The Future of AI: Horses for Courses - Task-Specific Models and Content Understanding

The world is abuzz about general-purpose models like GPT. It's hard to question their power and their versatility - you can accomplish a great deal with them. And yet we should not ignore the efficiency and simplicity of their smaller, more focused counterparts: task-specific models.

What Are Task-Specific Models?

The English saying “horses for courses” comes from horse racing, meaning some horses do better on certain tracks. It’s like saying you need the right tool for a specific job - and that's what task-specific models are.

Task-specific models are designed to excel at one particular use case. Unlike general-purpose models that can handle a wide range of tasks, these models are fine-tuned for specific applications.

In healthcare, these models could offer insights to help diagnose diseases by analyzing medical images and patient data. In finance, they could offer insights to help predict market trends and detect fraudulent activities. In manufacturing, task-specific models can help optimize production processes and predict equipment failures to reduce downtime and increase efficiency. It may seem counterintuitive that, even as LLMs become ever more powerful, more and more of the world's work may be accomplished with the help of task-specific models.

Task-specific models offer highly specialized solutions for a targeted set of problems, which could make them more efficient and cost-effective for the tasks for which they were designed.

Azure AI Content Understanding

Azure AI Content Understanding, a new Azure AI Foundry service, showcases the power of task-specific models. This AI service uses general-purpose and task-specific models to process unstructured data - like documents, images, audio, and video - and extract structured data from it. It does all of this with a simple API, and without any complicated prompt engineering.

For example, you can input a grant deed and extract details like the buyer, seller, city, and transfer tax, all without any prompt engineering. Or you can give it the audio recording of a contact center call and extract key information from that call. It's an easy way to get a useful, structured result that can be used downstream by automated systems or other AI agents.

Advantages of Task-Specific Models

Task-specific models are not limited to Content Understanding. Azure AI Foundry offers many of them, including models for speech, translation, language, and vision. These models are used today to caption live news and sports on TV, like Swedish Television does; to power the Read-Aloud function in the Microsoft Edge browser and on publications like USA Today; and to read reams of tax documents, like H&R Block does.

These models offer several advantages. First, they are optimized for their particular use case, resulting in faster performance and lower latency. Second, they are cost-effective; using these models is often cheaper compared to general-purpose models. Additionally, many task-specific models - such as those for Speech and Document Intelligence - support many more languages than general-purpose models. Some functions, like Document Translation which translates documents like PDFs while retaining their formatting, don't presently have an analog in the general-purpose model world.

Finally, task-specific models tend to be easier to use because they don't require prompt engineering and usually do not need fine tuning. These models say what they do and do what they say.

While general-purpose models like GPT are incredibly powerful, task-specific models can be the better choice for simpler, more focused tasks.

Watch my latest Cozy AI Kitchen demo with Microsoft's VP of Computational Design John Maeda to learn how you can use task-specific models to drive AI efficiency:

To begin exploring task-specific models, have a look at the Azure AI services section of Azure AI Foundry.