As artificial intelligence continues to reshape the technology landscape, developers are increasingly faced with the task of choosing which programming languages are most useful and effective in terms of AI development.
Given the rapid growth of AI and machine learning, is there a programming language that better meets the needs of this evolving field?
Java has been a programming staple since its introduction in 1995 and remains one of the most popular languages for web applications and processing large amounts of data. However, the relatively new developer Kotlin is hot on its heels.
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And although Python dominates in research and prototyping, Java and Kotlin are good options for production AI systems, especially in large enterprise environments. This is thanks to a recent update that made Kotlin easier to onboard and reduced the error rate by almost 30%.
Kotlin in action
Kotlin was developed by JetBrains in 2010 and announced by Google as the preferred language for Android in 2019. That year, the tech giant deemed it the technology of choice for leveraging business logic across platforms.
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Kotlin has quickly gained popularity due to its concise syntax, expressive language features, and seamless interoperability with Java.
It is cross-platform and allows developers to build apps for iOS, Android, desktop and web from one code base, eliminating discrepancies between platforms and reducing overall development effort.
Interoperability is also an important feature and Kotlin can be used alongside Java, meaning developers don't have to abandon or disrupt current workflows.
Kotlin is also tailored for programming business logic in areas such as payment processing and financial transactions, making it a practical choice for developing secure, scalable systems in industries such as e-commerce, fintech, and enterprise software.
So could Kotlin ever replace Java?
“Partly, yes,” says Egor Tolstoy, Kotlin project lead at JetBrains and board member of the Kotlin Foundation.
“Kotlin is perfect for multiple scenarios: Android development, server-side development, cross-platform programming, and as a first language to get started with programming,” he says. “Kotlin is on the rise today, and we see even more opportunities for growth. After Kotlin was approved by Google for Android development, more than 50% of Android developers switched from Java to Kotlin, and up to 20% of Java server developers also switched. Kotlin is the second largest language in the JVM ecosystem and is growing in popularity among Spring developers.”
Dependency on Java libraries
However, it is worth noting that many developers using Kotlin for AI still rely heavily on Java libraries due to Kotlin's Java interoperability.
And in the context of AI, Java has many advantages. It remains a robust choice due to its mature ecosystem, solid tools, and huge ecosystem of libraries and frameworks. Deeplearning4j, Weka, and MOA are particularly useful when it comes to machine learning, deep learning, and data mining tasks.
The Java principle of “write once, run anywhere” means that a developer can develop Java code on any device and run it on any other Java-enabled system that supports the Java Virtual Machine (JVM).
In addition, its strong parallelism support – AI tasks often require parallel processing – makes it a sensible choice for developing scalable AI systems.
Further considerations
As AI evolves, Java remains relevant with its established ecosystem and comprehensive tool sets for machine learning and data analytics. Java is arguably better suited for projects that integrate with existing Java frameworks or require access to a wider range of AI libraries.
Nevertheless, Kotlin is evolving rapidly and offers a contemporary and user-friendly programming environment that enables a more efficient and streamlined coding experience.
And for those who are venturing into new AI projects and value a concise, modern syntax, Kotlin is a worthy candidate.
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