The report by Fritz AI and Spell is based on data from a survey of 500 technical leaders from various industries who have current or previous experience with mobile app development projects. Here are some interesting takeaways from the report. About 40% of mobile app development companies have no expertise in machine learning (ML), while 47% have no active ML projects. The main ML use cases are natural language processing (NLP) and computer vision. Non-profit/education and eCommerce/retail have zero access to ML expertise at the time of the research.

The steady rise of mobile Internet traffic has provoked a parallel increase in demand for on-device intelligence capabilities. However, the inherent scarcity of resources at the Edge means that satisfying this demand will require creative solutions to old problems. How do you run computationally expensive operations on a device that has limited processing capability without it turning into magma in your hand? The addition of TensorFlow Lite to the TensorFlow ecosystem provides us with the next step forward in machine learning capabilities, allowing us to harness the power of TensorFlow models on mobile and embedded devices while maintaining low latency, efficient runtimes, and accurate inference. TensorFlow Lite provides the framework for a trained TensorFlow model to be compressed and deployed to a mobile or embedded application.

With over 2 billion active Android devices and over 1 billion active iOS users, the mobile market provides the most engaging and profitable market to build and sell new digital solutions. There are less than 4 million unique applications for each of these operating systems, with most of them performing the same or related functions. However, the arrival of cloud-based and device-based artificial intelligence tools provides a unique opportunity to recreate the mobile experience for existing apps, as well as build entirely new mobile apps that can only be possible through the use of AI-powered tools. There are a few challenges with harnessing this opportunity of mobile apps with AI capabilities, some of which include knowing what problems to solve, what applications to build, and how to tailor these applications so that existing app users can have improved mobile experiences. Below, I'll be sharing 5 exceptional mobile apps ideas made possible by integrating AI into the mobile experience.

We from Mammoth Interactive are here to tell you that your Android and iOS apps can become smarter, stronger and more convenient thanks to machine learning. Better yet, we'll show you how to build your very own intelligent software that grows with you. Machine learning is changing the world around us. ML began on computers, but the next big wave is machine learning for mobile. Have you ever thought: why can't my mobile device do more?

In conjunction with the Artificial Intelligence (AI) theme at IBM Think, Apple and IBM announced that the two companies are working together to deliver a mobile AI solution based on IBM Watson Services for Core ML. The partnership will leverage Apple's Core ML framework and Watson Studio (also announced) development tools allowing developers to design apps that will leverage the machine learning (ML) capabilities on Apple devices. Starting with the A11 processor, Apple integrated a dedicated neural engine for inference processing of trained ML models. While it is unusual to see Apple highlight a technical partnership, it is not unexpected. No mobile AI solution would be complete without a cloud solution for model training and development and IBM is a leader in cloud computing solutions and AI.