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...
When humans talk to themselves (raising my own hand here!), other humans cast a side-eye. When AI does it, it’s heralded as the Next Big Thing. The past two weeks saw the rise of AutoGPT and similar apps, called "AI agents”. AutoGPT has so far garnered the most attention, so we’ll focus on that one. (Another name you may come across often is BabyAGI.) AutoGPT was created by Toran Bruce Richards, a game designer. And now, like mirrors within mirrors in the dazzling game of illusion that is AI, people are already building apps on top of the agents, too. But what even is an agent (except for, a bit worryingly, the guardians of the virtual world in The Matrix movies)? What is an AI agent? In computer science, an "agent" is a program that functions independently and continuously to perform various tasks. These could include archiving computer files or retrieving electronic messages on a scheduled bas...