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Exploring the 9 Best Languages for AI Programming in 2023

In software development projects, artificial intelligence (AI) serves as a crucial component, empowering developers to fashion software with the remarkable ability to perceive, learn, reason, and tackle intricate problems, mirroring the cognitive processes of the human mind. Therefore, acquiring the knowledge and skills to apply AI techniques proves pivotal, particularly for individuals aspiring to embark on a programming-oriented career.

However, selecting the appropriate programming language is often daunting for many. Nonetheless, initiating your journey into this rapidly evolving domain can provide you with a competitive edge, enabling you to ride the tides of transformative change that lie ahead. To assist you in charting your educational path, we have comprehensively analyzed major programming languages, identifying the best languages for AI programming. AI remains a relatively nascent innovation, and thus, what may be deemed the industry standard today could undergo significant transformations in the ensuing years.

If delving into AI programming entices you, but you need help deciding where to commence, fret not, for numerous options exist to acquire coding proficiency. These options include self-teaching applications, coding bootcamps, and full-time college degree programs. Below, we will delve into the top choices for learning AI-focused programming, along with a comprehensive exploration of the best languages for AI programming to master for the purpose of AI development.

What Is Artificial Intelligence?

If you believe that artificial intelligence conjures up visions of ominous parallel realities, rest assured that you’re not alone. The notion of imbuing machines with human-like qualities, as exemplified in movies like Terminator, has instilled a sense of trepidation in many minds.

However, the likelihood of evil robotic entities threatening our livelihoods in the near future is remarkably slim. At its core, artificial intelligence (AI) refers to the development of intelligent machines that can exhibit human-like cognitive abilities.

In fact, if you’ve ever encountered a digital device that struggled to discern up from down or perform even the simplest of tasks, you would appreciate the capabilities offered by artificial intelligence. But you’re already benefiting from AI in various ways. Take, for instance, the voice-activated digital assistant residing within your smartphone. That, my friend, is AI technology right at your fingertips.

From a business standpoint, artificial intelligence presents a multitude of advantages. Many marketing automation tools, for instance, heavily rely on AI algorithms to create, coordinate, and optimize campaigns while gathering invaluable insights to enhance your brand’s effectiveness. And this is just one illustration of AI’s prowess.

In this digital transformation era, you can expect artificial intelligence to manifest in countless scenarios, working with human counterparts to offer proactive solutions to everyday challenges. The synergistic collaboration between humans and AI heralds a new frontier of possibilities.

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1. Python

Python | Best languages for AI programming
Python | Best languages for AI programming

Python, widely regarded as a high-level, general-purpose language, is a favorite among developers. Its appeal stems from its straightforward syntax and object-oriented approach, significantly contributing to code maintainability.

However, don’t be fooled by Python’s friendly demeanor—it possesses remarkable capabilities to handle extensive and intricate projects efficiently. It finds particular favor in data engineering, where its utilization remains prevalent. In addition, Python is one of the greatest languages for programming AI, as well as offering numerous libraries, frameworks, and tools specifically tailored for machine learning tasks.

Machine learning is a subset of artificial intelligence that involves using algorithms to train machines. Python’s supremacy in this domain is evident through TensorFlow, the most popular open-source machine-learning library. It empowers developers to train deep neural networks, thereby propelling the advancement of AI applications.

Source: TIOBE Index

Python’s repertoire extends beyond TensorFlow, boasting an array of pre-existing libraries and frameworks that further enhance its prowess in the AI domain. These include well-known names such as Pandas, SciPy, nltk, PyTorch, Keras, Theano, and scikit-learn. Leveraging these technologies, developers can delve into machine learning, natural language processing (NLP), and evaluating advanced mathematical expressions.

NLP, for instance, underpins the functionality of intelligent assistant applications like Google and Alexa, enabling them to comprehend spoken language and provide appropriate responses.

