Java in Artificial Intelligence – How is it Used?

Machine learning, data science, and artificial intelligence are the most exciting and sought after fields in the technological domain of late, and this is because the advancements in these spheres have markedly increased automation and business process outputs. From research institutions to corporate houses, every organization – large and small – is investing heavily into deeper research in these fields to develop astonishingly powerful data-driven products.


There are various programming languages that help build machine learning capabilities and data science products. While this is true, Java software development is fast becoming a preferred option for a range of applications, from enterprise solutions to mobile devices and applications and technologies aimed at consumers. 


Java was developed by a team of Canadian computer scientists led by James Gosling while they were employed by Sun Microsystems. After more than 2 decades of its existence, Java software development is still immensely popular, with increasing applications in various fields. 


Java programmers as well as agencies that provide Java development services are increasingly apprehending the vast applications of this programming language in the fields of artificial intelligence to build machine learning solutions, genetic programming, search algorithms, neural networks, and multi-robot systems. More and more Java development services are rapidly leveraging powerful features for AI projects such as its object-oriented nature and scalability. 


Since artificial intelligence has many algorithmic aspects, Java programmers can build different types of algorithms that can run smoothly. While the automation and machine learning capabilities of Java depend on how it is implemented in business processes, it provides cutting-edge features to develop intelligent products and services.


How is Java used in AI?

For developers who wish to use Java in AI processes, a basic knowledge of traditional algorithms and related concepts must be inculcated. There are many AI libraries in Java that can be implemented in multiple scenarios such as natural language processing, expert systems, neural networks & automatic programming.


Why Choose Java for Data Science and Machine Learning? 


Easy Maintenance 

Java’s decisive aspects such as access modifiers and indentations enable easy maintenance. Application testing with Java ensures that the code is impregnable and bug-free. Competent documentation helps with easy maintenance of applications. 



Flexibility and agility are hallmarks of Java, especially when it comes to running applications across multiple operating systems or environments. Java applications perform admirably on different CPUs and operating systems and help those that provide Java development services to build a single app version that can run on all Java-supported platforms, thus adding value to businesses. 



A very important aspect for any programming language, Java development services ensure that the applications built on it are robust and secure. 



Developers consider scalability as an important facet before they consider it consider for their projects, and Java makes application scaling much easier for data scientists and developers, which makes it an ideal option to build complex AI / ML applications, especially when they are being built from scratch.


Smooth Interfaces & In-built Redundancies

Java’s built-in garbage collector feature automatically deletes all useless data. It also incorporates Swing and Standard Widget Toolkit and supports visualization to create appealing graphics and interfaces.


Fast deployment

Java is optimal for speed-critical projects with its fast execution. Many popular social media applications such as Twitter, LinkedIn and Facebook rely on Java for their data engineering requirements.


Vast codebase and Libraries

Production codebases are generally written in Java, and developers can better understand critical aspects such as how data is being generated, submitting merge requests to such codebases, and deploy machine learning solutions by having a robust understanding of Java. It also has a vast array of libraries and tools for data science and machine learning. For e.g., Weka 3 is a Java-based library used for algorithms in data mining, data analysis, and predictive modelling. 


If you are looking for Java application development services to build AI / ML based applications, contact Smart Sight Innovations today to build next generation products by leveraging the power of this progressive language.

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