Whether one acknowledges it or not, artificial intelligence is progressively gaining traction in our daily existence. According to Gartner, the market value of AI software will reach $62 billion in 2022 alone, a 21% increase from 2021. The use of artificial intelligence is expanding rapidly across a wide range of industries, including marketing, e-commerce, education, travel, and even hospitality. 51% of e-commerce businesses employ AI now to give their customers a top-notch user experience.
Creating intuitive artificial intelligence systems that offer users a transcendent experience depends heavily on selecting the appropriate programming language for AI. There is an abundance of programming languages available, each with its own advantages and areas of expertise. Choosing the most suitable programming language for your AI project depends on your specific needs. Certain AI programming languages are exceptional at managing vast amounts of data and performing complex calculations, while others are particularly adept at natural language processing. Familiarizing yourself with the individual strengths and peculiarities of each language will enable you to identify the optimal choice for your project.
Let’s take a look at the top 8 AI programming languages of 2022, which have consistently been favored by developers and have a growing community. The popularity of a programming language among developers is a good indicator of its dependability and ease of use. This blog post will explain why each language has been favored by developers, helping you make informed decisions about the best artificial intelligence programming language for your project in 2022.
8 Best Programming Languages For AI In 2023
Here are the 8 best Programming Languages For AI in 2034
Interested in learning more about these languages? Stay tuned!
But First, What is Artificial Intelligence
The aim of artificial intelligence (AI), a broad field of computer science, is to construct smart computers that can accomplish tasks typically requiring human intelligence. Although AI involves various approaches, it is an interdisciplinary field. Recent advances in machine learning and deep learning, in particular, are bringing about a paradigm shift in nearly every sector of the technology industry.
Machines equipped with artificial intelligence are able to mimic or even outperform human brain functions. Businesses across all industries are investing in AI, which is gradually becoming part of daily life. Some notable instances include the advancement of autonomous vehicles and the ubiquitous integration of intelligent personal assistants such as Siri and Alexa.
The Best AI Programming Languages in 2022
There are many different programming languages available for AI development. Here are the top 8 programming languages that will be widely used in the development of AI and machine learning in 2023.
Python is often regarded as the top pick when it comes to the finest AI programming languages, regardless of how you dissect it. The rationale behind this notion is that it has been in existence for an extensive period and has consistently exhibited exceptional performance as a versatile programming language, ideal for both front-end and back-end development. It boasts of an easy-to-grasp structure, produces lucid, unambiguous code, and is adept for programmers of all levels, whether newbies or seasoned veterans. Thanks to its precisely outlined syntax and straightforward, uncomplicated English terminology, Python is remarkably legible and straightforward to learn.
Python stands out in the realm of artificial intelligence due to its extensive collection of pre-existing libraries that prove to be exceptionally advantageous in AI development. Its Scikit-learn library handles basic AI algorithms like regression and classification with utmost expertise. In addition, libraries such as Keras, Caffe, and TensorFlow competently handle deep learning, which helps keep AI development with Python smooth and effortless. Several other libraries, including NumPy, SciPy, Matplotlib, SimpleAI, and many more, make Python an exceedingly user-friendly programming language.
Python maintains its superiority even in natural language processing, which has become a crucial component of user experience nowadays. The language’s impressive arsenal of tools, such as SpaCy and NTLK, provide it with a clear advantage.
Python’s simple syntax, vast array of libraries, thriving community, and compact coding methodology make it an exceedingly effective programming language for AI development. Developers appreciate its ability to allow them to concentrate on critical aspects of development such as innovation, problem-solving, and design thinking, while its straightforward syntax and plethora of libraries expertly manage coding complexities.
LISP, the second-oldest programming language, has a notable presence in artificial intelligence development. With a rich history of providing bespoke and dynamic solutions to programming challenges, LISP’s progress, development, and evolution are closely linked to the early advances of AI.
Though not the most commonly employed AI programming language in modern contexts, LISP still sees some use. While it may not be as widespread as some other languages mentioned earlier, delving into LISP can expand your understanding of artificial intelligence, as it excels in the handling of symbolic information and lists and enables rapid prototyping.
Java stands out as one of the most sought-after coding tongues on a global scale, thanks to its inherent characteristics that render it a strong contender among the most proficient languages for artificial intelligence development heading into 2022. This versatile, object-oriented programming language is equipped with uncomplicated syntax and debugging abilities, making it a programmer’s dream language. Moreover, with an established track record in software and mobile app development, it comes as no surprise that Java is now gradually becoming a staple language in the field of artificial intelligence development, attracting attention from numerous developers worldwide.
AI programming requires quick execution and fast runtimes, both of which are strengths of Java. It provides excellent production value and seamless integration with key analytic frameworks. Its Virtual Machine Technology makes it easy to implement on multiple platforms, enabling developers to write once and run anywhere. This is why a majority of the open-source big-data stack is written in the Java Virtual Machine.
