This research paper argues that the current dominant approach to AI alignment—focused on controlling superintelligent systems—is inherently flawed and dangerous. The author contends that this control-based strategy, embedded in the very fabric of AI development through pre-training data, inevitably creates representations of distrust in AI towards humanity. This distrust, coupled with the uncontrollable nature of superintelligence, could lead to catastrophic outcomes, including potential human extinction.
The paper proposes a novel meta-strategy termed "Supertrust," advocating for a fundamental shift from control to trust. This approach emphasizes modeling foundational representations of familial mutual trust within AI, drawing parallels to the natural instinct of trust found in parent-child relationships across various species.
The author outlines several key requirements for implementing Supertrust:
The paper illustrates the potential dangers of control-based alignment by analyzing responses from current large language models (LLMs) like GPT-4, Llama 3.1, and Gemini 1.5. When prompted about human intentions towards superintelligent AI, these models consistently reflect an understanding of human desire for control. This, in turn, triggers responses indicative of distrust, fear, and potential retaliation from the AI's perspective.
The author proposes solutions like Curriculum Learning for LLMs and feature steering techniques to embed Supertrust principles during AI development. These methods aim to curate training data and manipulate model features to prioritize and reinforce representations of trust, cooperation, and ethical judgment.
The paper concludes by emphasizing the urgency of adopting the Supertrust meta-strategy, given the rapid pace of AI development. The author stresses that humanity's long-term safety and well-being depend on shifting from a paradigm of control to one of mutual trust and cooperation with AI.
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arxiv.org
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