Generative AI is an up-and-coming technology that has the potential to shape our future, substantially changing the way we operate in the evolving world around us. With its reach spanning numerous industries, Generative AI grasps the ability to make our everyday interactions more efficient and limit significant human errors. Despite possessing certain issues—largely ethical concerns—Generative AI harnesses the capabilities to bridge the barrier from our generation to the next.
Generative AI is a subset of artificial intelligence technologies designed to produce high-quality text, images, and other content based on the data they were trained on, using deep-learning models. It uses foundation models that are pre-trained on a large set of data and with multiple hyperparameters, requiring humans to design, develop, implement, and maintain it.
In 2010, near-perfect natural language translation was discovered, and around four years later, language models began to master the meaning of words. Advances made from 2017–2022 resulted in language models that serve as a foundation for customization. The year 2022 marked the arrival of the large language-foundational model ChatGPT, which possessed the capability to interact with humans using Natural language Understanding (NLU), allowing it to become so powerful and fundamentally change the way we live, work and conduct business in the world around us.
The main goals of any organization are to increase revenue, improve customer satisfaction and productivity of employees, and reduce costs. As these targets become more attainable with Generative AI products, every company aims to integrate them into their applications. Generative AI can perform tasks such as generating new ideas, summarizing texts, and searching knowledge-base documents that are already set up.
Taking a look at the different use cases of Generative AI across industries, we can see certain examples of real-world use-case scenarios. One such scenario includes retail companies integrating Generative AI via virtual chatbots, enhancing customer satisfaction and subsequently gaining trust, helping to increase the company revenue. Another scenario can be seen through AI-driven chatbots providing 24/7 customer support in contact centers, where Generative AI takes real-time data and combines it with the capabilities of a Knowledge Base and Large Language Models (LLMs), which are pre-trained by humans and can help to provide better suggestions and answers to the users. This will reduce operational costs and improve the efficiency of agents, allowing them to focus more on innovation.
In another essential sector, healthcare providers have started using Generative AI to optimize personalized and effective patient interactions. The use of generative AI has a wide range of applications in the healthcare industry, such as aiding in medical research and data analysis, personalized medicine, drug discovery and development, and medical imaging. As a result, they not only remain preferred providers, but they also improve staff efficiency and help provide better healthcare overall. Integration of Generative AI in healthcare frees up clinical resources from administrative tasks and enables healthcare professionals to focus on higher-value tasks.
In financial sectors, significant business potential is seen in process streamlining, personalized service, automated financial advisory, and compliance monitoring which enhances the customer experience. Generative AI automates routine tasks like data entry and fraud detection, reducing operational costs.
On the other hand, there are significant downsides to this technology, and the risks tied to generative AI are substantial and continuously developing. There are several challenges in leveraging AI technologies, ranging from the scarcity of credible and high-quality data to concerns about data accuracy and security. However, good governance enables better innovation, and there is a need to have guard rails, usage policies, and controls in place to overcome these issues. Technologies that provide trust, transparency, and security will become an important complement to Generative AI products.
There are concerns about the use of Generative AI and its potential impact on different industries and, subsequently, underlying jobs. There is a myth that Generative AI will eliminate jobs. If we look at history, we can see the potential fears in societies—when calculators and computers were invented—as there was an idea that they would overpower humans and replace skilled workers. However, these new inventions augmented our capabilities, and humans have become more productive in various fields and trained to work on more innovative ideas. The same can be said for the present-day Generative AI wave as well, as it also augments human abilities and generates new jobs in technology, such as Data Scientists, Machine Learning Engineers, AI Ethics managers, AI product managers, AI research scientists, AI ethics specialists, and Natural language processing engineers.
Lifelong learning and adaptability have always been the key to success. Instead of being threatened by Generative AI, we should focus on the innovative opportunities it presents. Learning and embracing this technology can help us ascend to the summit of technological advancements as we collectively work towards a more inclusive future for all.
About the Author:
Sreedevi Velagala has a background in Computer Science and Information Technology, kicking off her career as a developer in India and has an extensive career spanning over 20 years in the IT industry. Today, she is working as a Senior Architect at AWS. Throughout her career, Sreedevi has held leadership positions in esteemed financial organizations such as UBS (Union Bank of Switzerland), Barclays Capital, OCBC (Overseas-Chinese Banking Corporation Limited), Credit Suisse, and Citibank. Her extensive experience in various IT technologies, strategic leadership, project management, and navigating complex international landscapes has made her an invaluable asset in the world of cloud technology.
Sreedevi is working with Amazon Web Services (AWS) as a Senior Solution Architect. Here, she is heading numerous futuristic AI/ML projects including Generative AI capabilities such as Large Language Models (LLMs), vector databases and Retrieval Augmented Generation workflows. In addition, she has developed new technical assets such as AWS Guidance and Solutions which will include technical architectures, documentation and code. These reusable assets will simplify and accelerate the adoption of these technologies as they will provide customers with "pre-built" components. She is also an advisor to the customers on how AWS Solutions can lower the compute costs for Generative AI scenarios, educating customers and AWS technical Solutions Architects on how to use these technical assets.
Sreedevi is a recognized expert with extensive experience and a deep understanding of AI/ML and Compute domains, making her a trusted authority in these areas. This article encapsulates Sreedevi's vast knowledge and expertise in AI/ML and Generative AI, providing readers with valuable insights and perspectives from someone with hands-on experience in the field.
Bibliography sources:
1. https://www.gartner.com/en/topics/generative-ai
2. https://www2.deloitte.com/us/en/pages/consulting/articles/gen-ai-use-cases.html
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