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 ever-expanding realm of Artificial Intelligence (AI), a surprising source has led to a new solution. MIT researchers, seeking innovation, found inspiration in an unlikely place: the neural network of a simple worm. This led to the creation of so-called "liquid neural networks," an approach now poised to transform the AI landscape. Artificial Intelligence (AI) holds tremendous potential across various fields, including healthcare, finance, and education. However, the technology faces various challenges. Liquid networks provide answers to many of these. These liquid neural networks have the ability to adapt and learn from new data inputs beyond their initial training phase. This has significant potential for various applications, especially in dynamic and real-time environments like medical diagnosis and autonomous driving. The strengths of scaling traditional neural networks While traditional n...