What’s GenAI, LLM and what are the use cases in different domain, is it really helping?

Introduction : Generative AI (Gen AI) is one of the types of artificial intelligence technology that broadly describes machine learning systems capable of generating text, images, code or other types of content, often in response to a prompt entered by a user. For effective content generation “prompt engineering” techniques are very crucial. These days, Generative AI models are being integrated into applications, tools, and chatbots that allow users to provide input as questions or instructions and in return, AI model generates a human-like response

LLMs (Large Language Models) are a specialized class of AI model that uses natural language processing (NLP) to understand and generate humanlike text-based content in response. These large models store and retrieve relevant information then generate coherent and contextually accurate responses. Examples of LLMs include GPT-3, GPT-4.

So, GenAI and LLMs are different but their use cases are similar and they complement each other to solve business problems.

History (a step back): The genesis of Generative AI can be traced back to the mid-1950s when the concepts of artificial intelligence and machine learning (ML) were beginning to take shape. IT pioneers like Alan Turing and John McCarthy played a pivotal role in laying the foundation for GenAI when they proposed early models of computation based on the idea that machines could one day mimic human intelligence.

It wasn’t until the 2000s that GenAI began to gain momentum thanks to the advancements in machine learning and deep learning to create neural networks — interconnected layers of “neurons” that process and learn from data like the human brain. Trained to recognize patterns in datasets, neural networks are able to make predictions and decisions without being programmed to do so.

But the creative power of GenAI is thanks to a specific type of neural network developed in 2014 by Ian Goodfellow and colleagues called a Generative Adversarial Network (GAN). GANs revolutionized image generation by combining two neural networks into the architecture — a generator and a discriminator — which compete to improve the quality of the generated data.

Most recently, the Generative Pre-trained Transformer (GPT) series, particularly ChatGPT-3 has taken the world by an electrifying storm for its remarkable ability to generate human-like text from simple prompts, igniting global imagination about the creative potential of AI.

OpenAI, the company behind the ChatGPT series has played a major role in advancing the capabilities and adoption of Generative AI. GPT-1, GPT-2, and GPT-3 displayed incredible language generation capabilities but none have come close to GPT-4. The latest release is more powerful and sophisticated.

As per the Fortune Business Insights, the generative AI market size was valued at USD 29 billion in 2022 and is projected to grow to USD 667.96 billion by 2030. And, as per Bloomberg research Gen AI to become $1.3 Trillion market by 2032.

Considering the potential growth, all the industry leaders are launching various AI products to enable business use cases.

Top AI Products, Tools and LLMs

The coolest new AI products in the market this year are helping businesses become more productive and efficient by speeding up a wide variety of tasks and use cases.

Large language models (LLMs) like Microsoft 365 Copilot, Google Bard and OpenAI’s GPT-4 can be used to generate code, translate customer documentation, create marketing materials, answer questions and develop new product ideas.

Other hot artificial intelligent tools like Amazon Bedrock makes GenAI models accessible through an API, while Google’s Vertex AI Platform provides purpose-built MLOps tools for data scientist and machine learning engineers to automate, standardize and manage AI projects.

It appears that the possibilities and use cases for GenAI increase with every new version and iteration with the largest tech companies such as Google, Microsoft and Amazon, spending billions on building new AI tools. The most talked about startups across the globe right now are AI startups like Anthropic.

What are the predominant use case with GenAI?

As business look forward greater efficiency, innovation, automation in today’s competitive & fast paced market, GenAI has been crucial technology. Therefore, GenAI use cases are evolving and pretty much all the major domains and technological areas have use cases identified and more are evolving.

I’ve tried to capture few important use cases per industry, but the list can be more exhaustive.

Banking and Finance:

• Enhanced virtual assistants to increase the efficiency of human customer service representatives
• Fraud detention and prevention for anomalies
• Financial Forecasting based on historical data, trends and other factors
• Personalized marketing based on customer portfolio
• Boost the customer experience
• Enable financial advice based on customer history and interest
• Summarize large document
• Personalized financial recommendations

Health Care:

• Automated appointment booking and rescheduling
• Documentation and record-keeping automation
• Accelerate billing and claims processing
• Reduction in data entry and extraction information manually
• Patient outreach by sending personalized information and preventive care reminders
• Predicting drug adverse effects
• Repurposing Existing Drugs by predicting effectiveness against different diseases

Insurance Use Cases:

• Automated underwriting process
• Customer experience enhancement
• Accelerated claims processing for faster resolution
• Predictive analytics

Retail Industry Use Cases:

• Product and display design by analyzing current market trends and customer interactions, consumer preferences, and historic sales data
• Automated content generation
• Personalized marketing using marketing content for individual customers
• Product recommendations based on their buying history and preferences.
• Inventory management & supply chain optimization based on historical sales data, trends, seasonality etc.
• Virtual shopping assistants to help customers in their shopping journey, generating responses to their queries and guiding them through the purchasing process.
• Software Engineering Use Cases:
• Identify user needs and generate user stories
• Generate architecture diagram and data models
• Generate wire frame
• Code and unit test generation
• Test cases, test data generation and test automation
• Generating Infrastructure as code, CI/CD pipelines
• Incident analysis and problem resolution in production environment
• Anomaly detection and alerting

Risk and challenges with GenAI

As with any transformative technology, generative AI raises policy and ethical concerns. It’s also important to note that generative AI tools are not 100% accurate and they are known to hallucinate, fabricating information entirely. Moreover, generative AI models may replicate biases present in the training data, leading to the propagation of discriminatory practices post implementation. More widely, bias in AI systems can perpetuate social inequalities and reinforce unfair practices. Compliance with IP rights and licenses is another area of concern and there is a risk of inadvertently infringing copyright or violating licenses.

Conclusion

GenAI and LLMs can offer immense efficiency gain, reshape the future of various domains (health care, insurance, bank etc.), and accelerate time to market. However, considering the risk and challenges, it is essential to approach AI implementation with caution, considering the associated risk. To ensure the the success of these technology it is important to validate the results after the implementation and make sure “human must remain in charge”