Computer coding must be involved to implement any type of AI system, and there is a variety of programming languages that lend themselves to specific AI or machine learning tasks. Let’s look at which programming languages will be the most beneficial for your specific use cases.
We have composed a simple list showing which five programming languages are best to learn if you want to be successful in the artificial intelligence industry. Each has its own particular strengths and weaknesses for a given project, so consider your end goals before selecting a language.
These programming languages include:
Python is by far the most popular programming language used in artificial intelligence today because it has easy to learn syntaxes, massive libraries and frameworks, dynamic applicability to a plethora of AI algorithms, and is relatively simple to write.
Python supports multiple orientation styles; including functional, object-oriented, and procedural. In addition, its massive community helps to keep this language at the forefront of the computer science industry.
The disadvantages of Python include its lack of speed compared to some of the other languages, its less than optimal mobile coding capabilities, and the difficulty it has with memory-intensive tasks.
R is another machine learning programming language, that is relatively easy to understand. The most common uses of R are for data analysis, big data modeling, and data visualization. R’s abundance of package sets and variety of materials make it easy to work with on data-centric tasks.
The disadvantages of R includes its excess use of memory, lack of basic security (unable to embed into web applications), and the fact that it is rooted in an older programming language, S.
Java is object-oriented and includes strengths such as working well with search algorithms—a simplified framework that supports large-scale projects efficiently—and its ease of debugging code. In addition, it is supported by a well-established community and has a myriad of open-source libraries.
The disadvantages of Java include its lack of performance speed compared to other languages and the inefficient use of memory that comes with running on top of the Java Virtual Machine. These two shortcomings generally result in a third: the increased cost of hardware.
Scala is a highly scalable programming language that can handle large amounts of big data. Being multi-paradigm, Scala supports both object-oriented and functional styles of programming. Due to its concise code, Scala can be more readable and easier to write than other languages, similar to Java. Its speed and efficiency are what makes this language stand out for machine learning and AI models, with relatively error-free coding that is easy to debug when necessary.
The disadvantages of Scala include side effects that come with fulfilling both object-oriented and functional styles. Since this language is a combination of both programming styles, it can make understanding type-information more difficult. In addition, the option to switch back to an object-oriented style can be seen as a downside, as you won’t be forced to think functionally while you code.
Rust is a systems-level programming language. It was created with the intention of writing “safe” code, meaning that objects are managed in the program itself. This relieves the programmer of doing pointer arithmetic or having to independently manage memory. The inability to use excess memory often results in cleaner code, potentially making it easier to program.
The disadvantages of Rust include a slower compiler than other languages, no garbage collection, and codes that cannot be developed at the same rate as other programming languages, such as Python.
With Algorithmia, you can use multiple languages within one AI software
Algorithmia provides a machine learning architecture that invites programmers to pipeline models together, even if they’re written in different languages. This removes any need to translate algorithms into a certain language to be compatible with the rest of the algorithms in a monolithic architecture.
You can also reuse pieces of the software by calling them into the application whenever they’re needed, without copying and pasting them. Algorithmia helps organizations create better software, faster in this way.
Watch our video demo to learn how Algorithmia can help your organization increase efficiency in the last-mile machine learning process: deploying, serving, and managing models.