Android is another platform that Google has focused on and its efforts are clearly visible: Android occupies more than 87.9% of the market share. With the latest Android P just around the corner, it seems like a good time to take the best of both worlds: Machine Learning and Android and show why Android is more likely to beat its competitors using Google’s gigantic intelligence prowess and why the AI will. attract both regular users and developers.
Here are some reasons.
Google announced its virtual assistant in May 2016 during its annual Google I / O conference. Google described it as a “conversation assistant” and hoped it would provide “an ambient experience that extends to all devices.” And the feedback received has been overwhelmingly positive.
Of course, Google is not the only one trying to give its users a software hand. In fact, it’s not even the first: Apple released a beta version of Siri with its iPhone 4S almost a decade ago, October 2011. To say that software like Siri has come a long way in these 7 years would be a gross understatement. It seems all the tech giants are releasing their own wizards every two weeks. While the most prominent are Microsoft’s Cortana, Amazon’s Alexa, Samsung’s Bixby, Apple’s Google Assistant, and Siri, nearly all professional tester reviews reveal the one that manages to balance just about everything thrown at them. And that’s the Google Assistant.
It has proven its worth countless times in tasks ranging from speech recognition and contextual understanding to providing concise but detailed information for any user query.
Some would say it’s years ahead of other virtual assistants, although advancements like the Duplex are only confirming this.
Tech giants are recognizing the importance of incorporating machine learning into their products, and as our systems become more powerful and people generate more data than ever before, it’s no wonder why they do it. This is evident in companies that embrace and promote smart calculations.
Apple has been urging developers to use its relatively new CoreML framework that can be used to train machine learning models to develop applications for iOS. It’s too early to pass judgment on this move from Apple, but it’s pretty safe to say that the berry iPhone maker is late to the party.
Google released an open source framework called Tensorflow in 2015 after being tested and developed in-house for over 4 years. Since then, it has earned the industry standard badge and is one of the most active repositories on GitHub. It was developed with developers in mind and has multiple ports for different operating systems and also supports multiple programming languages to make a developer feel right at home.
Tensorflow Lite is Google’s goal to have native support for its deep learning models on Android phones. Applications like Gmail are already putting this to use by presenting something called “Smart Responses” which basically tries to understand the situation and context in a received email and will show some options that could be a good response to the above. Another famous application is Photos by Google, which uses deep learning, a popular form of machine learning, to recognize people from the images stored on the smartphone and suggest possible options, such as sharing them with the person or creating an album. completely new to them.
Simply put, Google has already started rolling out apps like Translator, Assistant, Photos, Gmail, etc. And it has created the necessary tools for developers to do the same with theirs. Which brings us to the next topic:
Extremely good developer support
Google has always been loved by developers. In addition to offering great opportunities like GSOC, it has released open source libraries like scikit-learn and TensorFlow that have been very popular and successful within the developer community.
Even Android, being open source, offers a lot of flexibility for developers and therefore naturally developers will be much more focused on creating scalable and optimized applications for this platform.
Google wants more and more people to enter this field of the machine and has endeavored to do so. One such example is the crash course in machine learning. It is a course from scratch aimed at developers who have almost no previous experience in the field of AI. Guides the user from the basics of linear algebra to next-generation convolutional neural networks.
Android developers received attention with the announcement of Tensorflow Lite, which is an ecosystem for said platform. It works seamlessly with the official Android IDE, Android Studio, to develop applications with the same level of consistency as before.
Google never ceased to amaze visitors and viewers at its 2018 developer conference. It showed something that Google developers had been hard at work on, called the Google Duplex.
It’s an extension of the already powerful Google Assistant that helps the user get through the day making appointments or booking services like ordering food at a store that doesn’t have an online presence or fixing a haircut at a salon for wear and tear.
It was introduced by Sundar Pichai, leaving the audience clapping. And why shouldn’t they? They witnessed an ancient test called the Turing Test that was supposed to be nearly a decade away from being solved, annihilated albeit in a very specific way.