Google’s I/O 2018 asserts one thing – the next wave of smartphones will run on a generous amount of Artificial Intelligence. Not just Google I/O. Even the recent Mobile World Congress (MWC) also had conversations that were largely revolving around Artificial Intelligence.
Major smartphones makers, led by Apple, Google, Samsung, and many others are creating operating systems, mobile apps and even smartphone that have Artificial Intelligence at their core.
McKinsey Global Institute estimates that the investments in Artificial Intelligence R&D made by tech giants by Google and Baidu to be in the range of $20 Billion to $30 Billion. In fact, Ai is ranked to be one among the 5 disruptive Technologies that are shaping up our future digital landscape.
Even if smartphone manufacturers are not developing their own AI systems, they are integrating existing options like Amazon Alexa, Google Personal Assistant into their devices to make them as intelligent as their human owners.
Artificial Intelligence and its subsets machine learning are seen as revenue-generators in several industries like healthcare, eCommerce, education, capital markets among many others. Above all, AI would have its biggest impact on mobile. The level at which mobile applications use Artificial Intelligence to drive user experience, personalization and data mining is drastically increasing.
AI-first Mobile Apps – The Future Mobile Landscape
Virtual personal assistants. This is the first image that most of us get when we think of a mobile infused with AI. but, the potential of AI in mobiles go beyond personal assistants.
Google’s Lens
Google Lens is a visual analysis tool that Google introduced in I/o 2017. It helps bring up relevant information about an image in the user’s device using Machine Learning & Deep Learning. Google Lens was initially launched as a Pixel exclusive. It was later rolled out to a wide range of Android devices.
Google Lens tweet – https://twitter.com/Google/status/864891667723300864
“With Google Lens, your smartphone camera won’t just see what you see, but will also understand what you see to help you take action.”
Google Lens can recognize everyday objects or places and provide related information like reviews, working, hours, addresses, etc. on a pop-up window. For instance, if you click the image a flower, and click on the Google Lens button, the app can provide you information line nearby florists, their working hours, contact details, address, and much more.
Using Machine Learning and Deep Learning, Google Lens can also provide several other capabilities like Smart text selection from images, searching for the selected text, searching for similar clothing or decor, nearby places and so on.
AI-based Language translation
Unsupervised machine learning can perform sophisticated tasks like real-time language translation, even without the help of a dictionary. The AI system can recognize texts given as textual, voice or graphical input by the user and translate it into any language of choice.
Google Translate AI which has created its own algorithm to find common clusters or patterns between various languages is a perfect example. Microsoft Translator, Siri, Facebook’s language translation are other examples of AI-based language translation.
Traditionally, language translation programs used to break down chunks of words or phrases in a sentence and process them individually. Since the translation was often done by referring to an onboard dictionary or a server, the results were prone to errors.
Unsupervised machine learning takes away that possibility of errors since the system learns continuously from user input. Moreover, AI systems come with several other capabilities for language translation including speech recognition, image and video recognition, handwriting recognition, etc. all of which makes it a powerful and independent language translation tool. Google’s Pixel earbuds which can translate language based on audio input is taking AI language translation to a whole new orbit.
Reliable transportation using Machine Learning
The biggest strength of machine learning is that it can learn user behavior patterns. The data mining of user behavior patterns helps fine-tune mobile applications to deliver a more personalized and smarter user experience.
Uber’s cab-hailing service analyzes millions of metrics collected on a real-time basis. The data so mined is used to train ML systems which can suggest optimal fare, best routes, preferred pickup locations and even estimate the predict peak timings for UBEReats.
Cab-hailing apps, food ordering apps, and even mobile shopping apps can leverage the self-learning capabilities of AI and machine learning systems to engage their users more. In fact, social apps like Instagram, Pinterest, Snapchat have already integrated AI to customize news feed for their users. The extent of usage might vary, but, almost every top downloaded mobile app on app stores today have a slice of AI infused into them.
Powering Smart Home Devices
From Amazon, Alexa to Google Home and the endless list of other virtual home assistants are all powered by Artificial Intelligence. These smart home devices thrive on voice recognition, speech recognition, and natural language processing to synthesize user commands into actionable items. The end result is a smooth user experience far advanced than a web browser or a mobile app.
Smart home devices make it easy to do everyday tasks like booking a cab through voice commands, set a reminder, make a voice call, play music from cloud playlists and much more.
Bringing it all together
Artificial Intelligence is all around us. We don’t recognize it always. Every single mobile app that we rely on for running our everyday digital lives comes with a heavy dose of Artificial Intelligence. Machine learning, deep learning, natural language processing and several other sub-fields of Artificial Intelligence make mobile apps a disruptive force.
