Top 10 Programming Languages For AI And Natural Language Processing
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.
Zapp2Photo/Shutterstock.Com
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. RustPerformance 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. PrologPerformance 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. WolframPerformance 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. HaskellPerformance 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. LispPerformance 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.
Click to continue reading and see the Top 5 Programming Languages for AI and Natural Language Processing.
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Disclosure: None. Top 10 Programming Languages for AI and Natural Language Processing is originally published on Insider Monkey.
Top 10 Programming Languages For AI And Natural Language Processing
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.
Story continues
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
Zapp2Photo/Shutterstock.Com
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. RustPerformance 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. PrologPerformance 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. WolframPerformance 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. HaskellPerformance 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. LispPerformance 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.
Click to continue reading and see the Top 5 Programming Languages for AI and Natural Language Processing.
Suggested Articles:
Disclosure: None. Top 10 Programming Languages for AI and Natural Language Processing is originally published on Insider Monkey.
Natural Language Programming AIs Are Taking The Drudgery Out Of Coding
"Learn to code." That three-word pejorative is perpetually on the lips and at the fingertips of internet trolls and tech bros whenever media layoffs are announced. A useless sentiment in its own right, but with the recent advent of code generating AIs, knowing the ins and outs of a programming language like Python could soon be about as useful as knowing how to fluently speak a dead language. In fact, these genAIs are already helping professional software developers code faster and more effectively by handling much of the programming grunt work.
How coding worksTwo of today's most widely distributed and written coding languages are Java and Python. The former almost single handedly revolutionized cross-platform operation when it was released in the mid-'90s and now drives "everything from smartcards to space vehicles," as Java Magazine put it in 2020 — not to mention Wikipedia's search function and all of Minecraft. The latter actually predates Java by a few years and serves as the code basis for many modern apps like Dropbox, Spotify and Instagram.
They differ significantly in their operation in that Java needs to be compiled (having its human-readable code translated into computer-executable machine code) before it can run. Python, meanwhile, is an interpreted language, which means that its human code is converted into machine code line-by-line as the program executes, enabling it to run without first being compiled. The interpretation method allows code to be more easily written for multiple platforms while compiled code tends to be focused to a specific processor type. Regardless of how they run, the actual code-writing process is nearly identical between the two: Somebody has to sit down, crack open a text editor or Integrated Development Environment (IDE) and actually write out all those lines of instruction. And until recently, that somebody typically was a human.
The "classical programming" writing process of today isn't that different from the process those of ENIAC, with a software engineer taking a problem, breaking it down into a series of sub-problems, writing code to solve each of those sub-problems in order, and then repeatedly debugging and recompiling the code until it runs. "Automatic programming," on the other hand, removes the programmer by a degree of separation. Instead of a human writing each line of code individually, the person creates a high-level abstraction of the task for the computer to then generate low level code to address. This differs from "interactive" programming, which allows you to code a program while it is already running.
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Today's conversational AI coding systems, like what we see in Github's Copilot or OpenAI's ChatGPT, remove the programmer even further by hiding the coding process behind a veneer of natural language. The programmer tells the AI what they want programmed and how, and the machine can automatically generate the required code.
Building the tools to build the tools allowing any tool to build toolsAmong the first of this new breed of conversational coding AIs was Codex, which was developed by OpenAI and released in late 2021. OpenAI had already implemented GPT-3 (precursor to GPT-3.5 that powers BingChat public) by this point, the large language model remarkably adept at mimicking human speech and writing after being trained on billions of words from the public web. The company then fine-tuned that model using 100-plus gigabytes of GitHub data to create Codex. It's capable of generating code in 12 different languages and can translate existing programs between them.
Codex is adept at generating small, simple or repeatable assets, like "a big red button that briefly shakes the screen when clicked" or regular functions like the email address validator on a Google Web Form. But no matter how prolific your prose, you won't be using it for complex projects like coding a server-side load balancing program — it's just too complicated an ask.
Google's DeepMind developed AlphaCode specifically to address such challenges. Like Codex, AlphaCode was first trained on multiple gigabytes of existing GitHub code archives, but was then fed thousands of coding challenges pulled from online programming competitions, like figuring out how many binary strings with a given length don't contain consecutive zeroes.
