No Phone, No Social Safety Net: Welcome to the'Offline Club' Across Europe's largest cities, people are gathering for semi-silent, offline hangouts, in search of an experience that isn't mediated through their smartphones. On cue, the room fell silent. A man seated to my left at a long wooden table began to scratch at a piece of paper with a coloring pencil. To my right, another guy picked up a book. Across the way, someone buried themselves in a puzzle.

Tesla shares fell nearly 9% on Friday, wiping about 60bn ( 45bn) from the company's value, after the long-awaited unveiling of its so-called robotaxi failed to excite investors. Shares in the electric carmaker tumbled to 217 at market close following an event in Hollywood, where the chief executive, Elon Musk, revealed a much-hyped driverless vehicle. The stock price is down roughly 12% year-to-date. Musk said the company would start building the fully autonomous "Cybercab" by 2026 at a price of less than 30,000, and showed off a van he claimed was capable of transporting 20 people around town autonomously – which he said would reshape cities by turning car parks into parks. Before the event, he tweeted: "And all transport will be fully autonomous within 50 years."

AI technology is quickly creeping into every industry, prompting new questions about whether online content comes from a human or a computer. The presence of advanced technology in the classroom may require conversations with students during this new school year. As artificial intelligence finds its way into more families' day-to-day routines, parents and teachers alike should be wary of how their kids are interacting with generative AI. This is according to SmartNews' head of trust and safety Arjun Narayan, who shared concerns during an interview with Fox News Digital. "As with any new technology, when it is very new, it's important to understand how you're engaging with that tech," said Narayan, who is based in Japan.

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future.

SmartNews' Head of Global Trust and Safety is calling for new regulation on artificial intelligence (AI) to prioritize user transparency and ensure human oversight remains a crucial component for news and social media recommender systems. "We need to have guardrails," Arjun Narayan said. "Without humans thinking through everything that could go wrong, like bias creeping into the models or large language models falling into the wrong hands, there can be very serious consequences for humanity." Narayan, who previously worked on Trust and Safety for Google and Bytedance, the company behind TikTok, said it is essential for companies to recognize opt-in and opt-outs when using large language models (LLMs). As a default, anything being fed to an LLM will be assumed training data and collected by the model.

The majority of human deaths and injuries are caused by traffic accidents. A million people worldwide die each year due to traffic accident injuries, consistent with the World Health Organization. Drivers who do not receive enough sleep, rest, or who feel weary may fall asleep behind the wheel, endangering both themselves and other road users. The research on road accidents specified that major road accidents occur due to drowsiness while driving. These days, it is observed that tired driving is the main reason to occur drowsiness. Now, drowsiness becomes the main principle for to increase in the number of road accidents. This becomes a major issue in a world which is very important to resolve as soon as possible. The predominant goal of all devices is to improve the performance to detect drowsiness in real time. Many devices were developed to detect drowsiness, which depend on different artificial intelligence algorithms. So, our research is also related to driver drowsiness detection which can identify the drowsiness of a driver by identifying the face and then followed by eye tracking. The extracted eye image is matched with the dataset by the system. With the help of the dataset, the system detected that if eyes were close for a certain range, it could ring an alarm to alert the driver and if the eyes were open after the alert, then it could continue tracking. If the eyes were open then the score that we set decreased and if the eyes were closed then the score increased. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80% and helps to reduce road accidents.

The purpose of this paper is to enhance the performance of the virtual assistant. So, what exactly is a virtual assistant. Application software, often called virtual assistants, also known as AI assistants or digital assistants, is software that understands natural language voice commands and can perform tasks on your behalf. What does a virtual assistant do. Virtual assistants can complete practically any specific smartphone or PC activity that you can complete on your own, and the list is continually expanding. Virtual assistants typically do an impressive variety of tasks, including scheduling meetings, delivering messages, and monitoring the weather. Previous virtual assistants, like Google Assistant and Cortana, had limits in that they could only perform searches and were not entirely automated. For instance, these engines do not have the ability to forward and rewind the song in order to maintain the control function of the song; they can only have the module to search for songs and play them. Currently, we are working on a project where we are automating Google, YouTube, and many other new things to improve the functionality of this project. Now, in order to simplify the process, we've added a virtual mouse that can only be used for cursor control and clicking. It receives input from the camera, and our index finger acts as the mouse tip, our middle finger as the right click, and so forth.

Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving way. Candidates for this approach are simulation methodologies for which there are fidelity differences connected with significant computational cost differences. Physics-informed Neural Networks (PINNs) are candidates for these types of approaches due to the significant difference in training times required when different fidelities (expressed in terms of architecture width and depth as well as optimization criteria) are employed. In this paper, we propose a particular multifidelity approach applied to PINNs that exploits low-rank structure. We demonstrate that width, depth, and optimization criteria can be used as parameters related to model fidelity, and show numerical justification of cost differences in training due to fidelity parameter choices. We test our multifidelity scheme on various canonical forward PDE models that have been presented in the emerging PINNs literature.

Healthcare's comfort level with artificial intelligence and machine learning models – and skill at deploying them across myriad clinical, financial and operational use cases – continued to increase in 2023. More and more evidence shows that training AI algorithms on a variety of datasets can improve decision support, boost population health management, streamline administrative tasks, enable cost efficiencies and even improve outcomes. But there's still a lot work to be done to ensure accurate, reliable, understandable and evidence-based results that ensure patient safety and account for health equity. There's no doubt that AI's application in healthcare has gone beyond "real" in 2019 to significant investment by providers and payers last year. This year, we've reported on deeper industry discussions focused on trust and best practices.

A growing issue in the development of realistic and entertain-ing interactive games is the need for mechanisms that support ongoing natural language conversation between human players and artificial non-player characters. Unfortunately, many methods for implementing natural language generation(NLG) induce a significant burden on the author, do not scale well, or require specialized linguistic knowledge. We formalize the notion of typed-templates, an extension of standard structures employed in template-based NLG. We further provide novel algorithms that, when applied to typed-templates, ameliorate the above issues by affording computational support for authoring and increased variation in utterance and scenario generation. We demonstrate the efficacy of typed-templates and the algorithms through a user study.