| > That’s not really how any of this works—you can’t simply throw a bunch of unrelated data at some algorithms and expect usable output. That is never what I claimed. First, note that I took a pretty (IMO) balanced view and indicated that this is still a hard setting. Second, note that I did indicate that sufficient training (i.e. labeled) data would be required. This is what was possible in 2016: https://www.theverge.com/2016/2/25/11112594/google-new-deep-...: "The new deep-learning program churns through millions of photos to determine the best match." Also see project of a fast.ai participant: "Which of the 110 countries a satellite image belongs to?" (point 13 here: https://forums.fast.ai/t/deep-learning-lesson-2-notes/28772) > (There’s also no such thing as “deep learning”.) - https://www.deeplearningbook.org/ - https://www.coursera.org/courses?query=deep%20learning - https://eu.udacity.com/course/intro-to-tensorflow-for-deep-l... - https://www.edx.org/professional-certificate/ibm-deep-learni... - https://www.deeplearning.ai - https://www.fast.ai/ > Yes, Google does have a lot of images of various locations from a top-down perspective, but that isn’t helpful for accurately determining a location from the images that Europol collects. You might be able to narrow it down to a probably country based colors and design patterns, but that’s hardly sufficient and not solid enough evidence to actually do anything. Maybe not completely, but again: being able to narrow it down would already be an incredible help, especially for outdoor pictures (which were also shown in the article's video). I never claimed that a model would completely replace the human process. Also, I find the downvotes (not saying you) on my initial comment to be in pretty bad form. I'm not Jeremy Howard or Andrew Ng, but don't think I was blowing smoke, and work in the area of data science and ML. |