Artificial intelligence is the new technology frontier. Programmers must align their skills with the changing technology platform. It means learning the appropriate and most efficient languages for AI development.
AI is evolving fast and looking for pioneers. If you can work fast and efficiently using a language, you will have an upper hand. Clients are also looking for the most advanced AI solutions. Any technician preparing for that future must learn the right language.
Here are the top programming languages for AI developers
Python is an elite and general-purpose programming language. Programmers prefer the language because it is simple and uses ordinary syntax. It has a straightforward approach that will enable you to focus on the project you are handling at the moment. It is a friendly programming language for beginners and gurus alike.
Python is also mysteriously potent. It can handle the simplest project with a beginner and deliver the best project. At the same time, expert programmers use Python to deliver the most complex products. It has been used to develop the most intense data engineering projects. It also delivers the best machine learning products for diverse markets.
The excellent results from the use of Python in machine learning have boosted the confidence of developers to use the language. They are certain that the language will deliver the desired results. Python has popular platforms that simplify AI development. The most popular is TensorFlow which is an open-source machine learning library. It allows developers to train and understand the deep neural networks involved in AI projects.
Developers using Python have other library options including Pandas, SciPy, nltk, PyTorch, Keras, Theano, and scikit-learn. The libraries and platforms also help with Natural Language Processing or NLP as well as the evaluation of advanced mathematical expressions.
In spite of the potential of Python programming language, it is one of the easiest to learn. You have numerous resources to use when learning, including free tutorials and test projects. The language will instantly multiply your development potential.
Experienced programmers knew the language as LISP. It ranks among the pioneers of AI development. Lisp was developed in 1958 and was originally used for list processing. In fact, the name is derived from Lis-t P-rocessing.
Within 4 years of origination, Lisp was capable of solving artificial intelligence problems. Over the last 60 years, it has scaled heights, turning it into one of the most reliable programming languages.
A difficult syntax dents the popularity of Lisp as a programming language. Consequently, developers have not accumulated sufficient libraries to make working with Lisp easier. However, Lisp has managed to establish dominance in some of the crucial AI project phases, including rapid prototyping, modification of programs into data, dynamic object creation, mandatory garbage collection, and the execution of data structures as programs.
Lisp has a reputation for simplifying thought processes. It makes it easy to transform an idea into a viable program or project. It has also become flexible as more people understand its potential in the development phase. This has resulted in more people using the language to deliver simple as well as complex programming products.
C++ has been in the programming platform for a while. It is popular for its simplistic approach to programming. Any programmer can understand the processes and deliver operable products.
C++ works perfectly for automation and machine operation. These are the precursors of AI learning. Because of its low-level programming capability, it has become the go-to language for AI developers.
C++ is perfect for building independent neural networks. You avoid depending on existing networks that might not meet your specific needs or may not integrate seamlessly. The neural networks are easy to build yet deliver some of the best results in the programming market. C++ helps you to translate the user codes in programs to a language that the target machines can understand and execute.
The simple yet effective nature of C++ neural networks has seen developers use the language for the most complex projects. It would be a huge mistake to underestimate the potential of C++. After all, it is the language used to develop most of the AI libraries in use today.
Programmers love Java because of its flexibility. You can use it on any platform and machine and still achieve excellent results. It is one of the most common programming languages, helping you to seamlessly integrate machines. It is referred to as a server-side language for a reason.
Java is the native language used in Android development. It is leading in mobile app development for projects integrating with AI gadgets and platforms. Some of the notable brands developed using Java include Google Assistant and FaceApp.
Java is also used outside the mobile app arena with tremendous results. A few of the machine-learning libraries are built using Java. They come with excellent Natural Language Processing capability as well as deep learning potential. If you are planning to use Java in your project, deep Java library, Open NLP, KubeFlow, Neuroph, and Java Machine Learning Library should be of interest. You will also love the object-oriented design that comes with the Java language. In Java, you have the perfect language to complete both simple and complex projects.
R is a language for amateur and professional statisticians. While the language is not too advanced for AI projects, it remains the best program to crunch numbers. Remember that AI is highly dependent on data to understand trends and predict future actions. R provides the basics you need to collect data and understand your numbers when modeling AI projects.
R will capture, analyze, and present your math accurately. It is better than the advanced languages of Python and Java at managing data and providing statistics. If you are working on an AI project, you should pay attention to OneR, Tm, and Gmodels. They will transform the potential of your project to capture and manage data.
Artificial Intelligence is an extremely fluid setting. One language is not yet enough to handle even the simplest project. You will be missing out on crucial capabilities brought by a blend of languages. Choose the segments that will help to actualize your project better from each language. The best AI projects have relied on multiple languages.
Dora Miller is a digital marketing enthusiast and assignment writer at MyPaperWriter. She helps students with homework on a variety of grades, disciplines, and topics. She is also enthusiastic about making the most of your college years. She shares personal experiences as well as insights from experts in different fields.