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.

Businesses must also create accurate forecasts. Such a forecast model typically looks at past data and overlays it with current factors such as economic trends, holidays, weather conditions, world events, and market changes, including competitive forces. Better forecasting will help improve inventory management and optimize cash flow. Dynamic pricing will help maximize revenue and increase profits and market share. This can be accomplished with the help of machine learning, and the savings are real.

During this period of progressive development and deployment of artificial intelligence, discussions around the ethical, legal, socio-economic and cultural implications of its use are increasing. What are the challenges and the strategy, and what are the values that Europe can bring to this domain? During the European Conference on AI (ECAI 2020), two special events in the format of panels discussed the challenges of AI made in the European Union, the shape of future research and industry, and the strategy to retain talent and compete with other world powers. This article collects some of the main messages from these two sessions, which included the participation of AI experts from leading European organisations and networks. Since the publication of European directives and guidance, such as the EC White Paper on AI and the Trustworthy AI Guidelines, Europe has been laying the foundation for the future vision of AI. The European strategy for AI builds on the well-known and accepted principles found in the Charter of Fundamental Rights of the European Commission and the Universal Declaration of Human Rights to define a human-centric approach, whose primary purpose is to enhance human capabilities and societal well-being.

During my internship at Taipei Medical University, I was working on developing predictive models for Chronic Kidney Disease. The project was an educational experience with challenges that arose from the complexity and large size of the dataset. In this article, I will share the key lessons I learnt that helped me boost my productivity as a beginning researcher. The ideas are quite general and applicable to most machine learning workflows. Some of them are obvious ones that we choose to ignore.

Joe Wieciek, Software Ops Manager at Brain Corp, shares the five most important lessons learned from powering the world's largest fleet of autonomous mobile robots (AMRs) operating in commercial indoor public spaces AI software technology today is used to build autonomous robots for the retail industry, malls, airports, hospitals and more. At Brain Corp, our groundbreaking work with our manufacturing partners has helped us build and sell several autonomous mobile robots (AMR) across several verticals and brands. Once the robots are deployed, our software operations team works diligently to ensure that every BrainOS -enabled robot performs well in the field, collecting data and insights via the cloud that we use to improve our software and systems, and ultimately create better user experiences. However, managing a handful of robots in the field is drastically different from managing a large global fleet. We learned that the hard way on our path to powering the largest fleet in the world of autonomous mobile robots (AMRs) operating in commercial indoor public spaces.

Stripe processes billions of dollars a year for some of the largest companies in the world. Enterprise Innovation talks to Michael Manapat, Stripe's head of data and machine learning products, about the lessons governments, large enterprises and online businesses alike can draw from Stripe's experience in building Radar, Stripe's machine-learning-powered fraud detection system. What key lessons can we learn from Stripe's experience in processing billions of dollars a year for global companies, especially in managing fraud, privacy and security? Manapat: In 2017 alone, Stripe Radar prevented US$4 billion in attempted fraud, directly helping the hundreds of thousands of companies who run their business on Stripe. From operating at this scale, we've drawn three important lessons that all online businesses, no matter where they are in the world, should consider when tackling fraud: They can be helpful, but also time consuming and not completely effective.

In this paper we outline the development of \Caesar{}, a domestic service robot with which we participated in the robot competition RoboCup@Home for many years. We sketch the system components, in particular the parts relevant to the high-level reasoning system, that make CAESAR an intelligent robot. We report on the development and discuss the lessons we learnt over the years designing, developing and maintaining an intelligent service robot. From our perspective of having participated in RoboCup@Home for a long time, we answer the core questions of the workshop about platforms, challenges and the evaluation of integrative research.