Artificial Intelligence in iOS

Transforming User Experiences and App Development

Artificial Intelligence (AI) has made significant strides across various sectors, and the iOS ecosystem is no exception. Apple has consistently integrated AI technologies into its products, creating smarter, more intuitive applications that enhance user experiences and streamline app development. This article delves into the ways AI is revolutionizing iOS development and its impact on users and developers alike.

Siri: The Intelligent Personal Assistant

One of the most prominent examples of AI in iOS is Siri, Apple’s voice-activated personal assistant. Siri leverages natural language processing (NLP) and machine learning to understand and respond to user commands. Siri’s capabilities have evolved over the years, allowing it to perform a wide range of tasks, from setting reminders and sending messages to providing weather updates and controlling smart home devices. Siri’s continuous learning from user interactions enables it to offer more personalized and accurate responses over time.

Core ML: Empowering Developers with Machine Learning

Apple’s Core ML framework is a powerful tool that brings machine learning to iOS apps. Core ML allows developers to integrate pre-trained machine learning models into their applications with ease. This framework supports various machine learning tasks, including image recognition, natural language processing, and predictive analysis. By leveraging Core ML, developers can create apps that offer intelligent features such as object detection, sentiment analysis, and personalized recommendations.

Example: Image Recognition with Core ML

Here’s a basic example of how to use Core ML for image recognition in an iOS app:

1. Prepare the Model: First, you need a trained machine learning model. Apple provides several pre-trained models, or you can train your own using tools like Create ML or third-party platforms.
2. Integrate the Model: Add the model to your Xcode project. Xcode automatically generates a Swift class for the model.
3. Use the Code:

import UIKit
import CoreML
import Vision

class ImageRecognitionViewController: UIViewController, UIImagePickerControllerDelegate, UINavigationControllerDelegate {

    // MARK: Properties
    @IBOutlet weak var imageView: UIImageView!
    @IBOutlet weak var resultLabel: UILabel!
    
    let model = try! VNCoreMLModel(for: MobileNetV2(configuration: .init()).model)

    // MARK: functions
    @IBAction func selectImage(_ sender: UIButton) {
        let imagePicker = UIImagePickerController()
        imagePicker.delegate = self
        imagePicker.sourceType = .photoLibrary
        present(imagePicker, animated: true, completion: nil)
    }
    
    func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [UIImagePickerController.InfoKey : Any]) {
        if let image = info[.originalImage] as? UIImage {
            imageView.image = image
            detectImageContent(image: image)
        }
        dismiss(animated: true, completion: nil)
    }
    
    func detectImageContent(image: UIImage) {
        guard let ciImage = CIImage(image: image) else { return }
        let request = VNCoreMLRequest(model: model) { (request, error) in
            if let results = request.results as? [VNClassificationObservation], let firstResult = results.first {
                DispatchQueue.main.async {
                    self.resultLabel.text = "\(firstResult.identifier) - \(Int(firstResult.confidence * 100))%"
                }
            }
        }
        let handler = VNImageRequestHandler(ciImage: ciImage)
        try? handler.perform([request])
    }
}

In this example, we use a pre-trained MobileNetV2 model to identify objects in an image selected from the photo library. The model’s prediction is displayed on the screen along with its confidence level.

On Storyboard UI we will create/drop only 3 elements which are UIImageView, UILabel and UIButton. We will connect IBOutlet of UIImageView and UILabel and connect IBAction of UIButton which is available in the code.

Conclusion

It’s very simple and easy to create AI based iOS application. Where user can browse image from photos and in the result can get what the object was there in the photo. In order to know about The Impact of Artificial Intelligence on Mobile Development click this link: AI Mobile

Github

https://github.com/faraz-zafar/imageRecognition