This work is a commentary of the article \href{https://doi.org/10.18716/ojs/phai/2025.2801}{AI Survival Stories: a Taxonomic Analysis of AI Existential Risk} by Cappelen, Goldstein, and Hawthorne. It is not just a commentary though, but a useful reminder of the philosophical limitations of \say{linear} models of risk. The article will focus on the model employed by the authors: first, I discuss some differences between standard Swiss Cheese models and this one. I then argue that in a situation of epistemic indifference the probability of P(D) is higher than what one might first suggest, given the structural relationships between layers. I then distinguish between risk and uncertainty, and argue that any estimation of P(D) is structurally affected by two kinds of uncertainty: option uncertainty and state-space uncertainty. Incorporating these dimensions of uncertainty into our qualitative discussion on AI existential risk can provide a better understanding of the likeliness of P(D).

Organizational decision-making is crucial for success, yet cognitive biases can significantly affect risk preferences, leading to suboptimal outcomes. Risk seeking preferences for losses, driven by biases such as loss aversion, pose challenges and can result in severe negative consequences, including financial losses. This research introduces the ABI approach, a novel solution designed to support organizational decision-makers by automatically identifying and explaining risk seeking preferences during decision-making. This research makes a novel contribution by automating the identification and explanation of risk seeking preferences using Cumulative Prospect theory (CPT) from Behavioral Economics. The ABI approach transforms theoretical insights into actionable, real-time guidance, making them accessible to a broader range of organizations and decision-makers without requiring specialized personnel. By contextualizing CPT concepts into business language, the approach facilitates widespread adoption and enhances decision-making processes with deep behavioral insights. Our systematic literature review identified significant gaps in existing methods, especially the lack of automated solutions with a concrete mechanism for automatically identifying risk seeking preferences, and the absence of formal knowledge representation, such as ontologies, for identifying and explaining the risk preferences. The ABI Approach addresses these gaps, offering a significant contribution to decision-making research and practice. Furthermore, it enables automatic collection of historical decision data with risk preferences, providing valuable insights for enhancing strategic management and long-term organizational performance. An experiment provided preliminary evidence on its effectiveness in helping decision-makers recognize their risk seeking preferences during decision-making in the loss domain.

AI Assistant in Adobe Acrobat, now in beta, is a new generative AI–powered conversational engine deeply integrated into Acrobat workflows, empowering everyone with the information inside their most important documents. As the creator of PDF, the world's most trusted digital document format, Adobe understands document challenges and opportunities well. Our continually evolving Acrobat PDF application, the gold standard for working with PDFs, is already used by more than half a billion customers to open around 400 billion documents each year. Starting immediately, customers will be able to use AI Assistant to work even more productively. All they need to do is open Acrobat on their desktop or the web and start working.

The scientific innovation in Natural Language Processing (NLP) and more broadly in artificial intelligence (AI) is at its fastest pace to date. As large language models (LLMs) unleash a new era of automation, important debates emerge regarding the benefits and risks of their development, deployment and use. Currently, these debates have been dominated by often polarized narratives mainly led by the AI Safety and AI Ethics movements. This polarization, often amplified by social media, is swaying political agendas on AI regulation and governance and posing issues of regulatory capture. Capture occurs when the regulator advances the interests of the industry it is supposed to regulate, or of special interest groups rather than pursuing the general public interest. Meanwhile in NLP research, attention has been increasingly paid to the discussion of regulating risks and harms. This often happens without systematic methodologies or sufficient rooting in the disciplines that inspire an extended scope of NLP research, jeopardizing the scientific integrity of these endeavors. Regulation studies are a rich source of knowledge on how to systematically deal with risk and uncertainty, as well as with scientific evidence, to evaluate and compare regulatory options. This resource has largely remained untapped so far. In this paper, we argue how NLP research on these topics can benefit from proximity to regulatory studies and adjacent fields. We do so by discussing basic tenets of regulation, and risk and uncertainty, and by highlighting the shortcomings of current NLP discussions dealing with risk assessment. Finally, we advocate for the development of a new multidisciplinary research space on regulation and NLP (RegNLP), focused on connecting scientific knowledge to regulatory processes based on systematic methodologies.

