I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long time. I was a bit surprised because I thought of him as someone very creative. However, it is not uncommon to hear scientists lament about their lack of creativity compared to academic superstars. I think about creativity in academia is a bit distorted and a straight view can help to feel less bad about one’s own creativity.
This morning I got an email about my blog post discussing the history of deep learning which rattled me back into a time of my academic career which I rather not think about. It was a low point which nearly ended my Master studies at the University of Lugano, and it made me feel so bad about blogging that I took two long years to recover. So what has happened?
In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century.
Convolution is probably the most important concept in deep learning right now. It was convolution and convolutional nets that catapulted deep learning to the forefront of almost any machine learning task there is. But what makes convolution so powerful? How does it work? In this blog post I will explain convolution and relate it to other concepts that will help you to understand convolution thoroughly.