Should AI models be like Swiss Army knives, versatile and handy in a variety of scenarios? Or do we prefer them as precision tools, finely tuned for specific tasks? In the world of artificial intelligence, and natural language processing specifically, this is an ongoing debate. The question boils down to whether models trained for specific tasks are more effective at these tasks than general models. Task-specific models: specialization and customization In my last blog post , we looked at the rise of personalized LLMs, customized for specific users. Personalized LLMs can be seen as an extreme form of task-specific model. Fans of task-specific models stress that these kinds of models are better suited for tasks involving confidential or proprietary data. This is obviously true. But some people also believe that specialized models necessarily perform better in their specific domains. It may sound logical, but the ans...
“The thing’s hollow – it goes on forever – and, oh my God, it’s full of stars!” - David Bowman, 2001: A Space Odyssey (novel) Last week, OpenAI co-founder Greg Brockman demoed some of the largely unreleased ChatGPT plugins at TED2023. At the same time, he provided more clarity on these plugins and their intended use-cases. Yesterday marked exactly one month ago that OpenAI tantalisingly announced that they will be introducing support for plugins in ChatGPT. We knew that this would enhance ChatGPT's capabilities beyond its built-in functionalities. But the specifics were a little bit unclear until now. Essentially, the various plugins will enable the language model to access real-time information from the web and other sources such as databases. Third-party services will allow it to perform actions such as booking a flight or ordering food on behalf of the user. And we will be able to access all of this func...