Top 6 Programming Languages For Artificial Intelligence

If your company is looking to integrate Artificial Intelligence, there are a few languages you should seriously consider adding to your developer's toolkit.
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Artificial Intelligence is on everybody’s mind—especially businesses looking to accelerate growth beyond what they’ve previously been able to achieve. With AI, your business can save time and money by automating and optimizing typically routine processes. Once AI is in place, you can be sure that those tasks will be handled faster and with more accuracy and reliability than can be achieved by a human being.

On top of that, AI is exponentially faster at making business decisions based on input from various sources (such as customer input or collected data). AI can serve as chatbots, in mobile and web applications, in analytic tools to identify patterns that can serve to optimize solutions for any given process and the list goes on. In fact, there’s very little that AI can’t boost. 

But to employ artificial intelligence in your company’s systems and services, you’re going to need software engineers who are up to the task. On top of that, those developers are going to need to know the best languages to use for AI. 

Which languages are those? There are several ones that can serve to make your AI integration dreams come true. Let’s dive in and take a look at what are some of the best languages available for Artificial Intelligence.

Python

Although Python was created before AI became crucial to businesses, it’s one of the most popular languages for Artificial Intelligence. Python is the most used language for Machine Learning (which lives under the umbrella of AI). One of the main reasons Python is so popular within AI development is that it was created as a powerful data analysis tool and has always been popular within the field of big data.

As for modern technology, the most important reason why Python is always ranked near the top is that there are AI-specific frameworks that were created for the language. One of the most popular is TensorFlow, which is an open-source library created specifically for machine learning and can be used for training and inference of deep neural networks. Other AI-centric frameworks include:

  • scikit-learn – for training machine learning models.
  • PyTorch – visual and natural language processing.
  • Keras – serves as a code interface for complex mathematical calculations.
  • Theano – library for defining, optimizing, and evaluating mathematical expressions.

Python is also one of the easiest languages to learn and use. 

Lisp

Lisp has been around since the 60s and has been widely used for scientific research in the fields of natural languages, theorem proofs, and to solve artificial intelligence problems. Lisp was originally created as a practical mathematical notation for programs, but eventually became a top choice of developers in the field of AI. 

Even though Lisp is the second oldest programming language still in use, it includes several features that are critical to successful AI projects:

  • Rapid prototyping.
  • Dynamic object creation.
  • Mandatory garbage collection.
  • Data structures can be executed as programs.
  • Programs can be modified as data.
  • Uses recursion as a control structure and not an iteration.
  • Great symbolic information processing capabilities.
  • Read-Eval-Print-Loop to ease interactive programming.

More importantly, the man who created Lisp (John McCarthy) was very influential in the field of AI, so much of his work had been implemented for a long time.

Java

It should go without saying that Java is an important language for AI. One reason for that is how prevalent the language is in mobile app development. And given how many mobile apps take advantage of AI, it’s a perfect match.

Not only can Java work with TensorFlow, but it also has other libraries and frameworks specifically designed for AI:

  • Deep Java Library – a library built by Amazon to create deep learning abilities.
  • Kubeflow – makes it possible to deploy and manage Machine Learning stacks on Kubernetes.
  • OpenNLP – a Machine Learning tool for processing natural language.
  • Java Machine Learning Library – provides several Machine Learning algorithms.
  • Neuroph – makes it possible to design neural networks.

Java also makes use of simplified debugging, and its easy-to-use syntax offers graphical data presentation and incorporates both WORA and Object-Oriented patterns.

C++

C++ is another language that’s been around for quite some time, but still is a legitimate contender for AI use. One of the reasons for this is how widely flexible the language is, which makes it perfectly suited for resource-intensive applications. C++ is a low-level language that provides better handling for the AI model in production. And although C++ might not be the first choice for AI engineers, it can’t be ignored that many of the deep and machine learning libraries are written in C++. 

And because C++ converts user code to machine-readable code, it’s incredibly efficient and performant.

R

R might not be the perfect language for AI, but it’s fantastic at crunching very large numbers, which makes it better than Python at scale. And with R’s built-in functional programming, vectorial computation, and Object-Oriented Nature, it does make for a viable language for Artificial Intelligence.

R also enjoys a few packages that are specifically designed for AI:

  • gmodels – provides several tools for the task of model fitting.
  • TM – a framework used for text mining applications.
  • RODBC – an ODBC interface.
  • OneR – makes it possible to implement the One Rule Machine Learning classification algorithm.

Julia 

Julia is one of the newer languages on the list and was created to focus on performance computing in scientific and technical fields. Julia includes several features that directly apply to AI programming:

  • Common numeric data types.
  • Arbitrary precision values.
  • Robust mathematical functions.
  • Tuples, dictionaries, and code introspection.
  • Built-in package manager.
  • Dynamic type system.
  • Ability to work for both parallel and distributed computing.
  • Macros and metaprogramming capabilities.
  • Support for multiple dispatches.
  • Support for C functions.

Julia can also be integrated with TensorFlow.jl, MLBase.jl, and MXNet.jl.

Conclusion

If your company requires the addition of Artificial Intelligence, you need to begin the process of integrating one or more of these languages. With the right development team, there’s no limit to what AI can do to help accelerate the growth of your company.

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