Part 8/14:
Numerical analysis and complex data analytics: Questions requiring deep data crunching or pattern recognition, like predicting purchasing behaviors based on multiple factors, are not yet best suited for gen models.
Traditional AI methods—clustering, neural networks, and data analytics—remain vital for such workloads.
Furthermore, the model's outputs are only as good as the data it was trained on. Garbage-in, garbage-out applies here. Moreover, the challenge of explainability—understanding how an AI model arrives at a conclusion—is more complex with gen AI due to its black-box nature.