Unravelling the mysteries of Artificial Intelligence

“Any sufficiently advanced technology is indistinguishable from magic.” - Arthur C. Clarke. Well! Convolutional Neural Network (CNN) is such a technology. How it does, what it does is truly indistinguishable from magic. Read our earlier post - “From Cats to Convolutional Neural Networks” , to understand why CNNs come close to human intelligence. Although the inner workings of a CNN can be explained, the magic remains. Fascinated by CNNs, we thought of coming up with as many questions about CNNs to understand the mystery of why it is able to classify images or any kind of input so well. What is convolution? What is pooling? Which pooling function is preferred - Max or Average? What is the role of activation functions in CNN? Why is Relu prefered in CNN rather than Sigmoid? Why adding more layers increase the accuracy of the network? What is the intuition behind CNN? What is stride? Is it necessary to include zero-padding? What is parameter

Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer. It will be a daunting task for humans to understand enormous amount of data that is generated all over the world. Machine Learning is helping scientists to use algorithms that learn from data and find patterns. Below is a list of few of the problems AI is working on to help find solutions which otherwise would not have been possible: Cancer Diagnostics : Recently, scientists at University of California (UCLA) applied Deep Learning to extract features for achieving high accuracy in label-free cell classification. This technique will help in faster cancer diagnostics, and thus will save a lot of lives. Low Cost Renewable Energy : Artificial-intelligence is helping wind power forecasts of u