Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing (NLP) for their impressive skills in language generation and other language-specific tasks. Though LLMs have been evaluated in various tasks, mostly in English, they have not yet undergone thorough evaluation in under-resourced languages such as Bengali (Bangla). In this paper, we evaluate the performance of LLMs for the low-resourced Bangla language. We select various important and diverse Bangla NLP tasks, such as abstractive summarization, question answering, paraphrasing, natural language inference, text classification, and sentiment analysis for zero-shot evaluation with ChatGPT, LLaMA-2, and Claude-2 and compare the performance with state-of-the-art fine-tuned models. Our experimental results demonstrate an inferior performance of LLMs for different Bangla NLP tasks, calling for further effort to develop better understanding of LLMs in low-resource languages like Bangla.

Large language models (LLMs) have been applied to tasks in healthcare, ranging from medical exam questions to responding to patient questions. With increasing institutional partnerships between companies producing LLMs and healthcare systems, real world clinical application is coming closer to reality. As these models gain traction, it is essential for healthcare practitioners to understand what LLMs are, their development, their current and potential applications, and the associated pitfalls when utilized in medicine. This review and accompanying tutorial aim to give an overview of these topics to aid healthcare practitioners in understanding the rapidly changing landscape of LLMs as applied to medicine.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. The assistant mimicked realistic and nuanced human speech patterns (complete with "ums" and "ahhs") as it made an appointment for a haircut and booked a table at a restaurant while in fluent conversation with a real person. Although the audience erupted in rapturous applause at the achievement, in the Twittersphere and beyond, observers were quick to question what they were hearing. Some called the likeness "scary," and others felt like a deception was at play -- with the human on the other end of the line completely unaware that they were speaking with a bot. But that's unfortunate because the truth of the matter is that voice AI has tremendous potential to empower consumers and deliver value to the businesses that deploy it -- provided there is a clear understanding of its purpose and of its limitations.

Given the outsized hold Artificial Intelligence (AI) technology has acquired on public imagination of late, it comes as no surprise that many are wondering what AI can do for the public health crisis wrought by the COVID-19 coronavirus. A casual search of AI and COVID-19 already returns a plethora of news stories, many of them speculative. While AI technology is not ready to help with the magical discovery of a new vaccine, there are important ways it can assist in this fight. Controlling epidemics is, in large part, based on laborious contact tracing and using that information to predict the spread. We live in a time in which we constantly leave digital footprints through our daily life and interactions.