Every few days I like to sit down like an old academic professor and just read and ponder what’s going on in the computer hardware and software industries. As of late it’s been interesting to dive into whatever Elon Musk is working on also; electric cars, solar energy, rockets, or even boring machines. Another thing I’ve had a curiosity in and continue to find interesting is machine learning, or as some call it in the mainstream movie media artificial intelligence.
I’ll tackle some of these technologies real quick so we all have a current situational report. Each of these topics seems to be a area of work that could use some extensive clarification. I’ll provide some of my own thoughts along with a few links of reference to dig in deeper, so that one doesn’t fall into the trap of uniformed oblivious media consumer.
Rockets (i.e. Space)
My History: I haven’t always paid attention to this space because I never had intention of working in the area, however who isn’t interested in rockets in some way. Well, considering my desire for other work, I was extremely fortunate where I grew up to be involved in rocketry. I grew up in Picayune, Mississippi which is a mere ~15-20 miles away from John C. Stennis Space Center.
I had the joy of experiencing rocket tests at John C. Stennis and seeing research into rockets as a child, and on some of my first paid computer related gigs. As one might suspect, they use computers to do rocket research and help with launches. Shocker right!
My Thoughts: The space we’re in right now is impressive. The market is actually getting involved in launching it’s own rockets, which means we’re likely only years or maybe a decade or two of having an economically sustainable rocket program. Hopefully NASA can work on more focused deep space missions now while Musk’s Space-X and others refine and perfect orbital rockets for satellites and all that mess.
SITREP:
- India is currently pushing the envelope with 104 satellites with a rocket, which is fueling even more of a space race today. The previous record was held by Russia with 37 satellites in 2014.
- Keep track of launches with the launch schedule.
- Nasa Space Flight is also a great source.
- Related Nasa links; Nasa Blog, Nasa Social Media, Nasa Youtube. Also check out the forums here per Peter Stephens @peterastephens.
- For Space-X check out; Space-X News, and the @SpaceX Account for quick tweets.
Machine Learning
My History: I’ve toyed with machine learning on and off again, working on pathing algorithms for objects to decide travel patterns to supervised learning algorithms. In the end I’ve generally ended up working on other things in my day to day work but I know this will be changing in the near future (next year or three). It’s an extremely interesting space of work and research.
My Thoughts: First, getting AI & ML (That’s artificial integlligence and machine learning) conflated, especially in the media, is starting to reflect the popularity of said space in the software industry. However, for the most part these two things are effectively the same thing. It’s just different words describing the industry space where we’re trying to make machines make decisions we deem intelligent based on available data.
That actually leads to many other discussions. What do we as humans deem intelligent and what happens when available data isn’t enough? But more words on that for another day. I know the questions are burning in the mind of every chief executive of something that wants this mythical AI they keep hearing about and paying voluminous amounts of money to their big data bad ass data science ninja architects to implement but rarely have answers for all of it.
The SITREP:
- Even though I’m not a fan of the site, here’s a pretty straight forward no bullshit description of machine learning.
- Here is a post that has some material on machine learning titled “Master the Basics of Machine Learning With These 6 Resources“ by Matt Fogel @mattfogel, it’s a good list to get started with.
Overall there’s a ton of material ending up on the web related to AI/ML, and my top suggestion is to start googling so you can pick and choose which aspects you want to read about in this space. One could dive in via the super technical aspect of how systems work that are being used for processing, how the algorithms work, or even working with data to model good decision results from data sets (i.e. diving into training). But it’s really a space that is awash in resources. Dive in!
End Words
That’s it for my industry instrospections for now. If you’re interested in reading about this, programming material, and related topics of this nature follow @PelotonTechio as I’ll be taking the site & blog at Peloton live in the coming days!
