The “I” nobody understands

The technology industry has spent the past three years debating artificial intelligence with the zeal of medieval theologians disputing angels on pinheads. Boardrooms have AI strategies, and governments have AI safety frameworks. LinkedIn has AI thought leaders, which is arguably the strongest case yet for existential risk. But somewhere beneath the acronym and the data centres in space, one central question remains unanswered. What, exactly, is intelligence?

We are developing systems we call intelligent, regulating systems we call intelligent, and worrying about systems we call intelligent, without a shared scientific consensus on what that term means when applied to humans, let alone machines. That is, to say the least, a problem.

The indefinable word

Ask a psychologist what intelligence is, and you’ll step into the epicentre of a fierce debate that has lasted for more than a century. The oldest and most statistically reliable answer comes from Charles Spearman, who in 1904 observed that people who did well on one kind of cognitive test also tended to do well on others. He called this underlying factor g, or general intelligence. The g factor is among the most replicated findings in psychology. It predicts academic performance, job performance, income, health outcomes, and even longevity, with a consistency that makes most social-science results look like coin flips.

And yet g tells you almost nothing about what intelligence really is. It is a statistical regularity, not a mechanism. Saying someone has high g is a bit like saying a car is fast. The measurement works, but the explanation is missing.

Howard Gardner tried to blow the whole thing up in 1983 with his theory of multiple intelligences, arguing that intelligence is not one thing but at least eight distinct varieties, from linguistic and logical-mathematical to musical, bodily-kinaesthetic, spatial, interpersonal, intrapersonal, and naturalistic. Teachers lapped this up. It confirmed their intuition…