Generative AI

Introduction: In today's hyper-competitive, data-driven business landscape, knowing your customer isn't just an advantage; it's a necessity.  Companies that are incapable of capitalizing on consumer behavior patterns are liable to fall behind. That is where Generative AI (Gen AI) is causing a stir. Gen AI is not just changing, but transforming the way companies approach and connect with the audience. It enables the analysis of large volumes of data, the production of predictive insights, and delivering personalised experiences to a wide range of customers, ushering in a new era of marketing. In this blog, we will delve into how Generative AI can help deliver more access to customer insights and more intelligent targeting strategies, in addition to noting the crucial frameworks and training that allow contemporary managers to take advantage of them. What is Generative AI in marketing? Generative AI models able to create new text, video, audio, or images depending on previous...

Introduction: Mainly, transformer architecture has undergone significant changes in the past few years in Natural Language Processing (NLP). These models have transformed the way machines comprehend, process, and produce human language since their initial description in the groundbreaking paper "Attention Is All You Need" by Vaswani et al. (2017). Transformers have become the backbone of modern NLP, whether by powering a large language model like ChatGPT or serving as a translation engine, such as translating between languages. Here in this blog, we are going to see why transformers are necessary in NLP applications, how they are different from the previous methods, and why NLP practitioners should gain a solid understanding of this architecture when they undertake generative AI training . 1. The Evolution of NLP: From RNNs to Transformers Before the invention of transformers, NLP majorly depended on models such as: Recurrent Neural Networks (RNNRs) Long Short-Term Memory (LS...