Uncertainty is a pervasive challenge in decision analysis, and decision theory recognizes two classes of solutions: probabilistic models and cognitive heuristics. However, engineers, public planners and other decision-makers instead use a third class of strategies that could be called RDOT (Risk-reducing Design and Operations Toolkit). These include incorporating robustness into designs, contingency planning, and others that do not fall into the categories of probabilistic models or cognitive heuristics. Moreover, identical strategies appear in several domains and disciplines, pointing to an important shared toolkit. The focus of this paper is to develop a catalog of such strategies and develop a framework for them. The paper finds more than 90 examples of such strategies falling into six broad categories and argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization. Overall, RDOT represents an overlooked class of responses to uncertainty. Because RDOT strategies do not depend on accurate forecasting or estimation, they could be applied fruitfully to certain decision problems affected by high uncertainty and make them much more tractable.

San Diego, California, March 28, 2023 (GLOBE NEWSWIRE) -- HUMBL, Inc. (OTCQB: HMBL) HUMBL announced today the launch of its Artificial Intelligence (AI) and Automated Machine Learning initiatives across its consumer, commercial and Latin America business units. On the commercial side, HUMBL kicked off its AI / Automated Machine Learning initiatives with the announcement of its first commercial sales contract in its HUMBL Latin America subsidiary, with the sale of AI / Automated Machine Learning services for a leading IT / Telecommunications provider in the Latin America region in the form of a $60,000 (USD) contract for initial deliverables and a total contract value of $195,000 (USD) over three years, pending the achievement of milestones by HUMBL Latin America. "Artificial Intelligence is an accelerant to the principles of web3," said Brian Foote, CEO of HUMBL. "The use of public data sets to create more autonomous, intelligent outcomes for consumers, as well as the corporations and governments that serve them, is an excellent use of automated machine learning technologies," continued Foote. "The use of AI can help our clients model for more predictive outcomes around things like credit scoring, default rates, churn rates, healthcare patterns and more; driving more tailored experiences for consumers, while driving revenues and improved efficiencies for corporations and governments."

Caution Regarding Forward-Looking Information This release contains forward-looking statements. Forward-looking statements, without limitation, may contain the words beliefs, expects, anticipates, estimates, intends, plans, or similar expressions. Forward-looking statements do not guarantee future performance. They involve risks, uncertainties and assumptions and actual results could differ materially from those anticipated. Forward looking statements are based on the opinions and estimates of management at the date the statements are made and are subject to a variety of risks and uncertainties and other factors that could cause actual events or results to differ materially from those projected in the forward-looking statements.

BOCA RATON, Fla., Nov. 15, 2022 (GLOBE NEWSWIRE) -- Totaligent, Inc. ("Totaligent" or "the Company") (OTCPK: TGNT) announces it has completed testing of its scalable Nvidia clusters and has started to build a super cluster, with 2.4 Terabytes of GPU ram and 18 Terabytes of system ram. Totaligent's new supercomputer will allow the Company's Artificial Intelligence to deliver nearly instantaneous data processing and modeling for its person-based digital marketing platform. "Having the power and speed to deliver near real-time results when building target audiences from billions of records for customers is critical to Totaligent's success and acceptance in the person-based digital marketing world. Now, when we append large datasets that used to take days to process, our AI completes the task in about a minute. The combination of data, speed, and a complete set of easy-to-use marketing tools, at an affordable price, will enable Totaligent to provide unparalleled results for its users upon the launch of its integrated digital platform," stated Ted DeFeudis, CEO.

The software-defined nature of the HRL131 means it is situationally aware, with the ability to adapt its scan pattern depending on the driving scenario to maximize safety. It's critical that manufacturers be able to test and validate these performance modes and the product's performance in diverse situations, which NVIDIA DRIVE Sim will uniquely enable.

Kellton Tech (BSE: KELLTONTEC) (NSE: KELLTONTEC), a global leader in next-generation digital transformation and enterprise intelligence solutions, announced that it has been selected as a technology partner by Hyderabad Metropolitan Water Supply and Sewerage Board (HMWSSB) to revolutionize its citizen service delivery and create world-class experiences. As a part of this engagement, Kellton Tech will leverage descriptive analytics and IoT-based smart metering technology to support HMWSSB with real-time information for planning, monitoring, and diagnosis of its systems and processes across its value chains. This holistic solution approach will help HMWSSB forecast demand and dynamically price resource consumption based on predictive analytics, thus promoting more efficient water use and ensuring fair pricing to citizens. Furthermore, HMWSSB will drive faster data-driven decisions, better optimize the government machinery and ecosystem for the timely elimination of inefficiencies, and curate frictionless experiences for the residents of Hyderabad Metropolitan today and in the future. Kellton Tech will also collaborate to help HMWSSB streamline its grievance redressal by introducing AI-based Metro Customer Care and addressing the needs of citizens and society with more agility and responsiveness.