"The power of the new AI comes from three sources: mechanisms, causal networks, and emergent properties. Mechanisms are combinations of interconnected parts whose interactions lead to regular changes. Causal networks are systems of causes based on multiple mechanisms. Emergent properties are ones possessed by whole systems but not by their components, because the novel properties result from interactions among the components and their functional mechanisms. Current AI systems are powerful because their mechanisms interact to produce causal networks with emergent properties that approximate human intelligence."
"Neural networks: Whereas human brains contain neurons connected by synapses that allow the neurons to interact, AI networks consist of mathematical vectors that simulate the interactions of artificial neurons with trillions of connections. Backpropagation learning: To get smarter, AI networks learn from experience by making predictions and propagating errors back through the networks to alter the connections between neurons"
New AI systems exhibit emergent properties such as language, problem solving, reasoning, and creativity. More than a billion people use AI models regularly for work and personal advice. Power derives from three sources: mechanisms, causal networks, and emergent properties. Mechanisms are combinations of interacting parts that produce regular changes. Causal networks are systems of causes built from multiple mechanisms. Emergent properties arise at the system level because component interactions produce novel functions. Key mechanisms include neural networks, backpropagation learning, attention, large-scale training, and specialized chips. The micro/macro emergence pattern also applies to other complex systems, including consciousness.
Read at Psychology Today
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