We talk a lot in this blog about programming frameworks and how they help developers do their jobs in various languages. It seems at times that for any given programming language there is an endless list of frameworks available. This is great for us, because it gives us an endless stream of material for the blog.
“Big data”—the gathering, manipulation, analysis, and reporting of data based on one or more data sets that are too large to be managed by traditional means—has had a big problem: Because of the vast quantity of data to be processed, a single computer, or even a high-end virtual or physical server with multiple CPU cores, is not up to the task of processing that much data efficiently. It’s much better to divide the work among several computers or servers operating in parallel.
An old metaphor, intended to explain the concept of “infinity,” states that an infinite number of monkeys, banging away at an infinite number of keyboards, would write code just as well as we humans can, with better commenting.
For much of its history, artificial intelligence (AI) technologies of all kinds have been relegated to computer science laboratories and arcane academic papers. As discussed many times in this space, only recently has the technology, specifically machine learning techniques, advanced to a state where developers at large can experiment with it without requiring a PhD in computer science.
There’s been a good deal of talk, in this blog and elsewhere, about the brave new world of the internet of things (IoT) and how it will transform our personal and business lives. The talk has been accompanied by no small amount of hype, with pundits proclaiming that there will be anywhere from hundreds of millions to trillions(!) of devices connected to the internet in the near future.
Images in apps and web pages are a bit like electricity, or the internet itself: You don’t notice them until they aren’t there. And when they aren’t there, the experience can be unpleasant.
When an image loads slowly or not at all, it’s easy to blame the network connection or the size of the image. However, there’s actually much more to it than that. An app’s ability to load images quickly depends in large part on the efficiency of its image processing routines, which use complex algorithms to load images as fast as possible without degrading image quality.
