“Artificial intelligence has recently beaten world champions in Go and poker and made extraordinary progress in domains such as machine translation, object classification, and speech recognition. However, most AI systems are extremely narrowly focused. AlphaGo, the champion Go player, does not know that the game is played by putting stones onto a board; it has no idea what a “stone” or a “board” is, and would need to be retrained from scratch if you presented it with a rectangular board rather than a square grid.” To build AIs able to comprehend open text or power general-purpose domestic robots, we need to go further. A good place to start is by looking at the human mind, which still far outstrips machines in comprehension and flexible thinking. This CACM article offers 11 clues drawn from the cognitive sciences—psychology, linguistics, and philosophy. Link
Insights for AI from the Human Mind
January 5, 2021 · decisionmanagementcommunity
Archived discussion
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jackjansonius · January 27, 2021
This is not about insights from the human mind, but about the architecture of the human psyche (Self, Soul, Mind), so we can safely say that this article is at least 20 years behind in the development of artificial intelligence. The notion of "Truly intelligent and flexible systems" is complete nonsense. Systems thinking does think in wholes rather than parts (the holistic principle), but does not ask the question of the being of those wholes. And thus there is no place in this thinking for an ontology (or a theory of being) and in connection with that: transcendence.
Jack Jansonius · April 25, 2024
With the rise of Large Language Models (LLMs), interest in ontology has only increased.
Jack Jansonius · February 10, 2026
Both indeterministic LLMs and deterministic business technology function as interpretation systems. Deterministic IT therefore requires an explicit meaning architecture (ontology) in order to remain stable and controllable. At the same time, indeterministic GenAI does not belong in core processes in an unregulated manner, but must be anchored in that same semantic basis. Without that layer, you get hallucinations on the AI side and spaghetti on the IT side.