Visualizing Magnitude: Graphical Number Representations Help Users Detect Large Number Entry Errors

Nurses frequently have to program infusion pumps to deliver a prescribed quantity of drug over time. Occasional errors are made in the performance of this routine number entry task, resulting in patients receiving the incorrect dose of a drug. While many of these number entry errors are inconsequential, others are not; infusing 100 ml of a drug instead of 10 ml can be fatal. This paper investigates whether a supplementary graphical number representation, depicting the magnitude of a number, can help people detect number entry errors. An experiment was conducted in which 48 participants had to enter numbers from a ‘prescription sheet’ to a computer interface using a keyboard. The graphical representation was supplementary and was shown both on the ‘prescription sheet’ and the device interface. Results show that while overall more errors were made when the graphical representation was visible, the graphical representation helped participants to detect larger number entry errors (i.e., those that were out by at least an order of magnitude). This work suggests that a graphical number entry system that visualizes magnitude of number can help people detect serious number entry errors

Interactive numerals

Although Arabic numerals (like {\textquoteleft}2016{\textquoteright} and {\textquoteleft}3.14{\textquoteright}) are ubiquitous, we show that in interactive computer applications they are often misleading and surprisingly unreliable. We introduce interactive numerals as a new concept and show, like Roman numerals and Arabic numerals, interactive numerals introduce another way of using and thinking about numbers. Properly understanding interactive numerals is essential for all computer applications that involve numerical data entered by users, including finance, medicine, aviation and science

Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

AI-driven methods have potential to minimise manual labour during planetary data processing and aid ongoing missions with real-time data analysis. This white paper focuses on key areas of AI-driven research, the need for open source training data, and the importance of collaboration between academia and industries to advance AI-driven research

Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

Over the past decades of NASA’s inner solar system exploration, data obtained from the Moon alone accounts for ~76%. Most of the lunar orbital spacecraft of the past and present carried imaging cameras and spectrometers (including multispectral and hyperspectral payloads), as well as a large variety of other passive and active instruments. For example, NASA’s Lunar Reconnaissance Orbiter (LRO) has been operating for more than 10 years, providing us with ~1206 TB of lunar data which amounts to ~99.5% of the total data contributed by NASA built instruments. Given recent advances in instrument and communication capabilities, the amount of data returned from spacecraft is expected to keep rising quickly. The white paper focus on potential components of AI and ML that could help to accelerate the future exploration of the Moon and other planetary bodies. The white paper highlights on selected AI/ML-based approaches for lunar and planetary surface science and exploration, the need for open-source availability of training, validation, and testing datasets for AI-ML based approaches, and need for opportunities to further bridge the gap between industry and academia for advancing AI-ML based research in lunar and planetary science and exploration