Ai for Business

  Understanding Generative Adverserial Networks - Part 1 This is a two part series on understanding Generative Adversarial Networks (GANs). This part deals with the conceptual understanding of GANs. In the second part we will try to understand the mathematics behind GANs. Generative networks have been in use for quite a while now. And so have discriminative networks. But only in 2014 did someone get the brilliant idea of using them together. These are the generative adversarial networks. This kind of deep learning model was invented by Ian Goodfellow . When we work with data already labelled, it’s called supervised learning. It’s much easier compared to unsupervised learning, which has no predefined labels, making the task more vague.  "Generative Adversarial Networks is the most interesting idea in the last ten years in Machine Learning." - Yann LeCun In this post, we’ll discuss what GANs are and how they work, at a higher , more abstract level. Since 2014, many variations...

  AI is not new; it has a 60-year history. It has seen many ups and downs. At the middle of last century (1950), the world came out of the world wars. There were two legacies of the wars - the nuclear bomb and the computer. Both changed the world dramatically. When scientists invented computers, AI was the first application on their mind. A single person is often credited for fathering both computers and AI - Alan Turing. His 1950 paper ‘Computing Machinery and Intelligence’ pioneers AI thought and proposed the famous Turing Test.  In 1956, a conference at Dartmouth College (near Boston in US) declared the name Artificial Intelligence officially. In these early days, there was great enthusiasm in scientists and supporters about AI. They were super-confident that AI will reach human capabilities in a decade. How innocent that sounds now! Initially, AI looked like it is living up to the promise. Scientists wrote programs that could play the game of checkers or prove mathema...

  Is your company ready for Predictive Analytics? Every business leader has now become aware of the application of AI/ML to Predictive Analytics. They would like to harness prediction using ML for the benefit of the company. There are some applications at the top of their priority list- sales and demand forecast, failure prediction, probability of prospect conversion, prediction of employee attrition and so on. One can easily see that if a business can foresee even a few of these important parameters, it can gain a solid advantage. But can every company gain from predictive analytics? Is there a classification or maturity level of organisations that indicates how well it can use ML for prediction? Let me put together a few ideas that will help you to analyse your company’s preparedness for predicting future. The evolution of data maturity In 1949, a scientist called Abraham Maslow put forward the theory of human motivation. Popularly called Maslow’s hierarchy of needs, his theory s...