By: Pierluigi Riti
October 9, 2020
What Programming Language Should I Learn For Artificial Intelligence
By: Pierluigi Riti
October 9, 2020
Artificial Intelligence is one of the hottest topics today, but what are the best programming languages to learn for those interested in AI? Aside from the math necessary to design the algorithm, some programming languages are used in the Artificial Intelligence area.
When we talk about Artificial Intelligence, the language one must know is Python. The huge number of libraries and the programming style one can adopt when writing the program make this the perfect choice for AI. Python allows the developer to use a Functional Programming style when writing the program. Using the Functional Programming style helps the developer precisely define the mathematical function, which can be very effective when designing a new architecture for an Artificial Intelligence application. Python also offers a very clean and flexible structure when writing the software, and the language is essentially available for every microprocessor architecture. This portability helps build the development community around the language and create the library ecosystem necessary for Artificial Intelligence.
Environments like Anaconda or Jupyter Notebook expand the Python language flexibility, adding the perfect statistical and mathematical analysis environment. Libraries like Numpy, Scikit-learn, or matplotlib make the perfect environment for building and training our Artificial Intelligence system.
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Julia's language, known as Julia, is relatively new. The language's first release was made in 2012, and the language was designed with High Performance in mind. During the Julia language development, agencies like NASA, NSF, DARPA, and NIH contributed to its development. Julia is designed to be a General Purpose Language, but the design characteristic of the language makes Julia the best choice when we want to work with:
- Data Visualization
- Machine Learning
- Data Science
- Scientific Programming
- Parallel Computing
Julia is the new rising star in the area of languages for Artificial Intelligence. The nature of the language and the high quality of libraries available make this language the perfect choice for the complex work needed for Machine Learning and Deep Learning.
Because Machine Learning and Artificial Intelligence strongly depend on statistical analysis, one of R's best languages is a language born for quickly making and expressing complex statistical formulas with a programming language. The R syntax allows the developer to write complex statistical formulas easily. R is quite different when compared with Python and Julia. It is not used to design and develop General Purpose Language but is essentially designed to solve Statistical and Mathematical problems. R's goal is essentially used to give to the statistician a language that can be used to solve statistical problems easily. With R, we can easily load and manipulate the dataset to produce highly accurate results. The ingratiation with the library for plotting the result and integrating with the Anaconda environment allows the engineer to create an environment for data and scientific analysis easily. R's weakness point is the syntax because it is not designed to be a General Purpose Language. Some of the constructors we find in languages like Python and Julia are not available. On the other hand, the statistical and mathematical constructors allow non-developers, like statisticians, to write a complete program. To make data analysis easy, R is the language to know.
If we talk about Artificial Intelligence, we can't escape using Matlab. Matlab is a language born with just one purpose in mind: manipulating matrices and plotting functions. Matlab uses a purely mathematical way to describe the function and to represent it graphically. For all the languages presented up until now, Matlab is, without a doubt, the most complex. Most projects are purely academic because of the complexity and the focused scope of the language. Knowing Matlab is always useful, particularly if one's interest in defining pure mathematical architecture around Artificial Intelligence.
Artificial Intelligence is a current hot cake. When we need to implement a new Machine Learning project or Deep Learning project, one critical element is selecting the language. Languages like Julia or Python are perfect when designing an AI application that an end-user needs to use. Languages like R and Matlab are needed when we need an AI application for strong mathematical analysis. All languages have weak points and strong points, and, like every other job, they involve software development, which is more of a technical choice.