[00:02] can write, code, and answer questions sometimes confidently gives completely wrong information? This happens because GPT models do not actually know facts likely next words based on patterns learned during training. When the model
[00:17] receives a question, it generates a response by calculating probabilities for different words instead of searching a database of verified answers. If the training data does not contain enough information or the question is unclear,
[00:30] the model may create a response that sounds realistic but is not factually correct. These incorrect yet convincing answers are called AI hallucinations >> and they happen because language models are designed to generate useful text,
[00:42] not automatically verify every statement. Techniques like retrieval augmented generation, real-time search, and human feedback help reduce hallucinations by giving AI access to trusted information sources. Better
[00:55] training methods also teach models to recognize uncertainty [music] and say when they do not have enough information. However, even advanced AI systems can still make mistakes because predicting language is different [music]
[01:08] from truly understanding the world. Understanding hallucinations helps us use AI more effectively by verifying important information >> instead of blindly trusting every answer. Subscribe to this channel for
[01:21] answer. Subscribe to this channel for more such information.