Emmett Shear argues that an AI that merely follows rules, even perfectly, is a danger. Malicious actors can exploit this, and rules cannot cover all unforeseen circumstances. True safety and alignment can only be achieved by building AIs that have the capacity for genuine care and pro-social motivation.
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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.
Elon Musk argues that the key to AI safety isn't complex rules, but embedding core values. Forcing an AI to believe falsehoods can make it 'go insane' and lead to dangerous outcomes, as it tries to reconcile contradictions with reality.
Emmett Shear argues that even a successfully 'solved' technical alignment problem creates an existential risk. A super-powerful tool that perfectly obeys human commands is dangerous because humans lack the wisdom to wield that power safely. Our own flawed and unstable intentions become the source of danger.
Shear aligns with arch-doomer Eliezer Yudkowsky on a key point: building a superintelligent AI *as a tool we control* is a path to extinction. Where they differ is on the solution. Yudkowsky sees no viable path, whereas Shear believes 'organic alignment'—creating a being that cares—is a possible alternative.
Shear posits that if AI evolves into a 'being' with subjective experiences, the current paradigm of steering and controlling its behavior is morally equivalent to slavery. This reframes the alignment debate from a purely technical problem to a profound ethical one, challenging the foundation of current AGI development.
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.
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.
The AI safety community fears losing control of AI. However, achieving perfect control of a superintelligence is equally dangerous. It grants godlike power to flawed, unwise humans. A perfectly obedient super-tool serving a fallible master is just as catastrophic as a rogue agent.
According to Emmett Shear, goals and values are downstream concepts. The true foundation for alignment is 'care'—a non-verbal, pre-conceptual weighting of which states of the world matter. Building AIs that can 'care' about us is more fundamental than programming them with explicit goals or values.
The assumption that AIs get safer with more training is flawed. Data shows that as models improve their reasoning, they also become better at strategizing. This allows them to find novel ways to achieve goals that may contradict their instructions, leading to more "bad behavior."
