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For most of my life, I’ve had some clear goals for that I’ve been working towards. Whether it was getting into boarding school, winning a debate tournament, reaching a math level, getting into college, getting certain grades, getting funding for my project, building a product, getting a job, getting into climate, etc. In this process, I’ve found that there’re 2 factors that drive my success in achieving these goals: clarity of purpose, and structure of action. To the extent I’ve got a meaningful job that benefits people and provides me financial security, I’ve got loving relationships, and I’ve got relative health, they’ve been derived from the times I’ve exhibited the strongest aptitude in those 2 factors.

Today, though, without a college application or desired job to work towards, I’m finding that there are many ways I can improve in my clarity and my structure. I’ve found myself identifying external or benchmarks with the notion that they will make me happy, while I haven’t invested enough in purpose and structure. Finding that purpose and structure in myself will be worth magnitudes more than any degree, job, material thing, or even accomplishment. So I’ve decided to take a year to set aside all other goals to focus on building the habit of finding and cementing clarity and structure in my life. In fact, I believe focusing on that will bring me closer to my goals than focusing on the goals themselves.

But what does clarity of purpose mean? What does structure of action entail? I must define these clearly to ensure that I don’t stray from the path.

clarity of purpose

This is the science of the self. Ultimately it comes down an inquisitive journey to answer the question, what am I here to do? it is an ongoing pursuit of knowledge driven by experemintation combined with reflection in an ongoing cycle, where instead of studying physical systems or abstract concepts, one focuses on seeking truth about oneself. There’re different strategies one can take on this path including mediation, writing, and experimenting with different projects. Like science, it’s a never ending journey not to find the right answer, but to be gradually less wrong through the discovery of new frameworks and evidence.

structure of action

This is the engineering of the self. Given whatever current understanding of what our purpose is, we need to develop and continuously evaluate techniques and tools to strive towards it, where instead of hardware or algorithms, these tools consist of habits. Like engineering, it’s best to start with a quick MVP and iterate where improvements can be made, not enough attention paid to this can lead to technical debt down the line as responsibilities grow, and being flexible and lean rather than overengineering is important to making sure this infrastructure is actually useful.

A never ending pursuit

Like science and engineering, the work of finding clarity of purpose and implementing structure of action is never done. I must commit myself to these pursuits on a regular basis. I believe something can be done to these ends every single day. It should be like eating, my day can’t go on without it.

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On Data and Being Honest

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As I stared at my 300th co-occurence number, I let out a groan. Why was I spending hours late into the night staring at countless static numbers? I could’ve been building something, or writing something, or brainstorming. Instead I was doing what felt like grunt work. Double-checking number after number. My co-worker was asked to independently reproduce my model results, and we had to make sure that the thousands of metrics and results we produced for the model were all exactly the same. I felt bad for the guy, having to spend hours into the night writing code that I had already written that did boring, predictable things, just to make sure we got the same numbers. This wasn’t a chance to be creative. It wasn’t even really a chance for reward, because the better we did our job, the more likely we were to have to deliver bad news and stress everyone out. I felt guilty. Were we doing this because I made evaluation mistakes earlier? Did my friend have to spend a late night just because the noob new grad’s code couldn’t be trusted? It isn’t easy being positive when you’re tired and staring at a monolithic table of numbers.

As an ML engineer, your success is driven by the success of the model you build. But in most cases, especially when you’re not at a big company, the same person who builds the model is also the person who writes the code to evaluate its success. People don’t need to be malicious and actively lie about their results to make mistakes in their process that lead to a misunderstanding of the data. They just have to be a little less skeptical, a little less curious about learning about the ways in which what they’ve built might fall apart, and by the time it’s clear that the analysis was misleading and something was wrong, it’s often way too late. The reality as a data scientist is that you know your data better than pretty much anyone else, and the questions you ask about the data, and especially the ones you don’t ask, drive the story that other people hear about this data. With the wrong incentives and attitude, this can be dangerous.