Politico became one of the first newsrooms last year to win a union contract that included rules on how the media outlet can deploy artificial intelligence. The PEN Guild, which represents Politico and its sister publication, environment and energy site E&E News, is now gearing up for another first. The union's members allege that the AI provisions in their contract have been violated, and they're preparing for a groundbreaking legal dispute with management. The outcome could set a precedent for how much input journalists ultimately have over how AI is used in their newsrooms. Last year, Politico began publishing AI-generated live news summaries during big political events like the Democratic National Convention and the US vice presidential debates.

The modern enterprise is facing an unprecedented surge in digital identities, with machine identities now significantly outnumbering human identities. This paper examines the cybersecurity risks emerging from what we define as the "human-machine identity blur" - the point at which human and machine identities intersect, delegate authority, and create new attack surfaces. Drawing from industry data, expert insights, and real-world incident analysis, we identify key governance gaps in current identity management models that treat human and machine entities as separate domains. To address these challenges, we propose a Unified Identity Governance Framework based on four core principles: treating identity as a continuum rather than a binary distinction, applying consistent risk evaluation across all identity types, implementing continuous verification guided by zero trust principles, and maintaining governance throughout the entire identity lifecycle. Our research shows that organizations adopting this unified approach experience a 47 percent reduction in identity-related security incidents and a 62 percent improvement in incident response time. We conclude by offering a practical implementation roadmap and outlining future research directions as AI-driven systems become increasingly autonomous.

A group of investors led by Elon Musk is offering about 97.4 billion to buy the nonprofit behind OpenAI, escalating a dispute with the artificial intelligence company that Musk helped found a decade ago. Musk and his own AI startup, xAI, and a consortium of investment firms want to take control of the ChatGPT maker and revert it to its original charitable mission as a nonprofit research lab, according to Musk's attorney Marc Toberoff. OpenAI CEO Sam Altman quickly rejected the unsolicited bid on Musk's social platform X, saying, "no thank you but we will buy Twitter for 9.74 billion if you want." Musk bought Twitter, now called X, for 44 billion in 2022. Musk and Altman, who together helped start OpenAI in 2015 and later competed over who should lead it, have been in a long-running feud over the startup's direction since Musk resigned from its board in 2018.

The past two decades have witnessed columnar storage revolutionizing data warehousing and analytics. However, the rapid growth of machine learning poses new challenges to this domain. This paper presents Bullion, a columnar storage system tailored for machine learning workloads. Bullion addresses the complexities of data compliance, optimizes the encoding of long sequence sparse features, efficiently manages wide-table projections, and introduces feature quantization in storage. By aligning with the evolving requirements of ML applications, Bullion extends columnar storage to various scenarios, from advertising and recommendation systems to the expanding realm of Generative AI. Preliminary experimental results and theoretical analysis demonstrate Bullion's superior performance in handling the unique demands of machine learning workloads compared to existing columnar storage solutions. Bullion significantly reduces I/O costs for deletion compliance, achieves substantial storage savings with its optimized encoding scheme for sparse features, and drastically improves metadata parsing speed for wide-table projections. These advancements position Bullion as a critical component in the future of machine learning infrastructure, enabling organizations to efficiently manage and process the massive volumes of data required for training and inference in modern AI applications.

The current study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job descriptions, with a focus on psychological characteristics. The proposed approach aims to evaluate the market demand for specific traits by combining relevant psychological constructs, text mining techniques, and an innovative measure, namely, the Semantic Brand Score. We apply the proposed methodology to a dataset of job descriptions for OM and SCM professionals, with the objective of providing a mapping of their relevant required skills, including psychological characteristics. In addition, the analysis is then detailed by considering the region of the organization that issues the job description, its organizational size, and the seniority level of the open position in order to understand their nuances. Finally, topic modeling is used to examine key components and their relative significance in job descriptions. By employing a novel methodology and considering contextual factors, we provide an innovative understanding of the attitudinal traits that differentiate professionals. This research contributes to talent management, recruitment practices, and professional development initiatives, since it provides new figures and perspectives to improve the effectiveness and success of Operations Management and Supply Chain Management professionals.

Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generation capabilities. Given the widespread use of such generative AI tools, in this work we investigate how these tools can be deployed in a non-safety critical, strategic traffic flow management setting. Specifically, we train an LLM, CHATATC, based on a large historical data set of Ground Delay Program (GDP) issuances, spanning 2000-2023 and consisting of over 80,000 GDP implementations, revisions, and cancellations. We test the query and response capabilities of CHATATC, documenting successes (e.g., providing correct GDP rates, durations, and reason) and shortcomings (e.g,. superlative questions). We also detail the design of a graphical user interface for future users to interact and collaborate with the CHATATC conversational agent.

A rush of interest in artificial intelligence (AI) has helped to fuel a $4tn (£3.2tn) rally in technology stocks this year, with the US Nasdaq exchange reaching its highest level since last August in a week that saw the chipmaker Nvidia poised to become the next trillion-dollar company. Some stocks seen as AI winners – such as semiconductor makers and software developers – have more than doubled in value as traders bet on massive growth in the industry, even as fears mount over waves of job losses as everyday tasks become automated. On Friday, the combined value of technology companies listed on the Nasdaq Composite share index reached $22tn, according to the international data firm Refinitiv, up from $18tn at the end of 2022. The AI rally has helped lift the index 23% so far this year. Nvidia, whose high-end chips are used to power the datacentres used by the new wave of generative AI products such as ChatGPT, could soon become the first chipmaker to be valued at more than $1tn.

Not long before his death in 2007, my father told me that he "thought he might have" coined the term information technology. It turns out he was right. In an article titled "Management in the 1980's," published in the November, 1958, issue of the Harvard Business Review, Harold J. Leavitt and his co-author, Thomas L. Whisler, identify a "new technology" that "has begun to take hold in American business, one so new that its significance is still difficult to evaluate." Since this technology "does not yet have a single established name," the article notes, "we shall call it information technology. It is composed of several related parts": "techniques for processing large amounts of information rapidly"; "the application of statistical and mathematical methods to decision-making problems"; and "in the offing, though its applications have not yet emerged very clearly . . . the simulation of higher-order thinking through computer programs." By the end of his life, my father had adopted a far more skeptical attitude toward the organizations he earned his living trying to understand and improve.

Machine Learning (ML) is changing DBs as many DB components are being replaced by ML models. One open problem in this setting is how to update such ML models in the presence of data updates. We start this investigation focusing on data insertions (dominating updates in analytical DBs). We study how to update neural network (NN) models when new data follows a different distribution (a.k.a. it is "out-of-distribution" -- OOD), rendering previously-trained NNs inaccurate. A requirement in our problem setting is that learned DB components should ensure high accuracy for tasks on old and new data (e.g., for approximate query processing (AQP), cardinality estimation (CE), synthetic data generation (DG), etc.). This paper proposes a novel updatability framework (DDUp). DDUp can provide updatability for different learned DB system components, even based on different NNs, without the high costs to retrain the NNs from scratch. DDUp entails two components: First, a novel, efficient, and principled statistical-testing approach to detect OOD data. Second, a novel model updating approach, grounded on the principles of transfer learning with knowledge distillation, to update learned models efficiently, while still ensuring high accuracy. We develop and showcase DDUp's applicability for three different learned DB components, AQP, CE, and DG, each employing a different type of NN. Detailed experimental evaluation using real and benchmark datasets for AQP, CE, and DG detail DDUp's performance advantages.