|Galileo Galilei. Portrait by Ottavio Leoni, 1624.|
Compared to current global positioning systems, Galileo introduces a few snazzy features. However, the feature that really stands out (in terms of its implications for ML) is its level of precision. Galileo will be accurate down to the metre range. Current general-purpose GPS receivers have an accuracy range of about 10-20 metres (or even worse at high altitudes). This means that, in practice, they only work as navigation systems for large, open areas. For example, integrated with a ML smartphone app, these systems are able to tell us where the nearest open museum is, what building we are looking at, or what part of town we are in.
With Galileo, provided a clear satellite signal is available, mobile devices will be able to detect which display case museum visitors are standing by (and provide personalised content at point of consumption); generate diagrams showing how people move around the gallery on a metre-by-metre basis and how long they spend interacting with each individual display (facilitating user data analysis for curators and events organisers); they could allow participants in educational crowdsourcing projects to geotag locations / learning resources / interesting features in libraries, archaeological sites, art galleries, etc. with a high level of precision (making it easy for other users to go back to that precise spot, find the resource and benefit from it); they could provide a new set of tools for blind students to engage with educational resources spatially in a classroom or lecture hall.
By reducing the error margin of existing geolocation systems from 20 metres to <1 metre, Galileo could open a massive range of new options for teachers and students in mobile learning.