In this article, we’ll discuss the top 10 programming languages for AI and Natural Language Processing. You can skip our detailed analysis of global market trends for NLP and AI development and trending programming languages for AI development and go directly to the Top 5 Programming Languages for AI and Natural Language Processing.
We have seen a recent boom in the fields of Artificial Intelligence (AI) and Natural Language Processing (NLP). Revolutionary tools such as ChatGPT and DALL-E 2 have set new standards for NLP capabilities. These tools are harnessing the power of language processing to store information and provide detailed responses to inputs.
In fact, according to research by Fortune Business Insights, the global market size for Natural Language Processing (NLP) is expected to witness significant growth. The market is projected to expand from $24.10 billion in 2023 to $112.28 billion by 2030, exhibiting a robust compound annual growth rate (CAGR) of 24.6%. This indicates a promising outlook for the NLP market, driven by the increasing demand for advanced language processing solutions across various industries.
With the presence of major industry players, North America is anticipated to dominate the market share of natural language processing. In 2021, the market in North America already accounted for a significant value of USD 7.82 billion, and it is poised to capture a substantial portion of the global market share in the forthcoming years. The region's strong position further reinforces its leadership in driving advancements and adoption of natural language processing technologies.
As the demand for AI and NLP continues to soar, the question arises: which programming languages are best suited for AI development? When it comes to AI programming languages, Python emerges as the go-to choice for both beginners and seasoned developers. Python's simplicity, readability, and extensive libraries make it the perfect tool for building AI applications. In addition, Python allows easy scaling of large machine learning models. Python, along with Lisp, Java, C++, and R, remains among the most popular programming languages in the AI development landscape.
The dominance of Python is further reinforced by the job market, where employers increasingly seek Python language skills. According to TIOBE Programming Community index, Python, SQL, and Java top the list of in-demand programming skills, with Python securing the first spot. With its versatility and ease of use, Python finds applications in various domains, including app and website development, as well as business process automation.
While the utilization of NLP and AI has become imperative for businesses across industries, some companies such as Microsoft Corporation (NASDAQ:MSFT), Amazon.com, Inc. (NASDAQ:AMZN), and Alphabet Inc. (NASDAQ:GOOG) have played a crucial role in driving recent advancements in these technologies.
Notably, Microsoft Corporation (NASDAQ:MSFT)’s significant investment of $10 billion in OpenAI, the startup behind ChatGPT and DALL-E 2, has made waves in the AI and NLP landscape. These tools have not only transformed the technological landscape but have also brought AI and NLP innovations to the general public in exciting new ways.
Also, Microsoft Corporation (NASDAQ:MSFT)’s Azure, as the exclusive cloud provider for ChatGPT, offers a wide range of services related to NLP. These include sentiment analysis, text classification, text summarization, and entailment services.
The significance of AI and NLP is palpable at Amazon.com, Inc. (NASDAQ:AMZN) as well. The widely recognized Alexa device, capable of playing your favorite song or providing product recommendations, exemplifies AI and NLP in action. Additionally, Amazon.com, Inc. (NASDAQ:AMZN)’s Amazon Web Services (AWS) provides cloud storage solutions, enabling businesses to complete their digital transformations.
The impact of AI and the recent surge in generative AI extends beyond Google's homegrown products, as parent company Alphabet Inc. (NASDAQ:GOOG) is actively investing in startups. Alphabet Inc. (NASDAQ:GOOG)’s venture capital arm, CapitalG, recently led a $100 million investment in corporate data firm AlphaSense, valuing the company at $1.8 billion.
So, if you are curious to discover the top programming languages for AI and NLPs, keep reading and delve into the realm of these exciting technologies.
Top 10 Programming Languages for AI and Natural Language Processing
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Our Methodology:
To rank the top 10 programming languages for deep learning and NLPs, we conducted extensive research to identify commonly used languages in these fields, considering factors such as community support, performance, libraries, ease of use, scalability, and industry adoption. We collected relevant data and evaluated each language on these criteria, assigning scores on a scale of 1 to 5. Higher scores were given to languages demonstrating more robust performance and broader usage in AI and NLP development. We sorted the list in ascending order of the best programming languages for machine learning applications.
Here is the list of the top 10 programming languages for AI and Natural Language Processing.
10. Rust
Performance Level: 3.5
Rust, known for its high performance, speed, and a strong focus on security, has emerged as a preferred language for AI and NLP development. Offering memory safety and avoiding the need for garbage collection, Rust has garnered popularity among developers seeking to create efficient and secure software. With a syntax comparable to C++, Rust provides a powerful and expressive programming environment. Notably, renowned systems including Dropbox, Yelp, Firefox, Azure, Polkadot, Cloudflare, npm, and Discord rely on Rust as their backend programming language. Due to its memory safety, speed, and ease of expression, Rust is considered an ideal choice for developing AI and leveraging it in scientific computing applications.
9. Prolog
Performance Level: 3.7
Prolog is a logic programming language. It is mainly used to develop logic-based artificial intelligence applications. Prolog's declarative nature and emphasis on logic make it particularly well-suited for tasks that involve knowledge representation, reasoning, and rule-based systems. Its ability to efficiently handle symbolic computations and pattern matching sets it apart in the AI and NLP domains. Prolog's built-in backtracking mechanism allows for elegant problem-solving approaches. With Prolog, developers can focus on specifying the problem's logic rather than the algorithmic details. These characteristics make Prolog an appealing choice for AI applications that involve complex inference, knowledge-based systems, and natural language processing tasks.
8. Wolfram
Performance Level: 3.8
Wolfram programming language is known for its fast and powerful processing capabilities. In the realm of AI and NLP, Wolfram offers extensive capabilities including 6,000 built-in functions for symbolic computation, functional programming, and rule-based programming. It also excels at handling complex mathematical operations and lengthy natural language processing tasks. Moreover, Wolfram seamlessly integrates with arbitrary data and structures, further enhancing its utility in AI and NLP applications. Developers rely on Wolfram for its robust computational abilities and its aptitude for executing sophisticated mathematical operations and language processing functions.
7. Haskell
Performance Level: 4
Haskell prioritizes safety and speed which makes it well-suited for machine learning applications. While Haskell has gained traction in academia for its support of embedded, domain-specific languages crucial to AI research, tech giants like Microsoft Corporation (NASDAQ:MSFT) and Meta Platforms, Inc. (NASDAQ:META) have also utilized Haskell for creating frameworks to manage structured data and combat malware.
Haskell's HLearn library offers deep learning support through its Tensorflow binding and algorithmic implementations for machine learning. Haskell shines in projects involving abstract mathematics and probabilistic programming, empowering users to design highly expressive algorithms without compromising efficiency. Haskell's versatility and fault-tolerant capabilities make it a secure programming language for AI applications, ensuring robustness in the face of failures.
6. Lisp
Performance Level: 4.3
Lisp, one of the pioneering programming languages for AI, has a long-standing history and remains relevant today. Developed in 1958, Lisp derived its name from 'List Processing,' reflecting its initial application. By 1962, Lisp had evolved to address artificial intelligence challenges, solidifying its position in the field. While Lisp is still capable of producing high-quality software, its complex syntax and costly libraries have made it less favored among developers. However, Lisp remains valuable for specific AI projects, including rapid prototyping, dynamic object creation, and the ability to execute data structures as programs.
