"Despite the impressive achievements of current generative AI systems, the dream of Artificial General Intelligence remains far away, notwithstanding the hype offered by various tech CEOs.[1] The reasons are easy to state, if hard to quantify. Human intelligence requires three primary features, none of which have been fully cracked: logic, associative learning, and value sensitivity. I'll explain each in turn."
"Logic was once thought to be the apotheosis of human reasoning and the key to human intelligence.[2] Getting machines to reproduce logical inference was a massive breakthrough in the mid-1950s, with Newell, Simon, and Shaw's Logic Theorist (1956)[3] and General Problem Solver (1957)[4], which were able to perform logical inferences and even prove some advanced mathematical theorems from the Principia Mathematica. The success reportedly prompted Simon to say to his students, "Over Christmas, Al Newell and I invented a thinking machine."[5]"
"Millions upon millions of dollars were subsequently spent in anticipation of "solving" the problem of intelligence. But it didn't work, not really. Logic-based AI-as useful as it remains today-proved brittle in the face of incomplete or contradictory information; perceptual inputs proved difficult (even impossible!) to capture in logical formulae; and as every schoolkid knows, logic is too hard to realistically be the whole or even the root of human cognition.[6]"
Despite impressive advances in generative AI, artificial general intelligence has not been achieved. Human intelligence relies on three primary features: logic, associative learning, and value sensitivity. Formal logical inference produced early breakthroughs in the 1950s, enabling machines to prove mathematical theorems, but logic-based approaches proved brittle with incomplete or contradictory information and cannot capture perceptual inputs effectively. Associative learning—recognizing co-occurrence of properties and predictive relations—is central to animal cognition and remains only partially captured by current AI systems. Value sensitivity—the ability to represent, prioritize, and act on goals and preferences—is not yet matched by AI, limiting adaptability and alignment with human-relevant ends. Bridging these gaps is necessary for genuine general intelligence.
Read at Psychology Today
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