For utilising emotions in text, there is a requirement of first discerning the corresponding emotion.

Once we have emotional information, we can make use of it to predict and assess the intention and context behind the piece of text. However, the question is, how do we extract emotional information? And once we effectively do that, how do we utilise thi information for specific use cases.

With the emergence of transformer architecture and homogenisation of this into Large Language Models, there has been a transformational change in the outlook of Generative AI and Artificial General Intelligence. Researchers, data scientists and industries are scrambling to commercialise and effectively leverage LLMs for increasing productivity. CEOs and top level managers are looking to cut costs by dehiring workforce which otherwise was being utilised for daily mundane chores. Low level developers are already threatened and signs of layoffs in the industry are visible. Technocrats are excited on the idea of ingenious application of LLMs to solve problems and revolutionise knowledge systems. The ripple effect is already visible with Gen AI courses being offered by Engineering and Management institutions. Enterprises are talking of enabling supply chain optimisation, fraud detection, customer and Human Resource management by leveraging LLMs and other Gen AI tools. The world is thus facing an ethical dilemma. There is lack of trust in genuine creativity, owing to the doubt in one’s mind that the product is a result of Gen AI rather than original work by a human being. Do we need to worry about this? I feel no; let’s embrace the technology and utilise it for creating enhancing our work by dedicating ourselves to creativity and to solving real world problems using these tools. We can view these tools as an advanced scientific calculator which leaves us with more time to ourselves to pause, contemplate and visualise solutions. I am of the opinion that the coming decade will witness accelerated growth in positive operationalisation of Gen AI towards a more cogent connected and evolved world. Some of the approaches to ensure fruitful and genuine utilisation of Allan’s and Gen AI a could be on the following approaches. - [ ] Enhancing Role of GANs for anomaly detection. - [ ] LLMs to evaluate other LLMs - [ ] NFTs for protecting genuine content - [ ] Deepfake detection - [ ] Authentic and synthetic discrimination using Trust based Framework.