The ability of machines to comprehend and produce language that is similar to that of humans has revolutionized sectors like customer service, healthcare, and finance thanks to the quick advances in Natural Language Processing (NLP), which are fueled by Generative Artificial Intelligence (AI) and Large Language Models (LLMs). However, because LLMs trained on large datasets may unintentionally absorb and reveal Personally Identifiable Information (PII) from user interactions, these capabilities also raise serious privacy concerns. Deep neural networks' intricacy makes it difficult to track down or stop the inadvertent storing and release of private information, which raises serious concerns about the privacy and security of AI-driven data. This study tackles these issues by detecting Generative AI weaknesses through attacks such as data extraction, model inversion, and membership inference. A privacy-preserving Generative AI application that is resistant to these assaults is then developed. It ensures privacy without sacrificing functionality by using methods to identify, alter, or remove PII before to dealing with LLMs. In order to determine how well cloud platforms like Microsoft Azure, Google Cloud, and AWS provide privacy tools for protecting AI applications, the study also examines these technologies. In the end, this study offers a fundamental privacy paradigm for generative AI systems, focusing on data security and moral AI implementation, and opening the door to a more secure and conscientious use of these tools.

In contemporary society, the issue of psychological health has become increasingly prominent, characterized by the diversification, complexity, and universality of mental disorders. Cognitive Behavioral Therapy (CBT), currently the most influential and clinically effective psychological treatment method with no side effects, has limited coverage and poor quality in most countries. In recent years, researches on the recognition and intervention of emotional disorders using large language models (LLMs) have been validated, providing new possibilities for psychological assistance therapy. However, are LLMs truly possible to conduct cognitive behavioral therapy? Many concerns have been raised by mental health experts regarding the use of LLMs for therapy. Seeking to answer this question, we collected real CBT corpus from online video websites, designed and conducted a targeted automatic evaluation framework involving the evaluation of emotion tendency of generated text, structured dialogue pattern and proactive inquiry ability. For emotion tendency, we calculate the emotion tendency score of the CBT dialogue text generated by each model. For structured dialogue pattern, we use a diverse range of automatic evaluation metrics to compare speaking style, the ability to maintain consistency of topic and the use of technology in CBT between different models . As for inquiring to guide the patient, we utilize PQA (Proactive Questioning Ability) metric. We also evaluated the CBT ability of the LLM after integrating a CBT knowledge base to explore the help of introducing additional knowledge to enhance the model's CBT counseling ability. Four LLM variants with excellent performance on natural language processing are evaluated, and the experimental result shows the great potential of LLMs in psychological counseling realm, especially after combining with other technological means.

Falls represent one of the most detrimental occurrences for the elderly. Given the continually increasing ageing demographic, there is a pressing demand for advancing fall detection systems. The swift progress in sensor networks and the Internet of Things (IoT) has made human-computer interaction through sensor fusion an acknowledged and potent approach for tackling the issue of fall detection. Even IoT-enabled systems can deliver economical health monitoring solutions tailored to pregnant women within their daily environments. Recent research indicates that these remote health monitoring setups have the potential to enhance the well-being of both the mother and the infant throughout the pregnancy and postpartum phases. One more emerging advancement is the integration of 'panic buttons,' which are gaining popularity due to the escalating emphasis on safety. These buttons instantly transmit the user's real-time location to pre-designated emergency contacts when activated. Our solution focuses on the above three challenges we see every day. Fall detection for the elderly helps the elderly in case they fall and have nobody around for help. Sleep pattern sensing is helpful for pregnant women based on the SPO2 sensors integrated within our device. It is also bundled with heart rate monitoring. Our third solution focuses on a panic situation; upon pressing the determined buttons, a panic alert would be sent to the emergency contacts listed. The device also comes with a mobile app developed using Flutter that takes care of all the heavy processing rather than the device itself.

An office worker answers it and hears his boss, in a panic, tell him that she forgot to transfer money to the new contractor before she left for the day and needs him to do it. She gives him the wire transfer information, and with the money transferred, the crisis has been averted. The worker sits back in his chair, takes a deep breath, and watches as his boss walks in the door. The voice on the other end of the call was not his boss. The voice he heard was that of an audio deepfake, a machine-generated audio sample designed to sound exactly like his boss.

Advanced cyberinfrastructure (CI) is critical to science and engineering (S&E) research. For example, over the past two years, CI resources (including those provided by the COVID-19 HPC Consortiuma) enabled research that dramatically accelerated efforts to understand, respond to, and mitigate near- and longer-term impacts of the novel coronavirus disease 2019 (COVID-19) pandemic.b Computer-based epidemiology models informed public policy in the U.S., and in countries throughout the world, and newly studied transmission models for the virus have been used to forecast resource availability and mortality stratified by age group at the county level.c Artificial intelligence and machine learning approaches accelerated drug screening to find candidate medicines from trillions of possible chemical compounds,d and differential gene expressions among COVID-19 patient populations have been analyzed with important implications for treatment planning.e Structural modeling of the virus has led to new insights, speeding the development of vaccines and antigens.

The National Science Foundation this week announced the formation of seven national AI institutes aimed at advancing technological innovation and bolstering the economy. AI research by the National Science Foundation will expand to a broader range of businesses across the U.S. economy through five new NSF AI institutes being created at a cost of $100 million. The new initiatives, which were unveiled Aug. 26 by the agency, will deepen the NSF's artificial intelligence research to expand the nation's workforce and drive new possibilities for a wide range of businesses, educational institutions, medicine, banking and other organizations. In a related announcement, two complementary AI research institutes are also being created by the U.S. Department of Agriculture over the next five years using $40 million in funding to expand AI research in farming and food processing. The new AI institutes will emphasize use-inspired research to help businesses and organizations, said James Donlon, program director for information and intelligent systems in the NSF's directorate for computer and information science and engineering (CISE) division.

The National Science Foundation today announced that UC Berkeley's RISELab has been awarded an Expeditions in Computing award, providing $10 million in funding over five years to enable game-changing advances in real-time decision making technologies. The award was one of three announced today for research teams pursuing large-scale, far-reaching and potentially transformative research in computer and information science and engineering. RISELab's award will be used to develop technology for an era in which artificial intelligence systems will make decisions that will play an increasingly central role in people's lives in areas such as healthcare, transportation and business. For example, the researchers say that these systems will revolutionize healthcare through early identification of patients at risk, cell-level diagnosis and treatment using nanoprobes, and robotic surgery. These systems could also reduce traffic congestion and help eliminate fatalities by powering autonomous vehicles and unmanned drones, or make businesses safer by detecting and defending in real-time against financial fraud and internet attacks.