Artificial intelligence has made its way into drug development, surgery and medical advice -- and now it's helping people improve the quality of their sleep. The Artificial Intelligence in Sleep Medicine Committee, which is part of the American Academy of Sleep Medicine, recently published a paper that highlights how AI is contributing to the field of sleep medicine. The committee looked at how AI is assisting in three areas: clinical applications, lifestyle management and population health. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Clinical applications involve the use of AI to diagnose and treat sleep disorders, while lifestyle management focuses on the use of consumer technology to track sleep data.

Bangla (or Bengali) is the fifth most spoken language globally; yet, the state-of-the-art NLP in Bangla is lagging for even simple tasks such as lemmatization, POS tagging, etc. This is partly due to lack of a varied quality corpus. To alleviate this need, we build Vacaspati, a diverse corpus of Bangla literature. The literary works are collected from various websites; only those works that are publicly available without copyright violations or restrictions are collected. We believe that published literature captures the features of a language much better than newspapers, blogs or social media posts which tend to follow only a certain literary pattern and, therefore, miss out on language variety. Our corpus Vacaspati is varied from multiple aspects, including type of composition, topic, author, time, space, etc. It contains more than 11 million sentences and 115 million words. We also built a word embedding model, Vac-FT, using FastText from Vacaspati as well as trained an Electra model, Vac-BERT, using the corpus. Vac-BERT has far fewer parameters and requires only a fraction of resources compared to other state-of-the-art transformer models and yet performs either better or similar on various downstream tasks. On multiple downstream tasks, Vac-FT outperforms other FastText-based models. We also demonstrate the efficacy of Vacaspati as a corpus by showing that similar models built from other corpora are not as effective. The models are available at https://bangla.iitk.ac.in/.

The upstart ChatGPT heralded an advent of conversational-AI platforms that can passably converse with humans based on a wide range of inputs. In addition to ChatGPT, which is made by the Microsoft-backed nonprofit OpenAI, other big tech companies are getting into the game with competing projects from Google (Bard) and Facebook (LLaMA). The rise of AI to kitchen-table prominence raises a question: Are insurance companies, which have been transforming digitally for years, ready to invest further in large language models and turn their precious customer relationships over to a chatbot? Forrester Principal Analyst Indranil Bandyopadhyay says that a big AI revolution in insurance isn't going to happen overnight. "I don't see the majority of the insurance industry going and jumping into these emerging technologies. It will take some time," Bandyopadhyay says.

Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can happen independently. It also provides natural benefits when deployed on the cloud since resources can be allocated dynamically to necessary components based on demand. Therefore, enterprises as part of their journey to cloud, are increasingly looking to refactor their monolith application into one or more candidate microservices; wherein each service contains a group of software entities (e.g., classes) that are responsible for a common functionality. Graphs are a natural choice to represent a software system. Each software entity can be represented as nodes and its dependencies with other entities as links. Therefore, this problem of refactoring can be viewed as a graph based clustering task. In this work, we propose a novel method to adapt the recent advancements in graph neural networks in the context of code to better understand the software and apply them in the clustering task. In that process, we also identify the outliers in the graph which can be directly mapped to top refactor candidates in the software. Our solution is able to improve state-of-the-art performance compared to works from both software engineering and existing graph representation based techniques.