Concerns associated with occupational health and safety (OHS) remain critical and often under-addressed aspects of workforce management. This is especially true for high-risk industries such as manufacturing, construction, and mining. Such industries dominate the economy of India which is a developing country with a vast informal sector. Regulatory frameworks have been strengthened over the decades, particularly with regards to bringing the unorganized sector within the purview of law. Traditional approaches to OHS have largely been reactive and rely on post-incident analysis (which is curative) rather than preventive intervention. This paper portrays the immense potential of predictive analytics in rejuvenating OHS practices in India. Intelligent predictive analytics is driven by approaches like machine learning and statistical modeling. Its data-driven nature serves to overcome the limitations of conventional OHS methods. Predictive analytics approaches to OHS in India draw on global case studies and generative applications of predictive analytics in OHS which are customized to Indian industrial contexts. This paper attempts to explore in what ways it exhibits the potential to address challenges such as fragmented data ecosystems, resource constraints, and the variability of workplace hazards. The paper presents actionable policy recommendations to create conditions conducive to the widespread implementation of predictive analytics, which must be advocated as a cornerstone of OHS strategy. In doing so, the paper aims to spark a collaborational dialogue among policymakers, industry leaders, and technologists. It urges a shift towards intelligent practices to safeguard the well-being of India's workforce.

Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly. A major limitation that has not been fully addressed occurs when the expert advice is imperfect, which can lead to poorer models. We propose a model that extends KBSVMs and is able to not only learn from data and advice, but also simultaneously improves the advice. The proposed approach is particularly effective for knowledge discovery in domains with few labeled examples. The proposed model contains bilinear constraints, and is solved using two iterative approaches: successive linear programming and a constrained concave-convex approach. Experimental results demonstrate that these algorithms yield useful refinements to expert advice, as well as improve the performance of the learning algorithm overall.

This paper uses the BERT model, which is a transformer-based architecture, to solve task 4A, English Language, Sentiment Analysis in Twitter of SemEval2017. BERT is a very powerful large language model for classification tasks when the amount of training data is small. For this experiment, we have used the BERT{\textsubscript{\tiny BASE}} model, which has 12 hidden layers. This model provides better accuracy, precision, recall, and f1 score than the Naive Bayes baseline model. It performs better in binary classification subtasks than the multi-class classification subtasks. We also considered all kinds of ethical issues during this experiment, as Twitter data contains personal and sensible information. The dataset and code used in our experiment can be found in this GitHub repository.

As the aging population increases and the shortage of healthcare workers increases, the need to examine other means for caring for the aging population increases. One such means is the use of humanoid robots to care for social, emotional, and physical wellbeing of the people above 65. Understanding skilled and long term care nursing home administrators' perspectives on humanoid robots in caregiving is crucial as their insights shape the implementation of robots and their potential impact on resident well-being and quality of life. This authors surveyed two hundred and sixty nine nursing homes executives to understand their perspectives on the use of humanoid robots in their nursing home facilities. The data was coded and results revealed that the executives were keen on exploring other avenues for care such as robotics that would enhance their nursing homes abilities to care for their residents. Qualitative analysis reveals diverse perspectives on integrating humanoid robots in nursing homes. While acknowledging benefits like improved engagement and staff support, concerns persist about costs, impacts on human interaction, and doubts about robot effectiveness. This highlights complex barriers financial, technical, and human and emphasizes the need for strategic implementation. It underscores the importance of thorough training, role clarity, and showcasing technology benefits to ensure efficiency and satisfaction among staff and residents.

We suggest a simple Gaussian mixture model for data generation that complies with Feldman's long tail theory (2020). We demonstrate that a linear classifier cannot decrease the generalization error below a certain level in the proposed model, whereas a nonlinear classifier with a memorization capacity can. This confirms that for long-tailed distributions, rare training examples must be considered for optimal generalization to new data. Finally, we show that the performance gap between linear and nonlinear models can be lessened as the tail becomes shorter in the subpopulation frequency distribution, as confirmed by experiments on synthetic and real data.

A Minnesota nursing home has begun using two AI-powered robots as care assistants for those with dementia or early-stage Alzheimer's. The Pepper and NAO robots, both built by Softbank, were customized by roboticists at the University of Minnesota, Duluth to interact and enhance the independence of residents at The Estates of Roseville, followed by another seven nursing homes. The four-foot-tall Pepper and two-foot-tall NAO are set to engage with patients through conversation and observation. The human-shaped robots can measure facial expressions, body language, and vocal tones while adjusting their own faces and voices to simulate human interaction. The robots are programmed to remind residents to exercise and eat, leading them in dance and telling jokes.

A 4-foot-tall droid named Pepper -- preprogrammed with hundreds of jokes -- is one of two robots now working at a nursing home in Roseville, Minnesota, entertaining residents and helping monitor their health. The big picture: Household robots are growing in utility and ubiquity, and this latest use seems to blend two trends we've noted: The availability of humanoid robots as party guests and entertainers and their use as companions and health monitors for the elderly. Driving the news: As part of a study by the University of Minnesota Duluth, two robots (designed by SoftBank Robotics) have been deployed at a nursing home owned by Monarch Healthcare Management.

The core idea is deceptively simple: every observable phenomenon in the entire universe can be modeled by a neural network. And that means, by extension, the universe itself may be a neural network. Vitaly Vanchurin, a professor of physics at the University of Minnesota Duluth, published an incredible paper last August entitled "The World as a Neural Network" on the arXiv pre-print server. It managed to slide past our notice until today when Futurism's Victor Tangermann published an interview with Vanchurin discussing the paper. We discuss a possibility that the entire universe on its most fundamental level is a neural network.

As far as Khan's aware, no one else has programmed Pepper, the world's first social humanoid robot designed by Japan-based SoftBank Robots, to act as what she calls "COVID gossip bots." It's all in the name of helping the elderly, especially during the pandemic when many are experiencing even higher levels of isolation. "We are using the concept that socialization often happens with some sort of gossip," she said. "We want to take that gossip element and use it in a positive manner. So what we're doing is we are creating an imaginary persona that is very similar to the person we are working with."

It's not every day that we come across a paper that attempts to redefine reality. But in a provocative preprint uploaded to arXiv this summer, a physics professor at the University of Minnesota Duluth named Vitaly Vanchurin attempts to reframe reality in a particularly eye-opening way -- suggesting that we're living inside a massive neural network that governs everything around us. In other words, he wrote in the paper, it's a "possibility that the entire universe on its most fundamental level is a neural network." For years, physicists have attempted to reconcile quantum mechanics and general relativity. The first posits that time is universal and absolute, while the latter argues that time is relative, linked to the fabric of space-time.