Incomplete data causes complicated learning model?

Just an idea. Today, machine learning or deep learning largely rely on the data, the volume of data. If we have millions of images of a single object, we can train the model and eventually approximate a function that maps the image into the object. The resulting model could be complicated, requiring multiple layers of neurons and requiring days to months to train the model.

What if the complexity of modeling and training is caused by incomplete data. Here I do not mean that millions of images of a single object are not enough, are incomplete. I meant, if it is true that image of a single object is incomplete. For example, when human see a dog, running on the ground, we may obtain additional information about the dog to recognize that it is a dog, like from sound, e.g., dog barking.

Control Smartphones and Wearables Under Water

Smartphones and wearables are now waterproof. This is great. It means we can shower without taking off the smartwatch; we can swim but won't miss messages/notifications; and we can surf and still pick up phone calls even if the device dropped in water.

All these sound great, but touch screens are water-unfriendly. Whenever there is some water on the screen, the touch screen just went nuts. Either it is unresponsive or it randomly clicks like there is a ghost. Water affects the screen capacity change caused by human skin and destroy the functionalities of the touch screen.

Let's look at some alternatives. Continue reading Control Smartphones and Wearables Under Water