Artificial intelligence (AI) has dominated the digital age, making significant strides in various sectors, from marketing to software development. According to a Deloitte survey, most AI projects are designed with market-oriented objectives, reflecting their immense potential for business growth.
This article explores artificial intelligence’s intricacies and the top eight programming languages best suited for AI development.
Realm of Artificial Intelligence
For those unfamiliar with the term, Artificial Intelligence, often called AI, refers to machines possessing an intelligence level comparable to humans. This complex field of study has opened up many possibilities, particularly in digital devices and software solutions.
While the idea of sentient machines might seem daunting to some, implementing AI technologies has proven beneficial in our daily lives.
AI has been integrated into numerous digital tools and platforms we often take for granted. From the voice assistant in your smartphone to sophisticated marketing automation tools, AI is the driving force that enables these platforms to deliver a smooth and user-friendly experience.
AI has become an indispensable part of our digital landscape in the digital transformation era, offering proactive solutions to various issues.
Programming Languages for AI Development
Developing AI-integrated software requires unique skills and a robust tech stack. This section aims to introduce you to the top eight programming languages that are ideal for AI development.
Python is a development language that is high-level and general-purpose due to its straightforward syntax and object-oriented approach. Despite its simplicity, Python is well-equipped to handle large and complex projects, making it an ideal choice for data engineering and machine learning applications.
Python’s most widely used framework for AI development is TensorFlow, an open-source machine learning library designed for training deep neural networks. Other noteworthy libraries and frameworks include:
These technologies are frequently employed for natural language processing (NLP), mathematical expression evaluations, and machine learning. Python’s versatility and ease of learning make it a top choice for AI development.
C++, a veteran in the programming world, is a low-level language that excels in hardware and machine interactions. Its prowess in handling AI models in production is noteworthy. Many renowned machine learning libraries were developed using C++, making it a popular choice in the AI community.
Java is a versatile server-side language known for its wide platform compatibility. It’s the native language for Android development, and many AI-integrated Android apps, like FaceApp and Google Assistant, have been developed using Java.
Java also boasts several machine learning libraries for deep learning and NLP applications, including:
- Deep Java Library
- Java Machine Learning Library
The language’s object-oriented design makes it a pleasure to work with, especially for AI projects.
R is a favorite among statisticians for being exceptionally adept at data analysis, which is crucial for AI development. Although it may not be the best language for AI, it’s superior to Python regarding number-crunching. Noteworthy R packages include:
Lisp, first introduced in 1958, is one of the oldest languages designed for AI development. While its complex syntax and intricate libraries may not appeal to everyone, Lisp’s capabilities in addressing AI problems are undeniable. It’s beneficial for:
- Rapid prototyping
- Dynamic object creation
- Program modifications
- Mandatory garbage collection
Despite its age, Lisp’s flexibility and ability to turn ideas into reality make it a valuable asset for any AI project.
Prolog dates back to 1972 and excels at pattern matching, making it an excellent choice for NLP applications. Prolog’s logic system is at the heart of its strength, allowing it to handle facts, rules, goals, and queries exceptionally well.
Haskell is a Language based on purely functional programming and statically typed, known for its flexibility and expressiveness. Its efficient memory management and reusable code practices make it an attractive choice for AI developers. Depending on the situation, Haskell only evaluates code when necessary, which can be advantageous or disadvantageous.
Julia, a recent AI development language roster addition, has a promising future. Its dynamic interface, impressive graphics, memory management, debugging capabilities, and metaprogramming features have earned it a spot in the top eight. Julia’s AI development packages include:
AI is a game-changer in the digital world. As machines become smarter and more capable of meeting our needs, the implications on a personal and business level are profound. Deploying the right programming language in your tech stack is the beginning of building successful AI software.