This year in language AI, we have followed many newsworthy developments in natural language processing (NLP), natural language generation (NLG), and natural language understanding (NLU), highlighting the most impressive trends and technological advancements in the process of machine learning (ML) for language-based applications. Throughout this discussion, our focus has been on the effectiveness of these algorithms at automating communications both within and beyond translation and localization, as well as the expansion and integration of multilingual language AI on a global scale. Yet, as we edge towards a new year, reflecting on the degree to which this technology has progressed and showcasing some of the major takeaways from this series will help to understand in what ways the language AI landscape might still change in 2022. Among many of the headlines dominating the field of language AI and machine learning in 2021 was the seemingly consistent development and focus on creating massive language models built around complex and highly sophisticated neural networks. Covering language AI in the news, Microsoft and Nvidia’s MT-NLG model, an experimental language generation AI, dwarfed models similar in size through its unmatched functionality in areas like reading comprehension, reasoning, and language inference. Similarly, the GTP-3 language model, a dominating presence at the frontier of language algorithms, became the cornerstone of comparison from which we saw its Chinese counterpart, the Yuan 1.0 emerge as leading non-English language model. Through all of this, it became apparent that massive language AIs are highly sophisticated and can near perfection in translation and text generation. Yet, as we discussed more recently, it is the smaller, equally efficient models that experts now see as closing the gap to natural human language. Notably, DeepMind’s RETRO model recently made wakes in the AI translation space as a much smaller model in comparison that was developed on a select collection of high-quality datasets in multiple languages. This 7-billion parameter algorithm with new features, like the external-memory analogous to a cheat sheet, represents a new approach to developing language AI – one that averts the high costs and lengthy training process associated with the much larger AI models. In the advancement of this impressive language technology, a major takeaway from this year is representative of developers focusing on creating smarter language models using a seemingly more cost-effective approach to carry out the same functions. As the new year approaches, it will become interesting to see how these models are developed and the innovative ways researchers navigate the complex field of ML to produce sophisticated and increasingly powerful AI of all sizes. A common theme mentioned throughout our series on language AI surrounds the deployment of this technology – specifically, how and where language algorithms are becoming integrated into markets to perform particular tasks. In our discussion on chatbots and virtual assistants for example, a growing demand for AI to automate and scale communications within industries has become synonymous with ways in which this cutting-edge technology can add value to businesses on a global scale. A major challenge of course, as evident throughout this series, has been finding ways to leverage ML to create language AI for prospective new markets of growth. Whether that means foreign or domestic markets, developing language AI that can meet growing demands for text and language automation will become increasingly important, especially in an era defined by heightened international trade, communication, and cross-cultural business. Linguistically diverse and localized algorithms for functions like chatbots and virtual assistants, for instance, have been an important utilization of this technology. Coinciding with the demand to integrate language AI on a global scale is the looming challenge of expanding the number of languages that AI models operate in to be effective in foreign markets. In our discussion on Yuan 1.0, the novel Chinese-language equivalent to GTP-3, it is evident that there exists a general lack of high-quality data to train language models outside of the primarily English-language markets. Even though Yuan 1.0 represents a stride in overcoming this challenge, developing sophisticated non-English language models relies on quality datasets spanning multiple languages. Similarly, our discussion on the Tunisian startup investing in ML to bridge the language barrier of Arabic dialects is representative of innovative approaches to overcoming this challenge. Though we cannot predict what AI advancements will be in the news in 2022, developers around the world will find new ways to leverage ML, eventually leading to an environment in which AI can carry out tasks in multiple languages to a similar degree. If this past year has taught us anything, it is that huge strides have been taken towards perfecting language automation in AI as opportunities for continued development and innovation in this field persist. Throughout our series, we have introduced developments that are important in the context of language generation and automation and discussed ways in which this technology is being used to expand into new markets. As we continue to monitor the growth of this industry into the new year, new horizons, and applications for meeting the demands for language AI will remain a central part for industries grounded in localization and translation services. To learn more about CSOFT’s innovative, technology-driven translation and localization solutions, visit us at csoftintl.com!