One of Python’s notable strengths lies in its accessibility and ease of learning, making it one of the best languages for AI programming. However, despite its impressive capabilities, Python remains incredibly beginner-friendly, welcoming aspiring AI programmers into its fold with open arms. 

RELATED: Python Coding Best Practices

2. Java

Java | Best languages for AI programming
Java | Best languages for AI programming

Java is revered as a server-side language, cherished by developers for its remarkable versatility. Thanks to its virtual machine, one of its standout features lies in its ability to be employed across diverse platforms.

While Android developers can work with Kotlin, Java remains the native language for Android app development. So it is no surprise that mobile app developers recognize the profitable prospects of integrating artificial intelligence into their applications. Prominent examples include the widely-known FaceApp and the indispensable Google Assistant, both of which showcase the infusion of artificial intelligence into Android apps through Java.

Beyond mobile apps, Java boasts a range of machine learning libraries catering to deep learning and natural language processing requirements. If you intend to utilize Java for your machine learning endeavors, consider exploring the following options: the Deep Java Library, Kubeflow, OpenNLP, Java Machine Learning Library, and Neuroph. These resources equip developers with the necessary tools to delve into machine learning.

Additionally, Java’s object-oriented design enhances its usability and makes it a favorable choice for AI projects. Furthermore, its well-established principles lend themselves seamlessly to the complexities of artificial intelligence development, facilitating smoother and more efficient workflows. Finally, Java’s versatility and widespread adoption in the industry further solidify its position as one of the best languages for AI programming.

READ: 10 Best Coding Languages for Data Science in 2023

3. C++

Renowned for its speed and efficiency, C++ stands as one of the best languages for AI programming among developers. Its ability to execute code swiftly makes it ideal for machine learning and neural network applications. Moreover, given the complexity often associated with AI-focused programs, leveraging a high-performance language like C++ can result in exceptionally well-performing software.

While C++ may not always be the first language that comes to mind for AI-focused applications, its widespread usage within the industry merits recognition. Although writing code in C++ may be more intricate, the language’s efficiency compensates for the added complexity. This makes C++ particularly suitable for resource-intensive applications, sometimes utilized with other languages to construct AI-centric programs.

Source: TIOBE Index

The esteemed library, OpenCV, is an ideal tool that employs C++ for AI-focused applications. OpenCV supports multiple programming languages, including C++, as a comprehensive library for machine learning and computer vision. With computer vision algorithms at its core, the library can process video input and extract valuable information, enabling tasks such as object identification, facial recognition, 3D scanning of real-life objects, and much more.

OpenCV provides a comprehensive documentation guide to aid programmers in incorporating C++ into their artificial intelligence projects. This resource equips developers with many modules and algorithms, encompassing capabilities like object detection, motion analysis, object tracking in videos, and machine learning.

4. Lisp

Lisp | Best languages for AI programming
Lisp | Best languages for AI programming

Lisp, an enduring language with a historical legacy, is one of the oldest programming languages still used for AI development. Its name, an abbreviation for “list processing,” reflects its original purpose and application.

Dating back to 1958, Lisp emerged as a language capable of tackling the challenges posed by artificial intelligence under the guidance of its creator, John McCarthy. Over the years, Lisp has retained its potency, although its complex syntax and complex libraries have resulted in fewer developers choosing Lisp as their primary language for AI endeavors.

Nonetheless, Lisp’s expertise shines in various AI projects where its unique capabilities can be a valuable asset. These include, but are not limited to:

  1. Rapid prototyping: Lisp facilitates quick iterations and experimentation, making it ideal for rapid prototyping in AI development.
  2. Dynamic object creation: Lisp’s dynamic nature allows for the seamless design and manipulation of objects during runtime, providing flexibility in AI applications.
  3. Execution of data structures as programs: Lisp’s distinctive design enables the execution of data structures as executable programs, opening doors to innovative AI algorithms and techniques.
  4. Modification of programs as data: Lisp’s ability to treat programs as data allows for dynamic code change during runtime, offering powerful capabilities in AI systems that require adaptability.
  5. Mandatory garbage collection: Lisp incorporates automatic garbage collection, relieving developers from manual memory management concerns and ensuring efficient resource utilization.

