machine learning

Generative adversarial networks are a machine learning method where two neural networks are competing against each other. The generative network tries to create an output based on the initial data and the discriminator network tries to detect if given input is real or generated by it's adversary. Repeating this process while feeding the results back to the network keeps improving both networks and in turn improving the quality of generated content.

Machine learning appears to be today's Maslow's hammer. As I have been following closely this field I have noticed that there seem to be new areas where machine learning is suggested to be applied every day. 

Sure it has the potential to transform many industries or at least make them more efficient. But still, it's not the Deus ex machina that magically solves all the problems. Those nails are still needed. Without them, there is nothing to learn from.

Yesterday we had an innovation day at work. During these days we have groups of people working on interesting new things trying to come up with new things we could start doing at work.

This time I was running a workshop around machine learning and chatbots. Our goal was to get familiar with basic machine learning tools and train a model that could be used to create a chatbot that could participate in casual conversations in our company chatrooms.