Top 5 Programming Languages For Building Artificial Intelligence Solutions

Can machines think? Until the 20th century, answering this question in the affirmative was next to impossible. But today, with the existence of Artificial Intelligence (AI), a branch of computer science, we have reached very close to answering that question in the affirmative. AI is concerned with building machines that can replicate human intelligence and can perform tasks that require human intelligence without the need for human intervention.

Major Goals of Artificial Intelligence:

• To plan future and improve decision making with better prediction models
• To analyze hidden patterns
• To enable hyper-personalization
• To increase productivity and operational efficiency
• To automate mundane tasks and free up time for creative work

Are you working on a new Artificial Intelligence (AI) project? As exciting as it may sound, deciding which programming language to use isn’t all fun. It can often result in a lot of confusion and even in choice paralysis among developers. If you’re trying to find out about the best possible programming languages to use for developing AI solutions, you’ve landed at the right place. Read on.

Below are the top 5 programming languages (with their pros, cons, and best applications) that developers and AI enthusiasts prefer to use for their AI projects.

Best Artificial Intelligence (AI) Programming Language

1. Python

Since Python as a programming language is simple to use and implement for complex AI projects, it is one of the most popular styles for AI development. Most of AI consists of Big Data, something for which Python is well suited. The pre-existing libraries in the programming language make development more straightforward and quicker.

Pros:

• A lot of pre-existing libraries and frameworks in the programming language make tasks simpler.
• It is simple and easy to learn.
• It supports several different styles of programming, including functional, object-oriented, and procedure-oriented.

Cons:

• Compared to Java, it has slower execution in AI development.
• It has a weak language for mobile computing which makes it unsuitable for mobile apps development.

Best Applications: Process automation projects and applications that are based on detection and recommendations, such as chatbots.

2. Java

When talking about programming languages for AI, how can we not talk about Java, the most portable, transparent and object-oriented programming language? Although it is more complicated than other programming languages, well-known organizations such as YouTube, Amazon and eBay use it for server-side.

Pros:

• It is simple to use and debug.
• It has robust security features.
• It simplifies the work of developers since it has an automatic memory manager.
• It offers better user interaction.
• It is an excellent choice for developing games.
• It also has the incorporation of Swing and SWT (the Standard Widget Toolkit), tools that make graphics and interfaces look appealing and sophisticated.

Cons:

• Not beginner-friendly
• It has less speed in execution and more response time.

Best Applications:

From game development to standard, web and mobile applications, Java is a programming language used for versatile purposes. It can be a right choice for Machine Learning and for developing AI games that need to learn how to play a game with a user, such as Chess. There is widespread use of the language in building Chatbot applications as well.

3. Lisp

Another commonly used programming language for artificial intelligence development, Lisp is a family of computer programming languages and has widespread use in the context of traditional AI. It is capable of symbolic processing information and its unique features that allow fast and flexible prototyping makes it appropriate for building AI solutions.

Pros:

• It has excellent prototyping capabilities.
• Its development cycle allows interactive evaluation of expressions and recompilation of functions or file while the program is still running.

Cons:

• It is one of the harder languages to learn and is not recommended for beginners.
• Its lack of support in the form of libraries make it a challenge for the language to handle modern requirements

Best Applications: This programming language is highly suitable for inductive logic projects and machine learning. It has the most widespread use in the area of traditional, symbolic AI. It is used in building chatbot applications too.

4. R

This is one of the most effective programming languages for statistical and AI-related projects. Biggies in the industry like Google, Uber, and Bank of America widely use this programming language for big data- and AI-related analysis. In the field of AI, it is suited for data manipulation, calculation, visualization, time-series analysis, clustering, and graphical representation.

Pros:

• It has lots of packages, libraries, and frameworks that have great use in the field of machine learning.
• It allows for easy implementation of machine learning algorithms.
• It is capable of transforming messy data into a structured form.

Cons:

• It is slower than Python in AI development.
• It is a complicated language- challenging to learn and not beginner-friendly.
• Since it has a weak origin, it does not have support for dynamic graphics.

Best Applications:

In addition to its extensive usage in banking, especially in fields for predicting different risks, the language is used in projects that involve cracking business-related problems. Projects related to data analysis, big-data modelling and visualization also make use of this language. Lastly, it is used for doing ML testing.

5. C++

Being the fastest computer programming language, C++ is widely popular and appreciated for AI projects that have time constraints. It is capable of using statistical AI techniques efficiently. Although it takes highly experienced developers to use C++ for AI development, it is a beneficial programming language for AI.

Pros:

• It runs on all platforms without any additional recompilation
• It provides faster execution of complex algorithms using statistical AI techniques.
• It leads to faster execution and response time
• It is useful in achieving organized data because of being a multi-paradigm programming

Cons:

• Highly complex language making it hard for beginners at using it for writing AI programs.
• It isn’t very secure.

Best Applications:

Projects based on search engines and the development of computer games. Since it allows extensive use of algorithms, it is a suitable choice for AI development in machine learning and building neural networks.

Final Thoughts

Programming AI is not only complicated but time consuming too. This is the reason coders prefer languages that offer quick response time.

While one language is more prevalent in ML projects (example: Python), the other is far better for developing desktop, mobile applications, and games (example: Java). While one is more challenging to learn, the other is beginner-friendly.

While it is painful and honestly unfair to pick any one of the programming languages as the best for AI development, the ones listed above make our top 5. Mostly, it all comes down to the unique requirements of the project.

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