Toggle navigation
Will Google’s TensorFlow Lite be the Game Changer for Machine Learning in Mobile Apps?

Will Google’s TensorFlow Lite be the Game Changer for Machine Learning in Mobile Apps?

  • Home
  • /
  • Blog
  • /
  • Will Google’s TensorFlow Lite be the Game Changer for Machine Learning in Mobile Apps?

Updated: May 20, 2017

This year’s Google I/O amazed the world with yet new incredible tech ideas. For Android app developers, it was a special treat as Google’s CEO Sundar Pichai announced in his keynote speech that now more than 2 billion people run the Android OS, including smartphones, tablets, Android TVs and Android wearable devices. Furthermore, with the release of Android O, developers have greater flexibility to customize their apps to become more user friendly and thus enhance user experience. Additionally, with the new OS Android Go, Pichai aims to capture the “next billion”. This OS will be able to run on devices with RAMs as low as 512 Mb! Google further outlined its road map for Google Assistant, Google Home, Virtual Reality and more. But, throughout, it was clear that the next primary objective of the tech giant is to dominate the field of Artificial Intelligence and Machine Learning.

io home post event 11 Source: Google Events

Release initially on November 9, 2015, Google’s open source platform for Artificial Intelligence, Machine Learning and Deep Learning called TensorFlow, has had a huge impact on AI research. TensorFlow has helped researchers predict cancer, ease machine translation, enhance image recognition, and speed up speech recognition. In February this year, Google announced that they were “excited to see people using TensorFlow in over 6000 open-source repositories online.“ Today, major companies like Uber, Twitter, Snapchat, Qualcomm, Airbnb, Ebay and several others implement TensorFlow.

The Launch of TensorFlow Lite

google io 2017 0284 Source: Google Events

Unfortunately, TensorFlow was primarily for computers, until now. In Google’s I/O, Dave Burke, the vice president of engineering for Android, announced the soon-to-be-released TensorFlow Lite for mobile. He said “TensorFlow Lite will leverage a new neural network API to tap into silicate specific accelerators, and over time we expect to see DSPs (Digital Signal Processors) specifically designed for neural network inference and training.” He further added “We think these new capabilities will help power the next generation of on-device speech processing, visual search, augmented reality, and more.”

This new library will allow developers to build Machine Learning apps which run faster while being less expensive on the system. The framework will enable development of deep learning models, and thus bring AI with a greater impact to the mobile platform. We could expect social media apps to become smarter by giving better recommendations. Photo, video or document editing apps could soon have cooler tools, enabling higher customizability and enhancing ease of use. New games could have better Augmented Reality graphics and smoother gameplay. Speech recognition and translation apps would become more user friendly, and way smarter. Most importantly, TensorFlow Lite will enable smaller companies to develop high quality AI apps, thus giving them an opportunity to grow significantly.

Any AI software requires training a huge dataset, which remains a major task, and a task which is computationally too expensive to be handled by smartphones. So in spite of TensorFlow Lite, the training of the models would still have to be performed on the cloud. But as Burke mentioned, Android O will introduce “a new framework” to hardware to enable accelerated neural computations. We can be sure that the future will see several modifications not only in software, but in hardware too. Thus, powered with these new technologies, we can push AI to new limits.


We're BitCot!

Need help? We design, build, and grow digital products across Android,iOS, and web.

Contact Now

    Share On:

    Apple Pay allows users to pay using their credit cards without a PIN or password. Apple Pay works by scanning the card's hologram. It can also be used to save card details. However, Apple Pay isn't available for all credit cards. According to the https://aucasinoslist.com/casinos/iphone-casino/, only the banks that accept it can approve it for use with this system. Then, you can use it to make payments and deposits at Apple Pay casinos. If you'd prefer to play at a casino that accepts payments via your Apple Pay, you can simply visit the website's Apple Pay page.

    Enquanto as estratégias de apostas positivas e negativas para jogos de cassino online podem ajudá-lo a ganhar uma pequena vantagem sobre o cassino, ambas podem diminuir drasticamente a sua banca. De acordo com o https://casinosnobrasil.com.br/, jogadores que são habilidosos o suficiente para usar ambas as estratégias podem ter dificuldade em perder uma aposta quando as marés acabarem. Eles devem apostar no que acham que é a melhor opção para eles. Estes sistemas não só são populares, mas também são extremamente eficazes.

    Raj Sanghvi BitCot CEO

    Author: Raj Sanghvi

    Raj Sanghvi is a technologist and founder of BitCot, a full-service award-winning software development company. With over 15 years of innovative coding experience creating complex technology solutions for businesses like IBM, Sony, Nissan, Micron, Dicks Sporting Goods, HDSupply, Bombardier and more, Sanghvi helps build for both major brands and entrepreneurs to launch their own technologies platforms.

    Visit Raj Sanghvi on LinkedIn and follow him on TwitterView Full Bio

    Free project quote

    Fill out the enquiry form and we'll get back to you as soon as possible.

    Contact Us: 858-683-3692

      Dave S

      Co-Founder- StompSessions


      I have Known BitCot for 4 years and have been impressed with the diversity and quality of BitCot work. With that solid foundation it was really easy to select BitCot as our development partner.