Artificial Intelligence with Java Programming

Java is an ideal language for developing artificial intelligence programs because it is object-oriented, highly reliable, and performant.

Artificial Intelligence

Artificial intelligence (AI) is a branch of computer science that focuses on creating intelligent machines that work and react like humans. AI is an interdisciplinary field of study that combines Computer Science, Mathematics, Natural Language Processing, Robotics, Psychology and Neuroscience to study how machines can be made capable of intelligent behaviour and understand the concept of human intelligence. AI is used to develop computer systems that are able to perceive and learn from their environment, reason and make autonomous decisions, and act in intelligent ways.

The programming language most commonly used for artificial intelligence (AI) is Python. Python is used for a wide range of AI applications, from basic search and decision-making algorithms to neural networks, natural language processing (NLP) and deep learning. Other programming languages commonly used for AI include Java, C++, and Prolog.

Goals of Artificial Intelligence

Following are the main goals of Artificial Intelligence:

1. Replicate human intelligence
2. Solve Knowledge-intensive tasks
3. An intelligent connection of perception and action
4. Building a machine which can perform tasks that requires human intelligence such as:

• Proving a theorem
• Playing chess
• Plan some surgical operation
• Driving a car in traffic

5. Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.

What Comprises to Artificial Intelligence?

Artificial Intelligence is not just a part of computer science even it’s so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.

To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:

• Mathematics
• Biology
• Psychology
• Sociology
• Computer Science
• Neurons Study
• Statistics

Why Should We Use Java?

Java is an ideal language for developing artificial intelligence programs because it is object-oriented, highly reliable, and performant. It provides the necessary frameworks and packages (such as Weka, JavaNNS, and JOONE) for efficient implementation of artificial intelligence programs. Additionally, because Java is a platform-independent language, it enables the creation of applications that can be deployed on any platform, facilitating their reach to a wide audience. Furthermore, the JVM and numerous free libraries provide debugging and scripting features that make the development of AI applications easier and faster. Finally, its large and active user community allows developers to share their experiences and code while getting help when they run into problems.

Artificial intelligence (AI) with Java programming is possible through various libraries that enable the development of Artificial Intelligence algorithms.

Some of the most popular libraries used for AI development with Java include DeepLearning4J, Weka, Apache Mahout, and Encog. They each provide access to different AI algorithms and offer a range of predictive modeling and machine learning functions.

DeepLearning4J is a machine learning library specialized for deep learning models such as convolutional neural networks, recurrent neural networks, and long-short term memory models. Weka provides natural language processing, classification, clustering and regression algorithms. Apache Mahout is used to create machine learning models using Hadoop or other distributed computing systems. Finally, Encog is an advanced machine learning library with various functions including researchers, reinforcement learning, clustering, and others.

Is Java good for AI?

This ‘cup of coffee’ is one of the oldest open source languages. It is used for AI as well as machine learning and has a Java Machine Learning Library (JavaML) that provides a collection of machine learning algorithms implemented in Java.

The Java artificial intelligence signifies coding with the help of a long list of libraries that Java provides for Java programming and consequently for Java AI programming that any data scientist can be benefitted from. In the AI landscape, Java is used for machine learning, neural networks, search algorithms, and genetic programming.

AI Libraries in Java

There is a great number of Java AI libraries. Take a look at the list of frameworks and platforms for the most popular fields.

For Expert Systems

Apache Jena — a framework to create web and linked data applications.

PowerLoom — a platform to build knowledge-based applications and reasoning systems.

d3web — a reasoning engine with many algorithms to solve given problems.

Eye — a reasoning engine to perform semi-backward reasoning.

Tweety — a collection of frameworks for logical aspects of AI and knowledge representation.

For Neural Networks

Neuroph — an open-source framework for neural network creation.

Deeplearning4j — a deep learning library for JVM that also provides API for neural network creation.

For Natural Language Processing

Apache OpenNLP — a toolkit to process the natural language text.

Stanford CoreNLP — a framework to perform NLP tasks.

For Machine Learning

Java-ML — a collection of machine learning algorithms.

RapidMiner — a data science platform that provides machine learning algorithms through GUI and Java API.

Weka — a collection of machine learning algorithms.

Encog — a collection of advanced algorithms.

For Genetic Algorithms

Jenetics — an advanced genetic algorithm.

Watchmaker — a framework for implementing genetic algorithms.

ECJ 23 — a research framework with support for genetic algorithms.

JGAP (Java Genetic Algorithms Package) — a genetic programming component.

Eva — a simple OOP evolutionary algorithm framework.

For automatic programming

Spring Roo — a lightweight developer tool.

Acceleo — a code generator for Eclipse which creates code from EMF models.

Advantages and Disadvantages of Java

Advantages of Java

1. Security: Security is integral for a Java design. The Java compiler, interpreter, and runtime environment are secured.
2. Stack Allocation: Java follows a LIFO (Last in First Out) system which helps in storing and retrieving the data easily.
3. Multithreaded: Using Java’s multithreading capability, a programmer can perform several tasks simultaneously in a program.
4. Rich APIs: Java offers APIs and a set of commands for database connection, networking, I/O, XML parsing, utilities, and much more.
5. Rapid Development Tools: The open-source development IDEs used for the coding of Java languages such as Eclipse and Netbeans provide a base for powerful application development with efficient coding and debugging.

Disadvantages of Java

1. Performance Issues: Java consumes more memory and is slower when compared to compiled languages such as C or C++ hence faces performance issues.
2. Complex Codes: Java codes are long and complex and are difficult to read and understand. The overly complex codes require one to explain everything in detail.

Roadmap for AI with Java

First of all, you should learn Java.

