The rapid advancements in autonomous vehicle software present both opportunities and challenges, especially in enhancing road safety. The primary objective of autonomous vehicles is to reduce accident rates through improved safety measures. However, the integration of new algorithms into the autonomous vehicle, such as Artificial Intelligence methods, raises concerns about the compliance with established safety regulations. This paper introduces a novel software architecture based on behavior trees, aligned with established standards and designed to supervise vehicle functional safety in real time. It specifically addresses the integration of algorithms into industrial road vehicles, adhering to the ISO 26262. The proposed supervision methodology involves the detection of hazards and compliance with functional and technical safety requirements when a hazard arises. This methodology, implemented in this study in a Renault M\'egane (currently at SAE level 3 of automation), not only guarantees compliance with safety standards, but also paves the way for safer and more reliable autonomous driving technologies.

Changes and updates in the requirement artifacts, which can be frequent in the automotive domain, are a challenge for SafetyOps. Large Language Models (LLMs), with their impressive natural language understanding and generating capabilities, can play a key role in automatically refining and decomposing requirements after each update. In this study, we propose a prototype of a pipeline of prompts and LLMs that receives an item definition and outputs solutions in the form of safety requirements. This pipeline also performs a review of the requirement dataset and identifies redundant or contradictory requirements. We first identified the necessary characteristics for performing HARA and then defined tests to assess an LLM's capability in meeting these criteria. We used design science with multiple iterations and let experts from different companies evaluate each cycle quantitatively and qualitatively. Finally, the prototype was implemented at a case company and the responsible team evaluated its efficiency.

DevOps is a necessity in many industries, including the development of Autonomous Vehicles. In those settings, there are iterative activities that reduce the speed of SafetyOps cycles. One of these activities is "Hazard Analysis & Risk Assessment" (HARA), which is an essential step to start the safety requirements specification. As a potential approach to increase the speed of this step in SafetyOps, we have delved into the capabilities of Large Language Models (LLMs). Our objective is to systematically assess their potential for application in the field of safety engineering. To that end, we propose a framework to support a higher degree of automation of HARA with LLMs. Despite our endeavors to automate as much of the process as possible, expert review remains crucial to ensure the validity and correctness of the analysis results, with necessary modifications made accordingly.

Discrete-choice models are a powerful framework for analyzing decision-making behavior to provide valuable insights for policymakers and businesses. Multinomial logit models (MNLs) with linear utility functions have been used in practice because they are ease to use and interpretable. Recently, MNLs with neural networks (e.g., ASU-DNN) have been developed, and they have achieved higher prediction accuracy in behavior choice than classical MNLs. However, these models lack interpretability owing to complex structures. We developed utility functions with a novel neural-network architecture based on generalized additive models, named generalized additive utility network ( GAUNet), for discrete-choice models. We evaluated the performance of the MNL with GAUNet using the trip survey data collected in Tokyo. Our models were comparable to ASU-DNN in accuracy and exhibited improved interpretability compared to previous models.

When the development team at Infinity Ward rolled out the massive playing area of Verdansk for "Call of Duty: Warzone," they viewed it as a starting point. After what game director at Infinity Ward Jack O'Hara describes as a short break, they turned their attention to building their next map -- Al Mazrah, the sprawling environment that is Call of Duty's biggest battle royale map to date, and serves as the battleground for "Warzone 2.0," which releases Nov. 16. "We started on this map straight after Verdansk," O'Hara said. "We kind of rolled from that one to a little bit of a breather and then we started laying the foundations for the next map, which is Al Mazrah. It's a chance to refine what we did last time and a chance to build on all the lessons."

We're now told that AI in general might have a mystical side. A professor of Philosophy, Classics, Religion, and Environmental Studies tells us that "Technology could be part of some bigger plan to enable us to perceive other dimensions." But he asks, "will we believe our machines when that happens?" Specifically, he wonders, What if your Siri claimed to have had a spiritual experience, or, as he puts it a "deeper-than-5G connection"?: As our machines come closer to being able to imitate the processes of our own minds, Pascal's story raises some important questions. First, can a machine have a private experience that is important to the machine but that it is reluctant to talk about with others?

Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area. The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility. "What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business. O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies. "Trying to estimate these sorts of things using standard techniques gets very tricky, because the databases are so big. The beauty of machine learning is that it's a different way to analyze the data," O'Hara said.

Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area. The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility. "What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business. O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies. Other Cornell co-authors are: David Easley, the Henry Scarborough Professor of Social Science in the College of Arts and Sciences and professor of information science in Computing and Information Science; and Marcos Lopez de Prado, professor of practice in Operations Research and Information Engineering in the College of Engineering and chief information officer of True Positive Technologies.

Kairos, the facial recognition startup that found itself in turmoil following the ouster of founder and then-CEO Brian Brackeen last October, has raised $4 million in funding from E. Jay Saunders, CEO of Domus Semo Sancus. This brings Kairos's total funding to $17 million. As of November, Kairos had just enough money to get through Q1 of this year. At the time, Brackeen was looking to raise $5 million for the company and had already secured $3.5 million from Beyond Capital Markets, contingent upon Brackeen rejoining the company. Fast-forward to today and Brackeen is still out of the company and the interim CEO, Melissa Doval, has been appointed permanent CEO.

The owner of the wrecked Fukushima No. 1 power plant is trying this week to touch melted fuel at the bottom of the plant for the first time since the disaster almost eight years ago, a tiny but key step toward retrieving the radioactive material amid a ¥21.5 trillion ($195 billion) cleanup effort. Tokyo Electric Power Co. Holdings Inc. will on Wednesday insert a robot developed by Toshiba Corp. to make contact with material believed to contain melted fuel inside the containment vessel of the unit 2 reactor, one of three units that melted down after the March 2011 earthquake and tsunami. "We plan to confirm if we can move or lift the debris or if it crumbles," Joji Hara, a spokesman for Tepco said by phone Friday. Tepco doesn't plan to collect samples during the survey. The country is seeking to clean up the Fukushima disaster, the world's worst atomic accident since Chernobyl, which prompted a mass shutdown of its reactors.