It seems like an age since I wrote one of my last posts while still at COS. In it I mention learning, and I really need a bit of a pick-me-up in that area right now. I am very tired from all the constant learning that I have been doing with the new job and the two courses for my Master’s degree. I think I am a bit mad… although it is doable and I am looking at next semester for something similar I really need a reminder as to why this is so hard.
I just finished listening to Freakonomics podcast “How to Become Great at Just About Anything” (which seems to a duplicate title, must be an important thing) and got to thinking about how much time I have put into coding, programming, thinking, and sometimes dreaming about it. Have I put in 10,000 hours? By school alone I would say that I have put in nearly 800 hours. If I take the last two years of work and figure that I have spent around half of that doing solid work, that’s another 2,000 hours. If I add in the robot challenge that started in January and ended in September I put in another 2-300 hours.
So I am around 7,000 hours short of being an expert from a purely number of hours point of view. However, I’ve experienced something very important that Jeffrey Spies, the CTO at COS, thought was extremely important for anybody to experience, especially the people working for him: Gain expertise by struggle.
Just to give you a small taste of contrast let’s take a look at the NASA robot challenge that I participated in. Or rather let’s go even further back to when I was interviewing at Green Bank Telescope in West Virginia in January 2015, that was embarrassing.
They asked me to come up with an idea of how to improve the telescope and how I would implement it, a pretty broad problem, but being familiar with telescope dishes and just having finished helping with aligning the dish for JCMT, I thought I could have a bit of insight. I suggested that the dish alignment could be fixed with Artificial Intelligence. Or Machine Learning. That’s it, I really didn’t have any idea of how to implement this “dish” dream. Unsurprisingly they didn’t like the very spartan answers I gave and I ended up working for COS.
At COS I was thrown into a realm that I had very cursory experience with, as well as starting on my Master’s… Why not have two completely new things at once?
December 2015 my brother was looking for someone to help his team with some computer vision issues for the NASA robot. “Oh yeah, that will be easy, I have heard about that, you just need to do…” And off we went, except that a majority of the starting time after an initial evening of following a few tutorials was spent in my usual way of working on hard problems, just worrying. A majority of the work ended up being in the week or two before the June competition out of near, pure panic. Thank goodness it was on such an even field.
But I knew that September would be harder and I needed to implement actual Machine Learning, not just think about all the wonderful things that it could do. So I started tinkering. By the time the second level of competition rolled around I had a mostly working system to get a model running to check sub-images out of an image.
Between work and competition and, so far, a large majority of my classes in Python I was actually becoming somewhat fluent in the language, so even if I was working down to the last minute on work, competition, or classes I was somewhat confident that I could arrive at a solution for the more fiddly problems. Yes, even if they were out of my immediate understanding.
With the formal Machine Learning course I have come across material that the professors are talking about where I know the bare basics, but they are talking several ideas beyond that. My immediate reaction is the old one: worry about it, later if possible. Maybe that would be a motto that I adopt, it has a nice ring to it, but I really need to toss it out. I cannot do that any more. I am not going to be an expert in a semester, but maybe I can at least have a decent conversation about it, in fact I had one earlier this week with a coworker.
I have quite a bit more stretching to do before I hit the 10,000 hours, but I really need to remember that it does take work and engagement, just like writing. The more I do so, the easier it seems, too bad I didn’t figure that out until after college.
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