Language Processing by Artificial Intelligence: A Crucial Question for Businesses in the Digital Age

The Global Rise of Artificial Intelligence and the Challenge of Multilingualism

At the heart of the global digital revolution is a game-changing technology: artificial intelligence. In 2023, global AI market revenues are estimated at $165 billion, expected to reach $1,591 billion by 2030. However, as we continue to integrate AI into multiple aspects of our lives and businesses, a particular challenge arises: the multilingualism of artificial intelligence.

With over 7,151 languages spoken worldwide, only 600 of them are significantly supported for online use. Therefore, for businesses seeking to serve a global customer, understanding the costs and challenges associated with integrating language into AI systems is crucial.

Key Languages for Artificial Intelligence

In 2023, English, Chinese (Mandarin), Hindi, Spanish, and French are the most spoken languages in the world. These five languages form an essential linguistic core for businesses looking to maximize their global reach.

This means that a large part of the linguistic world remains underrepresented online, representing both a challenge and an opportunity for businesses looking to integrate multilingual AI.


The Most Spoken Languages in the World in 2023 

(Sources : statistica)

Two Approaches to Language Processing: Machine Learning & Symbolic AI

Machine Learning: a powerful but data-demanding technology

Machine Learning, a subset of AI, is based on algorithms capable of learning from data without explicit programming. It is this technology that has made major advances possible in various fields, including natural language processing (NLP).

Machine Learning (ML) trains by analyzing vast volumes of data to deduce recurrent characteristics. In terms of natural language processing (NLP), this means that ML systems can absorb millions of pages of text in a given language and from there, deduce grammatical rules, word meanings, and sentence constructions.

However, despite its many benefits, ML encounters limitations when it comes to integrating new languages. Each new language requires significant training data volumes, implying high cost in terms of time and resources, which may not be available depending on the languages. Furthermore, machine learning can incorporate bias or alignement with human values when learning new languages.

Symbolic AI: a flexible and linguistically intelligent approach

Unlike Machine Learning, symbolic AI does not rely on massive data absorption to function. Instead, it focuses on explicit modeling of knowledge and decision-making processesIt is a branch of AI that uses symbols to represent knowledge and leverages a set of predefined rules to make inferences, solve problems, and learn new knowledge.

Regarding linguistic integration, symbolic AI can be very advantageous. It capitalizes on universal linguisticsa linguistic theory postulating the existence of principles and grammatical structures common to all human languages (Chomsky). By exploiting these common structures, it is possible to develop AI models that can quickly understand and process new languages without requiring a large amount of training data.

In other words, symbolic AI can understand a new language based on pre-established rules and linguistic structures, making the integration of new languages easier, faster, and less costly. As for words, it suffices to add a "dictionary" that would contain all of them. This is a significant advantage for easily integrating keywords specific to businesses. 

Thus, while Machine Learning offers significant advantages in many areas, symbolic AI is potentially a more effective and efficient for integrating new languages into AI systems. This decision should be made considering each company's specific environment, needs, available resources, and language specifics.

As an example, multilingual message processing is not analyzed in the same way. Indeed, a user can, for example, start a sentence in English and end it in French. Despite this mix,'s symbolic AI, by exploiting universal linguistic structures, can understand the message and different languages natively. Whereas with an ML technique, it would be necessary to train the model with all potential possibilities. This is much more time-consuming, tedious, and costly.

See also the article on Symbolic artificial intelligence and machine learning, the rise of disruptive technologies

Symbolic AI, an efficient solution for language processing

In conclusion, given the ongoing digital revolution and the rise of artificial intelligence, managing language multiplicity is a major challenge for companies operating globally. Machine Learning, despite its undeniable advantages, can be limited when it comes to quickly and efficiently integrating new languages, due to its need to rely on significant volumes of training data, which may not necessarily be available depending on the language. 

On the other hand, symbolic AI, thanks to its ability to leverage universal linguistics and its flexibility, emerges as a potentially more powerful and efficient solution for linguistic integration. Its strengths in handling polysemy, processing multilingual messages, and interpreting symbols make it a tool of choice to address linguistic challenges.

Digital transformation is a journey, and each company has its own objectives and constraints, especially considering all the business specifics. This is how the choice of technology is vital for achieving performance."