Part 1

1. Install the necessary tools for developing AI with Java
— Install the Java development kit (JDK)
— Install a suitable Integrated Development Environment (IDE) such as Eclipse or IntelliJ
— Install the additional development libraries for machine learning such as Apache Mahout or Weka

2. Learn the basics of Java language
— Learn the basic syntax and structures of the language
— Understand the difference between objects, classes, methods and variables
— Understand the concepts of inheritance, abstraction, and polymorphism

3. Explore the libraries for AI with Java
— Familiarize yourself with the different libraries and frameworks related to AI
— Learn how to use the APIs exposed by these libraries

4. Develop basic AI programs with Java
— Create basic AI algorithms such as search algorithms, classification algorithms, or clustering algorithms in Java
— Implement AI programs with the help of Java libraries

5. Extend AI capabilities with deep learning libraries
— Explore the libraries for deep learning such as TensorFlow, Keras, and PyTorch
— Learn how to use gpu computing to speed up your projects

6. Develop real-world AI applications
— Develop applications involving natural language processing, computer vision, or robotics
— Apply your AI skills to solve complex problems

Part 2

1. Reinforcement Learning: Reinforcement learning is the process of applying learning algorithms to enable AI agents to interact with an environment and optimize their performance. It is a popular AI technique that has gained mainstream attention due to DeepMind’s AlphaGo.

2. Natural Language Processing: Natural Language Processing (NLP) is a branch of AI that focuses on creating computer-enabled systems that can understand and generate natural human language. This can include machine translation, text analysis, text synthesis, and more.

3. Robotics: Robotics is another branch of AI that involves creating robotic systems that can interact with the world. Robotics has been widely applied in manufacturing, particularly in areas such as warehouses and automated vehicle production.

4. Computer Vision: Computer vision is a branch of AI that focuses on allowing machines to understand their physical environment by interpreting visual data. It is widely used in applications such as surveillance, facial recognition, autonomous driving, and medical imaging.

5. Deep Learning: Deep learning is a branch of AI that has recently become popular due to its widespread use in image recognition, natural language understanding, and pattern recognition. It is based on the concept of artificial neural networks and can be thought of as a way to make sense of large amounts of data.

6. Virtual Agents: Virtual agents are computer-generated characters that act like a human. They can converse and interact with people in virtual settings, such as chatbots or virtual customer service representatives.

Artificial Intelligence Algorithms

Artificial Intelligence algorithm is a broad field consisting of Machine Learning algorithms and Deep Learning Algorithms. These algorithms’ main goal is to enable computers to learn on their own and make a decision or find useful patterns. Artificial Intelligence algorithms Learn from the data itself. In a broader sense, learning can be divided into 3 categories:

• Supervised Learning: When input and output, both labels are known, and the model learns from data to predict output for similar input data.
• Unsupervised Learning: When output data is unknown, or it is needed to find patterns in data given, such type of learning is unsupervised learning.
• Reinforcement Learning: Algorithms learn to perform an action from experience. Here algorithms learn through trial and error, which action yields the greatest rewards. The objective is to choose actions that maximize the expected reward over a given amount of time.

According to problems that humans encounter and solve, there are three categories in which these algorithms can be divided to perform the same actions.

1. Classification: Humans do make decision-based on classification; for example, Will this shirt look good on me or not? So here, the human mind will process some algorithm with previous experience (data), and then the output will be yes or no. In the same way, these classification algorithms will take some input data and based on this. It will predict yes or No. Some examples of these algorithms are Naïve Bayes, logistic regression, SVM, etc.
2. Regression: Here, the output is continuous; there is no specific category. For example, what will be the temperature tomorrow? A human mind will think of the season and temperature of previous days and will predict some number. Some examples of these algorithms are Linear regression, gradient boosting, random forest, etc.
3. Clustering: Sometimes, we don’t have to make a decision on given input but distinguishing odd ones. For example, segregating different human race white people and black people or seeing a painting and finding different patterns. Some examples of these algorithms are K-means clustering, Hierarchical clustering, etc.

We will discuss one algorithm from each category:

1. Naïve Bayes

• It is used for the classification problem, and it assumes that predictors are independent and have one feature that does not affect another predictor feature.
• For e.g., orange may be considered as orange in color, round in shape, and about 6 cm in diameter. All these variables do not depend on each other or maybe on the existence of the another, but all these features independently contribute to the fact that fruit is an orange and that is the reason it is called ‘Naive’.
• Naive Bayes model is used for a very large data set. It is simple and outperforms highly sophisticated classification models also.
• Naïve Bayes Classifier is based on Bayesian probability used to calculate posterior probability (processing new evidence and refine the hypothesis at each step). In Naïve Bayes’ the probability with each independent variable is calculated and then with the Bayesian approach is used for predicting outcome.

2. Random Forest

• Random Forest is a version of Ensemble Learning and is used for regression problems though it can also be used for classification problems.
• Ensemble Learning: Take multiple algorithms or the same algorithm multiple times, and then it puts them together to make something much more powerful than the single decision tree.
• In Random Forest, we take a number of decision trees to predict output. Then we ensemble them with an ensemble algorithm that will further take count/average of output from all the decision trees and predict the final outcome.
• Ensemble Algorithms are more stable because any change in your dataset can affect your decision tree, but for them to affect the forest of a tree is far more difficult than gives stability to the model.

3. K-Means Clustering

This is an algorithm that is used for unsupervised learning. K is the number of clusters we want. We can understand it in 5 basic steps:

1. For K clusters, we chose k random data points which are called Initial Centroids.
2. Find Euclidian distance from data points to centroids and assign each data point to the closest centroid. (take mean and assign it to data points).
3. Recompute the Euclidian distance from new cluster positions to all the data points.
4. Take mean and assign this as new clusters centroid.
5. Keep repeating until convergence is met.

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