In recent history, Lisp has earned recognition as one of the best languages for AI programming for its remarkable flexibility and ability to transform conceptual ideas into tangible realities. While it may not be the first choice for all AI projects, Lisp’s unique strengths make it a notable contender for specific AI applications that demand its particular capabilities.

5. JavaScript

JavaScript | Best languages for AI programming
JavaScript | Best languages for AI programming

JavaScript, as per GitHub’s rankings, holds the distinction of being the most popular programming language worldwide. This comes as no surprise considering its integral role in shaping the modern web, empowering the interactivity we encounter daily. 

Given its relatively gentle learning curve, its versatility makes it a reliable choice for web developers. Furthermore, JavaScript is a good entry point for beginners venturing into AI or general web development, making it one of the best languages for AI programming to consider in 2023.

Why does JavaScript fit seamlessly into the realm of artificial intelligence? One compelling reason is its abundant selection of high-level tools and libraries tailored to machine learning. A prime example is TensorFlow.js, a framework that operates directly within web browsers, thus unlocking a myriad of possibilities for web developers. In addition, expanding your knowledge of browser-based AI applications can enable you to construct cutting-edge, AI-centric browser tools that redefine user experiences.

The TensorFlow.js demo section is a valuable resource, offering a collection of AI program examples and their corresponding code, all running seamlessly in web browsers. Among the showcased examples, you’ll find applications like a lip-syncing scoring application and a piano program that automatically generates music. These represent just a glimpse into the infinite array of applications made possible by browser-based AI technology.

JavaScript’s ubiquity, combined with its extensive tooling and libraries, positions it as a precise language for AI development within the web ecosystem. As you explore the realm of JavaScript-powered AI, you uncover limitless possibilities that can revolutionize web experiences.

6. R

R | Best languages for AI programming
R | Best languages for AI programming

R, favored by aspiring and seasoned statisticians, is a popular language in the field. While it may not be hailed as the ultimate language for artificial intelligence, R boasts exceptional prowess in number-crunching tasks.

In AI development, data takes center stage, and accurate data analysis and representation often require a mathematical approach. This is where R proves to be a valuable asset, illuminating the path through an otherwise daunting tunnel. R surpasses Python in its capabilities regarding handling numerical data, making it a practical choice for data-intensive AI projects.

For those delving into R for AI endeavors, exploring relevant packages can further enhance your workflow. Here are a few notable R packages worth considering:

  1. Gmodels: This package equips you with tools for efficient model fitting, enabling streamlined processes in AI development.
  2. Tm: Tm is a robust framework specifically designed for text mining applications, allowing you to extract valuable insights from textual data.
  3. OneR: Employed for implementing the One Rule Machine Learning classification, the OneR package provides a valuable resource for classification tasks within your AI projects.
Source: TIOBE Index

While R may not be the foremost language associated with AI, its proficiency in handling numerical data and its specialized packages make it an indispensable tool for statisticians and data scientists venturing into the best languages for the AI programming landscape. 

7. Julia

Julia | Best languages for AI programming
Julia | Best languages for AI programming

In programming, relying on multiple languages to strike a balance between high performance and reduced complexity is common. However, this approach often creates a tug-of-war between the two objectives. Moreover, many programming languages have accumulated years of design and development, resulting in potential complexities and challenges. To address these issues, Julia emerged as a solution.

Conceived at MIT in 2009, Julia entered the programming space to resolve these inherent problems. Flux, a powerful machine learning and AI stack was developed explicitly for Julia. Its key features include a built-in package manager and parallel solid and distributed computing support.

