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Here’s how Google Makes Android ready for AI



Artificial Intelligence has been a technology that has been a technology that has been on the rise in the technological world. Machine learning and Data Science have brought in ways to improve computations and have begun a change in the technological world. Artificial Intelligence is a product of the technology developed by them and has become a very popular way to provide improved customer experience in various software products.

With the rise of Artificial Intelligence, there has been an active involvement of many of the top businesses to develop their products with the inclusion of the technology to greatly enhance their customer experience and has helped them move forward in the automation of processes and moving ahead in the Artificial Intelligence integration race. One such top player is Google which has developed many of its products with Artificial Intelligence integrated into them and is now set to integrate it into its highly anticipated operating system, Android. In its annual developer conference, called the Google I/O, Google announced their product for developers called TensorFlow, which will “allow developers to access some awesome neural networking features in Android OS”. This shows the company’s intentions of implementing Artificial Intelligence deeply into their Android operating system.

Google has started investing heavily in the machine learning technology and has started pushing the machine learning specific hardware so as to make it work with the Cloud Tensor Processing Unit, that is, TPU for short, that are processing chips that have been developed so as to bring in a better Artificial Intelligence experience into the system. The hardware so developed will help in an improved and accelerated training of the system with newer Machine Learning algorithms and processing data in a better way using the existing Machine Learning algorithms. The company announced their TensorFlow Lite, an artificial intelligence development tool that would allow mobile app development company to incorporate AI into their app for the existing hardware in the smartphone market.

How will this technology improve the integration of Artificial Intelligence into the Android?

According to Dave Burke, the Vice President of Engineering working for Android, the TensorFlow Lite tool will enable the better development of a neural network API that will enable it to use the silicon-specific accelerators in the hardware for an improved Artificial Intelligence computing on the hardware. This will help in enabling the systems with capabilities that will enable the speech processing, visual search, augmented reality and other related processes, to be implemented on the device and not being done on a cloud server that will make it a quicker process to perform and integrate the functionality into the device. TensorFlow Lite is a resource library for Android apps for the developers to integrate Artificial Intelligence into their apps quickly using some state-of-the-art techniques. The TensorFlow Lite project is an open source project allowing the source code of it to be freely available for other developers for modifications and addition of features into the existing code. And, Google plans to provide as an over the air update as a neural network API in the coming Android updates. TensorFlow already supports Android and iOS, but TensorFlow Lite is a step forward in bringing Artificial Intelligence more into the Android apps.

How does this technology work for integrating Artificial Intelligence into the Android operating system?

The Artificial Intelligence focused chips in the TensorFlow Lite tool can help in developing Android phones that can handle advanced machine learning computations efficiently with minimum power consumption. As the numbers of Android apps that use machine learning grow to offer an enhanced user experience, the computations and processes required will reduce thus reducing the load on the phone as it will have enough previously learned data to work upon.

Due to the complexity of computations and the high requirements of time and power by the computational models, it has been very difficult to hardcode the machine learning instructions into the apps. It requires a powerful hardware and a highly skilled programming to integrate machine learning into the applications. As these are not available in the smartphones available to the consumers, these features were deemed impractical for a long time. The high requirements of hardware and processing time for the computations, the consumer smartphones do not provide it. The process requires high data storage and that cannot be made available in the limited storage on a smartphone. The apps that do have artificial intelligence or machine learning features in them, load the processed data to their massive data centers. The data is processed on the cloud and it requires a good connectivity of the consumer smartphone with the internet. The user must allow the app to have access to their data and have the permission of the user to send their data like texts, images, etc., to the company’s servers for processing. This uploading to and receiving a result from the server process also requires a rich connectivity with the server that the consumer smartphone may not be able to provide every time reliably.

TensorFlow Lite is what will reduce this requirement of a robust hardware and it will also bring in the computation of data to be done locally on the device rather than fetching the results from a remote server. It offers solutions to all these problems or rather difficulties, by a machine learning specific Digital Signal Processor (DSP for short). This will help the newer generation of apps to be more intelligent and produce quicker results for the user without needing any cloud storage or cloud processing. These apps may as well grow more and more intelligent as they learn more about the user and produce user specific results more quickly as a result of the Artificial Intelligence integrated into them. This will enable them to refine image recognition and speech recognition on the move, providing more computing power to the Android devices. These chips integrated by TensorFlow Lite from Google will really be a huge step forward in integrating Artificial Intelligence into the Android operating system.