For some time, I’ve been interested in learning about the Raspberry Pi. It’s little a bare bones computer that packs a big punch. And to top it off, it’s quite affordable. Through work I heard about a way to use a Raspberry Pi for an OS called Retropie. Retropie is an emulation platform that let’s you play scores of old games…if you have the digital files for them, of which many can be found with the help of Google.
I’m not much into modern video games, (as in games from the last 20 years or so), but I did play NES games back when I was in jr. high and high school. And I do still have my original NES, but it has a number of issues that make it less than reliable for playing. My kids are interested in the older games because I’ll actually join them when they play. And, quite frankly, because the older games are super fun to play and easy to learn.
Anyway, Retropie is a great way to learn how to use and get familiar with the Raspberry Pi. You simply, burn the Retropie image on a micro SD card, pop it in the micro SD card slot and boot it up! There are a few other things you need to know, but that’s the gist of it. Get a few games, a controller or two, have a monitor with an HDMI plug-in handy and you’re good to go. That’s a bit of an over-simplification, but please do explore Retropie and Raspberry Pi if you’re at all interested in this sort of thing and are looking for a good way to get familiar with the Raspberry Pi world.
Here are a couple key links:
These days efforts to revamp company culture are in vogue. I’m going to attempt to articulate what I see as a connection between machine learning and efforts to change company culture. Stay with me here a bit because the analogy doesn’t show up until the fourth paragraph and I need to share a little bit of background first. 🙂
One group leading the charge to change company culture is Partners in Leadership (https://www.partnersinleadership.com). They use a tool that identifies the following flow toward changing results. It’s a pyramid that moves from experiences to results in the following steps: EXPERIENCES >> BELIEFS >> ACTIONS >> RESULTS. According to the model, you start with the results you want to see as an organization and then move backward until you’ve arrived at the experiences that you need to create. The thinking is that experiences shape beliefs, which shape actions, which shape results. They maintain that you cannot simply skip ahead results until the rest of the house is in order first.
As for the experiences, they actually need to be high quality experiences. Partners in Leadership breaks these experiences into four types (big paraphrase here): 1) Easy to interpret, 2) Needing work to interpret, 3) Very little meaning, so there isn’t much to interpret, and 4) Experiences that, well, kind of did the opposite of what they were intended to do.
Now it is time for the machine learning analogy! Boiled down, machine learning is essentially learning from experiences (data) in order to shape beliefs (trained statistical models). These beliefs/models turn into actions (acting on the outcome of a model), which leads to results. Critical to this process is the experiential data and its interpretation (the model). We train our models by feeding data (experiences) into them. Why am I making this connection? Because organizations are really struggling to understand machine learning. Why not piggy back off of something that they’re learning already? Results from machine learning algorithms are no different results gleaned from an organizations’ cultural change initiatives. What data do you have that you can use to shape your statistical models? Which actions do you need to take to get results? You can change your culture and understand machine learning at the same time!