Julia proves to be especially advantageous in scientific computing and data analysis. This is because the language inherently supports parallelism, enabling the simultaneous execution of numerous processes. This parallel computing capability is of utmost importance in machine learning and AI-focused applications, thereby elevating the significance of Julia in the field.

While Julia’s AI ecosystem is still growing, it may not be as extensive as some options in other prominent programming languages. Nevertheless, the Flux website highlights several capabilities and tools within the library that can be applied to AI projects. These encompass computer vision tools, reinforcement learning tools, and more.

As Julia continues to gain traction, its unique characteristics and growing AI-focused ecosystem position it as one of the best languages for AI programming. The language’s parallel computing capabilities and dedicated packages, such as Flux, reinforce its relevance in the ever-evolving landscape of artificial intelligence. 

8. Haskell

Haskell | Best languages for AI programming
Haskell | Best languages for AI programming

Haskell stands out as one of the best languages for AI programming, offering a unique blend of being statically typed and purely functional. With its focus on flexibility and expressiveness, Haskell provides developers with a powerful toolset for tackling AI development challenges. 

One notable advantage of Haskell lies in its readiness for abstraction, significantly reducing the time spent on debugging errors. In addition, the language’s efficient memory management, robust type system, and emphasis on code reusability further contribute to its appeal.

However, one of the most intriguing aspects of Haskell is its lazy evaluation approach. In Haskell, code is evaluated only when necessary, introducing a unique characteristic to the language. While this can be seen as advantageous or disadvantageous depending on one’s perspective, it adds an exciting dimension to Haskell’s overall functionality.

Ultimately, Haskell’s allure for AI developers lies in its efficiency. With Haskell, developers can devote more time to conceptualizing and thinking through their algorithms and designs rather than being consumed by excessive typing. In addition, Haskell empowers developers to focus on the logic and ideation aspects of AI development, making it an enticing option for those seeking an efficient and thoughtful programming language.

9. Prolog

Prolog | Best languages for AI programming
Prolog | Best languages for AI programming

Prolog, a portmanteau of logic programming, has been around since 1972, and despite its age, it remains a viable option for suitable projects. Instead of relying on coded sequences in Prolog, you construct a fundamental framework consisting of facts, rules, goals, and queries. This unique approach makes Prolog particularly well-suited for pattern-matching tasks, especially in the context of natural language processing (NLP).

One notable historical application of Prolog is ELIZA, the first chatbot ever created. Developed using Prolog, ELIZA utilized Prolog’s logical system to assign values to words and rearrange them intelligently to generate meaningful questions. The logical foundations of Prolog encompass the definition of factual statements as facts, the specification of statements with additional conditions as rules, the determination of where submitted statements fit within a knowledge base as goals, and the guidance for making statements accurate and conducting the final analysis of facts and rules as queries.

Observing that these core principles form the essence of Prolog’s strength and prowess is fascinating. However, it is worth noting that with the advent of cloud technology, chatbots today have access to a wealth of data, enabling them to tap into vast repositories of new and old information. Consequently, modern chatbots had evolved to become significantly more intelligent and capable compared to the time when Prolog was predominantly used.

While Prolog may not be among the best languages for AI programming in today’s context, its historical significance and contributions to pattern matching and logic programming remain notable. The advancements in AI and the availability of more specialized languages and tools have opened up new horizons for creating highly sophisticated and adaptive AI systems.

Conclusion

In conclusion, we have explored several programming languages that are widely used and regarded as some of the best for AI programming. Each language has advantages and characteristics that cater to different aspects of AI development.

In the ever-expanding landscape of AI, the best languages for AI programming will continue to evolve, adapt, and expand. It is an exciting time for developers to be part of this journey, leveraging the strengths of these languages to shape the future of artificial intelligence. But, of course, the choice of language ultimately depends on the development team’s specific requirements, preferences, and expertise. 

By leveraging the strengths of these languages and exploring their respective ecosystems, developers can unlock the full potential of AI and drive innovation in this rapidly advancing field.

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