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  • Writer's pictureDonnovan Andrews

From Customer Service to Fraud Detection: How Generative AI is Reshaping Business Operations

Updated: Aug 20, 2023


Generative AI, which refers to AI systems that can generate text, images, and other media, has become more prevalent in recent years. It offers a new way of finding and exploring highly specific information. One example of generative AI is the ChatGPT plugin developed by OpenAI, which companies can use to develop AI systems that can interact with their customers.


Another trend in generative AI is the use of self-hosted large language models (LLMs). Self-hosted LLMs offer benefits such as sidestepping privacy issues that can come from connecting data with an OpenAI product. However, companies should consider both ChatGPT plugins and self-hosted LLMs when developing AI systems.


Domain-specific language models are another trend in generative AI. These models fine-tune general-purpose LLMs on specific data sets to develop information retrieval tools. If generative AI becomes more domain-specific, it will lose its mystique as something that knows everything and become embedded in our daily lives…potentially quelling the freak outs and Skynet comparisons.


Generative AI has the potential to change businesses in several ways. One example is how it can help companies with customer service. ChatGPT plugins can help answer customer questions and even provide product recommendations. Another example is how generative AI can help companies with research and development. Companies can use generative AI to explore new solutions to problems and generate new ideas.


Overall, generative AI has the potential to be a valuable tool for businesses. Companies should consider both ChatGPT plugins and self-hosted LLMs when developing AI systems. Domain-specific language models are an emerging trend that could make generative AI less frightening and threatening to humans. If generative AI becomes more domain-specific, it will lose its mystique as something that knows everything, and it will instead become embedded in our context.


Domain-specific generative AI refers to AI models that are trained on specific data sets and designed to generate content or perform tasks in a particular domain. Here are some examples of domain-specific generative AI:


1. Medical diagnosis and treatment planning: AI models can be trained on large medical data sets to generate diagnoses and treatment plans for specific medical conditions. These models can help doctors make more accurate diagnoses and provide personalized treatment plans for patients.


2. Language translation: AI models can be trained on specific language pairs to provide accurate translations for different languages. For example, an AI model trained on English and Spanish can provide accurate translations between the two languages.


3. Content creation: AI models can be trained on specific types of content, such as news articles or social media posts, to generate new content. This can help businesses create new content quickly and efficiently.


4. Fraud detection: AI models can be trained on financial data sets to detect fraud patterns and flag suspicious transactions. This can help financial institutions prevent fraud and reduce losses.


5. Speech recognition: AI models can be trained on specific speech patterns and accents to provide accurate speech recognition for different languages and dialects. This can help businesses provide better customer service and support.


Overall, domain-specific generative AI has many potential applications in various industries. By training AI models on specific data sets, businesses can create more accurate and effective AI systems that are tailored to their specific needs and less frightening to the general public.

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