Part 3/6:
However, this approach is not without its critics. Concerns have been raised about the potential for an "inbreeding effect," where the new models become too similar to the old ones, stifling innovation. There are also worries about the compounding of small mistakes as the synthetic data is used to train subsequent models, leading to a potential "collapse" in the quality of the AI systems.
Shifting Focus: Improving Models Post-Training
In response to these challenges, Open AI is shifting its focus towards developing techniques to improve models after the initial training phase. Reinforcement learning and human feedback are being used extensively to fine-tune models like ChatGPT, making them more helpful and safer.
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