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Let's get into the specifics, shall we? One of the major challenges to *robustness* is *data poisoning*. This is when malicious actors intentionally corrupt the training data used to build the AI model. The result? The AI system might start making incorrect predictions or behave in unpredictable ways. Then there is *model drift*, which happens when the performance of the AI model degrades over time. This can be caused by changes in the data distribution or the environment in which the system is deployed. As the environment how to troubleshoot youtube changes, the model may no longer be accurate. We also have to think about *adversarial attacks*, which involve crafting inputs designed to fool the AI system. Imagine feeding a self-driving car a stop sign that has been subtly altered so that the system reads it as a yield sign. Furthermore, *network latency and bandwidth limitations* can also pose significant challenges. When the components of a *distributed AI system* are communicating across a network, delays and limitations in bandwidth can impact performance, especially when handling large volumes of data.
