In a new series of interviews, we're meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the European Conference on Artificial Intelligence (ECAI 2025) . During the conference in Bologna, we caught up with Mario Mirabile who is studying for his PhD in trustworthy AI and multi-agent systems at the University of Santiago de Compostela and is a Research Fellow in human-AI interaction at the University of Bologna. Mario, along with co-authors Frida Hartman and Michele Dusi, was also the winner of the ECAI-2025 Diversity & Inclusion Competition, for work entitled . This award was presented at the closing ceremony of the conference. Could you start by giving us an introduction to the topic you are working on?

The goal of RoboCup is to make research in the area of robotics measurable over time, and grow a community that works together to solve increasingly difficult challenges over the years. The most ambitious of these challenges it to be able to play against the human world champions in soccer in 2050. To better understand what members of the RoboCup community believes to be the state of the art and the main challenges in the next decade and towards the 2050 game, we developed a survey and distributed it to members of different experience level and background within the community. We present data from 39 responses. Results highlighted that locomotion, awareness and decision-making, and robustness of robots are among those considered of high importance for the community, while human-robot interaction and natural language processing and generation are rated of low in importance and difficulty.

We’ve all heard the scare stories. The availability of endless data will allow organisations to become less reliant on the human workforce. Artificial Intelligence (AI) is going to be smarter than humans. And automation will take away lots of our jobs. How much of this is really true though? Despite advances in these technologies, like conversational AI, they’re just tools to be used in the endeavour of making our lives easier and organisations more productive. But even a tool with contextual and conversational capabilities can’t provide the unique flexibility of human touch and true ingenuity that we all desire and

ChatGPT, OpenAI's recently launched conversational bot which can write clear, simple sentences has become the talk of the town lately. Despite being in the test-only preview phase, OpenAI's new ChatGPT has compelled CEO Sundar Pichai-led Google's management to issue a "code red." The company may be on the verge of a technological shift that could completely transform it, a worry that permeates Silicon Valley and all things technological, reported The New York Times. Even though ChatGPT has only been available to the public for three weeks, the NYT report stated that despite occasionally producing harmful and false information, the bot has forced Google to create a rival in order to counter the "first serious threat to its main search business." Since its public launch, the platform has received over a million visitors.

It seems it is useful in many ways though it is not fully accurate as of now. You can try it at https://openai.com/blog/chatgpt/ Graphene is a two-dimensional form of carbon that has been studied extensively for its unique properties. It is made up of a single layer of carbon atoms arranged in a hexagonal lattice, and is known for its strength, flexibility, and high electrical and thermal conductivity. Graphene has many potential applications, including in electronics, energy storage, and biotechnology.

As a venture investor, I have the privilege of meeting amazing people. But there's one group of individuals who I think are something else. Women and men who are supremely talented, endlessly curious, passionately committed, and unconstrained by disciplinary boundaries. I think people like these are uniquely designed to solve the world's most critical and intractable problems. I refer to these extraordinary folks as "Missionary Misfits," and every so often, I'll introduce readers to one of them.

With the right technical infrastructure and data-literate work culture, the challenges with the adoption of data science and machine learning technologies can be easily addressed. Successful companies today need to be data driven. A survey by NewVantage Partners found that 92% of organizations are increasing their investments in data and artificial intelligence (AI) capabilities. On the flipside however, only 19% of companies feel that they are truly being data driven. This analytics gap continues to widen and conspires to impede organizational process.

Jon Crowcroft has been the Marconi Professor of Communications Systems in the Computer Laboratory since October 2001. He has worked in the area of Internet support for multimedia communications for over 30 years. Three main topics of interest have been scalable multicast routing, practical approaches to traffic management, and the design of deployable end-to-end protocols. Current active research areas are Opportunistic Communications, Social Networks, Privacy Preserving Analytics, and techniques and algorithms to scale infrastructure-free mobile systems. He leans towards a "build and learn" paradigm for research.

Culture remains one of the biggest hurdles to successful implementation of artificial intelligence (AI) technologies at the Defense Department, according to DOD leaders at multiple events this week. Some DOD officials believe people are becoming more comfortable using the term AI, but when it comes to understanding what AI really is, and the practical applications of it, not so much. According to Col. Mike Teter, Chief Data Officer and Deputy Director of Digital Superiority and J6 C4/Cyber Directorate at U.S. Space Command, building cultural trust really comes through educating and training, but it also changes the paradigm and timelines associated with traditional reporting up the chain. When you have the ability to have everyone in the chain from the lowest unit all the way to the president can see the same thing at the same time at scale, then it's really a cultural shift on both sides, Teter said during ATARC's ATARC's Ethical Uses of AI with the Federal Government event. "You're not going to have all the answers right away when you see something in the data," he said.

Predictive maintenance: Most industrial equipment is built to last and operate with minimal downtime. As a result, there is often limited historical data with which to work. Because unsupervised ML can detect anomalous behavior even in limited data sets, it can potentially identify developing defects in these situations. Here too, it can be used for fleet management, providing early warning of defects while minimizing the amount of data that needs to be reviewed. Quality assurance/inspection: A machine that's operating improperly can produce substandard product.