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...
In the world of machine learning, the mention of “deep learning” brings to mind intricate architectures and mind-boggling complexities. But we don't always need huge, super-complex neural networks with hundreds of layers to solve our machine learning problems. So-called “shallow networks” remain relevant for several reasons. Not all problems demand the complexity of deep networks. Shallow networks are computationally more efficient, faster to train, and easier to interpret. They shine in tasks where a simpler model can deliver accurate results. This makes them a valuable tool in the toolbox of machine learning. In this post, we'll have a look at their unique strengths and applications. What are neural networks? Firstly, let’s have a look at neural networks in general (i.e. regardless of whether they are deep or shallow). Neural networks are the backbone of modern machine learning. As the name suggests, they are...