The curse of being an engineer is that it’s not enough to simply complete a process, it has to be optimized as you go. Below are a few thoughts on optimizing the process of picking and juicing oranges:
Consistent incremental progress is, most of the time, just as good as occasional massive advancement. So, in the spirit of New Year’s Eve and all associated traditions, I’ve chosen to rethink how I go about the new year.
This post is not just for technical people: it’s a discussion about what encryption is and how we can bust a few myths going around in the media. No matter your job title, it’s always good to know the signs of bad security so that even if you can’t fix it yourself, you can throw a fit until someone else does.
Below is a pseudo-outline of mixed advice, tricks, and links that should give you a fighting chance in any technical interview. Remember, interviewers aren’t out to get you.
Artificial Intelligence is, as a field of study, larger than Machine Learning. In fact, ML is just a specific subset of AI. I wasn’t too clear on where exactly the line was drawn, but it turns out the accepted industry answer is that you can have AI without ML, but whenever you’re doing ML, that’s a form of AI.
Impostor syndrome is when you’re convinced that you’ve faked your way to where you are now and that it’s only a matter of time before the people around you start to catch on. The problem here is that knowing you have it doesn’t actually make it any better.
Knowing more about the internals of a problem can lead to better understanding and discussion across the board. Most algorithms that you’ll come across in practice aren’t going to be as simple as to fit into any one of these categories, but understanding the pieces of them allows you to get a better grasp of the system as a whole.