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...
Picture tiny protein architects effortlessly combining like pieces of an intricate puzzle to build nanoscale structures with mind-boggling precision. Dream or nightmare? These self-assembled protein structures hold the promise of creating entirely new materials with properties that defy our current imagination. But there are those who fear they also hold the key to the annihilation of all humankind… Welcome to the fusion of machine learning (ML) and protein synthesis. It’s not so far away as you might think. Say the words “artificial intelligence,” and most people today will probably think of the large language models like ChatGPT or any of the AI art generators . But many other ML techniques are used in various fields with equally exciting applications. Protein prediction and synthesis is one such area. ML is making remarkable advancements with implications for biotechnology and materials science. It works like t...