Google is testing a new way of training its AI algorithms directly on your phone
When big tech firms use machine learning to improve
their software, the process is usually a very centralized one. Companies
like Google and Apple gather information about how you use their apps;
collect it in one place; and then train new algorithms using this
aggregated data. The end result for users could be anything from sharper
photos on your phone’s camera, to better a search function in your
email app.
This method is effective, but the back-and-forth of
updating apps and gathering feedback is time-consuming. And it’s not
great for user privacy, as companies have to store data on how you use
your apps on their servers. So, to try and address these problems,
Google is experimenting with a new method of AI training it calls Federated Learning.
As the name implies, Federated Learning approach is all
about decentralizing the work of artificial intelligence. Instead of
collecting user data in one place on Google’s servers and training
algorithms with it, the teaching process happens directly on each user’s
device. Essentially, your phone’s CPU is being recruited to help train
Google’s AI.
Google is currently testing Federated Learning using its
keyboard app, Gboard, on Android devices. When Gboard shows users
suggested Google searches based on their messages, the app will remember
what suggestions they took notice of and which they ignored. This
information is then used to personalize the app’s algorithms directly on
users’ phones. (To carry out this training, Google has incorporated a
slimmed-down version of its machine learning software, TensorFlow,
into the Gboard app itself). The changes are sent back to Google, which
aggregates the, and issues an update to the app for all its users.
As Google explains in a blog post,
this approach has a number of benefits. It’s more private, as the data
used to improve the app never leaves users’ device; and it delivers
benefits immediately, as users don’t have to wait for Google to issue a
new app update before they can start using their personalized
algorithms. Google says the whole system has been streamlined to make
sure it doesn’t interfere with your phone’s battery life or performance.
The training process only takes place when your phone is “idle, plugged
in, and on a free wireless connection.”
As Google research scientists Brendan McMahan and Daniel
Ramage sum up: “Federated Learning allows for smarter models, lower
latency, and less power consumption, all while ensuring privacy.”
This isn’t the first time we’ve seen tech companies try
to mitigate AI’s thirst for user data. Last June, Apple announced its
own machine learning models would be using something called “differential privacy”
to achieve a similar aim using, essentially, statistical camouflage.
Methods like this are only going to become more common in the future, as
companies try to balance their need for user data, with users’ demands
for privacy. The end result, though, should still be better AI for all.
The article was published on : theverge
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