Top 9 AI Programming Languages in 2021

More and more often, we hear impressive news about Artificial Intelligence. The machines have learned to recognize phone scammers, diagnose glaucoma, detect illegal deforestation or approaching floods. The remarkable results of the developers’ work are excellent. What programming languages should you work with to create a new Jarvis? We will talk about the most popular AI programming languages in this article.

What is AI programming?

Artificial Intelligence (AI) is a system that is trained to imitate human behavior. It uses this skill to solve problems and further train to produce as little “machine” behavior as possible. This technology scares some people with the possibility of replacing a person completely, but this is impossible. Although AI already writes lullabies, it does it only according to a specific algorithm that the system has been taught.

How is Artificial Intelligence taught? Numerous illustrations of actions. For example, developers need AI to detect apples. They upload 10,000 images of apples to the system so that AI can learn to identify them by external signs. Then between a dog’s and an apple’s photo the system will choose the second one. Developers’ goal is to expand human capabilities and simplify the world by letting AI take care of routine tasks.

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PwC predicts that the AI market will grow to $15.7 trillion by 2030. This industry is exciting for businesses, as it can automate production and reduce labor costs. With its growing popularity, there is an increasing need for professional programmers and engineers ready to work hard and experiment. Programming languages also do not stand still since this branch of computer science requires a growing number of practical machine learning frameworks, models, languages.

Each AI programming language has its features that will help in creating and training AI. The only difficulty lies in the fact that for a specific task you need to choose one ideal from various options.

The best programming languages for Artificial Intelligence in 2021

Among the variety of programming languages, some are excellent for solving problems with artificial intelligence. The most popular are Python, Java, R, Scala, Lisp, and many others. Most AI engineers use them to create frameworks. With the help of these programming languages, templates are created for machine learning of systems and algorithmization of their actions.

Let's talk about programming languages for Artificial Intelligence more specifically.

1. Python

This dynamic programming language was created in 1995 as a multifunctional programming language. It was developed in 1991 and immediately established itself among developers. It meets generally accepted standards of simplicity. Recent surveys have shown that 57% of developers prefer to use Python (compared to C++).

The advantages of Python are an extensive community, platform independence, and many AI frameworks. Also, this language is easy to learn, and many beginners start with it.

Do not forget about the disadvantages of Python: because of its sophistication, it is inferior to other languages in speed. It is due to the fact that it is interpreted at runtime instead of being compiled to native code at compile time. It also consumes a large amount of memory. Python is perfectly modeled for file sharing.

Conclusion: Python is easy to learn, convenient, simple, and has a vast number of machine learning frameworks that support the community. The disadvantages are significant memory usage and low speed. Python is used for visualization, server, mathematical calculations, and machine learning.

2. Java

It was created in 1995 as a multifunctional programming language. It is easy to use and is often used by AI developers. Experts say that Java is similar to C and C++ in syntax, but its difference is that it is more autonomous. Java is unique — you can use it to modify the code at runtime. The language has a large developer community and tools for multithreading.

Java is used for data exchange, statistical analysis, deep learning, visualization, and AI programming.

Conclusions: Java is platform-independent, has a large supporting community, a high level of security, and the possibility of multithreading. The disadvantages can be attributed to high memory consumption, low performance, and readability. It is used in Artificial Intelligence to apply the Internet of Things, machine learning, and servers.

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3. C++

C++ is an extension of the C programming language, which was invented in 1985. It is a universal dynamic programming language; a distinctive feature of which is memory allocation — this enables you to create flexibility in complex data structures and processes.

C++ has some security issues due to friendly functions, global variables, and pointers. It doesn’t have a garbage collector like Java, so there may be problems with unnecessary data.

The TensorFlow platform for machine learning, which was mentioned above, was based on C++. Among the merits of this programming language are also the platforms Caffe, CNTK, mlpack, FANN.

Conclusions: C++ is excellent for those who value efficient memory access, performance, and system programming. The disadvantages can be attributed to a complex understanding of the language, a low level of security.

4. R

This programming language is great for AI because it perfectly copes with mathematical problems and makes it possible to work on different paradigms.

