The goal of AI language models is to reduce the need for human labour in areas like translation, customer service, and computing. They streamline routine tasks and, using the available data, produce novel insights. By leveraging enormous amounts of user data and information stored on their computers, the deep learning processes of such AI models are leveraged for instantaneous decoding of foreign languages.
About Google’s 1,000 Language AI Model
At a November 2022 AI conference, Google revealed its Model for 1,000 Languages. Google is developing an artificial intelligence language model which can accommodate over 400 languages in order to explore the potential of this enormous undertaking. There are claims that this model has the “biggest language coverage” of any current speech model.
What makes this language model unique from other AI models?
Many different types of AI language models are currently in use, either in commercial settings or in academic labs. In order to make these models better for a wide range of scenarios, Google launched the 1,000 Languages project. Its grand objective is to standardize on a single model for the world’s 1,000 most widely spoken languages. It includes both common and uncommon tongues, allowing them to coexist, communicate, and develop with one another.
Other AI language models –
To create natural text responses and carry out tasks like classification, simple summaries, address correction, answering inquiries, etc., OpenAI, an AI research firm, developed the GPT-3 (Generative Pre-trained Transformer 3) collection of models dubbed Da Vinci, Curie, Babbage, and Ada.
Meta develops AI-based language translation –
Its open-source M2M-100 model is touted as the first multilingual translation model that translates directly between 100 languages without using English as the default.
The Facebook parent firm is also investing heavily on translation utilizing artificial intelligence (AI) for both written and spoken languages, such as Hokkien. Google is also amassing information for languages that are commonly spoken but lack an online presence.
