Is Java the Best Language for Artificial Intelligence Projects?

September 24, 2021

As technology continues to advance in the 21st century, we see an introduction of more and more artificial intelligence solutions.

Developers use Haskell, Java, JavaScript, C++, Python, R, LISP, Prolog, among other programming languages, to develop AI solutions.

However, choosing a specific programming language for your AI projects is an uphill task.

According to Statista, Java is one of the most widely used programming languages worldwide as of 2021. But is Java the most popular language for AI projects?

In this article, we will look at how Java is used in AI, why you should develop your AI solutions in Java and how Java compares to other top programming languages in developing AI projects.

Table of Contents

How is Java used in AI?

Java provides many artificial intelligence frameworks that are beneficial to data scientists. They include Apache Jena, d3web, Tweety, and Neuroph.

Other frameworks are Stanford CoreNLP, Java Machine Learning Library (Java-ML), Weka, Jenetics, as well as Spring Roo.

In the artificial intelligence landscape, Java is used in:

  • Genetic programming.
  • Search algorithms.
  • Artificial neural networks.
  • Multi-robot systems.
  • Machine learning solutions.

Genetic programming

Genetic programming is a new method of generating computer programs derived from the model of biological evolution. These programs are ‘bred’ through continuous improvement of different projects.

These improvements are possible through stochastic variation of programs and selection based on prespecified criteria for judging the quality of a solution.

Evolutionary computing is a rapidly growing field of AI that helps to solve pre-described automatic programming and machine learning problems.

Genetic programming is comprised of artificial intelligence metaheuristics which is a high-level problem-independent algorithmic framework. It provides a set of guidelines or strategies to develop heuristic optimization algorithms.

Evolutionary algorithms are a subset of artificial intelligence. They imitate nature mechanisms to solve optimization problems.

Evolutionary techniques are applied to optimization problems, many of which are combinatorial optimization problems such as shortest paths, spanning trees, and matroid problems.

Others are multi-objective optimization problems that involve two or more conflicting optimization goals.

Genetic algorithms

Genetic algorithms is a stochastic global optimization algorithm inspired by the biological theory of evolution which performs an optimization procedure.

It starts with a set of solutions referred to as a population. The first set of solutions from the population are compiled to form a new population by iterating over the main loop of the algorithm in repeated fixed number or until there is no further improvement.

The ideology is that this new population will show better characteristics than the old population. In this algorithm, only the fittest individuals are picked for reproduction to increase the chances of reproducing better solutions (offspring).

Search algorithms

Most of the applications in use today have a search feature to helps users find relevant information quickly.

Retriving data stored in data structures such as arrays, maps, and lists is possible because of search algorithms. Search operations determine the responsiveness of apps to the end-users.

There is a long list of search algorithms, including the breadth-first algorithm, jump search, depth-first search algorithm, exponential search, and the Ubiquitous Binary Search.

The binary search method is arguably the best search algorithm, while the linear search algorithm is considered the most basic one.

Unlike the linear search algorithm that scans each element in an array starting from the first, the binary search method analyzes the list in sections. This has a significant benefit because it takes less time to search for an element.

Artificial Neural Networks

Artificial Neural Networks (ANNs) that are designed in Java perform many tasks. For instance, they forecast trends, learn from data, and recognize patterns.

With a highly interconnected network structure, ANNs can learn how to solve tasks by themselves. Their algorithms recognize correlations and hidden patterns in raw data.

ANNs also cluster and classify this data and continuously learn and become better with time.

Multi-robot systems

Robotic technology has significantly developed in recent years. Java is helping to program inexpensive robots that perform complex tasks.

A collection of robots collaborating to achieve clearly defined goals is referred to as a multi-robot system (MRS).

MRSs are applied in many real-life situations such as the military, disaster relief, smart home, and agriculture. They are preferred over single-robot systems due to their high efficiency, reliability, and scalability.

Machine learning solutions

Java is an important part of a typical machine learning cycle as it helps in data engineering and processing.

Machine learning models use engineered and processed data for unsupervised and supervised functions. Java programing provides a systematic and efficient way for machine learning models to process large collections of data and datasets.

Why develop AI in Java?

Developing artificial intelligence solutions in Java comes with many benefits. The main advantages are discussed below.

  • Java has a wealth of libraries useful for developing many AI use cases. These Java AI libraries are specific to machine learning, neural networks, natural language processing, and rule-based systems.
  • Java’s write once, run everywhere concept (WORA) allows programmers to develop their code on one system and run the same code (without recompilation) on other java-enabled systems.
  • The large community of Java developers is actively involved in creating AI and ML algorithms, as well as coding a wide range of AI-enabled applications. The Java official website supports the efforts to create ML and AI projects by providing features like scalability and object-orientedness.
  • As a multi-paradigm language, Java supports both object-oriented and procedural programming philosophies.
  • Java can support many types of algorithms. Besides, Java tools help to create appealing user interfaces and graphics.

Java versus other AI programming languages

R and Python are popular alternatives to Java, especially in sentiment analysis.

Machine learning scientists believe that Python is a strong choice for text analysis and R for text mining. Python provides CLIPS and NLTK libraries for sentiment analysis.

NLTK allows you to stem and parse sentences for low-level analysis based on word frequencies. Java has Stanford’s CoreNLP for sentiment analysis.

The CoreNLP resource provides simple tools such as stemming and lexicalization of words to complex features like dependency analysis.

Java is a preffered by those working on fraud detection, cyber-attacks, and network security. Other AI languages such as Python are less used in such areas.

Fraud detection and network security algorithms are popular in large enterprises such as financial institutions.

Most internal development teams in large organizations also use Java. Developers in less enterprise-focused areas such as NLP prefer Python due to its ease of use, especially when building high performing algorithms.

R is favored in bioinformatics and bioengineering fields. Whether inside or outside academia, R has a long-standing history of its use in biomedical statistics.

What is the best programming language for machine learning projects?

There is no single language that can be described as the best for machine learning and artificial intelligence.

Therefore, choose the language that best suits your AI project needs. For example, Python is liked for its simplicity and wealth of AI libraries. On the other hand, huge enterprises prefer to use Java for their AI projects.

Irrespective of the programming language you chose, you will still enjoy creating data-driven solutions in this exciting machine learning era.

Conclusion

AI projects developed in Java have many benefits. Furthermore, Java is simple to debug and has a hugely supportive community of developers.

It also has rich AI libraries, APIs, and frameworks that boost the software development process.

Further reading


Peer Review Contributions by: Rabo James Bature


About the author

Bridget Mwikali

Bridget is an undergraduate student pursuing a degree in Information Technology and Information Systems. She loves technical writing and managing databases.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to enged@section.io.