Urban Stories: Difference between revisions
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Client: David Russell, [[The Fusion Works]] <david@thefusionworks.com> | |||
The what3words service has become very successful, with its pitch that three random words can identify any location on the planet. If you’ve used it, you may have noticed that the random words are often strangely relevant to the Cambridge locations being specified - if not for one particular cell, then quite likely for the cell next to it. Your task is to use a combination of natural language processing and path optimisation methods to turn these into engaging stories, starting from the creative seed of places you have visited, or perhaps would like to visit. | The what3words service has become very successful, with its pitch that three random words can identify any location on the planet. If you’ve used it, you may have noticed that the random words are often strangely relevant to the Cambridge locations being specified - if not for one particular cell, then quite likely for the cell next to it. Your task is to use a combination of natural language processing and path optimisation methods to turn these into engaging stories, starting from the creative seed of places you have visited, or perhaps would like to visit. | ||
Latest revision as of 16:52, 12 November 2021
Client: David Russell, The Fusion Works <david@thefusionworks.com>
The what3words service has become very successful, with its pitch that three random words can identify any location on the planet. If you’ve used it, you may have noticed that the random words are often strangely relevant to the Cambridge locations being specified - if not for one particular cell, then quite likely for the cell next to it. Your task is to use a combination of natural language processing and path optimisation methods to turn these into engaging stories, starting from the creative seed of places you have visited, or perhaps would like to visit.
