John Voorhees:
Apple has announced that in September, when iTunes 12.7 is released, it will migrate iTunes U collections to Apple Podcasts. iTunes U courses will be available only through the iTunes U app on iOS.
iTunes U was launched in 2007 to offer downloadable collections of free educational content through the iTunes Store. In 2012, Apple introduced the iTunes U iOS app, which allowed educators to create iTunes U courses that go beyond audio and video by incorporating handouts, homework, quizzes, ebooks, and other content. Although courses are currently listed alongside collections in iTunes on macOS, courses are designed to work best in the iTunes U app, which is iOS-only.
This may break some courses, since it’s not clear whether Apple Podcasts supports ePub files. The audio will still be available in iTunes for Mac, as there is no Mac version of Apple Podcasts, but the Mac version of iTunes will lose the ability to view the course materials.
Apple Podcasts Datacide Education iOS iOS 11 iTunes iTunes U Mac macOS 10.13 High Sierra
Giuseppe Stuto (via Dan Masters):
The Piper Jaffrey data shows how commanding iPhones are in today’s smartphone landscape for teens. This is in line with our various surveying here at Fam, in which we have approximated over the past year that 75% of US teens use iPhones. In terms of why this may be the case, there are several factors to consider: design, iTunes, network effects, and of course what we believe to be the most important one, iMessage.
[…]
iMessage is a pre-installed platform on every single iPhone so obviously it will naturally have a ton of engagement. But it being pre-installed should not be a reason to discount it, especially when taking into account the level of saturation within the Gen-Z demographic and its dynamic user experience to date (relative to traditional SMS). Of course this is more of a subjective premise, however, after first hand observing how teens use iMessage over the past few years it is clear that they treat it as much more than a basic text message delivery service. It’s the center of their mobile social life, whether they themselves realize that or not.
Update (2017-08-23): John Gruber:
Here’s a Reddit thread chock full of anecdotes about how dominant iMessage and iPhones are among US teens.
Update (2017-10-23): Owen Williams:
It turned out that the only excuse I had for staying in Apple’s ecosystem was quite literally iMessage and that ditching it didn’t matter at all. If that was the only thing stopping me from switching to a Pixel 2, it was a pretty dumb excuse, and one that only self-reinforced over time.
[…]
The beauty of 2017 is that it really doesn’t matter — everyone’s on everything anyway, and if you really can’t reach them there’s always *gasp* SMS there to save the day. Almost everyone you know is on Facebook Messenger and WhatsApp, and for the others there’s Signal, Allo, Telegram, Slack, whatever.
iMessage iOS 10 iPhone
Matt Miesnieks (via iOS Dev Weekly):
To get 3D you need to have 2 views of a scene from different places, in order to do a stereoscopic calculation of your position. This is how our eyes see in 3D, and why some trackers rely on stereo cameras. It’s easy to calculate if you have 2 cameras as you know the distance between them, and the frames are captured at the same time. With one camera, you capture one frame, then move, then capture the second frame. Using IMU Dead Reckoning you can calculate the distance moved between the two frames and then do a stereo calculation as normal (in practice you might do the calculation from more than 2 frames to get even more accuracy). If the IMU is accurate enough this “movement” between the 2 frames is detected just by the tiny muscle motions you make trying to hold your hand still! So it looks like magic.
To get metric scale, the system also relies on accurate Dead Reckoning from the IMU. From the acceleration and time measurements the IMU gives, you can integrate backwards to calculate velocity and integrate back again to get distance traveled between IMU frames. The maths isn’t hard. What’s hard is removing errors from the IMU to get a near perfect acceleration measurement. A tiny error, which accumulates 1000 time a second for the few seconds that it takes you to move the phone, can mean metric scale errors of 30% or more. The fact that Apple has got this down to single digit % error is impressive.
[…]
Google also could easily have shipped Tango’s VIO system in a mass market Android phone over 12 months ago, but they also chose not to. If they did this, then ARKit would have looked like a catch up, instead of a breakthrough. I believe (without hard confirmation) that this was because they didn’t want to have to go through a unique sensor calibration process for each OEM, where each OEMs version of Tango worked not as well as others, and Google didn’t want to just favor the handful of huge OEMs (Samsung, Huawei etc) where the device volumes would make the work worthwhile. Instead they pretty much told the OEMs “this is the reference design for the hardware, take it or leave it”.
[…]
So ultimately the reason ARKit is better is because Apple could afford to do the work to tightly couple the VIO algorithms to the sensors and spend *a lot* of time calibrating them to eliminate errors / uncertainty in the pose calculations.
Android Artificial Intelligence Augmented Reality iOS iOS 11 Programming
Stephen Wolfram:
The answer, I think, is well enough that even though the edge of totality moves at just over 1000 miles per hour it should be possible to predict when it will arrive at a given location to within perhaps a second. And as a demonstration of this, we’ve created a website to let anyone enter their geo location (or address) and then immediately compute when the eclipse will reach them—as well as generate many pages of other information.
[…]
These days it’s easy to find out when the next solar eclipse will be; indeed built right into the Wolfram Language there’s just a function that tells you (in this form the output is the “time of greatest eclipse”)[…]
[…]
I have to say that I consider Newton in a sense very lucky. It could have been that it wouldn’t have been possible to work out anything interesting from his theory without encountering the kind of difficulties he had with the motion of the Moon—because one would always be running into computational irreducibility. But in fact, there was enough computational reducibility and enough that could be computed easily that one could see that the theory was useful in predicting features of the world (and not getting wrong answers, like with the apse of the Moon)—even if there were some parts that might take two centuries to work out, or never be possible at all.
History Math Mathematica Science Web Wolfram Language
