In this work, we establish a method for abstracting information from Computer Aided Engineering (CAE) into graphs. Such graph representations of CAE data can improve design guidelines and support recommendation systems by enabling the comparison of simulations, highlighting unexplored experimental designs, and correlating different designs. We focus on the load-path in crashworthiness analysis, a complex sub-discipline in vehicle design. The load-path is the sequence of parts that absorb most of the energy caused by the impact. To detect the load-path, we generate a directed weighted graph from the CAE data. The vertices represent the vehicle's parts, and the edges are an abstraction of the connectivity of the parts. The edge direction follows the temporal occurrence of the collision, where the edge weights reflect aspects of the energy absorption. We introduce and assess three methods for graph extraction and an additional method for further updating each graph with the sequences of absorption. Based on longest-path calculations, we introduce an automated detection of the load-path, which we analyse for the different graph extraction methods and weights. Finally, we show how our method for the detection of load-paths helps in the classification and labelling of CAE simulations.

The 2023 Ford Mustang Mach-E is equipped with the latest semi-autonomous BlueCruise highway driving system that can drive the car under certain circumstances better than the original version. The family of Formula One star Michael Schumacher is planning to take legal action against a German weekly magazine after it published an "interview" with the racer that was generated using artificial intelligence. A spokesperson for the Schumacher family pointed to published reports of legal action, according to Reuters. Family spokesperson Sabine Kehm confirmed to The Associated Press by email on Thursday that legal action is being planned over a "fake artificial intelligence interview by German outlet Die Aktuelle." Schumacher has not been seen in public since suffering a near-fatal brain injury in a skiing accident on a French Alps vacation in December 2013.

This week, a German tabloid tested the boundaries of AI passing itself off as reality. Celebrity magazine Die Aktuelle published a cover story in its April 15th issue about a supposed interview with Formula One driver Michael Schumacher; only at the end does it reveal that it was a hoax produced entirely by an AI chatbot. Schumacher's family told ESPN it plans to take legal action against the gossip rag. The article promised "the first interview" with Schumacher, who suffered a severe brain injury on a family ski trip in the French Alps in 2013. Since then, the celebrated driver -- arguably the greatest in Formula One history -- hasn't appeared publicly as his family guards his privacy.

Simply put, neuroethics explores the ethical, legal and social implications of neuroscience. So on a daily basis, I'm exploring things like how neuroscience and emerging neurotech will impact identity, personhood, privacy and, actually, pretty much life as we know it,

"F1 and Amazon Machine Learning Solutions Lab took a full year to build the algorithm that led to the fastest driver." Formula 1 has been working with Amazon Web Services (AWS) to rank their racers. After a year of algorithmic heavy lifting, the results are out now. Ayrton Senna, the three-time world champion from Brazil came out on top, followed by the seven-time champion, Michael Schumacher with a time differential of 0.114 second. Whereas current World Champion Lewis Hamilton featured at 3rd position with a relative time of 0.275 seconds.

Process extraction, a recently emerged interdiscipline, aims to extract procedural knowledge expressed in texts. Previous process extractors heavily depend on domain-specific linguistic knowledge, thus suffer from the problems of poor quality and lack of adaptability. In this paper, we propose a multi-task architecture based model to perform process extraction. This is the first attempt that brings deep learning in process extraction. Specifically, we divide process extraction into three complete and independent subtasks: sentence classification, sentence semantic recognition and semantic role labeling. All of these subtasks are trained jointly, using a weight-sharing multi-task learning (MTL) framework. Moreover, instead of using fixed-size filters, we use multiscale convolutions to perceive more local contextual features. Finally, we propose a recurrent construction algorithm to create a graphical representation from the extracted procedural knowledge. Experimental results demonstrate that our approach can extract more accurate procedural information than state-of-the-art baselines.

Schumacher's keynote, Voice user interface (UI): Forget everything you know about UI Design, will explore some of the ways that designers need to think differently about voice to deliver successful user experiences. Designing for the screen is inherently more defined and constrained than designing for voice interaction; in his talk, Schumacher will highlight considerations for organizations as they transition to'Voice First' from'Mobile First' design. Bringing a focus on artificial intelligence (AI) & UX, Lew will discuss core elements from a book he is co-authoring in which the future of AI is explored through interviews with AI experts. He will illustrate successes and failures of AI through case studies and present a UX framework to pave the way for future success. The UX Masterclass is hosted by members of UXalliance, a network of 25 leading UX companies around the world.

America's largest tech companies face a growing backlash over the potentially negative impacts of their strategic decisions and innovations. For example, companies like Apple, Facebook, Google and Microsoft are investing in artificial intelligence (AI) technologies and product roadmaps that will replace millions of jobs during the coming years. Experts in marketing, technology and social awareness say it's time for technology providers to assume greater responsibility for the personal pain that comes along with the collective gain. Emerging technology is at almost perpetual odds with the status quo, but U.S. society is coming to realize that dynamic can lead to job losses, unfair treatment of social services and a stain on civic engagement. The power and influence that some tech companies command is being reevaluated in light of the myriad ways people are being disenfranchised in some way by their actions.