A cat flap that automatically bars entry to a pet if it tries to enter with prey in its jaws has been built as a DIY project by an Amazon employee.
Ben Hamm used machine-learning software to train a system to recognise when his cat Metric was approaching with a rodent or bird in its mouth.
When it detected such an attack, he said, a computer attached to the flap’s lock triggered a 15-minute shut-out.
Mr Hamm unveiled his invention at an event in Seattle last month.
The presentation was subsequently brought to light by tech news site The Verge.
Mr Hamm used two of Amazon’s own tools to achieve his goal:
DeepLens – a video camera specifically designed to be used in machine-learning experiments
Sagemaker – a service that allows customers to either buy third-party algorithms or to build their own, then train and tune them with their own data, and finally put them to use
He explained that the most time-consuming part of the task had been the need to supply more than 23,000 photos.
Each had to be hand-sorted to determine whether the cat was in view, whether it was coming or going and if it was carrying prey.
Mr Hamm had to create a database of thousands of images to train the software
The process took advantage of a technique called supervised learning, in which a computer is trained to recognise patterns in images or other supplied data via labels given to the examples. The idea is that once the system has enough examples to work off, it can apply the same labels itself to new cases.
One of the limitations of the technique is that hundreds of thousands or even millions of examples are sometimes needed to make such systems trustworthy.
Mr Hamm acknowledged that in this case the results were not 100% accurate.
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