AI models: The future of machine intelligence AI models: The future of machine intelligence - Softweb Solutions

AI models: The future of machine intelligence

AI models: The future of machine intelligence
Author : Kirtan Vaghela   Posted :

What is artificial intelligence?

Artificial intelligence (AI) is the field of computer science that focuses on creating intelligent machines, especially intelligent computer programs.

What is machine learning?

Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn or improve performance based on the data they ingest.

What is an AI model?

AI models are computer programs that learn from data to make predictions or decisions. They are used in a wide range of applications, from self-driving cars to medical diagnosis. AI models are becoming increasingly accurate and sophisticated, thanks to the availability of large amounts of data and the development of new machine learning algorithms.

AI models vs. machine learning models

ML and AI are frequently discussed together, and the terms are occasionally used interchangeably, although they do not signify the same thing.

AI models are designed to replicate human intelligence using algorithms, whereas ML is designed to teach machines to operate and optimize themselves. With ML, the machine will learn from previous decisions to improve its efficiency over time.

All ML models are AI models, but not all AI models will necessarily be ML models. This is an important distinction to make and will help you understand this topic in greater detail.

The most popular AI models

Linear regression

It predicts the value of a dependent variable based on a given independent variable. Linear regression models are widely used in various industries, including banking, retail, construction, healthcare, insurance and many more.

Deep neural networks

Inspired by the neural network of the human brain, these are similarly based on interconnected units known as artificial neurons. DNN models find application in several areas, including speech recognition, image recognition and natural language processing (NLP).

Logistic regression

Logistic regression is a subset of ML which estimates the outcome and predicts one of two values for dependent variables. Logistic regression is the best AI model for solving a binary classification problem. This model is adept at predicting the value or class of a dependent data point based on a set of independent variables.

Decision trees

Decision trees are supervised ML algorithms considered to be one of the most efficient AI models. Decision trees can be used to solve both regression and classification problems. Moreover, rudimentary decision trees powered the earliest forms of predictive analytics.

Linear discriminant analysis

This is usually used when two or more classes are to be separated in the output. This model is useful for various tasks in the field of computer vision, medicine, etc.

Naive bayes

The model works on the assumption that the occurrence of any feature does not depend on the occurrence of any other feature. Since this assumption is almost never true, the model is called ‘naive’. It can be used for medical data classification.

Support vector machines

SVM is faster and performs better with limited samples. It can be used for classification, outlier detection and regression problems.

Learning vector quantization

Learning vector quantization (LVQ) is a type of machine learning algorithm that prepares a set of vectors to learn information that can be used to classify other unseen vectors.

K-nearest neighbors

KNN is an ML algorithm that groups input values to be graphed near one another. The disadvantage of this model is that speed slows down with the increase in data.

Random forest

A random forest is a collection of multiple decision trees. The combined results of every decision tree make a more accurate final prediction or decision.

Deep neuralnetworks:

Deep neural network models were originally inspired by neurobiology. A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. DNN processes data in complex ways by employing sophisticated mathematical modelling. DNN reduces the error rate in recognizing words in speech recognition and in image recognition.

The two key phases of neural networks are called training (or learning) and inference (or prediction).

  • The prediction accuracy of a neural net depends on its weights and biases.
  • The process of improving the accuracy of neural networks is called training.

Types of deep neural networks

Multi-layer perceptron (MLP):

It is a feedforward neural network, which means that data flows in one direction, from input to output, without looping back on itself. MLPs are often used for tasks like classification and regression.

Example of MLP on iOS:

Setting up your development environment:

  • Install Xcode, which is the integrated development environment for iOS app development.
  • Create a new iOS project or open an existing one.

Designing the neural network:

  • Import the necessary libraries and frameworks. You’ll need Core ML to deploy your trained model on iOS.
  • Design your MLP model architecture using a deep learning framework such as TensorFlow or PyTorch. Train and save the model in a format compatible with Core ML, typically in the ONNX (Open Neural Network Exchange) format.

Converting the model to Core ML format:

  • You can use Apple’s Core ML tools to convert your trained model to the Core ML format. This typically involves using the coremltools Python library. For example:

Integrating the model into your iOS app:

  • Add the.mlmodel file to your Xcode project.
  • In Xcode, create an MLModel class by selecting your .mlmodel file and using the “Create ML Model Class” option.

Use the model in your iOS app:

  • In your Swift code, you can use the generated YourMLPModel class to make predictions. Here’s a simplified example:


importUIKit
importCoreML

// Load the Core ML model
let model = YourMLPModel()

// Prepare input data (should match the model's input requirements)letinputData = YourInputDataType(...) 

// Make a prediction

iflet prediction = try?model.prediction(input: inputData) { let output = prediction.output// Access the model's output

// Process the output as needed in your app

}

Convolutional neural networks (CNN)

CNNs are most employed in computer vision. With the implementation of CNN, AI will learn all the features of input (it can be images or videos) to perform various tasks. Convolutional neural network applications include image classification (e.g., AlexNet, VGG network, ResNet, MobileNet) and object detection (e.g., Fast R-CNN, Mask R-CNN, YOLO, SSD).

Example:
Steps 1 to 4 are same as multi-layer perceptron.

Use the model in your iOS app:



importIKit
importCoreML

// Load the Core ML model
let model = YourCNNModel() 

// Prepare an input image in a format that matches the model's input requirements
let image = YourInputImageType(...) 

// Make a prediction
ifletpixelBuffer = image.pixelBuffer(), let prediction = try?model.prediction(image: pixelBuffer) { let output = prediction.classLabel// Access the predicted class label

// Process the output as needed in your app

}

Recurrent neural networks (RNN):

RNNs have been developed to address the time-series problem of sequential input data. RNN models are widely used in natural language processing (NLP) due to the superiority of processing data with an input length that is not fixed.

Example:
Step 1 to 4 is same as Multi-Layer Perceptron example.

Using the Model in Your iOS App:



import UIKit
import CoreML

// Load the Core ML model
let model = YourRNNModel()

// Initialize the model's initial hidden state and input
var hiddenState = YourInitialStateType(...)
var input = YourInputDataType(...)

// Generate a sequence using the RNN
var generatedText = ""
for _ in 0..<maxSequenceLength {
    if let prediction = try? model.prediction(input: input, hiddenIn: hiddenState) {
        let output = prediction.output // Access the generated output
        // Update the input and hidden state for the next time step
        input = YourInputDataType(output)
hiddenState = YourHiddenStateType(prediction.hiddenOut)
generatedText.append(output)
    } else {
        break
    }
}

Suggested: CNN vs. RNN vs. ANN

Conclusion

By understanding the different types of AI models and how they work, we can better position ourselves to leverage this powerful technology to solve some of the most pressing business challenges.

Softweb Solutions is a leading provider of AI consulting and development services. We have a team of experienced AI experts who can help you develop and implement AI solutions to address your specific business needs.

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