Today, I want to demonstrate you a great example of how object recognition technologies based on machine learning:
1) becoming widely available and do not require rare genius programming skills to get the result.
2) can be greatly trained even on a very modest in size data sets.
The article, that I have read some time ago, tells how a bird lover and part-time computer science professor, together with his students, taught the neural network to recognize the bird species and then — and that impressed me a lot – to distinguish individual species of woodpeckers, who flew to the bird feeder in his yard.
At the same time, 2450 photos in the training sample were enough to recognize eight different woodpeckers. The professor estimated the cost of a homemade station for the recognition and identification of birds at about $ 500. This is really can be called technology for everyone and machine intelligence in every yard.
Moreover, this technology can really help birds. As Lewis Barnett, the inventor of this technology wrote in his article : «Ornithologists need accurate data on how bird populations change over time. Since many species are very specific in their habitat needs when it comes to breeding, wintering and migration, fine-grained data could be useful for thinking about the effects of a changing landscape. Data on individual species like downy woodpeckers could then be matched with other information, such as land use maps, weather patterns, human population growth and so forth, to better understand the abundance of a local species over time»
As some people correctly noted, this technology has also some great commercial potential. Just imagine that camera traps will be able to recognize birds that harm your fruit trees and than activate a device that make a large noise to scare away pests.