What programming language for artificial intelligence is suitable for you? It is a crucial question for your company’s future. Every major tech business and even startups are working on artificial intelligence (AI), which has emerged as one of the hottest issues and largest study disciplines. It’s a tremendously broad topic that covers anything from simple calculators and self-driving cars to intelligent robots that could fundamentally alter the course of human history.
The core of AI is creating machines that are as intelligent as or more intelligent than humans. But how?
Better AI solutions are continuously being sought after by businesses. IDC projects that the market for artificial intelligence will reach $500 billion by 2024, with a five-year CAGR of 17.5% and total revenue of $554.3 billion. However, picking the right programming languages for AI software is the first step in developing effective solutions.
What programming language for artificial intelligence is the best?
The need for AI practitioners is rising as a result of this digital change. In fact, between 2020 and 2021, recruiting for AI-related positions surged by 165 percent. You must learn how to use AI programming languages that are supported by powerful machine learning and deep learning libraries if you want to work in the industry.
Artificial intelligence career paths are expanding, with artificial intelligence designers being one of them.
Programming languages come in a wide variety, from Java to Julia, so where do you begin? You have arrived at the right place if you are an AI enthusiast who is unsure about which coding language to use for your upcoming major project.
There are many AI programming languages. However, none of them can legitimately be referred to as “the best ai programming language.” Each has advantages and disadvantages. Yet these five are typically well-liked:
- Python
- JavaScript
- Java
- Scala
- R
Later, we shall dive into them and the others. But first, let’s examine the rationale behind their adoption by artificial intelligence engineers.
What programming languages do artificial intelligence engineers use?
A programming language is a computer language used to write instructions and transmit them to computers and other computer-based devices. To communicate with computers, software engineers and developers employ programming languages, which can be divided into five main types.
These subcategories include:
- Logic,
- Procedural,
- Object-oriented,
- Functional,
- Scripting programming languages.
AI engineers frequently use programming languages for scripting. When designing learning algorithms and automating processes that often require human involvement, this type of language offers a significant benefit over alternatives. Scripting programming languages are often interpreted—not compiled—into machine-readable languages.
So, let’s explore them!
Top programming language for artificial intelligence (2022)
There aren’t always universal, one-size-fits-all solutions in the realm of artificial intelligence. The needs and scale of your project will determine the AI programming language you use. If your project calls for substantial data analysis, consider using R, which was created to handle large data sets easily. However, Python would be better for implementing machine learning models in production than R.
Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence.
Each programming language has its own special strengths when it comes to programming for artificial intelligence. Others are built to perform numerical analysis, while some are excellent at natural language processing. Let’s analyze the numerous uses and benefits of the top AI programming languages and find what programming language for artificial intelligence suits you.
Python
As you can imagine, Python will be where we begin. Right now, Python can be thought of as the precursor to all other languages. The Python language’s straightforward syntax is the cause of its explosive success. Python is a great choice for Machine Learning engineering because of its easy syntax, freeing up a lot more time to prepare the fundamental structure. It is one of the best answers to the question of “What programming language for artificial intelligence is the best?”
The most popular usage of Python, an all-purpose, object-oriented, high-level programming language, is for scripting small to very large projects. Python has several applications in numerous industries on its own. Many engineers pick Python as their favorite language due of its straightforward syntax and use of English-language terms.
These technologies can also be used for sophisticated mathematical expression evaluation and natural language processing (NLP), in addition to machine learning.
Smart assistants like Google and Alexa use natural language processing (NLP) to comprehend what you’re saying and answer properly.
The nicest feature is how well Python integrates with other languages, like Java, for increased convenience. One of the greatest programming languages for artificial intelligence projects right now is Python, thanks to its ease of use and the large open source community that supports it.
The extensive range of support provided by AI libraries is one of the main benefits of Python for AI over other programming languages. Libraries are collections of tools that facilitate the application of specific ideas. The general-purpose Python languages can gain specialized AI features from these modules. Let’s examine the most widely used Python AI libraries in more detail.
TensorFlow
Google created and released the Python library TensorFlow for use in AI applications. The library is used to create machine learning-based AI applications. It comprises the majority of Google’s production AI services and supports the implementation of neural networks. Due to its capacity to parallelize workloads and scale quickly, TensorFlow is frequently employed by many AI practitioners. It has strong Google support and a vibrant developer community.
SciKit-Learn
Another Python module that manages the data, SciKit-Learn, is a crucial component of the AI workflow. Functions for classification, model selection, and data pre-processing are available in SciKit-Learn. Applications involving data mining and analysis frequently use it. Data management and organization are handled using this open-source framework in a way that makes it simple for the algorithm to consume.
