Part 4/9:
With the recent unveiling of the capabilities of GPT-4, which reportedly utilizes 1.8 trillion parameters to enhance performance, the expectation was that increasing size would yield superior results. However, the reality reveals a wall of diminishing returns where simply augmenting the scale of models and computational capacity does not significantly improve their performance. Current research suggests a reality where lesser data availability constrains the effectiveness of models, as the amount of data required for training could soon exceed the available data in existence.