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...
Imagine a digital coach guiding a model through data, teaching it tasks like distinguishing between cats and dogs, diagnosing illnesses from medical images, or forecasting stock market trends. This is the essence of supervised learning – a technique with applications ranging from self-driving cars to personalized recommendations. Supervised learning is often considered one of the easiest machine learning techniques to understand, especially for beginners. It is a type of machine learning where a model learns to make predictions or decisions based on labeled training data. In supervised learning, the algorithm learns to map input data to the correct output by observing examples of input-output pairs provided in the training dataset. The goal is for the model to generalize from the training data and be able to make accurate predictions on new, unseen data. Let’s take a step-by-step look at how supervised machine learnin...