Top 10 Programming Languages Used in IA
It is safe to say Artificial Intelligence (AI) is not an innovation in 2022, its origin dates way back in time. We have witnessed and gotten used to this technology area, which is improving daily.
Artificial intelligence adoption has incremented dramatically over the past 18 months, and many organizations across various industries have reported projects to expand their AI strategies this year.
AI can optimize not only operational processes but also create innovative business models, products, and services—which is why most business leaders feel optimistic about the future impacts of AI.
That said, businesses and individuals are inclined more toward AI development. AI development is a perfect choice with benefits like enhanced customer experience, smart decision-making, automation, minimum errors, and data analytics.
This digital trend has sparked a growing demand for AI-related hiring, which increased by 165% between 2020 and 2021.
While many programming languages will get the work done, it's essential to identify which will suit your project.
What are the top ten Programming Languages for AI?
Artificial intelligence is a field of diverse solutions for multiple applications, the choice of a particular AI programming language will be dictated by the project's scope and requirements.
For example, if the project involves extensive data analysis, R is a great option designed to crunch big numbers quickly.
However, it is not suited for deploying machine learning models in production, where Python would be a better pick for that task.
Each programming language brings unique features and strengths to the table regarding artificial intelligence programming.
Some are great for natural language processing, while others are explicitly designed to run numerical analyses.
Java is one of the top languages in the industry; this language has proven its ability to adapt to the latest innovations and technological advancements, which holds for AI as well.
Thanks to its robust, object-oriented programming language with simple syntax and easy debugging, Java has established itself as a go-to language for mobile apps that rely heavily on artificial intelligence.
Quicker execution and a faster runtime than Python in production environments make Java an ideal choice for AI, especially for machine learning projects that require performance while also providing easy scaling of large or complex AI applications.
Java's Virtual Machine Technology allows developers to build a unique version of an application that can run on multiple Java-based platforms while also working on the GUI and user experience, making them more appealing with the Standard Widget Toolkit.
Java is supported by many machine learning libraries like Weka, which is used for predictive modeling, and Massive Online Analysis, an open-source data mining solution.
Apache Hive, Hadoop, and Spark are popular big data processing solutions developed in Java, enabling effortless integration with these key analytic frameworks.
Java has created a rich support community for business applications like data analysis, deep learning, and Natural Language Processing.
Another one on the list is Python, which offers the least coding. Python is a general-purpose language with many applications, from backend development to data science and machine learning.
Python's intuitive syntax strives for readability, streamlining coding, and making the language easier to learn, making it an optimal choice for both new and experienced machine learning engineers and data scientists.
Thanks to its abundant variety of pre-designed libraries that optimize the AI development process, Python has proved itself as one of the most popular AI programming languages.
Its straightforward structure and tools like NTLK and SpaCy make Python an excellent programming language for natural language processing while also boasting exceptional documentation and community support.
At the same time, Python is suitable for development teams and data scientists without a solid coding background.
This relatively new dynamic programming language, developed by MIT in 2012, is focused on numerical analysis and computational science. Its popularity is on the rise, thanks partly to its performance, robust computational capacity, and script-like syntax.
An ever-growing support community has blossomed around the language alongside many machine learning dedicated libraries like TensorFlow.jl, and Scikit Learn.jl, Mocha.jl, Flux, and more.
Julia is the best development tool for data analysis and numbers, offering multiple high-end features for top-notch AI development.
If your project requires a dynamic interface, modern graphics, and data visualizations, Julia provides teams with the ideal tools for perfect execution.
Features like debugging, memory management, and metaprogramming make AI development with Julia a breeze.
Julia excels at seamlessly translating algorithms from research papers into code, which enables machine learning engineers to estimate and deploy a model in production using the same language.
Developed in the 1960s, Lisp is one of the oldest players in the AI development game, as it excels at efficient modification of code, problem-solving, rapid prototyping, and dynamic development. Some industry-standard successful projects built with Lisp are Routinic, Grammarly, and DART.
Despite its drawbacks, Lisp is still an established programming language for AI development.
AI pioneer John McCarthy, the originator of the term artificial intelligence, created LISP, which developers used to build classic AI applications like ELIZA, an early natural language processing system.
The second oldest programming language after Fortran remains relevant for machine learning-intensive applications based on its adaptability, efficient prototyping abilities, automatic garbage collection, and more.
