If your company wants to integrate artificial intelligence (AI) into its solutions, you should consider the following several languages by adding them to your developer tools.
Thanks to AI, your business can save time and money by automating and optimizing routine processes. After the introduction of AI, you can be sure that these tasks will be performed faster, with greater accuracy and reliability than a human can do.
In addition, AI makes business decisions significantly faster based on data from various sources. In fact, there’s almost nothing that AI can’t improve on.
But to use artificial intelligence in your company's systems and services, you will need software engineers who will cope with this task. In addition, these developers will need to know which languages are best used for AI.
Although Python was created before AI became crucial for business, it is one of the most popular languages for AI. Python is the most commonly used language for machine learning (which is under the auspices of AI). One of the main reasons of why Python is so popular in AI development is that it was created as a powerful data analysis tool and has always been popular in the field of big data.
As for modern technologies, the most important reason why Python is always at the forefront is that specialized AI frameworks have been created for this language. One of the most popular is TensorFlow, an open source library created specifically for machine learning and can be used for training and logical inference of deep neural networks. Other AI-oriented frameworks include:
- scikit-learn - for learning machine learning models.
PyTorch - visual processing and natural language processing.
Keras - serves as a code interface for complex mathematical calculations.
Theano - a library for defining, optimizing and calculating mathematical expressions.
Python is also one of the easiest languages to learn and use.
Java is an important language for AI. One of the reasons for this is how common and famous this language is in mobile app development. And considering how many mobile apps take advantage of AI, it's a perfect match.
Java can work not only with TensorFlow, but also with other libraries and frameworks specifically designed for AI:
- Deep Java Library- it is a library created by Amazon to create deep learning capabilities.
Kubeflow - it allows you to deploy and manage machine learning stacks in Kubernetes.
OpenNLP- it is a machine learning tool for natural language processing.
Java Machine Learning Library - it provides several machine learning algorithms.
Neuroph - it allows you to design neural networks.
Java also uses simplified debugging, and its easy-to-use syntax offers a graphical representation of data and includes both WORA and object-oriented templates.
C++ is another language that has been around for quite some time, but is still a legitimate contender to use AI. One of the reasons for this is the wide flexibility of the language, which makes it ideal for resource-intensive applications.
C++ is a low-level language that provides better processing of the AI model in a production environment. And while C++ may not be the best choice for AI engineers, one cannot ignore the fact that many deep learning and machine learning libraries are written in C++.
And because C++ converts user code into machine-readable code, it is incredibly efficient and productive.
R may not be an ideal language for AI, but it handles very large numbers fantastically, which makes it better than Python at scale. And thanks to R's built-in functional programming, vector computing, and object-oriented nature, it really makes it a profitable language for AI.
R also has several packages specifically designed for AI:
- gmodels - which provides several tools for fitting the model.
TM- a framework which is used for text mining applications.
RODBC - ODBC interface.
OneR - it allows you to implement the One Rule Machine Learning classification algorithm.
Lisp has existed since the 60s and is widely used for scientific research in the field of natural languages, proofs of theorems and solving AI problems. Lisp was originally created as a practical mathematical notation for programs, but over time it became the best choice of developers in the field of artificial intelligence.
Even though Lisp is the second oldest programming language still in use, it includes several features that are crucial for successful AI projects:
- Rapid prototyping.
Creating a dynamic object.
Mandatory garbage cleaning.
Data structures can be executed as programs.
Programs can be modified as data.
Uses recursion as a control structure, not iteration.
Great possibilities for processing symbolic information.
Read-Eval-Print-Loop to facilitate interactive programming.
More importantly, the man who created Lisp (John McCarthy) was very influential in the field of artificial intelligence, so most of his work has been implemented for a long time.
If your company requires the use of AI, you need to start the process of integrating one or more of these languages. With the right development team, there is no limit to what artificial intelligence can do to accelerate your company's growth.