What is the orthogonality thesis?

The orthogonality thesis is the claim that any^[1] level of intelligence is compatible with any set of terminal goals. In other words, values and intelligence are “orthogonal” to each other in the sense that agents can vary in one dimension while staying constant in the other dimension.

This means we can’t assume that an AI system that is as smart as or smarter than humans will automatically be motivated by human values.

On its own, the orthogonality thesis only states that unaligned superintelligence is possible, not that it is likely, or that AI alignment is difficult. It is invoked to counter the idea that future AI will converge toward human goals or morality, regardless of its design, as an automatic result of becoming smarter.

In addition to this “weak” version of the thesis, people have considered stronger versions. Eliezer Yudkowsky’s “strong form” of the orthogonality thesis says that creating AI systems with arbitrary goals is not only possible, but involves no special difficulty; in other words, that “preferences are no harder to embody than to calculate”.^[2]

While the orthogonality thesis is broadly accepted by the alignment research community, it has critics who come from a few distinct strands:

• Some moral realists assert that a sufficiently intelligent entity would discover and adhere to objective moral truths that humans would endorse upon reflection.

• Beren Millidge claims that the strong form of the orthogonality thesis is false within modern deep learning algorithms.

• Nora Belrose contends that, depending on how the thesis is interpreted, it’s either trivial, false, or unintelligible.

• Steve Petersen argues that the need for continuity of an agent's goals over time, together with the seemingly intrinsic underspecifiedness of goal representations, might cause AIs that have complex goals and that can understand their human creators to consider these creators to be previous versions of themselves, and aim to further these creators’ goals.