To do this, AlphaCode will generate as many as a million code candidates, then reject all but the top 1 percent to pass its test cases. The system then groups the remaining programs based on the similarity of their outputs and sequentially test them until it finds a candidate that successfully solves the given problem. According to a 2022 study published in Science, AlphaCode managed to correctly answer those challenge questions 34 percent of the time (compared to Codex's single-digit success on the same benchmarks, that's not bad). DeepMind even entered AlphaCode in a 5,000-competitor online programming contest, where it surpassed nearly 46 percent of the human competitors.
Now even the AI has notesJust as GPT-3.5 serves as a foundational model for ChatGPT, Codex serves as the basis for GitHub's Copilot AI. Trained on billions of lines of code assembled from the public web, Copilot offers cloud-based AI-assisted coding autocomplete features through a subscription plugin for the Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs).
Initially released as a developer's preview in June of 2021, Copilot was among the very first coding capable AIs to reach the market. More than a million devs have leveraged the system in the two years since, GitHub's VP of Product Ryan J Salva, told Engadget. With Copilot, users can generate runnable code from natural language text inputs as well as autocomplete commonly repeated code sections and programming functions.
Salva notes that prior to Copilot's release, GitHub's previous machine-generated coding suggestions were only accepted by users 14 to 17 percent of the time. "Which is fine," he said. "It means it was helping developers along." In the two years since Copilot's debut, that figure has grown to 35 percent, "and that's netting out to just under half of the amount of code being written [on GitHub] — 46 percent by AI, to be exact."
"[It's] not a matter of just percentage of code written," Salva clarified. "It's really about the productivity, the focus, the satisfaction of the developers who are creating."
As with the outputs of natural language generators like ChatGPT, the code coming from Copilot is largely legible, but like any large language model trained on the open internet, GitHub made sure to incorporate additional safeguards against the system unintentionally producing exploitable code.
"Between when the model produces a suggestion and when that suggestion is presented to the developer," Salva said, "we at runtime perform […] a code quality analysis for the developer, looking for common errors or vulnerabilities in the code like cross-site scripting or path injection."
That auditing step is meant to improve the quality of recommended code over time rather than monitor or police what the code might be used for. Copilot can help developers create the code that makes up malware, the system won't prevent it. "We've taken the position that Copilot is there as a tool to help developers produce code," Salva said, pointing to the numerous White Hat applications for such a system. "Putting a tool like Copilot in their hands […] makes them more capable security researchers," he continued.
As the technology continues to develop, Salva sees generative AI coding to expand far beyond its current technological bounds. That includes "taking a big bet" on conversational AI. "We also see AI-assisted development really percolating up into other parts of the software development life cycle," he said, like using AI to autonomously repair a CI/CD build errors, patch security vulnerabilities, or have the AI review human-written code.
"Just as we use compilers to produce machine-level code today, I do think they'll eventually get to another layer of abstraction with AI that allows developers to express themselves in a different language," Salva said. "Maybe it's natural language like English or French, or Korean. And that then gets 'compiled down' to something that the machines can understand," freeing up engineers and developers to focus on the overall growth of the project rather than the nuts and bolts of its construction.
From coders to gabbersWith human decision-making still firmly wedged within the AI programming loop, at least for now, we have little to fear from having software writing software. As Salva noted, computers already do this to a degree when compiling code, and digital gray goos have yet to take over because of it. Instead, the most immediate challenges facing programming AI mirror those of generative AI in general: inherent biases skewing training data, model outputs that violate copyright, and concerns surrounding user data privacy when it comes to training large language models.
GitHub is far from alone in its efforts to build an AI programming buddy. OpenAI's ChatGPT is capable of generating code — as are the already countless indie variants being built atop the GPT platform. So, too, is Amazon's AWS CodeWhisperer system, which provides much of the same autocomplete functionality as Copilot, but optimized for use within the AWS framework. After multiple requests from users, Google incorporated code generation and debugging capabilities into Bard this past April as well, ahead of its ecosystem-wide pivot to embrace AI at I/O 2023 and the release of Codey, Alphabet's answer to Copilot. We can't be sure yet what generative coding systems will eventually become or how it might impact the tech industry — we could be looking at the earliest iterations of a transformative democratizing technology, or it could be Clippy for a new generation.