LISP excels in contexts that involve computing symbols and symbolic expressions thanks to its ability to process symbolic information. Other popular programming languages have adopted many of LISP's powerful features, like Python's list comprehensions and LINQ in C#.
Statisticians developed R for statistical computing, which can quickly crunch vast data sets; as a statistical programming language, it is one of the ideal options for projects requiring intensive statistical computations.
Diverse industries, like education, finance, telecommunication, pharmaceuticals, life sciences, and more, have adopted it, partly for being the language that supports tech giants like Microsoft, Google, Facebook, Uber, Airbnb, etc.
R includes built-in and user-generated packages for graphical devices, tools, import/export capabilities, statistical techniques, and data modeling support, making it a language that allows developers to work efficiently on deep learning models.
R is widely adopted in statistical software development, data analysis, and data visualization, making it a top choice for machine learning projects requiring extensive data analysis. It offers a powerful toolset for model training and evaluation.
R's interactive environment is also perfect for rapid prototyping and experimentation with new problems.
At the same time, it's different from the industry standard for deploying machine learning models in production environments, but R is an excellent tool for exploratory work in model selection.
Prolog, short for Programming in Logic, was developed in 1972 in a rule-like form. Its primary usage is in projects that involve computational linguistics and artificial intelligence, especially those built on architecture, including symbolic reasoning, databases, and natural language processing, making it the perfect language support for research regarding artificial intelligence.
While employed for automated planning and theorem proving, Prolog still has limited usage. IBM Watson uses this declarative, logical programming language to parse natural language when processing human-generated questions.
The core of Prolog's expression lies in relationships known as rules, which the interpreter queries in a database to solve a problem. Once the user provides the rules and the desired solution, Prolog solves the rest.
Prolog's pattern-matching feature benefits natural language processing and intelligent database search, combined with its tree-based data structuring, making Prolog a highly flexible and adaptable AI developing framework.
Scala executes on the Java Virtual Machine (JVM) environment. It is compatible with Java and JS and offers many robust features like pattern matching, browser tools, flexible interfaces, and high-performing functions.
Scala is one of the best options for AI development, and it has impressed developers in that area.
This general-purpose language, which debuted in 2004 as an alternative to Java, supports object-oriented and functional programming.
Developers can integrate Java and Scala stacks interchangeably, while Scala supports many JVM libraries and shares familiar syntax features with other widely adopted programming languages.
Android applications are often written in Java, which makes Scala's compatibility with Java a priceless language for developing AI-intensive Android applications, as it can handle complex algorithms and stream data at scale.
Scala is also widely adopted for interfacing with big data processing engines like the Scala-built Apache Spark, which helps its growing popularity for creating machine learning models.
Every company looks for high-performance, fast, and safe software development, making Rust an ideal option, as it is a general-purpose programming language teams love using for AI development.
With a syntax similar to C++, Rust boasts the addition of memory safety and garbage collection prevention.
Rust works at the backend of many industry-standard platforms like Dropbox, Yelp, Firefox, Azure, Polkadot, Cloudflare, npm, Discord, etc.
Memory safety, performance, and ease of expression make Rust the ideal option for building AI applications that rely on scientific computing.
Haskell is a functional programming language built on the semantics of Miranda. While it counts with limited support, it is another valid option for programming languages for AI development, as it provides pure functionality and abstraction capabilities that make it very flexible.
One of Haskell's strengths is code reusability, which comes in handy for developers, along with type system, memory management, and support for embedded, domain-specific languages vital to AI research.
Haskell has found a niche in academia, with tech behemoths like Microsoft and Facebook employing Haskell to build frameworks that manipulate schematized data and fight malware.
Haskell's HLearn library offers algorithmic implementations for machine learning, while its Tensorflow binding supports deep learning.
Haskell allows users to code highly expressive algorithms without sacrificing performance, and the language is ideal for projects that involve abstract math and probabilistic programming.
With Haskell, users can represent a model with just a handful of code and read the lines they've written like mathematical equations.
In this way, Haskell can aptly convey the complexity of a deep learning model with clean code that resembles the model's actual mathematics.
These top ten programming languages are a suitable match for AI development, giving teams several programming languages to choose from, depending on how easy or technical the project requirements are.
One way to tackle the decision is by looking at the popular apps already on the market. What do these applications do?
How do their essential or interactive functions work, and what programming languages do they use?
This information can help teams choose the correct programming language and framework for their projects.
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