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.

Neural tangent kernels (NTKs) have been proposed to study the behavior of trained neural networks from the perspective of Gaussian processes. An important result in this body of work is the theorem of equivalence between a trained neural network and kernel regression with the corresponding NTK. This theorem allows for an interpretation of neural networks as special cases of kernel regression. However, does this theorem of equivalence hold in practice? In this paper, we revisit the derivation of the NTK rigorously and conduct numerical experiments to evaluate this equivalence theorem. We observe that adding a layer to a neural network and the corresponding updated NTK do not yield matching changes in the predictor error. Furthermore, we observe that kernel regression with a Gaussian process kernel in the literature that does not account for neural network training produces prediction errors very close to that of kernel regression with NTKs. These observations suggest the equivalence theorem does not hold well in practice and puts into question whether neural tangent kernels adequately address the training process of neural networks.

You would think that Arm, which arguably has been the vanguard in the smartphone and PC industry push for improved power efficiency, would double down on that strategy in its plans for 2025. PCWorld sat down at CES 2025 with Chris Bergey, senior vice president and general manager for Arm's client line of business. Bergey is responsible for both the smartphone as well as the laptop and tablet business, where Arm's designs are licensed by companies like Qualcomm and Apple, who tweak and eventually manufacture them as finished goods. Arm provides multiple types of licenses, but the two most common types are a core license, where a customer will buy a verified core that includes an Arm Cortex CPU, Mali GPU, or other intellectual property. Arm also sells architectural licenses to companies like Apple, which gives them the freedom to design their own cores from scratch, though they must be fully compatible with the Arm architecture.

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.

This research introduces LegalScore, a specialized index for assessing how generative artificial intelligence models perform in a selected range of career exams that require a legal background in Brazil. The index evaluates fourteen different types of artificial intelligence models' performance, from proprietary to open-source models, in answering objective questions applied to these exams. The research uncovers the response of the models when applying English-trained large language models to Brazilian legal contexts, leading us to reflect on the importance and the need for Brazil-specific training data in generative artificial intelligence models. Performance analysis shows that while proprietary and most known models achieved better results overall, local and smaller models indicated promising performances due to their Brazilian context alignment in training. By establishing an evaluation framework with metrics including accuracy, confidence intervals, and normalized scoring, LegalScore enables systematic assessment of artificial intelligence performance in legal examinations in Brazil. While the study demonstrates artificial intelligence's potential value for exam preparation and question development, it concludes that significant improvements are needed before AI can match human performance in advanced legal assessments. The benchmark creates a foundation for continued research, highlighting the importance of local adaptation in artificial intelligence development.

Natural Language Processing (NLP) is an essential subset of artificial intelligence. It has become effective in several domains, such as healthcare, finance, and media, to identify perceptions, opinions, and misuse, among others. Privacy is no exception, and initiatives have been taken to address the challenges of usable privacy notifications to users with the help of NLP. To this aid, we conduct a literature review by analyzing 109 papers at the intersection of NLP and privacy policies. First, we provide a brief introduction to privacy policies and discuss various facets of associated problems, which necessitate the application of NLP to elevate the current state of privacy notices and disclosures to users. Subsequently, we a) provide an overview of the implementation and effectiveness of NLP approaches for better privacy policy communication; b) identify the methodologies that can be further enhanced to provide robust privacy policies; and c) identify the gaps in the current state-of-the-art research. Our systematic analysis reveals that several research papers focus on annotating and classifying privacy texts for analysis but need to adequately dwell on other aspects of NLP applications, such as summarization. More specifically, ample research opportunities exist in this domain, covering aspects such as corpus generation, summarization vectors, contextualized word embedding, identification of privacy-relevant statement categories, fine-grained classification, and domain-specific model tuning.

