There has been an increased global demanded for a more personalised mobile experience, so a widespread adaption of deep learning and AI in the mobile app development industry is inevitable. We can forget about latency issues that crop up in cloud computing and mobile sensing. Close to zero latency is here, with real-time data processing speeds which delivers optimum results.
With the introduction of Bionic smartphone chips by Apple, built-in neural processing units help neural networks run directly on-device at an amazing speed. Using Google’s ML Kit and Apple’s Core ML, deep learning libraries like Keras and TensorFLow Lite, our developers can create products with fewer errors, faster data processing and lower latency.
A huge advantage of on-device machine learning is that it can offer users an accurate, seamless user experience. Since you do not have to question if data has been sent for processing, you will get an improved privacy, data protection and user security. Also, with neural networks on mobile devices, you do not need to connect to the internet to access every feature of your app. By using mobile device computing capabilities to implement deep learning, it has greatly improved mobile device usability.
Why You Should Be Incorporating Deep Learning Algorithms:
Augmented Reality and Immersive Capabilities
Using Apple’s ARKit and Google’s ARCore platforms, developers can build AR apps that can juxtapose digital environments and objects with realistic settings. Mobile-based AR’s immersive capabilities are having a significant impact on entertainment, travel, retail and other industries. Looking at brands Like Sephora or Lacoste, they allow their customers to try on products with AR apps and a growing number of shoppers prefer to check out products on their phones before taking the plunge, especially in today’s times where people can try things on in-store. Interactive AR games like Ghostbusters World, Ingress and Pokemon received a cult following and received extensive press. Also, if you’re looking for navigation, Google Maps Live View will provide you with real-time navigation.
Privacy and Security
On-device machine learning has made things easier in complying with General Data Protection Regulations (GDPR) and the California Consumer Privacy Act (CCPA). It guarantees security in your data, as you don’t need to upload encryption, biometrics or to a cloud or server for processing.
On-device automatic encryption is another useful smartphone feature that protects your content with a pattern, password, PIN and allows access to your data only when you unlock your phone. This way, if your device is stolen or it is lost, the chance of someone having access to your data is negligible. The iPhone’s Face ID feature is an example of a more secure smartphone experience. The on-device neural networks in the Apple smartphone chips process and safely store user facial data. The identification happens on the device, so your security and privacy remain unimpeded. Google Pixel 4’s Face Unlock tech, uses 3D IR depth mapping to create your face models for face recognition and stores them on an on-device Titan M6 security chip. Face Unlock works well with the 1Password app which offers users biometric security by eliminating the chance of fraud identity.
Speech recognition involves transducing or transforming input sequences into output sequences using recurrent neural networks (RNN), deep neural networks (DNN), convolutional neural networks (CNN), and other architectures. Developers struggled with the issue of latency which will create delays between the automated assistant’s response and the request given. But, we can get around this by using the compact recurrent neural network transducer (RNN-T) tech in mobile devices.
RNN-Ts are sequence-to-sequence models. Rather than following methods that have been followed previously of processing an entire input sequence before producing an output, however, they maintain a steady continuity in their input processing and output streaming. This facilitates the processing of real-time speech. We can see this in Google Assistant, which can process consecutive voice commands without faltering and without requiring you to say ‘Hey, Google’ after before each request. This creates a more conversational flow, and the assistant will follow your instructions precisely. Want to find a photo in one of your folders? Looking for a guided route to a friend’s place? Consider it sorted.
High Quality Photos
High quality photographs are an important criterion for buyers when selecting smartphones, which they can get with many of the latest models. These come equipped with the hardware components: image central processing units (CPUs), image signal processors, deep learning image algorithms, and neural processing units that have catapulted smartphones into a new realm from traditional cameras.
Accurate Image Recognition
When you pair on-device machine learning with image classification technology, you can identify and receive detailed information in real-time with about anything you encounter. Looking to read a foreign language text? Scan it with your mobile device and get an accurate instant translation. See a piece of furniture that you are keen on? Scan it to get information about where you can buy it and the price. By facilitating image recognition in real-time, apps like Calorie Mama, Google Lens and Leafsnap are increasingly mobile’s devices’ usability and learnability and enhancing user experience.
The possibilities of on-device machine learning are endless. With the increase of efficient intelligent algorithms, increase in power for AI chips and deeper neural networks, digital products incorporating deep learning mobile will be the expected standard in retail, banking, health care, data analytics and various other industries. As deep learning capabilities continue to improve, mobile devices’ usability features will evolve alongside and will continue to flourish.