Deploying autonomous robots in safety-critical domains requires architectures that ensure operational effectiveness and safety compliance. In this paper, we contribute the Safe-ROS architecture for developing reliable and verifiable autonomous robots in such domains. It features two distinct subsystems: (1) an intelligent control system that is responsible for normal/routine operations, and (2) a Safety System consisting of Safety Instrumented Functions (SIFs) that provide formally verifiable independent oversight. We demonstrate Safe-ROS on an AgileX Scout Mini robot performing autonomous inspection in a nuclear environment. One safety requirement is selected and instantiated as a SIF. To support verification, we implement the SIF as a cognitive agent, programmed to stop the robot whenever it detects that it is too close to an obstacle. We verify that the agent meets the safety requirement and integrate it into the autonomous inspection. This integration is also verified, and the full deployment is validated in a Gazebo simulation, and lab testing. We evaluate this architecture in the context of the UK nuclear sector, where safety and regulation are crucial aspects of deployment. Success criteria include the development of a formal property from the safety requirement, implementation, and verification of the SIF, and the integration of the SIF into the operational robotic autonomous system. Our results demonstrate that the Safe-ROS architecture can provide safety verifiable oversight while deploying autonomous robots in safety-critical domains, offering a robust framework that can be extended to additional requirements and various applications.

Studies on the use o f AI tools in CS course s focus on prescriptive uses of AI tools that preemptively assign a use case to the tool. To our knowledge, this is the first user study that surveys CS students on how they use AI tools for their coding tasks by personal choice. We surveyed 26 CS students and practitioners with various programming experiences using AI tools for coding tasks (i.e., writ e, debug, etc.). When asked about the most common resources they have used to write a 300 - line program from scratch, blog entries ( e.g., Stack Exchange, etc.) were their top choice, followed by AI coding assistants (e.g., GitHub Copilot, etc.). When asked about resources to debug code, AI chatbots (e.g., ChatGPT, etc.) were the most common choice, followed by blog entries. AI coding assistants are used more for writing code, while AI chatbots are used for debugging tasks. Respondents with all programming experience prefer online resources for coding tasks - whether AI - powered or not - rather than direct human help from peers an d instructors.

We may earn revenue from the products available on this page and participate in affiliate programs. Arduino kits are great for teaching students about science, technology, engineering, and math. The Interaction Design Institute in Turin, Italy, created Arduino in 2005 to provide people of all ages with an easy, inexpensive way to build electronic devices and control them with rudimentary code. By making Arduino an open-source platform, the Institute made the technology freely available to anyone, which led to a vast array of starter kits. Today, everyone from young children to seasoned professional techies uses them to build everything from simple devices that turn on the lights to robots controlled remotely via Wi-Fi. Given their popularity, there are a lot of kits available, like our best overall pick, the Official Arduino Starter Kit.

Artificial intelligence (AI) is becoming increasingly ubiquitous in our daily lives, from the recommendations we receive on social media to the autonomous vehicles being tested on our roads. Yet, for many people, AI remains shrouded in mystery, and the thought of interacting with it can be intimidating. However, there are several steps you can take to familiarize yourself with AI and gain a better understanding. First and foremost, it's essential to understand what AI is and how it works. At its core, AI is the use of computer algorithms to perform tasks that typically require humans, such as recognizing patterns, making decisions, and learning from experience.

Every year, Stack Overflow conducts a survey of its users to help inform the development of its community and platform. This year, more than 80,000 developers shared how they learn, the tools and languages they use, and provided all sorts of feedback valuable to Stack Overflow's direction. The results also present a snapshot of developers and development as of when the survey was conducted. The results of the 2021 Stack Overflow Survey were recently shared publicly, along with commentary and insight provided by Stack Overflow. We will take a look at some of the more interesting data points as they pertain to data science, data scientists, and all of the many data-related positions and those professionals who fill them.

If you think technology has only credited the Retail, F&B, eCommerce, Financial, and Agriculture verticles, then it's time for you to amaze yourself with the known yet unknown fact, the educational sector. The black & whiteboard to the projector screen and after COVID-19 it's the technology that makes education alive among the students. And it's important to acknowledge that the students enjoy the use of technology at every level of their learning process. Like how workplace digital transformation helped with high productivity and employee retention rate, similarly, creative ways to use technology in the classroom has created an excitement for learning among the students. The use of technology in teaching and learning has made life easier for both the teachers and students, the communication between the students and teachers are no more in a distress spot, right from the academic calendar schedules to lessons with media and visuals, technology integration has truly benefited education institutes.

When schools closed this spring, many parents, including me, felt overwhelmed and underwater trying to help our children participate in distance learning. Every day seemed to usher in a new way for my husband and me to fail at reading emails, managing logins, printing worksheets, troubleshooting tech problems, photographing assignments, and keeping track of class Zooms. Being an educator as well as a parent gave my experience a particularly nightmarish quality, as if I were somehow both the driver and pedestrian in this collision. As a teacher, I participated in a flurry of trainings on using various apps to make videos, find e-books, host meetings, use data, and share student work, but as a parent, I could not keep up. It doesn't have to be this hard.

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If you're working on a Machine Learning (ML) project, you probably know how hard it can be to find the right dataset for your model. You might worked on your problem, defining it as a ML problem but now have no data to train your model. Maybe you do have some data, but it's not enough to feed those data-hungry neural networks. Or maybe you do have lots of data, but it's not correctly labeled. Who said ML was easy?

As Artificial Intelligence has become one of the most significant forces driving innovation and economic development, this societal transformation requires new knowledge and an additional set of skills. Just as knowing a BIM software has become a prerequisite for most architecture jobs, understanding or even knowing how to use AI-related tools would become a desirable asset, if not a requirement in the future. However, with a vast array of information available, how does one begin to venture into this topic? The following is a compilation of online resources, lectures, and courses, that could provide a better understanding of the field and how to incorporate it into the practice of architecture. What does Artificial Intelligence represent, what is the difference between machine learning and deep learning?