Introduction
Java is a widely used programming language based on object-oriented programming. It provides platform-independent functionality using a two-step process, which includes compiling source code into bytecode and executing that bytecode on any device via the Java Virtual Machine (JVM). Java is a popular language because of their various features, such as robustness, security, ecosystem, APIs, a large and active community, strong industry adoption, and better career opportunities.
At the same time, the software development landscape is undergoing a major transformation driven by Artificial Intelligence (AI). Together, Java and AI are shaping the future of intelligent and enterprise-grade applications. In this article, we’ll cover the basic concepts of Java and AI, along with working examples that involve coding.
Core concepts of the Java programming language
In the Java programming language, the core concepts include Object-Oriented Programming System (OOPs), fundamental syntax elements like variables, and data types. It also includes control flow mechanisms such as loops and conditional statements. Given below is the detailed description of these concepts in Java.
Object-Oriented Programming
It is a programming paradigm that organizes software design around objects, which contain both attributes (data/properties) and behavior (methods/functions). OOPs is built on the following four primary skills.
- Encapsulation: This is the practice of bundling the data (variables) and the methods (functions) that operate on that data within a single unit. It hides the internal state of working from the outside world, controlling access to its features and preventing misuse.
- Inheritance: It is a mechanism that allows a new class (subclass or childclass) to inherit certain properties from an existing class (baseclass or parent class). This activity promotes code reusability and establishes a relationship between classes.
- Polymorphism: It allows objects of different classes related by inheritance to be treated as objects of a common superclass. It enables a single method to perform different tasks based on the object type.
- Abstraction: Abstraction is a mechanism in Java that aims at hiding the complex internal details of an architecture and showing only the necessary information to the user.
Example
The given below example demonstrate the working of OOPs concept in Java.
// abstraction using an abstract class
abstract class Animal {
private String name; // Encapsulation: private data
public Animal(String name) {
this.name = name;
}
public abstract void makeSound();
public void eat() {
System.out.println(name + “is eating. ”);
}
// Encapsulation: public getter method
public String getName() {
return name;
}
}
// Inheritance
class Dog extends Animal {
public Dog(String name) {
super(name);
}
// Polymorphism
@Override
public void makeSound() {
System.out.println(getName() + “ barks: Woof! Woof!”);
}
// Inheritance
class Cat extends Animal {
public Cat(String name) {
super(name);
}
// Polymorphism
@Override
public void makeSound() {
System.out.println(getName() + “ meow: Meow. ”);
}
}
public class Demo {
public static void main(String[] args) {
// creating objects of derived class
Dog myDog = new Dog(“Buddy”);
Cat myCat = new Cat(“Whiskers”);
// calling inherited and overridden methods
myDog.eat();
myDog.makeSound();
myDog.fetch(); // Dog-specific method
myCat.eat();
myCat.makeSound();
System.out.println(“\nDemonstrating polymorphism with an array: ”);
Animal[] pets = {myDog, myCat};
for (Animal pet: pets) {
pet.makeSound();
}
}
}
Basic Concepts of Artificial Intelligence: Understanding the key concepts and Terminology
The main concept in AI is to train models on large datasets to find patterns, which improves the performance over time, and using algorithms for the better efficiency of training models. Given below are the core concepts of AI.
- Machine Learning (ML): The core subset of AI where systems learn from data to identify patterns and make decisions, rather than being programmed for every task.
- Deep Learning (DL): Deep Learning is a subset of Machine Learning that uses multi-layered artificial neural networks offering powerful, automated, and high-accuracy analytical capabilities.
- Natural Language Processing (NLP): This technique allows machines to read, listen, understand, and talk in human language. It enables computers to understand text, speech, emotions, and the intention behind human words.
- Computer Vision: It is a field of artificial intelligence (AI) that allows AI to interpret visual information from images and videos. It is used in facial recognition and autonomous vehicles.
- Reinforcement Learning: This technique allows machine learning models to learn through a trial-and-error environment, which involves receiving rewards for the correct actions and penalties for incorrect ones. The model optimizes its performance over time.
- Generative AI: Generative AI creates bots and tools that create new content, including text, images, music, audio, and code by learning from massive pre-existing datasets. The examples of generative include ChatGPT, Wondershare Filmora, ElevenLabs, etc.
Evolution of Java in the field of AI
Java’s role in AI is rapidly evolving from a traditional enterprise-backend language to a robust platform for building and deploying scalable and production-ready AI systems. Java for building AI applications offers features such as enterprise integration because of its security features and strong integration capabilities with other enterprise technologies. Given below is the list of evolution of Java which can benefit in AI field.
- From Backend to AI Integrator: Java provides seamless integration with AI capabilities into existing business logic rather than replacing Python for initial model training. Java excels in the deployment and operationalization of AI models at scale.
- Focus on Enterprise Needs: Java programming language provides strengths in security, memory management, concurrency and platform independence which makes this programming language ideal for demanding and production-grade AI applications.
- AI-specific Frameworks: Java’s ecosystem provides several powerful AI-specific frameworks and libraries such as Deeplearning4j, Weka, Encog, Neuroph, Apache Mahout, and Apache OpenNLP that are designed for tasks ranging from traditional machine learning to modern generative AI applications.
