Apple Has Bet on Future by Integrating Core ML and ARKit to iOS Platform

Augmented Reality/ iOS

By releasing Core ML and ARKit, Apple has bet on future and established its technical superiority in the mobile app technologies. SysBunny is analyzing core concepts and reactions of releasing both frameworks in the iOS app development industry with the current post.

Introduction:

In the June 2017, Apple has announced the release of two frameworks, Core ML and ARKit. The Core ML is a Machine Learning API, and ARKit is Augmented Reality SDK. Both are altogether different technologies but have a high significance in shaping the future. To learn their impacts on our life let’s understand both one-by-one.

Core ML – Machine Learning Technology on iOS Platform

Apple is working in machine learning (ML) and artificial intelligence (AI) since long and Siri is an apparent result of it.

With Core ML, Apple has made Machine Learning for everyone

Apple’s Journey from NLP to ML

During the period of iOS 5, it has introduced natural language processing (NLP) through NSLinguisticTagger framework, and at the time of iOS 8, the Metal framework arrived with enhanced GPUs capabilities to provide immersive gaming experiences.

In 2016, Apple has developed an Accelerated framework to process signals and images using the Basic Neural Networks for Subroutines (BNNS) technologies. Now, it has placed Core ML framework on the top of both Metal and BNNS frameworks.

The former two frameworks required a centralized server to process the data for NLP and AI processes. With the intro of Core ML on the top, there is no need to send data outside the devices and the data processing accomplished with powerful A9, A10, and A11 chips. It also strengthens the data security within the iOS devices.

Understanding Core ML

To understand how Core ML is working, we can divide the process into two steps. The first one is the creation of Training Model by applying ML algorithms to the available training data sets. The next one is to convert training model into a file as the Core ML Model file.

The Core ML Model file helps developers to integrate high-level AI and ML features. The entire function flow of Core ML API helps to make the system “Intelligent” predictions.

For iOS developers, X-code IDE is capable of creating Objective-C/Swift wrapper classes once the model Core ML Model included in the app project. The Core ML model consisting of class labels and input/output as well as can describe the layers in the framework.

Apple has made decent efforts to help iOS developers to provide maximum support to develop customized AI and ML solutions with ease and speed.

Core ML Models Core ML Model Tools Core ML Model Supports
Apple has unveiled five different Core ML models for third-party developers, such as:
  • Places205-GooLeNet
  • Inception V3
  • ResNet50
  • SqueezeNet
  • VGG16
The Core ML Models also supports other t ools including
  • libSVM
  • XGBoost
  • Caffe
  • Keras
The Core ML Models also supports other ML Models like
  • Tree ensembles
  • Neural networks
  • SVM (support vector machines)
  • Regression (linear/logistic)

ARKit – Augmented Reality Technology on iOS Platform

The phenomenal success of Pokémon Go has grabbed many eyes in the mobile app market and inspired the entire app industry to take the serious note of Augmented Reality as an upcoming and stable technology in the near future.

In the WWDC in June 2017, Apple has announced the release of ARKit as a Augmented Reality framework for iOS developers. However, Apple is not the first company in the industry for AR application support and growth, other players like Amazon with Alexa, Microsoft with HoloLens, and Google with Project Tango have already established their presence well before the Apple.

The history of Apple reveals one thing that Apple always bring innovative and long-lasting technologies in the market. The same goes true for AR technologies too. It has taken the entire different approaches to provide AR experiences in the iOS devices.

Understanding ARKit Functionality

The existing AR frameworks and tools developed by the rivals of Apple are based on the creation of three-dimensional models, and that requires AR specific hardware, processors, network, sensors, and software.

To cut it short and provide high-end AR experiences, Apple has introduced a revolutionary VIO (Visual Inertial Odometry) technology, which is blending the camera data with CoreMotion data with higher accuracy without any additional hardware or software help.

In the AR rendering, technically, virtual objects are placing in the image of the environment or before the eye retina through AR devices like headsets, glasses, and lenses. The standard AR devices create virtual points in the image of physical environment using GPS or other location tracking algorithms for external calibration.

However, in the case of ARKit, it uses projected geometry (World Tracking) technology, which helps in tracing a set of points in the environment around the iOS device and these points are updating in real-time as the device is moving. Thus, ARKit eliminates the entire process of external calibration.

In the world tracking process, ARKit framework detects the plans in the physical environment to place the virtual objects. Similarly, framework determines the availability of lights in the real world and adjust lighting for the virtual world. Moreover, it also determines light effects like shadows from the perspective of real-world lighting sources.

ARKit – A Superior AR Technology

The AR activities in Facebook AR apps/devices are confined up to its camera app only while Project Tango requires separate and customized hardware to display AR activities. Against these, ARKit is compatible with all iOS devices and required no additional hardware or software to render the AR activities.

Therefore, it is a superior technology in the field of AR and holds strong prospects for iOS developers to create innovative AR apps for iPhone and iPad devices.

Moreover, Apple has decided to infuse dual camera on iOS 7 and other advanced versions. It is to support AR applications to gauge the distance correctly between two viewpoints and ease the triangulation process to enhance depth sensing and better zooming.

Therefore, iOS handsets can create depth maps with pinpoint accuracy and enable differentiation between background objects and foreground objects.

Conclusion:

Machine Learning (ML) framework by iOS platform provides extensive support for deep learning technologies with the help of more than 30 layer types. The app developers can use NLP APIs to add features for language identification, part of speech, access recognition, face tracking barcode detection, and much more.

With ARKit and high-speed A9/A10/A11 processors, iOS developers can create customized VR experiences over the physical world for iOS AR applications. Thus, both technologies seem promising for coming days and bet of Apple is right on right time and with right tools.

If you were thinking developing your mobile app leveraging Augmented Reality and Machine Learning technologies, SysBunny has talented iOS developers to hire at competitive marketing rates.

Would you like to know more about mobile app development skills and our quality apps? Contact our team, please.

Hemant Parmar is the one who pushes the boundaries on a daily basis which made him a veteran mobile app consultant. Owning the company that deals with various mobile app development projects, Hemant is also specialized in never-been-done, one of a kind developer. Gathering vast knowledge and adding a business background with over 9 years of experience makes him enthusiastic consultant. With that, Hemant is professionally the one who masters transparent solutions for his clienteles to help them build and maintain a strong relationship in the market. Feel free to get in touch with him on hemant@sysbunny.com.

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