With the increasing demand for AI skills, careers in this field make it an attractive option for people interested in software.
According to a recent report by PwC, global GDP is forecast to increase by 14% ($15.7 trillion) by 2030, thanks to AI. This makes AI the most important commercial opportunity in today’s economy.
If you want to take advantage of this opportunity, you may be wondering where to start. What is the fastest way for a career in AI, or the best programming language for AI? There is no exact answer, however, many technology pages offer suggestions of the following 5 programming languages:
Python pioneers in AI development for the following reasons:
Less code requirements: AI has a lot of algorithms. Checking all of them can become a daunting task. So Python is often useful. Language has “test when you code” method to reduce the testing process.
Integrated Library: Python libraries are convenient for AI developers. For example, Pybrain for Machine Learning, Numpy for Scientific Computing, and Scipy for Advanced Computing.
Flexibility and independence: A good thing about this language is that it can run on different operating systems with a few changes in code. That saves time because you do not have to test the algorithm on every single operating system.
Community: The Python community is very active – you can find a more experienced developer to help you solve the problem.
Popularity: The popularity of Python is increasing. There are lots of courses, open source projects and comprehensive articles that will help you master this language immediately.
C++ is a solid choice for an AI developers. To get started, Google used the language to create the TensorFlow library. Although most developers have switched to “easier” programming languages like Python, there are still many basic AI functions built with C++. In addition, it is the choice for high-level AI diagnosis.
To use C++ to develop AI algorithms, you must be a real developer with experience.
Lisp is one of the oldest languages and most suitable for development in AI. It was invented by John McCarthy, the father of Artificial Intelligence in 1958.
Lisp has its own powerful features. For example, Lisp allows you to write self-modifying code fairly easily. Or you can expand the language in a way that is more relevant to a particular domain, thus creating a language specific to the domain.
It is one of the most popular programming languages in overall development and is also a suitable language for AI development.
Flexibility for data security: Given the GDPR and general concerns about data protection, it is important to ensure data security of the customer. Java provides the flexibility to create different customer environments, thus protecting one’s personal information.
Strong ecosystem: Many open source projects are written in Java. Language acceleration evolves a lot compared to its alternatives.
Impressive Community: There are many experienced developers and professionals in Java who are open to sharing their knowledge and expertise. In addition, there are many open source projects and libraries that you can use to learn AI development.
Prolog is a less common choice than the 4 choices above. However, Prolog is still useful for AI developers. Most people beginning to use admit that this is a convenient language to express relationships and goals:
You can declare events and create rules based on those events. This allows the developers to respond and explain the different queries.
Prolog is a simple language for a kind of problem solution development. Another good news is that Prolog backtracking support for overall algorithm management will be easier.