Artificial intelligence (AI) is the Future, so learning AI and ML is important. In this article, we are going to discuss about Best programming language for artificial intelligence.
Constant transformations in technology have efficaciously changed the way we interact with machines. These technological transformations have opportunely upgraded us to AI-powered products and devices. With these advancements, the importance of artificial intelligence in our day to day life is only meant to be growing in the coming times.
Artificial Intelligence, after remaining stagnant for several years, has now become an integral part of our professional as well as daily lives. AI has undergone a powerful acceleration to become a one-stop solution for hundreds of unique cases and situations. With this remarkable growth, AI has marked a striking impact on the various industries. It has become a significant tool for the businesses of every spectrum to enhance their productivity and achieve their full potential.
Undoubtedly, AI programming languages are the core reason behind these advancements in Artificial Intelligence. With this recent boom in AI, a career in Artificial Intelligence has now become a significant option for people having an interest in data science and software engineering.
As there are numerous AI programming languages, the projects having a specific approach require different development objectives. Therefore, learning one specific AI programming language is of no use. In this scenario, you must be wondering where you should start.
So, to help you get rid of all the confusion and make a clear vision in your mind, I have enlisted the best programming language for Artificial Intelligence that benefits from a salubrious ecosystem of tools, support packages, and a wide community of programmers.
List of Best Programming Languages for Artificial Intelligence
1. Python
Python is the first language in the list to learn when it comes to AI or machine learning. Python, with its extensive community support and pre-built libraries such as PyPi, Pybrain, NumPy, Pandas, and SciPy helps accelerate AI development.
It works on multiple orientation styles, which include functional, object-oriented, and procedural models. Moreover, Python is helpful to prototype algorithms developed for AI and ML. Also, the wide community of Python makes it lead the pack of the programming languages among the computer science industry.
Software developers and computer science experts prefer Python over any other programming language, as it is very simple to write, execute, and process automation. It has created a standard in the industry and ranks as the most favourable choice for Artificial Intelligence, machine learning, and deep learning.
Pros
- Easy to write.
- Less coding required (Best feature)
- Widely spread community.
- Number of libraries, frameworks, packages available.
Cons
- Slower execution than Java.
- Mobile application development and game development is not supported.
2. LISP
LISP is another widely-used programming language for artificial intelligence development. Lisp is the oldest AI programming language still usable for AI and ML. Lisp has effectively transformed itself to become the strongest and dynamic language in coding.
Lisp offers a favor of liberty to its developers. It also provides flexibility in prototyping and experimentation in AI development, which eventually evolved into a standard AI language.
Pros
- Fast and efficient in coding.
- supported by compilers instead of interpreters.
- Automatic memory manager.
- garbage collection.
- offers specific control over systems.
Cons
- Requires the configuration of new software and hardware to accommodate its use.
3. R Language
R language was founded and created in 1995 by Ross Ihaka and Robert Gentleman. R-language is an interpreted and dynamically designed language that has created a major boom in the world of data science & technology for long. R stands as one of the best programming languages for Artificial Intelligence and the environment used to analyze and control the data for statistical purposes.
With the use of R-language, developers can build a well-structured production quality plot, which includes mathematical symbols and formulae at the required places. A number of large-scale organisations such as Google, Uber, etc. are using R for data analysis, big-data modeling, and visualization purpose.
R language is most commonly used for data analysis, big data modeling, and data visualization. R-languages’ wide structure of package sets and variety of materials makes it operate swiftly on data-centric projects.
The cons of R-languages comprise a lack of standard security (incapability to embed into web applications), excessive use of memory, and its rooting integration with its older programming language.
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4. JAVA
Since its emergence in the industry, Java has evolved itself into a highly compact, maintainable, and transparent programming language supported by the number of wealthy libraries. Java is an acclaimed language among the developers as it is very user-friendly, easily debuggable, and supports all the platforms.
Java runs across all the platforms swiftly without any additional recompilation, as the virtual machine technology lets the code run and execute on all the Java supported platforms. It is used in the development of a wide range of applications like desktop applications, games, web platforms, and mobile applications. Java is a prominent option for Machine Learning.
Pros
- High user-friendliness.
- Flexible nature.
- Platform-independent.
- Highly portable and easy maintenance.
- Easy to debug.
- Simplified work with large-scale projects.
- Graphical representation of data.
- Sophisticated graphics and interfaces with Standard Widget Toolkit.
- Seamless access to big data platforms.
- Improved user interaction.
Cons
- Lack of performance speed.
- Inefficient use of memory on java virtual machine.
5. Julia
Julia is a general-purpose AI programming language. This high-level and dynamic language can be used to write and execute any program. This language comprises features that are optimized and designed for high-performance computational science and numerical analysis.
Unlike other AI programming languages, Julia isn’t exactly the popular and go-to language. That is the reason it doesn’t have that much of community support and library packages.
Key points
- Utilizes native or non-native libraries or frameworks for machine learning (ML).
- Designed to focus on numerical computing .
- Multiple dispatch capabilities.
- Parametric polymorphism .
- Holds the ability to translate various algorithms from research papers into code
- Reduces model risk and improves safety.
- Built-in package manager.
- Support for Multiple dispatches.
- Direct support for C functions.
6. C++
C++ is developed by Bjarne Stroustrup as an extension of the well-known and widely used C programming language. C++ is a general-purpose language and can be considered as Pure Object-oriented, as every entity is the object. It has support for Low-level memory control.
It is quite often used as a compiled language, and a number of vendors give C++ compilers, and it is accessible on multiple platforms as it includes free software foundation, Microsoft, Intel, LLVM, Oracle, and IBM. C++ is strongly recommended by Artificial Intelligence experts for various AI-powered projects such as search engines, development of neural networks to facilitate faster execution of complex algorithms and computer games.
Key Points
- Easy to find solutions for complex problems.
- Comprises rich in library functions and programming tools.
- Supports OOP and used to achieve organized data.
Conclusion
While it’s a tough task to select any one language as the best programming language for Artificial Intelligence, the five languages listed above would aptly make anyone’s top 10. There are several other coding languages that can be considered for AI, including Scala, Haskell, and Prolog.
Every AI programming language has its pros and cons. Analyzing the constant transformations in these languages, it won’t be long when AI development would become more comfortable compared to the latest scenarios.
The best thing about these languages is Community support; they are making daily new packages & extensions, also maintaining them to make the programming easier.