Tag Archives: Apps

The Getaround App Is The Airbnb For Cars And Might Revolutionize Ridesharing

Walk into any city and you’ll see about a million different modes of transportation. You can hail a taxi, grab an Uber or Lyft, rent a bike or car, pick up a Car2Go or Zipcar—the list goes on and on. But an app service in Seattle is looking to change things up.

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Report: Turkish President Erdogan Claims Uber’s Turkey Business Is ‘Over’

Turkish President Recep Tayyip Erdogan told attendees at a Ramadan dinner on Friday that Uber’s branch in his country is “over” and the interior ministry gave orders to end its operations, per Bloomberg.

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What Neural Networks, Artificial Intelligence, and Machine Learning Actually Do In Your Apps

When an app claims to be powered by “artificial intelligence” it feels like you’re in the future. What does that really mean, though? We’re taking a look at what buzzwords like AI, machine learning, and neural networks really mean and whether they actually help improve your apps.

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What Neural Networks, Artificial Intelligence, and Machine Learning Actually Do In Your Apps

When an app claims to be powered by “artificial intelligence” it feels like you’re in the future. What does that really mean, though? We’re taking a look at what buzzwords like AI, machine learning, and neural networks really mean and whether they actually help improve your apps.

Just recently, Google and Microsoft both added neural network learning to their translation apps. Google said it’s using machine learning to suggest playlists. Todoist says it’s using AI to suggest when you should finish a task. Any.do claims its AI-powered bot can do some tasks for you. All that’s just from last week. Some of it is marketing fluff to make new features sound impressive, but sometimes the changes are legitimately useful. “Artificial intelligence,” “machine learning,” and “neural networks” all describe ways for computers to do more advanced tasks and learn from their environment. While you may hear them used interchangeably by app developers, they can be very different in practice.

Neural Networks Analyze Complex Data By Simulating the Human Brain

Artificial neural networks (ANNs or simply “neural networks” for short) refer to a specific type of learning model that emulates the way synapses work in your brain. Traditional computing uses a series of logic statements to perform a task. Neural networks, on the other hand, use a network of nodes (which act like neurons) and edges (which act like synapses) to process data. Inputs are then run through the system and a series of outputs are generated.

That output is then compared to known data. For example, say you want to train a computer to recognize a picture of a dog. You’d run millions of pictures of a dog through the network to see what images it decided looked like dogs. A human would then confirm which images are actually dogs. The system then favors the pathways through the neural network that led to the correct answer. Over time and millions of iterations, the network will eventually improve the accuracy of its results.

To see how this works in action, you can try out Google’s Quick, Draw! experiment here. In this case, Google is training a network to recognize doodles. It compares the doodle you draw to examples drawn by other people. The network is told what the doodles are and then trained to recognize future doodles based on what the past ones look like. Even if your drawing skills suck (like mine do), the network is pretty good at recognizing basic shapes like submarines, house plants, and ducks.

Neural networks aren’t the right solution for everything, but they excel at dealing with complex data. Google and Microsoft using neural networks to power their translation apps is legitimately exciting because translating languages is hard. We’ve all seen broken translations, but neural network learning could let the system learn from correct translations to get better over time. We’ve seen a similar thing happen with voice transcription. After introducing neural network learning to Google Voice, transcription errors were reduced by 49%. You may not notice it right away and it won’t be perfect, but this type of learning genuinely makes complex data analysis better which can lead to more natural features in your apps.

Machine Learning Teaches Computers to Improve With Practice

Machine learning is a broad term that encompasses anything where you teach a machine to improve at a task on its own. More specifically, it refers to any system where a machine’s performance at completing a task gets better solely through more experience performing that task. Neural networks are an example of machine learning, but they are not the only way a machine can learn.

For example, one alternative method of machine learning is called reinforcement learning. In this method, a computer performs a task and then it’s graded on the result. The video above from Android Authority uses a chess game as an example. A computer plays a complete game of chess and then it either wins or loses. If it wins, then it assigns a winning value to the series of moves it used during that game. After playing millions of games, the system can determine which moves are most likely to win based on the results of those games.

While neural networks are good for things like pattern recognition in images, other types of machine learning may be more useful for different tasks like determining what kind of music you like. To wit, Google says its music app will find you the music you want when you want it. It does this by selecting playlists for you based on your past behavior. If you ignore its suggestions, that would (presumably) be labeled as a failure. However, if you choose one of the suggestions, the process it used to give that suggestion is labeled as a success, so it reinforces the process that led to that suggestion.

In cases like this, you might not get the full benefit of machine learning if you don’t use the feature a lot. The first time you open Google’s music app, your recommendations will probably be pretty scattershot. The more you use it, the better the suggestions get. In theory, anyway. Machine learning isn’t a silver bullet, so you could still get junk recommendations. However, you’ll definitely get junk recommendations if you only open the music app once every six months. Without regular use to help it learn, machine learning suggestions aren’t much better than regular “smart” suggestions. As a buzzword, “machine learning” is vaguer than neural networks, but it still implies that the software you’re using will use your feedback to improve its performance.

