AI image generators, like DALL-E, Stable Diffusion, Midjourney, and Bing Image Creator, have made significant strides in recent years, producing impressive and often striking images. However, they still struggle with certain tasks, such as generating accurate and detailed maps or producing coherent and correctly spelled text. One of the reasons for these shortcomings is that these AI models are built on artificial neural networks that are trained on vast amounts of data but lack a true understanding of the world. They do not possess an inherent comprehension of what the text or symbols they generate mean, and they do not understand 3D objects or text when it appears in images.
For instance, when asked to generate an image of a person walking a dog in a park, the AI image generator might produce a result that is disappointingly inaccurate or unspecific, as it doesn't have a clear understanding of what a person or a dog looks like, let alone the specific details of the park or the person's appearance. Similarly, when it comes to text generation, AI models can struggle with spelling words correctly or producing coherent and meaningful sentences. This is because they lack a true understanding of language and context, and instead rely solely on patterns in the data they were trained on. Despite these limitations, AI image generators are continually improving, and researchers are actively working on developing new techniques to overcome these challenges and create even more sophisticated and realistic images and text.