Recent advances in Large Language Models (LLMs) have enabled human-like responses across various tasks, raising questions about their ethical decision-making capabilities and potential biases. This study investigates protected attributes in LLMs through systematic evaluation of their responses to ethical dilemmas. Using two prominent models - GPT-3.5 Turbo and Claude 3.5 Sonnet - we analyzed their decision-making patterns across multiple protected attributes including age, gender, race, appearance, and disability status. Through 11,200 experimental trials involving both single-factor and two-factor protected attribute combinations, we evaluated the models' ethical preferences, sensitivity, stability, and clustering of preferences. Our findings reveal significant protected attributeses in both models, with consistent preferences for certain features (e.g., "good-looking") and systematic neglect of others. Notably, while GPT-3.5 Turbo showed stronger preferences aligned with traditional power structures, Claude 3.5 Sonnet demonstrated more diverse protected attribute choices. We also found that ethical sensitivity significantly decreases in more complex scenarios involving multiple protected attributes. Additionally, linguistic referents heavily influence the models' ethical evaluations, as demonstrated by differing responses to racial descriptors (e.g., "Yellow" versus "Asian"). These findings highlight critical concerns about the potential impact of LLM biases in autonomous decision-making systems and emphasize the need for careful consideration of protected attributes in AI development. Our study contributes to the growing body of research on AI ethics by providing a systematic framework for evaluating protected attributes in LLMs' ethical decision-making capabilities.

Increasingly many AI systems can plan and execute interactions in open-ended environments, such as making phone calls or buying online goods. As developers grow the space of tasks that such AI agents can accomplish, we will need tools both to unlock their benefits and manage their risks. Current tools are largely insufficient because they are not designed to shape how agents interact with existing institutions (e.g., legal and economic systems) or actors (e.g., digital service providers, humans, other AI agents). For example, alignment techniques by nature do not assure counterparties that some human will be held accountable when a user instructs an agent to perform an illegal action. To fill this gap, we propose the concept of agent infrastructure: technical systems and shared protocols external to agents that are designed to mediate and influence their interactions with and impacts on their environments. Agent infrastructure comprises both new tools and reconfigurations or extensions of existing tools. For example, to facilitate accountability, protocols that tie users to agents could build upon existing systems for user authentication, such as OpenID. Just as the Internet relies on infrastructure like HTTPS, we argue that agent infrastructure will be similarly indispensable to ecosystems of agents. We identify three functions for agent infrastructure: 1) attributing actions, properties, and other information to specific agents, their users, or other actors; 2) shaping agents' interactions; and 3) detecting and remedying harmful actions from agents. We propose infrastructure that could help achieve each function, explaining use cases, adoption, limitations, and open questions. Making progress on agent infrastructure can prepare society for the adoption of more advanced agents.

Generative AI has had a profound impact on biomedicine and health, both in professional work and in education. Based on large language models (LLMs), generative AI has been found to perform as well as humans in simulated situations taking medical board exams, answering clinical questions, solving clinical cases, applying clinical reasoning, and summarizing information. Generative AI is also being used widely in education, performing well in academic courses and their assessments. This review summarizes the successes of LLMs and highlights some of their challenges in the context of education, most notably aspects that may undermines the acquisition of knowledge and skills for professional work. It then provides recommendations for best practices overcoming shortcomings for LLM use in education. Although there are challenges for use of generative AI in education, all students and faculty, in biomedicine and health and beyond, must have understanding and be competent in its use.

The development and deployment of artificial intelligence (AI) systems, with their profound societal impacts, raise critical challenges for governance. Historically, technological innovations have been governed by concentrated expertise with limited public input. However, AI's pervasive influence across domains such as healthcare, employment, and justice necessitates inclusive governance approaches. This article explores the tension between expert-led oversight and democratic participation, analyzing models of participatory and deliberative democracy. Using case studies from France and Brazil, we highlight how inclusive frameworks can bridge the gap between technical complexity and public accountability. Recommendations are provided for integrating these approaches into a balanced governance model tailored to the European Union, emphasizing transparency, diversity, and adaptive regulation to ensure that AI governance reflects societal values while maintaining technical rigor. This analysis underscores the importance of hybrid frameworks that unite expertise and public voice in shaping the future of AI policy.