A new study in Robot Learning has introduced a robotic system that combines machine learning with decision-making to analyse water samples. The approach enables robots to detect, classify, and distinguish drinking water on Earth and potentially other planets.
Researchers used a hybrid method that merged the TOPSIS decision-making technique with a Random Forest Classifier trained on the Water Quality and Potability Dataset from Kaggle. By applying data balancing techniques, classification accuracy rose from 69% to 73%.
The robotic prototype includes thrusters, motors, solar power, sensors, and a robotic arm for sample collection. Water samples are tested in real time, with the onboard model classifying them as drinkable.
The system has the potential for rapid crisis response, sustainable water management, and planetary exploration, although challenges remain regarding sensor accuracy, data noise, and scalability. Researchers emphasise that further testing is necessary before real-world deployment.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
Communication, empathy, and judgment were dismissed for years as ‘soft skills‘, sidelined while technical expertise dominated training and promotion. A new perspective argues that these human competencies are fundamental to resilience and transformation.
Researchers and practitioners emphasise that AI can expedite decision-making but cannot replace human judgment, trust, or narrative. Failures in leadership often stem from a lack of human capacity rather than technical gaps.
Redefining skills like decision-making, adaptability, and emotional intelligence as measurable behaviours helps organisations train and evaluate leaders effectively. Embedding these human disciplines ensures transformation holds under pressure and uncertainty.
Career and cultures are strengthened when leaders are assessed on their ability to build trust, resolve conflicts, and influence through storytelling. Without funding the human core alongside technical skills, strategies collapse, and talent disengages.
Would you like to learn more about AI, tech, and digital diplomacy? If so, ask our Diplo chatbot!
Two Spanish scientists question the thesis of ‘two Ukraine’: pro-West or pro-Russian by analysing a data set on violent events in Ukraine since January 2021.
The analysis shows that conflicts can arise from many factors beyond simple East-West binary optics and the solution is not to split Ukraine in two.
According to the authors, lack of data is the greatest problem in this scientific method.
As opposed to other fields, like engineering, obtaining reliable and high-quality data about social and political events is a major challenge.
The greatest challenge to using statistical models and scientific methods in diplomacy will be finding timely, reliable, and usable data.
