Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation, in marketing management. In this paper, we explore the transformative potential of LLMs along with the current applications, future directions, and strategic recommendations for marketers. In particular, we focus on LLMs major business drivers such as personalization, real-time-interactive customer insights, and content automation, and how they enable customers and business outcomes. For instance, the ethical aspects of AI with respect to data privacy, transparency, and mitigation of bias are also covered, with the goal of promoting responsible use of the technology through best practices and the use of new technologies businesses can tap into the LLM potential, which help growth and stay one step ahead in the turmoil of digital marketing. This article is designed to give marketers the necessary guidance by using best industry practices to integrate these powerful LLMs into their marketing strategy and innovation without compromising on the ethos of their brand.

Apple has pulled a new iPhone feature released just three months ago after users slammed it for spreading misinformation. The tech giant removed its AI notification summaries for news and entertainment apps after the system falsely reported a news article. The summary of the BBC article suggested that Luigi Mangione, 26, the alleged assassin of the CEO of UnitedHealthcare, had shot himself. It read: 'Luigi Mangione shoots himself; Syrian mother hopes Assad pays the price; South Korea police raid Yoon Suk Yeol's office,' in reference to three articles that had supposedly been published by the BBC. Mangione has been accused of shooting Brian Thompson, 50, at point-blank range as he was walking to a Manhattan hotel where his company was holding an investor conference on December 4. He is currently being held in a Brooklyn federal jail.

A groundbreaking chatbot created in the 1960s has been painstakingly reconstructed from archived records and run for the first time in over half a century, as part of an effort to preserve one of the earliest examples of artificial intelligence. ELIZA was written by computer scientist Joseph Weizenbaum at MIT in just 420 lines of code. The AI model is extremely rudimentary in comparison to today's large language models (LLMs) like ChatGPT but wowed researchers at the time with…

AgiBot promises to reshape our understanding of robotic learning and adaptation. AgiBot, a pioneering Chinese artificial intelligence and robotics company, has introduced a transformative open-source dataset called AgiBot World Alpha. This comprehensive collection represents a significant milestone in humanoid robot training, capturing intricate data from over 100 robots across diverse real-world scenarios. By providing an unprecedented window into robotic movement and interaction, AgiBot has created a multidimensional resource that promises to reshape our understanding of robotic learning and adaptation. The AgiBot World Alpha dataset is more than a simple data collection.

Squid Game is not a subtle show. It is impossible to misinterpret its very obvious message that the games are bad, and people should NOT be driven to such desperation by a merciless capitalist system that they will murder each other for rich people's entertainment. I would not be the first to point out that there is some conflict in the fact that we, the viewers, are watching all these competitors get killed for our entertainment, but still: despite the violence, despite the shock value, there is no ambiguity around the narrative intention. In this spin-off video game from Netflix, by contrast, the games are not bad. They are supposed to be fun.

Jon Voight spoke to Fox News Digital while promoting his film "Reagan" and weighed in on the family drama between his daughter, Angelina Jolie, and her ex, Brad Pitt. A happily-ever-after with whom a woman assumed to be Hollywood hunk Brad Pitt quickly turned into a living nightmare. On Jan. 12, the French television channel TF1 aired an episode of its show "Sept à Huit," which told the story of a 53-year-old interior designer named Anne who revealed that she had lost 830,000 euros (approximately 850,000) in personal funds because she thought she was sending money to a cancer-ridden Pitt. Through falsified documents and images as well as artificial intelligence, Anne believed she was speaking to, and eventually in a relationship with, the 61-year-old actor. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? A woman was conned into believing she was in a relationship with Brad Pitt after being contacted by someone claiming to be the actor on Instagram.

Keir Starmer this week announced a 50-point plan that aims to give the UK world leader status in artificial intelligence and grow the economy by as much as 47bn a year over a decade. The multibillion-pound investment, which seeks to create a 20-fold increase in the amount of AI computing power under public control by 2030, has been framed as a gamechanger for businesses and public organisations. The reaction to the announcement has been mixed, given it is far from clear that the much-hyped potential of AI will result in the level of economic benefit forecast. Many are concerned that the technology could lead to widespread job cuts, while others fear a destruction in the value and growth of the creative industries after learning of proposals to make it easier for AI companies to mine artistic works for data, for no cost. Despite such concerns, for many in the world of business the AI revolution is already here and transforming their industries.

Unmanned Aerial Vehicles (UAVs), commonly known as Drones, are one of 21st century most transformative technologies. Emerging first for military use, advancements in materials, electronics, and software have catapulted drones into multipurpose tools for a wide range of industries. In this paper, we have covered the history, taxonomy, architecture, navigation systems and branched activities for the same. It explores important future trends like autonomous navigation, AI integration, and obstacle avoidance systems, emphasizing how they contribute to improving the efficiency and versatility of drones. It also looks at the major challenges like technical, environmental, economic, regulatory and ethical, that limit the actual take-up of drones, as well as trends that are likely to mitigate these obstacles in the future. This work offers a structured synthesis of existing studies and perspectives that enable insights about how drones will transform agriculture, logistics, healthcare, disaster management, and other areas, while also identifying new opportunities for innovation and development.

The standard language modeling (LM) loss by itself has been shown to be inadequate for effective dialogue modeling. As a result, various training approaches, such as auxiliary loss functions and leveraging human feedback, are being adopted to enrich open-domain dialogue systems. One such auxiliary loss function is Bag-of-Words (BoW) loss, defined as the cross-entropy loss for predicting all the words/tokens of the next utterance. In this work, we propose a novel auxiliary loss named Bag-of-Keywords (BoK) loss to capture the central thought of the response through keyword prediction and leverage it to enhance the generation of meaningful and interpretable responses in open-domain dialogue systems. BoK loss upgrades the BoW loss by predicting only the keywords or critical words/tokens of the next utterance, intending to estimate the core idea rather than the entire response. We incorporate BoK loss in both encoder-decoder (T5) and decoder-only (DialoGPT) architecture and train the models to minimize the weighted sum of BoK and LM (BoK-LM) loss. We perform our experiments on two popular open-domain dialogue datasets, DailyDialog and Persona-Chat. We show that the inclusion of BoK loss improves the dialogue generation of backbone models while also enabling post-hoc interpretability. We also study the effectiveness of BoK-LM loss as a reference-free metric and observe comparable performance to the state-of-the-art metrics on various dialogue evaluation datasets.

Composing music for video is essential yet challenging, leading to a growing interest in automating music generation for video applications. Existing approaches often struggle to achieve robust music-video correspondence and generative diversity, primarily due to inadequate feature alignment methods and insufficient datasets. In this study, we present General Video-to-Music Generation model (GVMGen), designed for generating high-related music to the video input. Our model employs hierarchical attentions to extract and align video features with music in both spatial and temporal dimensions, ensuring the preservation of pertinent features while minimizing redundancy. Remarkably, our method is versatile, capable of generating multi-style music from different video inputs, even in zero-shot scenarios. We also propose an evaluation model along with two novel objective metrics for assessing video-music alignment. Additionally, we have compiled a large-scale dataset comprising diverse types of video-music pairs. Experimental results demonstrate that GVMGen surpasses previous models in terms of music-video correspondence, generative diversity, and application universality.