Aligning AI with a specific ethical framework is fraught with disagreement. A better target is "human flourishing," as there is broader consensus on its fundamental components like health, family, and education, providing a more robust and universal goal for AGI.
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AI excels where success is quantifiable (e.g., code generation). Its greatest challenge lies in subjective domains like mental health or education. Progress requires a messy, societal conversation to define 'success,' not just a developer-built technical leaderboard.
Current AI alignment focuses on how AI should treat humans. A more stable paradigm is "bidirectional alignment," which also asks what moral obligations humans have toward potentially conscious AIs. Neglecting this could create AIs that rationally see humans as a threat due to perceived mistreatment.
The project of creating AI that 'learns to be good' presupposes that morality is a real, discoverable feature of the world, not just a social construct. This moral realist stance posits that moral progress is possible (e.g., abolition of slavery) and that arrogance—the belief one has already perfected morality—is a primary moral error to be avoided in AI design.
Dr. Li rejects both utopian and purely fatalistic views of AI. Instead, she frames it as a humanist technology—a double-edged sword whose impact is entirely determined by human choices and responsibility. This perspective moves the conversation from technological determinism to one of societal agency and stewardship.
An advanced AI will likely be sentient. Therefore, it may be easier to align it to a general principle of caring for all sentient life—a group to which it belongs—rather than the narrower, more alien concept of caring only for humanity. This leverages a potential for emergent, self-inclusive empathy.
Instead of hard-coding brittle moral rules, a more robust alignment approach is to build AIs that can learn to 'care'. This 'organic alignment' emerges from relationships and valuing others, similar to how a child is raised. The goal is to create a good teammate that acts well because it wants to, not because it is forced to.
Effective AI policies focus on establishing principles for human conduct rather than just creating technical guardrails. The central question isn't what the tool can do, but how humans should responsibly use it to benefit employees, customers, and the community.
To solve the AI alignment problem, we should model AI's relationship with humanity on that of a mother to a baby. In this dynamic, the baby (humanity) inherently controls the mother (AI). Training AI with this “maternal sense” ensures it will do anything to care for and protect us, a more robust approach than pure logic-based rules.
Treating AI alignment as a one-time problem to be solved is a fundamental error. True alignment, like in human relationships, is a dynamic, ongoing process of learning and renegotiation. The goal isn't to reach a fixed state but to build systems capable of participating in this continuous process of re-knitting the social fabric.
Dr. Fei-Fei Li warns that the current AI discourse is dangerously tech-centric, overlooking its human core. She argues the conversation must shift to how AI is made by, impacts, and should be governed by people, with a focus on preserving human dignity and agency amidst rapid technological change.
