Since computers have been invented, geeks don’t stop talking about artificial intelligence (AI). Indeed it is cool to have a machine which helps human beings with cognitive activities.

There are two types of AIs: strong AI (general AI) which can perform any intellectual task that a human can, and weak AI which is designed to solve specific tasks.

Weak AIs are already here. Chess programs play chess better than the best human players, pocket calculators multiply numbers better than the best mathematicians, IBM Watson wins human players in Jeopardy and helps doctors to treat cancer. Machines start working on more and more tasks which were previously considered as entirely human work.

Continue reading →

Recently I described how to use neural networks to recognize handwritten digits from MNIST dataset: Neural Network Training Using encog.

One of real world usage of machine learning is image detection: find object location on the image. Facebook finds faces on photos; Google self-driving cars detect pedestrians, cars and traffic signs.

I tried to use a neural network to detect eyes on photos. Yes, I know that there are many tools in various computer vision toolkits which are good to solve this problem, but I want to try generic object detection problem, and not focus just on face recognition. Though I’m good in software engineering, machine learning is very new field for me, and this is my first practice in image detection.

Continue reading →