How to start programming in Artificial Intelligence: languages, tools and recommendations

Nacho Palou    18 January, 2023

There is a very close relationship between Big Data and Artificial Intelligence (AI): Big Data is about capturing, processing and analysing large amounts of data. When this data is combined with capabilities such as machine learning and predictive analytics, more value is extracted from that data.

This makes it possible, among other things, to find patterns in this data that are “invisible” to the human eye, allowing us to predict and prevent events, offer personalised experiences of use and consumption of products and services, hold care conversations and even create content.

Knowing how to program Artificial Intelligence allows you to develop countless solutions and take advantage of the enormous potential offered by Big Data, Artificial Intelligence and Internet of Things.

Furthermore, in 2023 the demand for professionals qualified in the development of Artificial Intelligence solutions will continue to grow, because “AI will be present in all the digital transformation processes of companies” in numerous sectors, according to data from Fundación Telefónica’s Employment Map.

Programming languages for AI development

Elena Díaz, head of the Centre of Excellence in the AI of Things product team at Telefónica Tech, is passionate about programming languages focused on exploiting data.

As an expert, to program in Artificial Intelligence Elena recommends learning these programming languages:

  • Python is the most widely used programming language for the development of Artificial Intelligence applications. It has many libraries and tools for machine learning, such as TensorFlow, PyBrain or PyTorch, among others. You can start with Python with an experiment for everyone.
  • R is also a programming language widely used for data analysis, data visualisation and machine learning, especially in the field of statistics.
  • SQL is the standard query language for relational databases, widely used in the fields of Big Data and Artificial Intelligence. Knowledge of SQL is essential for the management and analysis of large datasets, essential in the field of Artificial Intelligence.

However, as Elena explains, although right now these are the most widely used programming languages in the field of Artificial Intelligence, “we always have to be aware of evolutions and adapt new languages and always be in a continuous learning process”.

“Once we learn to program, it is relatively easy to switch from one language to another”.

Elena Díaz, Telefónica Tech.

Other languages that also apply to the development of Artificial Intelligence include:

  • Java and C++ are more advanced programming languages and are also used for the development of artificial intelligence applications, including high-performance developments such as neural networks and machine learning algorithms.
  • JavaScript, a very popular language in web development that is increasingly used in the field of Artificial Intelligence, especially for the development of machine learning applications oriented to users accessing them through apps or web browsers, for example.

The programming language will depend very much on your specific preferences and needs – it will even depend on your previous programming experience, if you already have some – and also on what you want to achieve or the project you are going to work on.

How to develop your Artificial Intelligence skills

The first thing you need to do if you are interested in starting programming in Artificial Intelligence is to learn the basics of it. In this sense, it is important to “overcome conceptual, mathematical or technical barriers” and understand basic concepts of artificial intelligence, such as machine learning, computer vision and natural language processing.

In addition, you will also find it helpful to:

  • Learn a programming language such as the aforementioned Python, R, Java and C++, which are widely used in the development of artificial intelligence applications. Choose one and dedicate time to learning it.
  • Practice with problems and projects: It is important that you practice with real problems and projects. You can find datasets and problems on websites such as OpenAI or Kaggle.
  • Learn about AI tools and libraries: there are many AI tools and libraries, such as TensorFlow, PyTorch, scikit-learn and Keras. They allow you to build and train AI models easily and you can use them in your projects.
  • Take every opportunity to keep learning: Artificial Intelligence is constantly evolving, so it is important to keep acquiring and updating skills. You can keep up to date with trends, new techniques and technologies through blogs and articles on the subject, webinars, talks and courses (there are many free ones) and by participating in online groups and forums.

Elena recommends those interested in Artificial Intelligence to “discover what you like the most and go deeper into it. Specialising in what most motivates you and helps you to continue to grow.”

Featured photo: Kelly Sikkema / Unsplash