Artificial Intelligence Just Means Anything That’s “Smart”

Just like neural networks are a form of machine learning, machine learning is a form of artificial intelligence. However, the category of what else counts as “artificial intelligence” is so poorly defined that it’s almost meaningless. While it conjures the mental image of futuristic sci-fi, in reality, we’ve already reached milestones that were previously considered the realm of future AI. For example, optical character recognition was once considered too complex for a machine, but now an app on your phone can scan documents and turn them into text. Describing such a now-basic task as AI would make it sound more impressive than it is.

The reason that basic phone tasks can be considered AI is because there are actually two very different categories of artificial intelligence. Weak or narrow AI describes any system that’s designed for a narrow task or set of tasks. For example, Google Assistant and Siri—while powerful—are designed to do a very narrow set of tasks. Namely, take specifics series of voice commands and return answers or launch apps. Research into artificial intelligence powers those features, but it’s still considered “weak.”

In contrast, strong AI—otherwise known as artificial general intelligence or “full aI”—is a system that can perform any task that a human can. It also doesn’t exist. If you were hoping that your to-do list app would be powered by a cute robot voiced by Alan Tudyk, that’s a long way off. Since virtually any AI you’d actually use is considered weak AI, the phrase “artificial intelligence” in an app description really just means “it’s a smart app.” You might get some cool suggestions, but don’t expect it to rival the intelligence of a human.

While the semantics may be muddy, the practical research in AI fields is so useful you’ve probably already incorporated it into your daily life. Every time your phone automatically remembers where you parked, recognizes faces in your photos, get search suggestions, or automatically groups all your vacation pictures together, you’re benefitting either directly or indirectly from AI research. To a certain extent, “artificial intelligence” really just means apps getting smarter, which is what you’d expect anyway. However, machine learning and neural networks are uniquely suited to improving certain kinds of tasks. If an app just says it’s using “AI” it’s less meaningful than any type of machine learning.

It’s also worth pointing out that neural networks and machine learning are not all created equal. Saying that an app uses machine learning to do something better is a bit like saying a camera is better because it’s “digital.” Yes, digital cameras can do some things that film cameras can’t, but that doesn’t mean that every digital photograph is better than every film photograph. It’s all in how you use it. Some companies will be able to develop powerful neural networks that do really complicated things that make your life better. Others will slap a machine learning label on a feature that already offered “smart” suggestions and you’ll ignore it just the same.

From a behind-the-scenes standpoint, machine learning and neural networks are very exciting. However, if you’re reading an app description that uses these phrases, you can just read it as “This feature is slightly smarter, probably” and continue doing what you’ve always done: judging apps by how useful they are to you.

Illustration by Sam Woolley.

The Weather Channel App Now Tells You the Best Time to Go for a Run

The Weather Channel App Now Tells You the Best Time to Go for a Run

iOS/Android: Most of us run our best when it’s cool and cloudy, so in the summer that can mean taking a close look at the hourly forecast to find the best time to beat the heat. Now, the Weather Channel app can do that work for you.

The app’s home screen now boasts a “GoRun forecast” rating the current running weather on a scale of 1 to 10, with 10 being the best. By default, it seems to flag dry 70-degree days as perfect for running, but you can adjust the ratings by setting your own preferences.

Hit “Details” and you can check out the hour-by-hour ratings for today and tomorrow, plus a running forecast for the rest of the week. Try it yourself at the links below.

The Weather Channel

The Weather Channel | iTunes

The Weather Channel | Google Play Store

Google Maps Driving Mode Is Your Essential In-Car AI

You’re probably used to getting turn-by-turn directions to your next destination with Google Maps, but there’s also a pretty-well-hidden Driving Mode just for… well, driving. It alerts you to traffic problems, directs you to nearby gas pumps and stores, and is useful for those times when you already know your route or don’t even have a destination in mind.

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Fences 3 Adds Support for High DPI Monitors and Full Windows 10 Compatibility

Fences 3 Adds Support for High DPI Monitors and Full Windows 10 Compatibility

Windows: Fences, one of our favorite apps for organizing your desktop, just got better with version 3. Now you can minimize fences to just their title bars so you can hide your desktop clutter while keeping everything easy to find.

There aren’t any dramatic changes to the trusty organization app but all the little improvements add up: it now supports high DPI displays, and you can also choose whether the fences are transparent or translucent, blurring the image behind them. The update is $4.99 if you’ve purchased a previous version or $9.99 for new users.

Fences | Stardock

The Best System Monitor for iPhone

The Best System Monitor for iPhone

At a glance, system monitors might not seem as useful on your iPhone as they are on a desktop computer, but they can pack in a lot of good data. This includes detailed battery life breakdowns, storage space, data speeds, and more. For the average user, our favorite system monitor for the iPhone is Omnistat.


