This exercise using Stable Diffusion via DreamStudio offered an intriguing insight into how different AI models interpret and represent complex and abstract ideas. Each piece was a unique blend of the expected and the unexpected, reflecting the distinctive capabilities and limitations of the AI models.
The arena of Artificial Intelligence (AI) and Large Language Models (LLMs) continues to be an ever-evolving landscape. In a recent article, the focus was on how even the most advanced AI language models like Google’s BERT and OpenAI’s ChatGPT grapple with one fundamental aspect of human language: negation.
As we delve deeper into the world of AI art generation, we find ourselves continually intrigued by the capabilities and limitations of these innovative models. One model we’ve been experimenting with is Stable Diffusion, a tool that has provided us with a range of unique, sometimes surprising, artistic renditions. However, as with any technology, it has its limitations. Let’s take a closer look at some of the challenges we’ve encountered and speculate on possible reasons behind them.