On imitated problem solving | Imagico.de


  • On imitated problem solving

    As many of you know for a few years now we have a new trend in remote sensing and cartography that is called Artificial Intelligence or Machine Learning.
    Those in decision making positions at companies like Facebook and Mapbox who try to push AI or Machine Learning into cartography (see here and here) are largely aware of these limitations. If they truly believed that AIs can replace human intelligence in mapping they would not try to push such methods into OSM, they would simply build their own geo-database using these methods free of the inconvenient rules and constraints of OSM. The reason why they push this into OSM is because on their own these methods are pretty useless for cartographic purposes. As illustrated above for principal reasons they produce pretty blatant and stupid errors and even if the error rate is low that usually ruins things for most application. What would you think of a map where one percent of the buildings are in the middle of a road or river or similar? Would you trust a self driving car that uses a road database where 0.1 percent of the roads lead into a lake or wall?
    What Facebook & Co. hope for is that by pushing AI methods into OSM they can get the OSM community to clean up the errors their trained mechanical kids inevitably produce and thereby turn the practically pretty useless AI results into something of practical value – or, to put it more bluntly, to change OSM from being a map by the people for the people into a project of crowd sourced slave work for the corporate AI overlords.
    Computers should perform work for humans, not the other way round.
    In other words: You should do exactly the opposite of what Facebook and Mapbox are doing in this field.

    #osm #ai #machine_learning & #mapbox as usual :p

    https://seenthis.net/messages/670955 via b_b