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>>Example, http://www.optical-illusionist.com/imagefiles/dalmation.jpg http://www.optical-illusionist.com/imagefiles/dalmation.jpg - to see the dog, you really

>>Example, http://www.optical-illusionist.com/imagefiles/dalmation.jpg http://www.optical-illusionist.com/imagefiles/dalmation.jpg - to see the dog, you really do have to know rather a lot about dogs (Disclaimer: Cynical Plug) This is exactly the sort of AI project that I'm working on: being able to recognize patterns even with lots of noise or incomplete information. I tried a show HN submission [1] but there was absolutely no interest. :( Question: Does anybody know if google is yet at this level? Would google's AI be able to recognize the dalmatian? I think my AI algorithm would be able to recognize the dog but I'm still not at that stage yet. Really need to get more computer power before that. Thanks for the image though, you've given me some really good testing ideas. [1] https://news.ycombinator.com/item?id=14057043 https://news.ycombinator.com/item?id=14057043

runako 10y ago
> Question: Does anybody know if google is yet at this level? Would google's AI be able to recognize the dalmatian? Apparently not, based on trying out the palmation image here: https://cloud.google.com/vision/ https://cloud.google.com/vision/
phkahler 10y ago
If that was not a still image the dog would be far more visible. Once you incorporate motion tracking in 2D many things will suddenly become apparent. Pro-tip: For a moving viewer, anything in a 2D image that doesn't move horizontally or vertically is on a collision course with the camera. This is why deer seem to "come out of nowhere" because they are not moving within the field of view as they run into a car, and could actually remain hidden behind the A-pillar the entire time they are running toward the road right up until you hit em.