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  • Writer's pictureNatalie Bentivegna

Java and AI

AI seems to be everywhere these days, advances in technologies such as ChatGPT have propelled its popularity and prevalence in ways that it had never reached before, sparking conversations left, right, and centre about how it will fit into society's future and the impact it will have on us all. Being a Java Recruitment Specialist, I was more interested in how AI ties into the Java market so I hope you find this blog as insightful as I found it to write.


First of all, for those who are new here, Java is an object-oriented programming language and platform that runs on a wide range of devices, and can be used to create mobile applications, standalone applications as well as web and enterprise applications etc. to give you an idea of the impact Java still has on today’s market - Java has been used by Twitter to support more than 400 million tweets per day, Netflix, another big player, uses Java to facilitate pretty much every step in its process:


What does Java have to do with AI, you may ask? Well actually, quite a lot!

AI applications are written in backend languages, one of the most popular choices for said backend language happens to be Java, amongst some others such as Python, C++, and R. LinkedIn, Uber, Spotify, and Amazon rely on Java developers to build their AI solutions. In light of this, you cannot deny that Java is a globally trusted language, relied upon by some of the world's biggest names. As the technology has progressed, Java has been deemed one of the most versatile and well-suited language for AI programming and here’s why:

  • Java is already incredibly popular in mainstream development so if you wish to build an AI product, you don't have to up-skill from scratch to use it in ML and AI development

  • As a result of its prevalence, there is an endless amount of Java-related communities out there where you can find answers to your questions, Stack Overflow, Java News on Reddit, and Java Help on Redditt to name a few.

  • Java has features such as object oriented-ness and scalability that are essential for creating AI and machine learning projects.

  • There are AI libraries that already exist in Java, such as Natural Language Processing and neutral networks.

  • Despite being a slightly higher-level language, in comparison to some (ie. C++), Java has a reputation for being a relatively easy-to-use language.

  • Java APIs are very powerful with capabilities that developers can leverage. They have additional features that don’t ache to be downloaded separately.

  • Portability is one of the significant benefits of Java software development. Being able to move Java code from platform to platform makes things much easier, and you can run code no matter what computer you have. Java programs work the same on any device.

  • Java follows a LIFO (Last in First Out) system which helps in storing and retrieving the data easily.

  • Java is a very secure language - it provides a security management feature that allows users to specify exact access rules for each application. The Java compiler, interpreter, and runtime environment are secured.

  • Java was designed to be easy to maintain and modify, enabling developers to update code without major difficulties. Java's maintainability is largely due to its object-oriented programming (OOP) principles.

  • Using Java’s multithreading capability, a programmer can perform several tasks simultaneously in a program.

  • Java also supports many more advanced features such as TensorFlow, Kubeflow, OpenNLP, Neroph, Java Machine Learning Library, and Deep Java Library.

If you’re a techie and you want something a bit more technical to sink your teeth into you can read a bit more about Java’s suitability for AI here - https://bit.ly/3GkuiZj


Java also has a set of AI libraries and frameworks that are very useful in AI programming. Here is the list of them:


  1. Apache Jena: For building synthetic web and data applications from RDF data.

  2. PowerLoom: Used for creating intelligent, knowledge-based applications and reasoning systems.

  3. Deeplearning4j: A deep learning JVM library providing API for neural network creation.

  4. Apache OpenNLP: For the processing of natural language text.

  5. RapidMiner: Provides machine learning algorithms through GUI and Java API.

  6. Jenetics: This is an advanced genetic algorithm.

  7. Watchmaker: This is a framework for implementing genetic algorithms.

  8. JGAP (Java Genetic Algorithms Package): As the name suggests, it is a component of genetic programming.

  9. Eva: An Object Oriented Application (OOP) algorithm framework.

  10. Acceleo: This is an eclipse code generator for creating code from EMF models.


There are some disadvantages to be considered when it comes to using Java for AI development, these include:

  • Performance Issues: Java consumes more memory and is slower when compared to compiled languages such as C or C++ hence faces performance issues.

  • 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.

All in all, the use of programming languages in building AI applications will depend on the specific project requirements, the team building the application, and the platform being used. But Java obviously holds a firm place of importance in the AI ecosystem and I'm very excited to watch new technologies emerge from these developments.





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