Making new friends is hard. Finding out what trousers exist in the world other than black ones is also, apparently, hard. Fortunately, for an AI-enabled generation of students, help with the complexities of campus life is just a prompt away. If you are really stuck on an essay or can't decide between management consulting or a legal career, or need suggestions on what you can cook with tomatoes, mushrooms, beetroot, mozzarella, olive oil and rice, then ChatGPT is there. It will to listen to you, analyse your inputs, and offer up a perfectly structured paper, a convincing cover letter, or a workable recipe for tomato and mushroom risotto with roasted beetroot and mozzarella. I know this because three undergraduates have given me permission to eavesdrop on every conversation they have had with ChatGPT over the past 18 months.

Hand-specific localization has garnered significant interest within the computer vision community. Although there are numerous datasets with hand annotations from various angles and settings, domain transfer techniques frequently struggle in surgical environments. This is mainly due to the limited availability of gloved hand instances and the unique challenges of operating rooms (ORs). Thus, hand-detection models tailored to OR settings require extensive training and expensive annotation processes. To overcome these challenges, we present "RoHan" - a novel approach for robust hand detection in the OR, leveraging advanced semi-supervised domain adaptation techniques to tackle the challenges of varying recording conditions, diverse glove colors, and occlusions common in surgical settings. Our methodology encompasses two main stages: (1) data augmentation strategy that utilizes "Artificial Gloves," a method for augmenting publicly available hand datasets with synthetic images of hands-wearing gloves; (2) semi-supervised domain adaptation pipeline that improves detection performance in real-world OR settings through iterative prediction refinement and efficient frame filtering. We evaluate our method using two datasets: simulated enterotomy repair and saphenous vein graft harvesting. "RoHan" substantially reduces the need for extensive labeling and model training, paving the way for the practical implementation of hand detection technologies in medical settings.

LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. Cultural adaptation has applications across several creative industries and requires intimate knowledge of source and target cultures during translation. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper we define the task of cultural adaptation and create an evaluation framework to benchmark different models for this task. We evaluate the performance of modern LLMs for cultural adaptation and analyze their cross cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation including cultural biases and stereotypes. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.

For any company these days, the Recommendation system has become a vital part, every company wants to give a personalised experience to the user and for that Recommendation, systems are the best choice. LET'S UNDERSTAND WHAT IS A RECOMMENDATION SYSTEM… Let's say you want to buy a t-shirt from Amazon, you went to their website and type black t-shirt, You will see some Black T-shirts on your screen, Simple right??? Now let's say you liked some t-shirts on the first page and went inside to see them, lets say you select the third t-shirt from the left (BLACK PANTHER ONE), you checked its reviews, ratings, etc. Now you came back to the first page and select some different black t-shirts let's say with a round collar or maybe t-shirts with a particular brand etc. Now if you pay attention, Amazon is collecting every information, every click of yours, whenever you are going to a particular brand or particular pattern, Amazon has started to know your likings, disliking. It is the same like let's say you have gone to the nearest market to shop for a t-shirt with a new friend, the new friend did not know anything about your liking or disliking and he is just observing you, he is noticing every action of yours, What patterns you are choosing?? What brands you are choosing?? What color are you opting for?? Amazon is that unnecessary friend who is keeping a watch on you every time you are buying something on its website.

Before diving deep into the heavy explainable AI (artificial intelligence) concepts let us look at Rohan's story and understand "WHAT IS EXPLAINABLE AI?" & "WHY IS IT NEEDED?" Rohan was a machine learning engineer at a leading company and was very sick and had symptoms of lung cancer. He went to his doctor and discussed the issue and with him. The concerned doctor asked him to get some tests done and said "I can only come to a conclusion after that". Rohan got his tests done and showed the reports to the doctor. The doctor was certain of the diagnosis but still wanted to know more about his condition.

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft. We are only 6 days away from PASS Summit 2016 (@sqlpass) which kicks off next Wednesday, October 26th, in Seattle. SQL PASS Summit (#SQLSummit, #sqlpass) is the world's largest and most intensive technical training conference for Microsoft SQL Server (@SQLServer) and BI professionals. But more than that, it's a conference – planned and presented by the SQL Server community for the SQL Server community (#sqlfamily). It has the most technical sessions, the largest number of attendees, the best networking, and the highest-rated sessions and speakers of any SQL Server event in the world.