How I Taught Myself to Critique AI Research [Storytime Saturdays]
How I refined my articles without having anyone to critique them.
Hey, it’s your favorite cult leader here
On Saturdays, I will cover stories/writeups covering various people’s experiences 📚📚. These stories will help you learn from the mistakes and successes of others. These stories will cover various topics like Leadership, Productivity, and Personal/Professional Development. Use these to zoom ahead on your goals 🚀🚀
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Before we begin, RIP to Akira Toriyama. Great art moves us spiritually, and DBZ is no exception. I hope a small part of his legacy is honored through the work I put into creating beautiful articles.
Writing has been the best career decision I’ve made in my life. It’s given me exposure to new potential clients, acts as an additional source of revenue, and allows me to stay updated on the most important developments in the industry. Even if you don’t explicitly want to be a writer/publish for the public, writing for yourself can help with a few benefits-
It acts as a store of knowledge so that you can refer to things later.
Writing things down allows you to spot your own cognitive blindspots and understand where you might be diverging from the answer. This is critical in getting to a high level relatively quickly.
Writing about a source/topic forces you to engage with it more deeply, helping you understand it a level that’s more profound than just reading about it. This is a variant of the Feynmann Principle- you understand something better by teaching it.
These benefits are one of the reasons why I ask the clients whom I work with for interview preparation to take diligent notes as they solve problems. It allows us to spot their weaknesses and game plan for their individual tendencies and weaknesses.
When I started writing, I had to overcome a few challenges:
Being self-taught I didn’t have the same context as an ‘educated’ person who would have a clearer understanding of what was important to academics (the people I wanted to write to impress). This becomes doubly clear with AI Research papers, where there are some great resources for intro-level information (“what is a neural network etc”) but not so much for the cutting-edge stuff.
I also didn’t have a rigorous or well-defined learning path- which made it hard for me to understand the gaps in knowledge and to work on what I didn’t know (especially with my unknown unknowns).
I didn’t have a peer group/network that could critically evaluate my work and give me feedback. No one in my circles interacted with Machine Learning Research, so I had no one to tell me if I was on the right track or what mistakes my analysis contained.
In this article, we will be looking at how I addressed those challenges as I was getting started. I hope the way I did it can help all of you in your journeys. I applied similar game plans (with some tweaks) to developing my Leetcode Solutions, Understanding System Design, and other important concepts, so I’m sure this game-plan will help you a ton.
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