In this blog post, Anna Nadibaidze explores the main themes found across common visuals of AI in the military domain. Inspired by the work and mission of Better Images of AI, she argues for the need to discuss and find alternatives to images of humanoid'killer robots'. Anna holds a PhD in Political Science from the University of Southern Denmark (SDU) and is a researcher for the AutoNorms project, based at SDU. The integration of artificial intelligence (AI) technologies into the military domain, especially weapon systems and the process of using force, has been the topic of international academic, policy, and regulatory debates for more than a decade. The visual aspect of these discussions, however, has not been analysed in depth. This is both puzzling, considering the role that images play in shaping parts of the discourses on AI in warfare, and potentially problematic, given that many of these visuals, as I explore below, misrepresent major issues at stake in the debate.

Battery life has long been a key limiting factor in drone use. Although there are commercial models that can stay aloft for 45 minutes or longer on a single charge, being able to keep drones in the air for longer would be helpful for many purposes. Researchers at the University of Southern Denmark have been working on that issue for several years by developing drones that can recharge directly from power lines. This time around, the scientists attached a gripper system to a Tarot 650 Sport drone, which they customized with a electric quadcopter propulsion system, an autopilot module and other components. When the drone's systems detect that the battery is running low, the device employs its camera and millimeter-wave radar system to pinpoint the closest power line, as New Atlas notes.

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals.

Increasingly, social robots are being used for support in educational contexts. But does the sound of a social robot affect how well they perform, especially when dealing with teams of humans? Teamwork is a key factor in human creativity, boosting collaboration and new ideas. Danish scientists set out to understand whether robots using a voice designed to sound charismatic would be more successful as team creativity facilitators. "We had a robot instruct teams of students in a creativity task. The robot either used a confident, passionate -- ie charismatic -- tone of voice or a normal, matter-of-fact tone of voice," said Dr Kerstin Fischer of the University of Southern Denmark, corresponding author of the study in Frontiers in Communication.

Researchers at TH Ingolstadt and the University of Southern Denmark have studied the effects of AI opinions on humans. Their study shows that machine-generated moral perspectives can influence people, even when they know the perspective comes from a machine. In their two-step experiment, the researchers first asked ChatGPT to find solutions to different variants of the trolley problem: Is it right to sacrifice the life of one person to save the lives of five others? The researchers received different advice from ChatGPT. Sometimes the machine argued for human sacrifice, sometimes against.

A robot that is able to take throat swabs from coronavirus patients using a 3D printed arm was developed by a team of researchers from Denmark in just four weeks. The University of Southern Denmark says the world's first fully automated throat swab robot will be be able to test the first COVID-19 patients by late June. Using disposable 3D printed parts, the robot holds a swab and hits the exact spot in the throat where a sample needs to be collected every time. It puts the swab in a glass and screws the lid on to seal the sample without human input - reducing the risk of exposing healthcare workers to the deadly virus. A team of ten researchers for the Industry 4.0 Lab at the University of Southern Denmark worked around the clock to produce the prototype of the robot.

The Region of Southern Denmark, with help from SAS, has become the first place in the world to implement a complete system for monitoring hospital-acquired infections. Professor Jens Kjølseth Møller at Lillebaelt Hospital is the brain behind the new system, which is made possible by SAS Analytics. Kjølseth Møller expects the system to reduce the number of infections during hospitalization by one-third, significantly increasing patient safety. "It is unsatisfying that patients admitted to Danish hospitals are at risk of further illness," says Peder Jest, Medical Director at Odense University Hospital. "The work of providing a high degree of patient safety and good infection hygiene is, therefore, a key focus area for the Region of Southern Denmark. With SAS, we now have the ability to monitor and predict the risk of hospital-acquired infections at a patient level."