Earlier quoted context omitted.
The top 3 classes in your example are actually correct - it is a color photo of a human. But we expect it to get much better over time. Only real world usage will allow us to make real improvement - and that's why we are eager to release early. We are also believe that the first applications (e.g., classifying animals or plants or landmarks in dedicated apps) will have narrower use case that give better accuracy.
The top 3 may be correct, but they aren't very useful. What could I do with this information? What feature could I build? Also, the other results are very wrong. (i.e., Watson is more confident that this is a dog than a person. And I have no idea where it got "Long Jump" from). This makes it hard for me to trust Watson. Is the recommendation that I incorporate a "confidence in Watson" metric, and ignore most of the r…
One example of a use is at Kiva we require borrowers to have a picture of themselves posted for their loan. But sometimes we get pictures of things like goats or cows instead (those are kind of nice to but gotta follow policy). Currently this is something we have to manually review for, but if we could automate that review piece it would save a lot of time (especially if at some point it could also count the number of humans in a photo).
