Which Languages Are Ideal for Creating AI?

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According to IDC, the worth of the AI industry is expected to increase to $554.3 billion by 2024 from $499.8 billion in 2019, as businesses continue to invest in more advanced AI solutions, fueling industry growth.

Choosing the appropriate programming languages for creating AI software is the initial step in developing efficient solutions.

Here, we have collated the top 10 programming languages for developing AI in 2023.

Let’s begin from the start.


Due to its high-level architecture and versatility in applications, including Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS), Web Development (WDA), Desktop Development (DDA), Networking Development (NDA) and Scientific Computing (NNC), Python is widely regarded as one of the most popular programming languages. As a result, it is an essential tool for AI programmers.

Python is extremely capable for Artificial Intelligence (AI) development, benefitting from its collection of specialized libraries such as Keras, Pytorch, Scikit-learn, MXNet, Pybrain, and TensorFlow.

Python is ideal for NLP and AI applications, thanks to its clear syntax, modular scripting, and vast array of text processing tools.

Python is synonymous with its readability, conciseness and ease of use, making it an excellent language for beginners. Additionally, there is a huge community of Python enthusiasts and users.

Python is employed by several leading organizations, such as NASA, Amazon, Google, Instagram, Reddit, JP Morgan Chase, Intel, Netflix, IBM, Pinterest, Facebook and more.


A favoured AI programming language is Lisp, previously spelt as LISP.

The programming language, Lisp, was initially created in 1958 and named after its early use in the Artificial Intelligence field, as early as 1962.

Although its complicated syntax and costly libraries have caused its popularity to decline, Lisp continues to be capable of producing high-quality software. Its capabilities may still be beneficial for many Artificial Intelligence (AI) projects.

  1. Rapid Modeling
  2. Objects that are generated dynamically.
  3. Executing data structures as programs
  4. Adjusting software to new data
  5. Weekly waste collection requirements


Advice for Hiring Python Developers

Many Artificial Intelligence (AI) companies use Java for their server-side requirements. This popular language is favoured by programmers due to its specialized virtual machine, enabling work in different development environments. The majority of Android applications are coded in Java, which is the platform’s primary language.

Java-based Android applications, such as Snapchat, FaceApp, and the well-known Google Lens, employ Artificial Intelligence (AI). There are various Machine Learning libraries that can be utilized in Java, with applications extending beyond mobile devices.

  1. Comprehensive Java Resource
  2. Cubeflow
  3. OpenNLP
  4. Java Machine Learning Framework
  5. Neurophysiology

C++ is the optimal language for AI development.

C++ is a long-standing low-level programming language, excelling in hardware and machinery operation, but may be less suitable for complex software development.

C++ is a low-level language, an excellent option for building fundamental AI models. Programmers can use C++ to construct neural networks from scratch and convert user code into machine language.


R has been acknowledged by seasoned data scientists as a leading language for Artificial Intelligence. While it may not be the most favorable choice for AI programming, it excels in intricate computations.

Data engineering is a crucial component of Artificial Intelligence and Machine Learning. R is a programming language and open-source software platform utilized for statistical analysis and visual data representation. If statistical analysis is required, R is a superior choice when compared to Python.

Leading Languages for Recruiting AI Developers

Prolog is a declarative language extensively utilized for computational linguistics and natural language processing (NLP) models. It has exceptional utility for symbolic reasoning, database applications, linguistic parsing and NLP tasks, making it an indispensable tool.

By 2023, there is expected to be a resurgence in the popularity of Prolog, a programming language widely employed in theorem proving, expert systems, automated planning and type systems.


One of the exceptional attributes of Haskell is its capability to override operators in a type-safe mode using type classes.

Lambda expressions, pattern matching, type classes, list comprehension, and type polymorphism are some of the features that make Haskell a compelling language for academic research. Additionally, Haskell is a secure programming language for AI development, boasting of its adaptability and efficient error management capabilities.

The Wolfram Language is the ultimate choice for AI programming.

Wolfram delivers dependable and effective computing results, providing symbolic computing, functional programming and rule-based programming with access to over 6,000 pre-defined functions. It boasts of incredible handling capabilities for unstructured data and datasets.

Wolfram is renowned for its computer algebraic language due to its ability to perform complex mathematical functions and extensive natural language processing assignments.


Julia is a new entrant in the AI programming scene yet earns regular inclusion among the top selections for this purpose. Despite its small audience, it enjoys unwavering recognition in the AI community.

Julia, recognized for its data visualization software, has gained widespread popularity thanks to its visually attractive graphics and user-friendly interface. Furthermore, Julia outshines others when it comes to memory management, metaprogramming, and bug fixing.

Julia offers several appealing features including direct support for C functions, a dynamic typing system and potential for parallel and distributed computing. It also provides a variety of packages for probabilistic programming, together with pre-existing models for AI development.

  1. MLJ.jl
  2. Flux.jl
  3. Terminology: Metalhead

Rust and similar languages are suitable for AI programming.

Rust is a versatile language specifically designed for diverse applications, with a syntax similar to that of C++. The added benefit is that it ensures memory safety without relying on garbage collection. Moreover, reference counting can be disabled if necessary.

Rust boasts several benefits such as faster processing, enhanced security, and concurrent programming capabilities. These factors contribute towards its popularity as a top choice language for software development. Its wide adoption in industries and large-scale systems has also marked it as a preferred alternative for building AI applications.

The use of Rust is evident in products such as Firefox, Dropbox, Yelp, npm, Cloudflare, Azure, Deno, Discord, and Polkadot.

Future Considerations

AI is revolutionizing the way we live, and its impact is here to stay. With its diverse applications such as self-driving cars and automated grammar correction, businesses have now got the opportunity to hire remote AI professionals and harness their skills to the fullest.

At Works, we recognize the significance of building a proficient software development team that can effectively deploy advanced AI solutions. Our Intelligent Talent Cloud backed by AI empowers you to discover, evaluate, match, and manage the most exceptional programmers worldwide. For further information about how we can assist you, please visit our website.

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