Striving to be Uncomfortable
We grow up striving to be correct and get comfortable because it is rewarded. Remember those Quick Math sheets we would get in Elementary school? You were rewarded for doing very simple math quickly. Rewarded for practicing speed over learning newer harder material. I obviously learned new material or else no progress would be ever made, but I was always rewarded for staying within my own comfort zone of gradual learning. We always build upwards rather than starting with a hard concept and diving down. This way of learning is used all through our lives into adulthood, and we become used to starting off easy and comfortable, and turning away from seemingly difficult, uncomfortable problems.
I love math — abstract algebra, group theory, Combinatorics are such amazingly interesting topics for me, but I bet your first reaction to me saying “I love math” is, “Well he must be good at math”. This is not the case. To be quite honest, I struggle with it a lot. It is a hard, complex topic that I don’t get right away and in many cases I have to spend longer on these topics than many of my peers. But I really do love math. It is this incredible topic that’s constantly expanding in its knowledge, it helps support me in software development, but most of all I love it because it makes me uncomfortable.
A topic that is difficult should not be a deterrent. Difficulty should be an attraction. Something that is difficult means there is so much at its core you have yet to learn; many layers to peel away to get the root of understanding it. An example I’m experiencing as I write this is learning a Machine Learning API developed by Google called TensorFlow. Now for me I don’t want to just learn how to use this specific API, I need to understand the root of what it is, which is Machine Learning. I put TensorFlow on hold until I had some sort of base in Machine Learning to jump off of. I started reading a textbook called “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville . As I was reading it I found my knowledge in statistics (a concept highly used within Machine Learning) was too lacking for my liking, specifically Bayesian statistics. I put Machine Learning on the burner until I had a comfortable knowledge in Bayesian statistics, so now I’m currently reading a textbook called “Doing Bayesian Data Analysis” By John K. Kruschke.
Whew. From my own example I would like people to draw on this core idea I want to get across. Topics that are hard have more depth for learning than something that is easy, and immediately give you a goal. Don’t start at easy and ramp up. Take advantage of things that are hard because they are built on previous knowledge, which already have a link to this thing you are interested in. If you would like to learn programming, start by picking a project that you would think would improve your life in some way. Automating some process you do for work, getting rid of the 45mb excel spreadsheet your work uses as a database (WHY?!). From there find all the branches that lead to this problem and follow them down. Want to learn math? Stop avoiding those hard courses in University, take them, let them kick your ass.
Get really good at failing — learn to fail better every time you get out there.
There is such a thing as too hard though. For example, if you are brand new programmer, maybe it isn’t the best to try to recreate the Google Search Engine. The reason for this is there is a point where the tree becomes so tall you lose sight of the top, diving so far down that you lose track of where to go. That being said, this is fine on occasion — if your goal is to learn for the sake of learning then by gosh go out and see what it would take to recreate the Google Search and get lost in the knowledge that can come from it.
Comfortability leads to a fear of failure which can kill innovation and learning. Do not do things because you’ve gotten comfortable doing it that way. Who knows you may have a thought that may have never been voiced or thought, all because you strived to dig into the nitty gritty hard parts.
The most damaging phrase in the language is “We’ve always done it this way!”
— Rear Admiral Grace Murray Hopper
Thanks for everyone that took the time to read this post,
Ryan
P.S : Holidays reduced my time so this post is shorter than I normally would like!