Pybrain
A modular library designed for AI newcomers is called Pybrain, which stands for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library. It includes Python-compatible neural network and reinforcement learning algorithms that are easy to combine. It is also frequently used to train and implement popular AI algorithms quickly.
Python is used by many organizations, including Google, NASA, Amazon, Instagram, Reddit, JP Morgan Chase, Intel, IBM, Netflix, Facebook, and Pinterest.
Java
Java is a crucial language for AI, as should be obvious. The language’s widespread use in the creation of mobile apps is one explanation for this. And it makes great sense given how many mobile apps use AI. It is one of the oldest answers to the question of “What programming language for artificial intelligence is the best?”
Because of its “write once, run anywhere” programming philosophy, Java, which was initially introduced more than 20 years ago in 1995, is employed by AI programmers. Java is meant to have the fewest dependencies possible, so it can be run on any platform with little effort.
The most significant benefits of Java as an AI language include its ease of use, quick debugging, portability, and autonomous memory manager. Swing and SWT have been integrated into Java, which gives the interfaces and graphics a sleek, contemporary appearance.
Along with supporting TensorFlow, Java also provides other libraries and frameworks made especially for AI:
- Deep Java Library
- Kubeflow
- OpenNLP
- Java Machine Learning Library
- Neuroph
JavaScript’s AI features enable seamless interaction and operation with other source codes, including HTML and CSS. Like Java, JavaScript has a sizable developer community that aids in development. AI programming is made easier by libraries like jQuery, React.js, and Underscore.js. JavaScript can control front and backend operations, including multimedia, buttons, and data storage.
The FaceApp and the practical Google Assistant are two instances of Android apps featuring Java-based artificial intelligence.
R
Data science, a field that heavily relies on AI, frequently uses the computer language R. Data science is the discipline of processing and analyzing data with the aid of AI, utilizing statistics and math, in order to identify trends. Data transformation, preparation, and analysis are just a few of the data science tasks that the software’s libraries can be used for.
Most of R’s advantages come from its ability to process statistical data. They comprise time-series analysis, grouping, visualization, and linear and nonlinear modeling. It is a wonderful option for creating machine learning algorithms since it can store and access data efficiently.
Beginners in programming or AI shouldn’t use R because of its challenging learning curve. The benefits of learning R are substantial in business environments where huge amounts of data are handled.
LISP
Now, LISP deserves a lot of the credit for what we see from AI. It is the second-oldest language overall and the oldest language still in use for AI processes. With its expanded scope for transforming ideas into reality, LISP has traditionally been regarded as a tool for top AI businesses. By emphasizing precision, the language sets itself apart from existing AI languages.
Who are the precursors of artificial intelligence?
What was the first AI programming language called?
LISP is the answer. The 1958 invention known as Lisp takes its name from one of its initial implementations, “List Processing.” By 1962, Lisp had developed enough to meet the demands of artificial intelligence.
However, because of its numerous shortcomings, LISP language use is declining. The fact that the language is still utilized for logical solutions and is well-known for achieving miracles adds it to the list.
Scala
In addition to using object-oriented programming, Scala is a functional programming language. The building of websites and web-based applications, as well as data processing, make use of one of the industry’s most succinct high-level programming languages. With a very difficult syntax, this popular programming language is incredibly adaptable.
Scala is scalable, as its name suggests. It is a strong choice for AI engineers because it supports parallelism and has several excellent artificial intelligence libraries. Scaladex, a database of all Scala libraries, including those for artificial intelligence, will be available once you have learned Scala for AI.
JULIA
While Julia lacks a sizable user base and community, it provides a wealth of premium tools for creating superior AI. Julia is one of the best development tools for handling data analysis and numbers.
Julia gives you the necessary capabilities for flawless execution if you need to create a dynamic interface, eye-catching graphics, or data visualizations. This language develops AI simply because of its debugging, memory management, and metaprogramming features.
Julia is the finest option for AI tasks using machine learning. It includes a variety of packages, including Flux.JL, Turing.JL, MLJ.JL, and Metahead.JL.
Haskell
Based on the semantics of the Miranda programming language, Haskell is a functional programming language. Haskell provides safety and speed over all else in scenarios involving machine learning. Haskell has found a niche in academia due to its support for embedded, domain-specific languages that are essential to AI research; however, tech behemoths like Microsoft and Facebook have enlisted Haskell to create frameworks that manage schematized data and combat malware, respectively.
