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...
If you’ve spent any time at all on social media recently, the Pope in a white puffer jacket or “Harry Potter by Balenciaga” (and its several spin-offs) may have caught your eye. Of course, AI-generated art and video are everywhere these days. To those of us who have only recently woken up to the AI revolution, the technology feels very recent. Arguably the most popular AI image generator, Midjourney, is not even a year old. Yet, computer-generated art has been slowly advancing for more than half a century and very quickly for the last decade or so. The evolution of neural networks, a crucial component of modern AI-generated imagery, started even longer ago – all the way back in the 1940s. Algorithm art: The early days In some sense, the birth of AI-generated imaging can be traced back to the 1960s. This is when researchers started exploring the use of computer algorithms to create digital images. One of the earliest examples of this is the work of A. Michael Noll...