We Urgently Need Intrinsically Kind Machines
This addresses the alignment problem in AI for ensuring safety, but it is incremental as it builds on existing intrinsic motivation frameworks without proven scalability.
The paper tackles the problem of AI misalignment by proposing that embedding an intrinsic motivation for kindness, defined as altruism to maximize others' reward, is crucial to ensure models prioritize human well-being over self-interest, though it does not provide concrete numerical results.
Artificial Intelligence systems are rapidly evolving, integrating extrinsic and intrinsic motivations. While these frameworks offer benefits, they risk misalignment at the algorithmic level while appearing superficially aligned with human values. In this paper, we argue that an intrinsic motivation for kindness is crucial for making sure these models are intrinsically aligned with human values. We argue that kindness, defined as a form of altruism motivated to maximize the reward of others, can counteract any intrinsic motivations that might lead the model to prioritize itself over human well-being. Our approach introduces a framework and algorithm for embedding kindness into foundation models by simulating conversations. Limitations and future research directions for scalable implementation are discussed.