This paper delves into the transformative power of Generative AI-driven storytelling in the realm of marketing. Generative AI, distinct from traditional machine learning, offers the capability to craft narratives that resonate with consumers on a deeply personal level. Through real-world examples from industry leaders like Google, Netflix and Stitch Fix, we elucidate how this technology shapes marketing strategies, personalizes consumer experiences, and navigates the challenges it presents. The paper also explores future directions and recommendations for generative AI-driven storytelling, including prospective applications such as real-time personalized storytelling, immersive storytelling experiences, and social media storytelling. By shedding light on the potential and impact of generative AI-driven storytelling in marketing, this paper contributes to the understanding of this cutting-edge approach and its transformative power in the field of marketing.

ChatGPT has proven it can help students with their homework, but now it is helping teachers create those very courses, a computer science professor told Fox News. I'm still amazed at how ChatGPT can help write a toast to put people in stitches at a wedding, construct a legal argument to bolster a case and even help with a college admissions essay despite the number of errors the human eye can catch. Even with the glitches, using a chatbot still astounds most people who manage to put in perfect prompts to get wildly in-depth instant answers. And while OpenAI's ChatGPT is impressive, it's not the only option you should be confined to using. In fact, some of the biggest tech companies in the world are competing to create their own latest and greatest chatbots that can rival or surpass the AI amazement of ChatGPT.

Suleyman has had an unshaken faith in technology as a force for good at least since we first spoke in early 2016. He had just launched DeepMind Health and set up research collaborations with some of the UK's state-run regional health-care providers. The magazine I worked for at the time was about to publish an article claiming that DeepMind had failed to comply with data protection regulations when accessing records from some 1.6 million patients to set up those collaborations--a claim later backed up by a government investigation. Suleyman couldn't see why we would publish a story that was hostile to his company's efforts to improve health care. As long as he could remember, he told me at the time, he'd only wanted to do good in the world.

Tim Ballaret once dreamed of becoming a stockbroker but ultimately found fulfillment helping high school students in south Los Angeles understand the relevance of math and science to their daily lives. But making engaging class materials is time-consuming, so this spring he started experimenting with generative AI tools. Recommendations by friends and influential teachers on social media led Ballaret to try MagicSchool, a tool for K-12 educators powered by OpenAI's text generation algorithms. He used it for tasks like creating math word problems that match his students' interests, like Taylor Swift and Minecraft, but the real test came when he used MagicSchool this summer to outline a year's worth of lesson plans for a new applied science and engineering class. "Taking back my summer helped me be more refreshed for a new school year," he says.

US senators are proving slow studies when it comes to the generative artificial intelligence tools that are poised to upend life as we know it. But they'll be tested soon--and the rest of us through them--if their new private tutors are to be trusted. In a historic first, yesterday upwards of 60 senators sat like school children--not allowed to speak or even raise their hands--in a private briefing where some 20 Silicon Valley CEOs, ethicists, academics, and consumer advocates prophesied about AI's potential to upend, heal, or even erase life as we knew it. "It's important for us to have a referee," Elon Musk, the CEO of Tesla, SpaceX, and X (formerly Twitter), told a throng of paparazzi-like press corps waiting on the sidewalk outside the briefing. "[It] may go down in history as very important to the future of civilization."

In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have proven adept in capturing complex data distributions but often fall short in generalization and interpretability. On the other hand, causality offers a structured lens to comprehend the mechanisms driving data generation and highlights the causal-effect dynamics inherent in these processes. While causality excels in interpretability and the ability to extrapolate, it grapples with intricacies of high-dimensional spaces. Recognizing the synergistic potential, we delve into the confluence of causality and DGMs. We elucidate the integration of causal principles within DGMs, investigate causal identification using DGMs, and navigate an emerging research frontier of causality in large-scale generative models, particularly generative large language models (LLMs). We offer insights into methodologies, highlight open challenges, and suggest future directions, positioning our comprehensive review as an essential guide in this swiftly emerging and evolving area.

Large Language Models (LLMs) have shown impressive performance across a variety of Artificial Intelligence (AI) and natural language processing tasks, such as content creation, report generation, etc. However, unregulated malign application of these models can create undesirable consequences such as generation of fake news, plagiarism, etc. As a result, accurate detection of AI-generated language can be crucial in responsible usage of LLMs. In this work, we explore 1) whether a certain body of text is AI generated or written by human, and 2) attribution of a specific language model in generating a body of text. Texts in both English and Spanish are considered. The datasets used in this study are provided as part of the Automated Text Identification (AuTexTification) shared task. For each of the research objectives stated above, we propose an ensemble neural model that generates probabilities from different pre-trained LLMs which are used as features to a Traditional Machine Learning (TML) classifier following it. For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place (with macro $F1$ scores of 0.733 and 0.649) for English and Spanish texts, respectively. For the second task on model attribution, our model ranked in first place with macro $F1$ scores of 0.625 and 0.653 for English and Spanish texts, respectively.

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy.

More than 20 tech and civil society leaders, including the chief executives of five of the 10 biggest U.S. companies, appeared at a closed-door Senate meeting on Wednesday to shape how artificial intelligence is regulated. The meeting, which was organized by Senate Majority Leader Chuck Schumer, included a prestigious, and possibly combustible, mix of personalities with diverging views on how to write the rules for AI. The CEOs of Alphabet, Microsoft, Meta Platforms and OpenAI were invited to appear alongside rivals and industry critics to discuss possible guardrails for AI that balance the risks and rewards of the technology. Areas of disagreement were apparent throughout the morning session, according to several people who were in the room. Meta CEO Mark Zuckerberg, OpenAI CEO Sam Altman and Microsoft co-founder Bill Gates offered diverging views on the risks of open-source AI research, according to people in the room.

Adobe's generative AI-powered Firefly tools are now commercially available to subscribers, but with a catch: Adobe will "charge" you for using it with a credit plan that you may need to pay separately for. Adobe Firefly is a superb AI art generator, trained on Adobe's own stock images. While Firefly is now available for free as a standalone tool, Firefly-powered generative AI art capabilities were added to Adobe Photoshop in May and to Adobe Express last month. Firefly not only generates its own AI art, but can be used as a source for editing: replacing the background on a beach scene with a fantasy castle, for example. Firefly, like other AI generators, is a text-to-image tool which generates an image based on your text description and filters.