Engineers can improve these issues by training AI with specific models, but spelling issues are not expected to resolve quickly due to the complexity of language. Some models are taught to avoid generating text altogether, while others are continuously updated to address specific issues. However, these AI shortcomings can be useful in identifying misinformation in images. Despite rapid improvements, these AI models are still limited in their capacity and often make small, local errors that can be noticed by a keen observer.
Key takeaways:
- Despite advancements in AI, it still struggles with spelling and details like the number of fingers on a human hand.
- Image generators use diffusion models to reconstruct an image from noise, while text generators use complex math to match patterns in prompts.
- Engineers can improve these issues by augmenting their data sets with training models specifically designed to teach the AI what certain things should look like.
- AI's shortcomings in spelling and details can be useful in identifying misinformation, as random strings of letters or incorrect details can betray an image's synthetic origins.