From the course: Understanding Generative AI in Cloud Computing: Services and Use Cases

Introduction to cloud-based generative AI models for text data

From the course: Understanding Generative AI in Cloud Computing: Services and Use Cases

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Introduction to cloud-based generative AI models for text data

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- [Instructor] Generative AI models can learn patterns and structures in text data. The only thing that matters right now is that these models can produce coherent and relevant text based on the input. It's also important to remember that these systems can understand all types of information, including speech, writing, and data streamed indirectly. Intricate details of how this works is not crucial at this point. For now, all you need to know is the basic idea of each model. The training process involves exposing the model to vast amounts of text data. This allows it to learn grammar, vocabulary, and semantic relationships. Most of the current models are trained off data from the internet, which is why they're good at organizing and formatting information. However, some models can learn from specific data such as your customer data. So what are the benefits of cloud-based generative AI models for text data? Let's look at a few of them. Some of them, which you may have used today. Language translation. These cloud-based generative AI models can produce accurate translations between different languages, breaking down language barriers. We've all used translation applications such as Google Translate, which can be useful when traveling to new countries where you don't speak the language. Content generation. Cloud-based generative AI models can output human-like text in writing or simulating speech. Chances are, if you've leveraged productive AI systems such as ChatGPT, you've already used the service to assist in writing a letter, an article, or summarizing a research paper. Chatbots and virtual assistants. Conversational AI has been popular for years. This includes applications and customer service and virtual assistance. Chatbots powered by generative AI models can understand and respond to user queries conversationally. Improving customer interactions such as support experiences. We've all used these systems on our phones, smart watches or digital assistance. Data augmentation. Generative AI models can be used to create synthetic data to augment existing data sets for training machine learning models. This model helps address data scarcity problems and reduces the need for extensive data collection efforts, saving processing, and storage resources. Creative writing and storytelling. These models can create storylines and assist in the creative process for content creators, screenwriters, and many other types of users. For example, as ChatGPT or other generative AI services to write a fictional story about you. The important thing to remember here is that a variety of models exist, and are available to leverage regenerative AI in the cloud. It's like knowing the number and types of tools you have in the shed, and thus being able to use them to solve different problems.

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