AI frameworks that are available for R:

● Gmodels (model fitting);

● Tm (Text mining);

● OneR (Machine learning).

R is also helpful for a comprehensive set of libraries, supporting the community, integration with C, C++, Fortran, and creating high-quality graphs. On the other hand, R is not easy to learn; it works slowly and is not 100% safe.

Conclusion: R is famous for such advantages as efficient data processing and analysis, a large community, and the possibility of extensibility. Among the disadvantages we will highlight the difficulty in learning the language, low performance, and security problems.

5. Prologue

This language is best suited for AI programming and especially for natural language processing and chatbots. The very first and well-known chatbot ELIZA was based on a Prologue.

The logic of this language lies in the relationship between facts and rules. It is used for proving theorems, planning, and processing natural language. If logical queries occupy an important place in your task, prolog is suitable. It perfectly identifies problems in AI and has a large set of extensions.

Prolog is not the most straightforward language to learn because it does not offer tools for debugging. AI frameworks created based on Prolog: Zamia-AI (open-source speech and language processing), mlu, cplint, and cplint_datasets.

Conclusions: Prolog has proven itself among AI engineers to be successful in launching built-in inference mechanisms, to have declarative semantics, and extensibility. The disadvantages include the complexity of the language, poor testability, and complex logic.

6. Lisp

It is the second-highest programming language globally (created in 1958, a year younger than Fortran). It allows rapid prototyping, the creation of dynamic objects. Lisp also has a garbage collector, which speeds up its work.

Some world-famous projects were created using Lisp dialects — among them Reddit and HackerNews.

It is smaller than C++ or Java, which allows it to perform tasks faster and be easier to maintain. Among the disadvantages, we can list the complexity of the structure of this programming language and the inconvenience for reading.

Lisp libraries for machine learning: CLML, mgl (machine learning), Antik (digital code), mgl-mat and LLA.

Conclusions: Lisp is efficient, fast; you can create your own dialects and interpretations of it. Among the downsides is poor readability.

7. Haskell

Created in 1990, Haskell is a statically typed programming language. It is very secure, which is why AI engineers have more flexibility in error handling. Haskell has impressive abstraction capabilities, built-in memory management, and the ability to use code independently. Also, this language is clear with readable code.

Haskell is a lazy language, which makes it challenging to allocate memory. The most popular Haskell library for machine learning is HLearn, which is used for deep AI learning.

Conclusion: Haskell has strong abstraction capabilities and built-in memory management. Its disadvantages are a small community and memory problems.

8. Julia

Julia is a dynamic programming language that is great for computing and analysis. Julia is useful for AI in several of its features. A dynamic type system, a built-in package manager, macros, support for multiple shipments, and functions. It is used for machine learning and integration with TensorFlow. jl, MLBase.jl.

Julia has visualization and data processing, support for parallel and distributed computing. All this allows you to use this language for deep machine learning.

Conclusions: Julia has high performance and automated memory management. As for the disadvantages, we can only mention the lack of community support.

9. Scala

Scala is a reasonably young programming language that was created in 2004. It is efficient and straightforward to use, with object-oriented and functional programming concepts. It is Java compatible, which makes it easier to use libraries for these languages.

Among Scala’s disadvantages, the most widespread are the lack of community support, low implementation, and backward compatibility restrictions.

Conclusion: Scala is great for machine learning and data processing systems; it is efficient and easy to use. However, it is notorious for its poor, backward compatibility and low implementation.

Choosing a programming language for AI development

It is essential for the successful launch of the project, so you need to choose AI Programming Language wisely. Before choosing one of the above languages, one should clearly understand the task they need to accomplish. Each of the listed options is suitable for specific goals and results. It is also worth paying attention to the experience of your team. What they can work with, what is given to them as quickly as possible.

One more important detail is the actual scale of the project. If you and the team choose the programming language suitable for your scale, feel free to start working.

It’s time to turn ideas into reality! Technorely has been engaged in AI and neural connections for a long time — we have considerable experience in voice recognition technology. We are ready to help you in creating and improving such a project. To discuss the details, fill out the contact form.