Artificial intelligence uses different programming languages. Java is one of the foremost software programming languages that AI uses. Java for Artificial intelligence can build several applications, particularly larger enterprise applications, necessitating integration, scalability, and robustness.
Which Features of Java Make it Preferable for Artificial Intelligence?
Extensive Ecosystem:
Java has an expansive standard library with many classes, interfaces, etc., built for use across multiple programming tasks. With this standard library’s offering, Java helps simplify several fundamental programming tasks. The in-built library allows developers to lessen their effort and save time while working on basic functionalities. All such functionalities make Java an exceptional programming language for AI integration and machine learning.
Scalability:
Java exhibits scalability. It means Java programming language can support large-scale systems easily. Due to this feature, Java is the best choice for large organizations that use complex applications. As well as to build Artificial Intelligence systems on a large scale, such as fraud detection and recommendation engines.
Platform Independence:
Among the various features of Java, platform independence is one of the most powerful features. During the compilation process, the source code is converted first into an intermediate form known as bytecode. Then, the code enters the Java Virtual Machine (JVM), which enables the code to execute on any platform. For this reason, Java programming language is famous for its “write once, run anywhere” feature, which allows Java technology to be used across various applications. Likewise, Java finds extensive deployment across Artificial intelligence models across multiple platforms.
Python or Java for Artificial Intelligence?
Though both programming languages have extensive applications in artificial intelligence, they are best suited for different areas of choice. While Python finds a better edge, developers also prefer Java to build applications in specific areas.
Python and Java for Artificial Intelligence: Supporting Features
Libraries:
Python features extensive libraries such as sci-kit-learn, PyTorch, and TensorFlow. In contrast, Java for artificial intelligence offers limited choices, with only Weka and Deeplearning4j. However, these Java libraries are powerful, making them a good choice for various applications that are limited to certain areas.
Community Support:
Python provides good development and community support for research in AI or ML areas. Likewise, Java offers strong support for enterprise and backend development.
Integration:
Though integration is possible with Python, it is restricted to specific fields only. However, Java proves excellence to integrate, particularly for web services and enterprise apps.
Performance:
Java for artificial intelligence performs better than Python due to the multithreading feature. Python, though, is sometimes slow; almost all AI tasks find the speed comfortable.
Easy to Use:
Python is very easy to use due to its concise syntax and dynamic typing. In contrast, Syntax in Java is verbose.
Purpose:
Python is best suitable for developing research or academia-related applications, while Java anf artificial intelligence finds extensive applications to develop highly reliable and real-time systems.
In summary, Python is better for machine learning, deep learning, and research fields. Java and artificial intelligence is ideal for dealing with high-performance and scalable AI applications across enterprise environments.
List of Frameworks and Libraries of Java for Artificial Intelligence:
Below are some Java tools that can find applications in AI development.
Weka:
It contains a machine-learning library that is suitable for data mining. Java API and GUI are its offerings for training models.
Deeplearning4j:
It is a deep learning Java framework. It helps integrate with Spark and Hadoop while supporting GPU acceleration and neural networks.
Neuroph:
It is best suitable for developing smaller projects in the educational field as it is a lightweight neural network framework.
Real-World Example of Java across Artificial Intelligence Applications:
Java is used extensively across financial management, especially to detect fraud and perform automatic trading systems. Similarly, enterprise AI systems are adopting Java to integrate AI models and perform backend logic. Extensive use of Java in IoT and robotics.
Thus, due to some exceptional features, Java is finding its application across AI/ML technologies.