Some things are more art than science. And while software development is definitely a science, testing it has more than a few artistic aspects to the process. Dan Valderrama, QA Engineer at AndPlus, talks about the typical two-week sprint and how the company ensures a quality, shippable product at the end of it.
A long time ago, in a galaxy far, far away, the inhabitants had access to advanced medical care provided by robots. The Star Wars medical droids had the ability to diagnose and treat their patients with extraordinary knowledge and numerous built-in surgical tools. They had extra added bonus skills in reasoning and communication with their patients—any species from any planet speaking any language. Their bedside manner was, of course, impeccable.
You’ve read a number of articles in this space about different types of machine learning, with a high-level view of how they work and the types of technologies that machine learning will enable in the future. “But,” you’re thinking, “What are some of the ways machine learning is being applied right now, to do useful work outside the laboratory?”
Imagine, if you will, a world in which you can do, in a car, all the things you’re not supposed to do while driving: talking on your phone, texting, watching “Laverne and Shirley” reruns, putting on makeup, playing the trombone, etc. You will be able to do those things because you won’t actually be driving the car; the car will drive itself to your destination.
If you buy into the hype, you might believe an army of intelligent robots is on the march RIGHT NOW, coming to TAKE YOUR JOB.
Lately the term "Machine Learning" gets kicked around like a hackysack in a stoners basement. It's becoming a ubiquitous buzzword because it really is exciting technology that's finally making its way into our lives, but a large majority of the buzz is nothing more than lazy marketing. Apple is one of the tech giants that's bringing machine learning into the mainstream in effective and even mindblowing ways.
