Over the years, machines have learned how to generate acceptable bits and pieces of English. Advances in artificial intelligence have boosted these developments. The devices have now turned their attention to other languages. 

For instance, there are AI language models with algorithms fluent in German, Italian, Spanish, and English. The algorithms build on recent advances in machine learning which enable machines to handle language with what seems like an accurate understanding. 

The algorithms draw on what they learn from reading the web to develop logical articles on a given topic. Moreover, they can provide answers to general knowledge questions convincingly. However, the answers usually differ based on where the algorithm was developed. 

For example, when you ask these algorithms "which is the best sports team in history," they will answer based on their origin. For instance, a US-based model will tell you Chicago Bulls or New York Yankees is the best sports team in history.   

It Is Not Straightforward

Getting machines to understand language has been a big challenge for AI. Language is a powerful phenomenon as it offers various ways in which words and concepts combine to present countless thoughts and ideas. However, decoding the meaning of words also presents a challenge to AI algorithms because of frequent vagueness.  Additionally, it is impossible to put all the language rules into a computer language. 

For example, Japanese ranks as one of the most complicated languages in terms of complexity; this implies that it is challenging to master for humans. As a result, it might be challenging to capture all the concepts of the Japanese language in a computer program. 

BERT

However, further research revealed a powerful type of massive neural network specializing in language learning. The neural network is known as Bidirectional Encoder Representations from Transformers (or BERT).

This new development showed that machine learning could result in new language understanding. As a result, it inspired machine learning specialists to explore the possibilities. One year later, a startup from the US demonstrated a model built by feeding massive amounts of text from the web. 

However, the new model required enormous computing power that would cost billions of dollars. However, it would unlock a new level of understanding in the machine. The company later released another model that can generate paragraphs of comprehensible text on a given theme. 

AI experts suggest that the large language models have a remarkable ability to understand how the world works. They do it by simply reading all the information available. Additionally, some of the algorithms are said to be very statistical. 

For example, they learn how to re-generate patterns of words and grammar available in a language. This means that they are capable of removing stuff that doesn't make sense and outrageously false facts. Moreover, they also get rid of hateful language. 

The AI Language Models Could Be Misused

There is also an emerging issue where AI language models might be misused. For instance, their ability to generate coherent text on any subject could cause them to be used to create spurious spam, fake news, and reviews. 

Some experts argue that operators of disinformation could invest heavily in experimenting with these language models. They worry that it might be impossible for AI to detect disinformation generated by AI. For example, a tweet might not have enough information for AI to determine whether a machine created it. 

Additionally, experts also suggest that there could be knotty types of bias lying in wait within the massive language models. For example, studies have revealed that language models trained on Chinese internet content are likely to reflect the censorship that is supposedly associated with the continent. 

Moreover, the models predictably capture and reproduce slight and unconcealed biases based on race, gender, and age of the language they consume. This could include intolerable ideas and statements. 

Other experts warn that these big language algorithms could fail in unanticipated ways. For example, some language model developers have stated that their models could do more than they thought. 

So, although the companies state that they will vet all their algorithm users, it becomes nearly impossible by the day. This is because more tools are proliferating and becoming accessible, making it challenging to detect misuses.

Another challenge is that people with resources such as money and machine learning graduates could easily replicate a model. Moreover, cloud computing platforms provide tools that make it effortless to build neural networks on a massive scale. 

Final Thoughts

Having AI models learning other languages is great, right? Well, that would only be true in an ethical, social setting. However, that does not mean that it is a bad thing that should be done away with. 

Industry experts should continue to put more effort into understanding the capability of the models they create. They should also find ways of monitoring the use of these language algorithms as they could be used for the wrong reasons. Nevertheless, exciting times lie ahead!