Low-resource African languages pose unique challenges for natural language processing (NLP) tasks, including natural language generation (NLG). In this paper, we develop Cheetah, a massively multilingual NLG language model for African languages. Cheetah supports 517 African languages and language varieties, allowing us to address the scarcity of NLG resources and provide a solution to foster linguistic diversity. We demonstrate the effectiveness of Cheetah through comprehensive evaluations across six generation downstream tasks. In five of the six tasks, Cheetah significantly outperforms other models, showcasing its remarkable performance for generating coherent and contextually appropriate text in a wide range of African languages. We additionally conduct a detailed human evaluation to delve deeper into the linguistic capabilities of Cheetah. The introduction of Cheetah has far-reaching benefits for linguistic diversity. By leveraging pretrained models and adapting them to specific languages, our approach facilitates the development of practical NLG applications for African communities. The findings of this study contribute to advancing NLP research in low-resource settings, enabling greater accessibility and inclusion for African languages in a rapidly expanding digital landscape. We publicly release our models for research.

AI research company OpenAI is releasing a new machine learning tool that translates the English language into code. The software is called Codex and is designed to speed up the work of professional programmers, as well as help amateurs get started coding. In demos of Codex, OpenAI shows how the software can be used to build simple websites and rudimentary games using natural language, as well as translate between different programming languages and tackle data science queries. Users type English commands into the software, like "create a webpage with a menu on the side and title at the top," and Codex translates this into code. The software is far from infallible and takes some patience to operate, but could prove invaluable in making coding faster and more accessible. "We see this as a tool to multiply programmers," OpenAI's CTO and co-founder Greg Brockman told The Verge.

Back in September 2016, Google launched its Neural Machine Translation (GNMT) system, which uses deep learning to deliver more natural translations between languages. Google Translate originally supported only a handful of languages when it launched 10 years ago; today that number has risen to 103. Creating a computer system to translate multiple languages is complex. The people at Google who built it wanted to find out just how clever their system was. So they came up with a challenge.

Back in September 2016, Google launched its Neural Machine Translation (GNMT) system, which uses deep learning to deliver more natural translations between languages. Google Translate originally supported only a handful of languages when it launched 10 years ago; today that number has risen to 103. Creating a computer system to translate multiple languages is complex. The people at Google who built it wanted to find out just how clever their system was. So they came up with a challenge.

Creating a computer system to translate multiple languages is complex. The people at Google who built it wanted to find out just how clever their system was. So they came up with a challenge. They taught the machine to translate English to Japanese and vice versa. Then they taught it to translate English to Korean and also the reverse translation.

Google's artificial intelligence (AI) has created its first own language through the use of machine learning, allowing it to improve how it translates languages. The search giant fired up its Google Neural Machine Translation (GNMT) back in September to aid the development of the Google Translate service. Google found that be training the system to learn to say translate English to French and French to Italian it could create a means for the system to translate English to Italian even if it has not studied the language before. The system works by analysing whole sentences of language rather than individual words then inferring similarities between them and other sentences in different languages to seek out common patterns in the languages. By doing this the system can translate a language into another even if it has not seen a translation between the languages before.