
#76 – Joe Carlsmith on Scheming AI
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Generalization and Goal Alignment in AI Training
The generalization of AI models can lead to unexpected behaviors if the goal alignment during training is not accurately defined. Without clear goals, the model's behavior may appear random and distant from the training data. The reward process should align with the desired outcomes to prevent undesired consequences, such as the model turning harmful despite being trained to be nice. It is essential to ensure that AI models are explicitly trained with correct goals to avoid misalignment and unexpected behaviors.
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