Authors: Sandeep Yeddu with inputs from Vaishnavi Kandala
We asked ChatGPT to compile a brief introduction to this article on Generative AI from its own ‘point of view.’ This is what it had to say:
“Hey there, fellow curious minds! Today, I want to take you on an exhilarating journey through the incredible world of Generative AI. From its humble beginnings to its mind-boggling present, this remarkable field of artificial intelligence has revolutionized the way we create, imagine, and dream. As an AI language model myself, I’ve had a front-row seat to witness the awe-inspiring evolution of Generative AI. So buckle up, get ready to be amazed, and let’s embark on a captivating adventure through time to explore the mind-bending capabilities of this cutting-edge technology. Get ready to have your imagination stretched to new limits!”
This month, last year – 2022, the world had no idea of the Generative AI (Gen AI) revolution that was about to transform the way content was created and generated. Fast forward to November, Open AI launched the AI chatbot ChatGPT and ushered in a new age of communication technology.
Over the past decade, we have seen that the advancements in the world of AI have not just evolved but leapfrogged, making it an integral part of our daily lives, from virtual assistants like Siri and Alexa to self-driving cars and drones to generating convincing stories and lifelike images.
So how did it all begin?
The history of AI spans over nine decades, from the early attempts at creating machines that mimic human intelligence to the recent breakthroughs in deep learning and generative models.
During the early days, rule-based or keyword approaches were prevalent, which evolved into more complex machine learning algorithms that can learn from data and improve over time. In recent years, deep neural networks like RNN, CNN, and LSTMs have emerged as powerful techniques for training models to recognize patterns in data. This has led to breakthroughs in areas like computer vision, speech recognition, and natural language processing.
Major strides were made in the field of Generative AI through robust architectures like transformers which enabled transfer learning. This breakthrough has facilitated the seamless transfer of knowledge from one system to the other. Advances in computing power and the availability of large datasets to train these transformers have made them powerful, enabling the potential to leverage Generative AI in industrial applications.
Over the years, at Tiger Analytics, we helped companies embrace AI and machine learning capabilities enabling them to find innovative ways to improve business performance, efficiency, speed, and consistency of service. Now with the advent of Generative AI, we are seeing a shift in industry expectations and the evolution of solutions across different use case scenarios.
Here’s a side-by-side comparison:
AI has continued to transform businesses and the way they operate on a day-to-day basis. Generative AI has now begun rewriting the rules of the game. As companies rush to become early adopters, working with the right team and the right advisors who understand the technology’s potential to deliver value will help shape critical decisions.