The HLearn library in Haskell provides deep learning support through its Tensorflow binding and algorithmic implementations for machine learning. Haskell is the best language for projects involving abstract math and probabilistic programming because it enables users to design extremely expressive algorithms without losing efficiency.
Users of Haskell can interpret their code like mathematical equations and utilize a small amount of code to express a model.
C++
Although it is low-level and has been around for a while, C++ is still widely used. It is one of the best answers to the question of “What programming language for artificial intelligence is the best?”
This indicates that C++ performs well with hardware and machines but less so with software’s more theoretical aspects.
However, C++’s ability for low-level programming makes it ideal for managing production AI models.
Using C++, you can create neural networks from scratch and convert human code into something that computers can understand.
C++ was used to develop several of the most infamous machine learning libraries.
These factors contribute to the continued popularity of C++ in the artificial intelligence community. Don’t undervalue what C++ is capable of.
Prolog
Look at Prolog for a more logical technique to program an AI system. Instead of following a series of coded instructions, software that uses it adheres to a fundamental set of facts, rules, goals, and questions.
Prolog has the ability to recognize patterns and match them, locate and organize data logically, and automatically go backward in a process to discover a better route. The strongest application for this language in AI is problem-solving, where Prolog looks for a solution—or several—to the situation.
The usage of it in chatbots and virtual helpers like IBM’s Watson is the result. Consider how straightforward but useful these clever communication methods are. Although Prolog may not be as flexible or user-friendly as Python or Java, it can be of great use.
GO
Go is an open-source programming language that makes it simple to create trustworthy, effective, and efficient software. It is a recent arrival in the field of programming. It is one of the newest answers to the question of “What programming language for artificial intelligence is the best?”
In an era of multicore processors, computer networks, and big codebases, Google adopted Go in 2012 after it was first conceptualized in 2007. Go was designed to increase programming productivity. The goal of the designers was to address common complaints about other languages while maintaining many of their beneficial traits.
To assist you in creating scalable applications, GO combines the performance of classic C++ & Java with all the simplicity of Python.
This language has given a new generation of coding geeks a lot of optimism. It is new and lags behind Python, JS, and Java programs. However, it is steadily rising to the top of the list of languages that can be learned and used soon.
RUST
High-performance, quick, and secure software development is what everyone wants, and Rust makes it possible. Developers adore using it as a general-purpose programming language for the creation of AI. Rust’s syntax is comparable to C++’s, but Rust also provides memory safety and forgoes garbage collection.
Many well-known systems, like Dropbox, Yelp, Firefox, Azure, Polkadot, Cloudflare, npm, Discord, etc., use Rust as their backend programming language. Rust is the ideal language for developing AI and using it in scientific computing because of its memory safety, speed, and ease of expression.
How to choose an artificial intelligence programming language?
Outline the objectives and outputs of your project before choosing an AI language. Determine the resources needed to finish the activities at hand by evaluating the tasks at hand. Consider each AI programming language’s accompanying tools and libraries before deciding which is most appropriate for your project.
You should start the process of integrating one or more of these languages if your business needs to integrate artificial intelligence. There is no end to what AI can achieve to speed up your company’s growth with the correct development team.
For beginners, popular mainstream languages like Python, Java, and C++ are frequently good options. However, you should consider each language’s specific benefits and drawbacks in light of your objectives.
Artificial intelligence programming for beginners
If you want to work as an AI engineer, the first computer language you should learn is Python. You can start learning other programming languages once you have learned Python and its uses in AI development. The most important programming language for AI developers is Python, and most of them never learn any other languages during their careers.
The majority of these resources are open source, so anyone can use them for nothing. You may install Python packages straight on your computer for little to no money, and there are a ton of online forums where you can get instructional materials. The best method to learn Python is through bootcamps.
Conclusion
“What programming language for artificial intelligence is the best?” is a hard question. But thanks to many libraries and easily accessible instructional resources, AI programming is now more accessible than ever. A beginner’s introduction to AI programming is made even simpler by the abundance of online training resources for well-known languages like Python and Java.
The best programming languages for artificial intelligence projects are those that were covered above. It just comes down to selecting a project that best meets your needs. With a basic grasp of the project, you may quickly select the best language and increase your company’s efficiency.
Something more than a hype, artificial intelligence is here to stay. AI is present in everything, from autonomous driving to phrase correction. This popularity has created an excellent environment for businesses trying to generate additional AI improvements like artificial intelligence customer services.
