WordPress Tavern states:
WordPress now powers 27.1% of all websites on the internet, up from 25% last year. While it may seem that WordPress is neatly adding 2% of the internet every year, its percentage increase fluctuates from year to year and the climb is getting more arduous with more weight to haul.
Linking to these statistics from W3Techs. Â Impressive!
Those who think that WordPress is just a blogging system are far from the truth…
WordPress 4.7 is just around the corner (this month).  Here is a field guide, detailing what are the changes (and there are plenty!) and what to pay attention to during and after upgrade of your site, as well as what plugin and theme developers should check for the maximum compatibility with the upcoming release.
Holly Molly, that’s a lot of changes!
Over 447 bugs, 165Â enhancements, 8Â feature requests, and 15Â blessed tasks have been marked as fixed in WordPress 4.7.
Pascal Birchler of the WordPress blogs some interesting research he did in the area of handling preferred language and how different systems – ranging from browsers, wikis, and social networks to all kinds of content management systems – approach and solve the problem.
Drupal
Drupal 8 has a rather powerful user interface text language detection mechanism. There is a per session, per user and per browser option in the detection settings. However, users can only choose one language, so they cannot say (in core at least) that they want German primarily and Spanish if German is not available. But the language selected by the user is part of the larger fallback system, so it may fall back further down to other options.
The Language fallback module allows defining one fallback for a language, while the Language Hierarchy module provides a GUI to change the language fallback system. It allows setting up language hierarchies where translations of a site’s content, settings and interface can fall back to parent language translations, without ever falling back to English. This module might be the most interesting one for our research.
Apart from the research itself, I think this is an interesting example of how complex some seemingly simple features are.
https://twitter.com/vmbrasseur/status/804739968950104064
Slashdot has the details for the story, if you haven’t heard it yet. Â Inappropriate? Maybe. Â But then again, where do you draw the line of what’s inappropriate in the sponsor’s bag? Â (Beer and other alcoholic beverages are very welcome, for example.)
I tend to take things on the lighter side, considering it to be somewhat entertaining and mildly funny.
I know, I know, this blog is turning into an Amazon marketing blow-horn, but what can I do? Amazon re:Invent 2016 conference turned into an exciting stream of news for the regular Joe, like yours truly.
This time, Amazon Rekognition is announced, which is an image detection and recognition service, powered by deep learning. This is yet another area traditionally difficult for the computers.
Like with the other Amazon AWS services, I was eager to try it out. So I grabbed a few images from my Instagram stream, and uploaded them into the Rekognition Console. I don’t think Rekognition actually uses Instagram to learn about the tags and such (but it is possible). Just to make it a bit more difficult for them, I’ve used the generic image names like q1.jpg, q2.jpg, etc.
Here are the results. Firstly, the burger.
This was spot on, with burger, food, and seasoning identified as labels. The confidence for burger and food was almost 99%, which is correct.
Then, the beer can with a laptop in the background.
Can and tin labels are at 98% confidence. Beverage, drink, computer and electronics are at 69%, which is not bad at all.
Then I decided to try something with people. Here goes my son Maxim, in a very grainy, low-light picture.
People, person, human at 99%, which is correct. Portrait and selfie at 58%, which is accurate enough. And then female at 53%, which is not exactly the case. But with him being still a kid, that’s not too terrible.
Let’s see what it thinks of me then.
Human, people, person at 99% – yup. 98% for beard and hair is not bad. But it completely missed out on the duck! :) I guess it returns a limited number of labels, and while the duck is pretty obvious, the size of it, compared to how much of the picture is occupied by my ugly mug, is insignificant.
Overall, these are quite good results. This blog post covers a few other cases, like figuring out the breed of a dog and emotional state of people in the picture, which is even cooler, than my tests.
Pricing-wise, I think the service is quite affordable as well:
$1 USD per 1,000 images is very reasonable. The traditional Free Tier allows for 5,000 images per month. And API calls that support more than 1 image per call, are still counted as a single image.
All I need now is a project where I can apply this awesomeness…