Connected and software-defined vehicles promise to offer a broad range of services and advanced functions to customers, aiming to increase passenger comfort and support autonomous driving capabilities. Due to the high reliability and availability requirements of connected vehicles, it is crucial to resolve any occurring failures quickly. To achieve this however, a complex cloud/edge architecture with a mesh of dependencies must be navigated to diagnose the responsible root cause. As such, manual analyses become unfeasible since they would significantly delay the troubleshooting. To address this challenge, this paper presents SDVDiag, an extensible platform for the automated diagnosis of connected vehicle functions. The platform enables the creation of pipelines that cover all steps from initial data collection to the tracing of potential root causes. In addition, SDVDiag supports self-adaptive behavior by the ability to exchange modules at runtime. Dependencies between functions are detected and continuously updated, resulting in a dynamic graph view of the system. In addition, vital system metrics are monitored for anomalies. Whenever an incident is investigated, a snapshot of the graph is taken and augmented by relevant anomalies. Finally, the analysis is performed by traversing the graph and creating a ranking of the most likely causes. To evaluate the platform, it is deployed inside an 5G test fleet environment for connected vehicle functions. The results show that injected faults can be detected reliably. As such, the platform offers the potential to gain new insights and reduce downtime by identifying problems and their causes at an early stage.

Nuro is a robotics start-up whose mission is to accelerate the benefits of robotics for everyday life. We have an elite team of entrepreneurs and engineers, designers, and scientists. We believe AI and robotics are at the cusp of transforming daily life and we are dedicated to building meaningful products with this technology. Join us and play a critical role in our mission. The Systems Engineering team is responsible for the requirements, architecture, and validation of autonomous driving capabilities across engineering disciplines.

We are a next-gen cybernetics start-up backed by a few top-tier investors (led by NEA). We aim to push the boundaries of what intelligent systems are capable of achieving both autonomously and in collaboration with humans. Before starting Neo Cybernetica, our CEO founded the unicorn AI company DataRobot and led for almost a decade while working directly with worldwide customers across many industries. You can expect to be part of something exciting at the contour of human knowledge. We are looking for an Embedded Systems Engineer to join our fast-growing team of highly skilled professionals and work on breakthrough robotics technology.

And so the risk here is not that the AI is truly sentient but that we are well-poised to create sophisticated machines that can imitate humans to such a degree that we cannot help but anthropomorphize them--and that large tech companies can exploit this in deeply unethical ways. As should be clear from the way we treat our pets, or how we've interacted with Tamagotchi, or how we video gamers reload a save if we accidentally make an NPC cry, we are actually very capable of empathizing with the nonhuman. Systems engineer and historian Lilly Ryan warns that what she calls ecto-metadata--the metadata you leave behind online that illustrates how you think--is vulnerable to exploitation in the near future. In her section of the work, Suzanne Kite draws on Lakota ontologies to argue that it is essential to recognize that sapience does not define the boundaries of who (or what) is a "being" worthy of respect. This is the AI ethical dilemma that stands before us: the need to make kin of our machines weighed against the myriad ways this can and will be weaponized against us in the next phase of surveillance capitalism.

Argo AI is a global self-driving products and services company on a mission to make the world's streets and roadways safe, accessible, and useful for all. Our technology is built to enable commercial services for autonomous delivery and ridesharing in cities. With experienced leaders in the field and collaborative partnerships with some of the world's top consumer brands, we're working block by block, city by city to empower people and businesses to be more successful. We're individuals driven by strong values to solve complex problems together. Come join us to reimagine the human journey.

Director of AI/ML Engineering at Armis Industries Remote › 100% remote position (in the US) (Posted Mar 6 2022) About the company Armis Industries is a Saint Louis-based, deep technology startup developing next generation Artificial Intelligence/Machine Learning-enhanced, autonomous and unmanned vehicle systems and related data analytics technology for both government and commercial applications. Job description Armis Industries is a St. Louis-based deep technology startup focused on developing the next generation of unmanned and fully autonomous vehicle systems for aerospace, defense and industrial applications. We develop full-stack autonomous systems, with in-house physical vehicle design, command and control development, and mission information analytics and decision making. Machine Learning is the foundation of our command & control, autonomous decision making, as well as sensor data analytics capabilities. We are building machine learning systems that can operate reliably in complex, real-world environments and that can be easily adapted to new locations and missions.

Do you ever ask yourself why your machine learning model isn't used? Why do so few people really believe in the power of machine learning rather than these old dashboards? When I was working in a football club, I made a data visualization showing player performances during the season. It was a really simple tile plot. But when football people saw it on my screen they engaged quite quickly.

From cars to planes, the future of transportation is already here--and is changing rapidly. Software engineering is increasingly central to both the development and maintenance of all kinds of vehicles. That means more people need to start thinking like systems engineers. Dale Tutt, vice president of aerospace and defense industry for Siemens Software, says this means companies must offer more training and planning for those designing and developing vehicles of the future. "As you try to address the talent gap, there's a lot you can do to help make the tools easier to use. By better integrating the tools and by bringing in technologies like AI to help automate the generation of different design concepts and the analysis of those concepts using simulation tools, you can extend the capabilities of the system so that it helps empower your engineers," says Tutt. "Companies that are the most successful at adopting systems engineering are doing it because systems engineering, and the tools being used are becoming almost like the DNA of their engineering organization. Everyone is starting to think a bit like a systems engineer, even in their normal job. The tools and the ecosystem that you use to do systems engineering has a large role in facilitating adoption." Nand Kochhar, the vice president of automotive and transportation for Siemens Software, says a systems engineering approach can extend more broadly, as engineers think about how cars and vehicles connect to everything else in their environments. "In a smart city, the system has become the city itself. Take a vehicle in the city, for example. The definition of the system has moved from the single vehicle to include the flow of traffic in the city and to how the traffic lights operate. You can extend that expansive ecosystem to other aspects like building management, for example, into the smart city environment," he says.

After a thankfully uneventful seven-month journey, NASA's Mars 2020 mission is set to safely reach the Red Planet and insert itself into orbit on Thursday ahead of deploying the Perseverance rover and Ingenuity helicopter prototype that it's been toting down to the planet's surface in search for evidence of ancient microbial life. However, this expedition has been in the works for far longer than Perseverance has been travelling through interplanetary space. First announced in 2012, the mission marks the culmination of nearly a decade's work by hundreds of machinists, designers, rocket scientists and engineers at NASA's Jet Propulsion Lab. But not just anyone can get hired there, working for the world's premiere spacecraft production facility and building equipment that will grace the surfaces of neighboring planets. For Mohamed Abid, a Deputy Chief Mechanical Engineer on the Mars 2020 mission, the path to working at the JPL began in Tunisia, where he grew up.

Note: this is an opinion piece, feel free to share your own opinion so we can continue to move our field in the right direction. In every field, we get specialized roles in the early days, replaced by the commonplace role over time. It seems like this is another case of just that. Machine Learning Engineer as a role is a consequence of the massive hype fueling buzzwords like AI and Data Science in the enterprise. In the early days of Machine Learning, it was a very necessary role.