Linguistic Intelligence in AI

- Overview 

Natural Language Processing allows machines to understand the meaning of phrases spoken and written by humans, either by text or by voice. 

Linguistic artificial intelligence (AI) is a subset of AI that helps computers understand, interpret, and generate human language. It's based on research and development in AI, including neural networks, deep learning, natural language processing (NLP), and natural language generation (NLG). 

Linguistics is a central technology in AI, and is used to develop things like:

• NLP: Algorithms that help computers understand, interpret, and produce human language. NLP is the foundation of language-based AI.
• Speech recognition: Technologies that translate spoken words into written text, allowing for seamless communication between humans and machines.

AI techniques like NLP, machine learning (ML), and deep learning (DL) help researchers analyze and understand language with unprecedented efficiency and precision. The integration of AI techniques into linguistics research has revolutionized the way language is studied and analyzed. 

AI has also had a significant influence on how languages are spoken and understood, with its integration into daily life through technologies like Siri, Alexa, and Google Assistant. AI has the potential to both preserve and endanger linguistic diversity, and has been used in areas like terminology clean-up and visual instruction tuning:

• Terminology clean-up: Automated tools can check terminology for linguistic accuracy, consistency, and conformity with term development standards.
• Visual instruction tuning: Processes that translate image-text pairs into actionable data for the AI, generating instruction-following data from images.

Linguistic AI revolutionizes how content is created, managed, translated, and understood across languages worldwide. It encompasses AI systems and technologies designed to comprehend, process, generate, and interact with human language.

Prominent examples of AI systems include large language models like GPT and BERT, and virtual assistants like Siri and Alexa. 

- How Does Linguistic AI work?

Linguistic AI relies on content to understand and transform, and it can also be used to generate new content. Let’s consider the basic building blocks of linguistic AI to better understand how it can be used:

• Language understanding: it involves applying deep learning algorithms to extract meaning from large amounts of content. It's more than just converting words into the data structures a computer needs to interpret the data. For example, semantic artificial intelligence is a type of artificial intelligence that analyzes content through NLP to understand the meaning of content. This process assists individuals and organizations at all stages of the content management and translation management process.
• Language transformation: it involves a machine-first approach to translation. Machine translation is a simple example of how it can globalize chatbots and increase translation efficiency.
• Language creation: it involves utilizing LLMs to produce new content. LLMs are an artificial neural network that, after training with a large amount of text data, can generate natural language text or perform language-related tasks. This can give writers or translators a head start on content generation or generative translation. It is important to note, however, that we are not suggesting that machines will write the next great novel, or that creative writing and translation will no longer require manual input. 

- Linguistic AI Capabilities in Translation Technology

The language industry has been significantly disrupted by AI, with developments appearing thick and fast. Examples of linguistic AI capabilities in translation technology include:  

Neural machine translation (NMT): Uses existing translation memory and termbase data to train systems, which then provide automatic post-editing feedback. NMT can capture the contextual essence of the original text, not just its literal meaning.

• Content analysis: Extracts domain classifications and keywords to help project managers focus on the big picture.
• Learning capabilities: AI systems can analyze large amounts of multilingual data to learn the nuances, idioms, and contextual intricacies of various languages. This can lead to more accurate and culturally sensitive translations.
• Contextual translation: Some generative AI translation tools use context, or prompts, to deliver accurate translations. For example, you can provide context like brand voice, tone, industry, and audience.
• Dialect detection: Advanced systems can detect dialects and regional variations without users indicating the source language.

AI translation can also process complex language, such as slang, colloquialisms, and cultural references. However, AI systems still require human supervision for quality control, especially for content that involves deep cultural understanding or specialized industry knowledge.

[More to come ...]