Ai for Business

  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...

(This article is a reproduction of the lectures I have given in Engineering Colleges in Mumbai, for students and faculty) In a previous article, I have suggested the DTOP (Data-Technology-Objective-Possibilities) framework (see https://www.linkedin.com/pulse/4-hints-get-started-ai-your-company-devesh-rajadhyax)to analyse AI use cases in a company. Today I am going to use the same framework to explain some applications of AI to Computational Biology(CB). In this article I will focus more on the data aspect. There is a reason. Biological data is probably the most important kind data for us. However, very few engineers have a good understanding of this data. I will be very happy if this article encourages some engineers to study biological data in detail. Understanding and experience with this data will become a much wanted skill in near future. Let me first put CB use cases in DTOP.   Data : Biological data is of many types. I will mention three major types here and explain one ...

  Most companies are working on Digital Transformation today, and Artificial Intelligence is a critical part of that transformation. Two questions immediately present themselves- 1.    What is Digital Transformation and how it is different from the IT/ICT transformation that is happening since for than four decades? 2.    Why is AI a critical part of this transformation? Let me take the first question. Digital Transformation is actually a cognitive revolution. It is a more humanlike way of making sense of the world around. And this is our clue to the most important difference between IT and Digital- ‘IT systems are not humanlike. They don’t try to make sense of the world around them. They create a small world of their own and everyone follows the rules of that world. The input has to be given in the way they demand and output will be available in forms that they prefer. You better fall in line. Digital, or Cognitive systems would want to fit ...