Okay. I ran across something about – it’s almost meta about learning. It was on Lifehacker and it’s “How to get started in machine learning and robotics.” Before we go on, next week we’re going to be interviewing Chris DeBellis on robotic perception using Mask R-CNN, and with that in mind as a future thing and thinking about crossing machine learning and robotics - this was interesting not only because I knew that was coming, but also because it talks about these two gentlemen… And I’m not gonna say their names, because I’ll butcher it terribly; the link is in the show notes… But I noticed that one of them at least was 20 years old (maybe both of them), and they were trying to get started in this; they were involved in a hackathon and they just kind of talked about some of their lessons learned about how to get started in this field, and there’s so many people, whether you’re 20 or 40 or 60, there’s so many people that are starting to move into this that I thought that they had a really great perspective.

A couple of the key things that they said, that if you’re starting out – they referred to it as “cross the streams”, and what they meant by that is to think out of the box, and not think about the problem you’re trying to solve in the way everybody that came before you might have solved that, with previous technologies… With new advancements happening so fast and with robotics - it may not just be from an algorithmic standpoint, it might be the sensors that you’re using and where sensors are applied and how they’re combined, and stuff…

They basically said “Go for something that other people aren’t necessarily doing and see if you can make it work.” Then the next thing they said is “Get an assignment”, and that is to make it real. They were involved in a hackathon, and in that perspective they had a set time limit to knock some code out. With that time approaching quickly, you have to produce whatever you can in a short amount of time… But that forced them to really think quickly and act on it quickly, and see what they could produce. That assignment, they said, made a big difference.

[ ] And finally, when you have your assignment, they said, break down your project; instead of being overwhelmed and saying “Oh my gosh, we’ve taken this very ambitious assignment in terms of how we’re gonna approach it, and we have a set timeline…” They just said “Break it down into pieces”, just like you would if you were a software engineer or any one of many other things. It’s a project, and a project is a big thing that’s composed of lots of little things. They said that they would basically divide it and conquer the project, and were able to use open source tools like Pandas, which I know you mentioned in our last conversation… And they were able to turn out a good product.

I just thought it was a great attitude with some great practical advice for doing practical AI at an entry level, and I wanted to share that with our listeners.