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The reasoning behind choosing artificial intelligence over machine learning in robotics often hinges on cost implications associated with potential errors. When robotics systems make mistakes, the consequences can be costly—not only in terms of material loss or downtime but also regarding safety and liability issues. For instance, errors in automated processes could lead to expensive repairs, injury to human operators, or significant disruptions to operational efficiency. Thus, using AI, which typically encompasses a broader set of capabilities including the ability to adapt and learn more holistically from data, can mitigate the risks and expenses associated with mistakes in robotics.

In contrast, while cost-effectiveness and hardware requirements are important considerations, they don't directly address the implications of mistakes in operational settings. Outdated algorithms are also not a direct financial reason; rather, the focus should be on the capability and robustness of systems to prevent costly errors in the first place. Therefore, selecting AI is often justified by the need to reduce financial risks associated with operational errors in robotic applications.