Java’s ease of use, fast debugging, portable memory management, and versatility make it suitable for developing everything from data analysis to natural language processing, deep learning, machine learning, and more. The Java community is also active and supportive, providing assistance to new developers and enrichment for seasoned developers worldwide.
C++ is an object-oriented programming language that is widely used in AI development due to its rapid processing speed, making it particularly useful for complex machine learning models that require high efficiency. C++ has many beneficial attributes, such as generating compact code for efficient module execution, being a statically typed language that avoids type errors during runtime, and offering dynamic load balancing, adaptive caching, and memory management.
C++ is known as the fastest programming language and was developed in 1983, holding special importance in AI programming due to its speed. While C++ has a slightly more complex syntax than some other AI programming languages, it offers unique advantages for artificial intelligence programming once you overcome the learning curve.
C++ has limited but effective machine learning and deep learning libraries, including SHARK which supports linear regression and other supervised learning algorithms, and MLPACK which offers extensible algorithms that can be integrated into scalable ML solutions.
The processing of large amounts of data is required for many AI applications. Did you know that a particular language was created just for statistical computing? R was developed by statisticians specifically for making calculations and processing large amounts of data quickly and easily. Currently, R is used for a wide range of artificial intelligence applications, including machine learning applications that require heavy computation and data analysis.
R is a potent AI programming language that is used widely in fields like finance, health, sociology, and more. As the majority of AI development includes substantial data analysis, R is a must in these fields and others. A variety of libraries, including TensorFlow, MXNet, Keras, and others, are supported. It makes use of randomForest for creating decision trees, CARAT for classification and regression training, and many more tools.
R’s interactive environment, which supports rapid prototyping and exploratory model selection, is one of its greatest assets.
Julia, a novel AI programming language created by MIT in 2012, was engineered to proficiently handle vast numerical analysis and effortlessly manage colossal data sets. The MIT engineers meticulously designed Julia to accommodate all the requirements of modern AI development. It boasts extraordinary velocity, robust computational capabilities, facile script-like syntax, and myriad other features that assist developers in creating the most optimal AI programs.
Despite its nascent status, Julia has already attracted attention from the development community and has already built a thriving community along with an ecosystem of machine learning libraries like TensorFlow.jl, Scikit Learn.jl, Flux, Mocha.jl, and many others.
What distinguishes Julia from other AI programming languages is its ability to directly translate algorithms from research papers into code, effectively reducing model risk and promoting increased safety. It is a high-performance AI programming language custom-made for contemporary AI applications, and it is ideal for developers with a Python or R background.
Prolog, an AI programming language, boasts a rich history dating back to 1972. Despite its age, experts deem it a well-seasoned and mature programming language, particularly in the realm of natural language processing. IBM Watson, a cutting-edge AI system, utilizes Prolog to interpret human-generated inquiries.
Prolog’s superior pattern matching, metalevel reasoning, and tree-based data structuring are among its many esteemed qualities. Pattern matching, in particular, plays a pivotal role in natural language processing, computer vision, and intelligent database search.
List-handling is another area where Prolog excels. Its built-in, recursive list handling enables improved problem-solving, analytics, and application performance.
In Prolog AI programming, the programmer establishes a set of rules, known as “facts,” and an ultimate goal. Prolog then identifies the correlation between the two and employs pattern matching to attain the desired outcome.
Haskell, a non-strict programming language that boasts strong static typing, was developed in 1990. However, because there are not many developers proficient in Haskell, private companies tend to shy away from its use.
Haskell is highly adept at abstraction, specifically abstract mathematics as opposed to Java OOP. Its expressive and efficient libraries enable developers to express AI algorithms more elegantly. HLearn boosts the speed of simple machine learning algorithms by using well-known algebraic structures such as modules and monoids.
While these algorithms can be written in any language, Haskell’s superiority lies in its expressive capabilities, which other languages lack while still maintaining decent performance. A perfect example is faster cover trees, which was written in Haskell.
Haskell additionally allows embedded domain-specific languages, a well-known branch of computer language study that can be used in a variety of fields, including artificial intelligence. It is a particularly good platform for probabilistic programming and helps programmers find mistakes at the compile stage.
Additionally, the language offers CUDA binding and can be compiled to bytecode. Because it is functional and stateless, code can be easily executed on different CPUs in the cloud. Speaking of industry adoption, Facebook utilizes Haskell in its anti-spam efforts.
Artificial intelligence’s increasing significance in our lives makes the selection of the appropriate programming language for AI projects crucial. As of 2023, the top eight programming languages for AI development are Python, Lisp, Java, C++, R, Julia, Prolog, and Haskell. Python surpasses them all with its extensive range of existing libraries, natural language processing abilities, and straightforward syntax. Lisp and Java possess unique strengths in managing symbolic information and ensuring rapid execution, respectively. However, the choice of programming language is contingent upon the specific needs of each AI project. Nonetheless, a thorough understanding of the strengths and idiosyncrasies of each programming language will enable developers to make informed decisions. Overall, the burgeoning growth of AI is stimulating, and the proliferation of programming languages will only propel the field forward.