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In January 2014, Google splashed £400 million on buying the London-based artificial intelligence firm DeepMind. At the time, it wasn't clear what Google, and now parent company Alphabet, would get for its money. Four years later, DeepMind's team that focuses on developing AI for Google is starting to pay off.

Google's launch of its latest mobile operating system, Android Pie, involves DeepMind's largest real-world machine learning roll-out to date. And there's an ambitious aim for its AI. It's looking to solve one of the modern smartphone's most frustrating features: poor battery life.


Since Spring 2017 – well before the release of developer previews of Android Pie, formerly known by its codename "P" – DeepMind's London-based team started working with its Google counterparts. The result was the introduction of two AI systems within the operating system. Adaptive Battery, which aims to stop apps sucking-up battery life in the background, and Adaptive Brightness, that automatically adjusts the screen depending on the environment the phone is in.

Android engineer Ben Murdoch says the first data from the Android Pie developer, beta, and general release versions shows the system is working. Apps running in the background of Android devices are waking up the central processing unit (CPU) 30 per cent less and the data being transferred through Wi-Fi and mobile signals has been reduced up to 20 per cent in some cases, he says. Both of these reduce the strain on the battery.

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"We have seen a reduction in what we call the variance," Murdoch adds. "Most users are familiar with the day every now and then where your battery seems to drain much faster than you're expecting or it normally does. We call these unpredictable events 'bad battery days'. We're reigning in those bad battery days."

Despite Android Pie's public rollout, the systems are still in relatively early stages. The public version of the operating system was made available for download on August 6 but is still only available on a small number of phones. There are more than two billion devices running versions of Android, but the majority are on older operating systems. (Google's most recent breakdown, issued before Pie's release, says only 14 per cent of devices are on Oreo.)


So, how does the AI behind the potential battery saving tech work? "The model is a deep convolutional neural network," says Chris Gamble who works in DeepMind's team that develops products for Google. Convolutional neural networks are commonly used across the field of machine learning and have been tested in everything from self-driving cars to image recognition techniques.

DeepMind's AI analyses how people with Android devices use their apps. "It's got two layers and it uses the timestamps of app opens to predict when the app is going to be opened next," says Gamble. The machine learning model learns patterns of app usage – stripping away apps names and details to stop them being treated with any bias – to predict which ones are used regularly. Each app is then assigned a probability of how likely it is to be opened. "If two apps are used in the same way they'll likely get the same predictions because they'll have the same input data," Gamble says. "But the fact is they could be two completely different apps."

Battery life is saved by the AI classifying each app into one of four buckets. Each of these buckets has different restrictions placed upon it, which stop certain phone behaviours from happening. Murdoch says these four buckets comprise one for active apps, which are being used or most likely to be used next; a working set, which may be used soon; frequently used apps; and those that are rarely used.

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The apps placed in the working collection can run unrestricted and those placed in the lower priority buckets have different limitations on them. "As apps start to find themselves in the working set, frequent, and rare bucket the restrictions increase," Murdoch says. "Constraints on those jobs can be things such as: the device must be charging or the device must have a network connection." Other restrictions can be stopping an app's ability to set alarms that wake the phone up. Apps can also be restricted from responding to messages they receive through the cloud and those within the rarely used category can have their background and network activity completely restricted.

This can have an impact on user experience. When Android Pie users turn Adaptive Battery on it warns: "notifications may be delayed". So, if you don't use Facebook regularly on your phone, you may see delays in when notifications are pushed through. Apps are scanned every hour for their predicted usage and the AI processing is all done on each individual device.

Previously, DeepMind has turned its AI to Google's data centers. Its machine learning is directly controlling how giant buildings full of servers and internet infrastructure is kept cool, and the firms claim the setup saves energy. Phones are a different business, though.

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"Doing machine learning on mobile devices is extremely complicated," says Gamble. While more powerful than ever before, phones still don't have anywhere near the computation power of larger systems that can rely on more resources to process data. "One of the things we did identify was the first iteration of the model was quite computationally intensive," Gamble adds. This is particularly important for phones that aren't high-end models. Adaptive Battery and Brightness were initially tested on Google's on Pixel devices but was extended to other phones as it was moved away from a prototype version.

To get around any problems with the machine learning model as it becomes more widely used in the real world, Android and DeepMind have made it possible to update the AI before the release of Android Q in 2019. "The models are built and deployed in their own Android APK that we have the flexibility to update through the Play Store as we see fit," Murdoch says. Google can push updates to the machine learning whenever it feels enough improvement has been made. "One of the key things we intend to do as time goes on is monitor how the models are performing in the field and tune them as necessary."

At present, it's impossible to fully say how effective the machine learning technique will be and whether there will be any significant improvement in battery life on phones. Where people will complain is if the AI goes wrong. Murdoch says: "Ideally, if users don't notice that's our biggest success."