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Mobile Machine Learning May 11, 2017

Posted by kristenkozmary in Machine Learning.

The emergence of smartphones within the last two decades has changed the way humans interact with one another. Providing new ways to connect and access information, the smartphone has become an integral part of life. New technologies have allowed phones to be able to complete tasks that once could only be accomplished by super computers. Now, smartphones have the ability to learn without being programmed, otherwise known as machine learning.

Deloitte has predicted that over 300 million smartphones will have machine-learning capability in 2017 [1]. This will allow smartphones to perform machine learning tasks without having to be connected to a network. Machine learning tasks on smartphones include indoor navigation, image classification, augmented reality, speech recognition, and language translation [1]. Traditionally, machine learning capabilities on smartphones could only be performed by collecting data on the smartphone, send it to a data center for processing and training, and then send back the results to the smartphone. Local machine learning on smartphones cuts out the “middle-man” and processes the information directly on the smartphone. This increases the speed at which machine learning tasks can be done. Local machine learning is also more secure because private user data doesn’t need to be sent outside the network.

According to Business Insider Australia, technologies such as health and life predictions and user moods and emotions are going to become more prevalent in 2017 due to machine learning [2]. The data collected could aid the mental and physical of health of users by giving reports on well-being. Business Insider Australia also makes the claim that machine learning on smartphones can also help to protect against cyberattacks, although specific details were not provided.

Google recently published an article about the capabilities of machine learning on smartphones [3]. In this article, Google introduces a new approach to machine learning called Federated Learning which “enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud” [3]. The approach works by first downloading the current model to the smartphone. The phone then improves the model based on user data and “summarizes the changes as a small focused update” [3]. These updates only occur when the phone is plugged in and on a wireless connection, so that it won’t affect the phone’s performance. The update is then sent to the cloud and aggregated with other user updates to form a change in order to improve the shared model. The researchers at Google created a Secure Aggregation protocol for the aggregation of user updates. This protocol uses cryptographic techniques and only allows the server to decrypt information if there is enough user participation. This means that a small update from a single smartphone cannot be decrypted and read. The Federated Learning approach allows machine learning on smartphones to be faster because data doesn’t need to be sent to the cloud, which also ensures privacy [3].

Machine learning on smartphones has become a possibility because of better performance in processing units. In April 2017, Google announced its custom ASIC for machine learning called the Tensor Processing Unit (TPU) [4]. Google boasts that “its TPUs are 15x to 30x faster than contemporary CPUs and GPUs” [4]. With this faster processing, we’ll soon see large increases in capabilities of machine learning on smartphones.



[1] https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/articles/tmt-predictions.html

[2] https://www.businessinsider.com.au/machine-learning-in-your-smartphone-is-the-megatrend-of-2017-2017-1

[3] https://research.googleblog.com/2017/04/federated-learning-collaborative.html

[4] http://www.silicon.co.uk/cloud/datacenter/google-custom-ai-chips-208833



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