Crowdsourcing without Open Sourcing

»Because any­one can con­tribute to its plat­form, it gets updat­ed every day.« says the CEO. Noth­ing real­ly new from an AI start­up, despite mak­ing head­lines with MITs tech­nol­o­gy review: The com­pa­ny Map­il­lary crowd­sources com­mon knowl­edge to cap­i­tal­ize it by con­vert­ing it to valu­able data that is then cir­cu­lat­ed out of the hand of the com­mons, where it was orig­i­nal­ly sit­u­at­ed.

Map­il­lary uses crowd sourced imagery (that is with­out pay­ing for it) to cre­ate addi­tion­al data that would help autonomous cars to dri­ve »more save­ly«. While MIT Tech­nol­o­gy Review tries to describe the com­pa­ny as »Wikipedia of map­ping« it is clear­ly not. The com­pa­ny is pri­vate­ly owned and does­n’t give away the data in the sense of a pub­lic knowl­edge (e.g. donat­ing it to open street maps). Parts of the data is access­able via an API though and tem­porar­i­ly free »for char­i­ties and for edu­ca­tion­al or per­son­al use«.

The rather impu­dent mar­ket­ing is acknowl­edged at the arti­cles end, when stat­ing: »This sto­ry was cor­rect­ed to make clear the images are crowd­sourced but the under­ly­ing code is not open source.«

https://www.technologyreview.com/s/612825/open-source-maps-should-help-driverless-cars-navigate-our-cities-more-safely/

Why does adversarial.io tack­le this? The answer might be in an text by Eykholt et al.; Robust Phys­i­cal-World Attacks on Deep Learn­ing Mod­els. https://arxiv.org/abs/1707.08945