Think of ChatGPT as a blurry jpeg of all the text on the Web. It retains much of the information on the Web, in the same way, that a jpeg has much of the knowledge of a higher-resolution image, but if you're looking for an exact sequence of bits, you won't find it; all you will ever get is an approximation. But, because the approximation is presented in the form of grammatical text, which ChatGPT excels at creating, it's usually acceptable. So you're still looking at a blurry jpeg, but the blurriness occurs in a way that doesn't make the picture as a whole look less sharp.
- This article critically examines the capabilities and limitations of large language models like ChatGPT, comparing them to lossy text-compression algorithms.
- The model struggles with understanding fundamental principles and generating original content.
- Potential applications of these models in search engines, web content generation, and assisting original writing are explored, along with the challenges associated with their reliability, accuracy, and potential for misinformation.
- The importance of considering the benefits and limitations of large language models in future AI research and development is emphasized.