I’d like to project the Google Maps as a tool example a bit into the future. A future I find looks plausible:
The route service has been expanded by Google to avoid traffic congestion by—basically—giving different people different recommendations. Lets assume that Google has sufficiently many devices giving feedback which route is actually used (this may happen due to simply tracking movement of the devices, Google Cars, whatever). Lets also assume that the route service is intended to incorporates further data about its users, e.g. locations previously visited, preferrred locations (these could be e.g. gleaned from search profiles, but also from incorporating or tracking otrher sources). Now that is a heavy amount of data to integrate. How about using a self-optimizing algorithm for dealing with all that data and dispense the ‘best’ recommendations over all users. That looks like a recipe where the simple sounding “give me a route from X to Y” effectively means “considering all the information I have about this user and lots of other users desiring to travel near X and Y how do I best ensure the ‘goal’ of getting him there and minimizing traffic jams”. This could have lots of unexpected ‘solutions’ which the algorithm may learn from feedback from e.g. users stopping in their ‘preferred’ locations along the route, locations which lead to more accidents (e.g. if for some reason non-arrival isn’t counted negatively). Exercise for the reader: What else can go wrong here?
And how would the Maps AI explain its operation: “I satisfy human values with cars and shopping.”
I’d like to project the Google Maps as a tool example a bit into the future. A future I find looks plausible:
The route service has been expanded by Google to avoid traffic congestion by—basically—giving different people different recommendations. Lets assume that Google has sufficiently many devices giving feedback which route is actually used (this may happen due to simply tracking movement of the devices, Google Cars, whatever). Lets also assume that the route service is intended to incorporates further data about its users, e.g. locations previously visited, preferrred locations (these could be e.g. gleaned from search profiles, but also from incorporating or tracking otrher sources). Now that is a heavy amount of data to integrate. How about using a self-optimizing algorithm for dealing with all that data and dispense the ‘best’ recommendations over all users. That looks like a recipe where the simple sounding “give me a route from X to Y” effectively means “considering all the information I have about this user and lots of other users desiring to travel near X and Y how do I best ensure the ‘goal’ of getting him there and minimizing traffic jams”. This could have lots of unexpected ‘solutions’ which the algorithm may learn from feedback from e.g. users stopping in their ‘preferred’ locations along the route, locations which lead to more accidents (e.g. if for some reason non-arrival isn’t counted negatively). Exercise for the reader: What else can go wrong here?
And how would the Maps AI explain its operation: “I satisfy human values with cars and shopping.”