AI’s Dirty Little Secret: Understanding Overfitting
In this video, Sabine Hossenfelder discusses a fascinating aspect of artificial intelligence (AI) that often goes unnoticed: the phenomenon of why AI models, particularly large language models (LLMs), work as well as they do despite the expectation of overfitting. She explains that overfitting, a common problem in statistical models where the model fits the training data too closely and performs poorly on new data, does not happen as frequently in modern neural networks, and the reason for this remains unknown.
Sabine illustrates that while traditional models exhibit a clear overfitting point, where increasing the number of parameters leads to worse performance on new data, neural networks demonstrate a ‘double descent’ phenomenon. After an initial increase in error due to overfitting, further increasing the number of parameters surprisingly improves performance on new data. This unexpected behavior challenges conventional understanding and has yet to be fully explained.
She speculates that one possible reason for this could be that during training, neural networks naturally stabilize on a fit dominated by a few relevant parameters, fine-tuning the rest. However, this explanation remains speculative. Sabine believes that understanding this phenomenon could provide insights into the workings of the human brain and the emergence of complexity in general.
She also emphasizes the importance of understanding the software we rely on, especially as AI becomes more integrated into various aspects of life. The video concludes with a recommendation to explore courses on Brilliant.org for those interested in learning more about neural networks and large language models.
The video highlights the need for further research into the underlying mechanisms of AI models to better understand their capabilities and limitations.