Platform: iPhone (and iPad)
Price: $1.99
Download Page


  • Customizable Notification Center widgets let you decide what data is shown and where it shows up
  • Universal app for iPad and iPhone. Also includes Apple Watch support
  • Shows activity and stats for: device name, device model, current OS version, current OS build number, device uptime, Wi-Fi details, mobile carrier data usage, download and upload speed, storage information, CPU usage, battery details, and more
  • Choose when your data plan resets so you can always track mobile data usage accurately
  • Estimates remaining battery time

Where It Excels

Omnistat’s biggest strength are the Notification Center widgets. A system monitor is something you want quick access to, and Notification Center widgets are a clever way of doing that. With Omnistat, you can customize which stats appear as widgets, and any time you want to take a glance at them, just pull down on the Notification Center. Omnistat gives each activity its own widget, so you can customize the layout in Notification Center easily.

Beyond that, Omnistat provides the details most people want. This includes battery life, including estimations for remaining talk, text, and data time. You can also easily track Wi-Fi and cell data usage. For data usage, Omnistat supports creating an automated reset date for cell data so it’s always in time with your data plan. If you’re running on a 16GB iPhone or you’re just always against the wall with remaining space, the storage widget is extremely helpful for keeping your remaining storage space in check. Omnistat has plenty of other widgets, from network details to device CPU usage, so it should have the data you need access to the most.

Where It Falls Short

Omnistat excels because of the inclusion of Notification Center widgets. However, Omnistat is not the most extensive system monitor available. While it does track most activities the average user wants, it’s missing a lot of data for anyone looking for a more granular approach. Likewise, Omnistat gives a lot of overview data, but you can’t focus on more specific information, like what hours you tend to use more data, a history of Wi-Fi networks, or anything else like that.

The Competition

Omnistat is great for the average person looking to glance at a few broad bits of information, but if you want to dig really deep into data, it’s not the app you want. Thankfully, the system monitor space is pretty packed full of solid apps.

For those who love massive amounts of system details, System Monitor Ultimate (Free) is worth a look. System Monitor Ultimate displays a ton of data about your CPU, GPU, network, active connections, and plenty more. System Monitor Ultimate is not exactly the best looking system monitor around nor is it packed with features, but it’s free and displays just about every bit of data you can track on an iPhone. There’s no Notification Center widget support, but if widgets aren’t your thing, System Monitor Ultimate is the app you want.

If you’re looking for the same amount of data as System Monitor Ultimate with more interactive features, then System Status ($2.99) fits the bill. On top of monitoring a number of data points, network information, battery, and memory, System Status also shows you file statistics, detailed page statistics, tracks three minutes of background activity, and allows you to export all those charts over email. If you love to look at and save activity monitor data, but don’t care about the widgets, System Status does the job.

Finally, Omnistat isn’t the only system monitor with widgets, Usage Widget (Free/99¢) and SnapStats (Free) both include Notification Center widgets alongside basic system monitors. Unlike Omnistat, both apps display all the stats in a single widget, so you can’t move them around or customize them quite as much. That’s a preference thing though, so if you don’t mind all your data being jammed into one spot, both apps are worth a look.

Lifehacker’s App Directory is a new and growing directory of recommendations for the best applications and tools in a number of given categories.

TaxiLater Lets You Schedule an Uber for Later

TaxiLater Lets You Schedule an Uber for Later

iOS: Uber is great for finding a ride when you need it. And if you want to schedule a car in advance, TaxiLater lets you do just that: set a pickup time and location in the future.

Once you download the app, log in with your Uber account, then select your time, location, and car type. Once you submit requests, you can manage them from the app at any time and cancel your ride if necessary (just swipe left and hit delete).

The app’s developer, Joshua Meier, says TaxiLater doesn’t store your Uber information. Instead, it uses the Uber API to get an access token, keeping your actions private and secure. There are also no additional charges for using the app, aside from the standard Uber rate, of course. Meier says he currently doesn’t make money with the app. He told us:

I built TaxiLater because I knew it was a much needed feature that could be useful for so many people. I wanted anyone to be able to use it.

You can set your request for days in the future, but keep in mind—if you’re late, Uber will charge you, so make sure to keep track of your requests within the app.

Meier is an independent developer, and for now, the app is only available on iOS, but he says an Android version is in the works, along with additional features for the app in general. To try it out for yourself, head to the link below.

TaxiLater | Apple Store

CNN’s Politics App Simplifies and Keeps You Up to Date on Election News

CNN’s Politics App Simplifies and Keeps You Up to Date on Election News

iOS: It’s hard to keep up with all the headlines, breaking news, and poll results during an election year, and this election year is an especially chaotic one. If you have trouble keeping up with it all, CNN’s new Politics app breaks it down for you.

We’ve briefly mentioned CNN’s other news apps before. This one is a bit different as it tracks all election data—election results, breaking news, delegate math—and simplifies it visually. You swipe side to side to see the major headlines of the day, and if you swipe up and down, you can get into the data. You can also sign up for daily recaps and personalized alerts.

Of course, it’s a CNN app, so you can expect most of the sourcing to come directly from CNN. Still, it’s a simple way to stay up to date and get a briefing of the election news and results every day. Unfortunately, there’s no Android version available. However, all Politics is a decent alternative, as it sums up the latest 2016 coverage, too.

To check out CNN’s app for yourself, head to the link below.

CNN Politics | Apple Store