Emergence

Emergence in the field of artificial intelligence refers to a phenomenon in which complex collective behaviors or structures arise through the interaction of simple individuals or rules.In artificial intelligence, this kind of Emergence can refer to the high-level features or behaviors learned by the model. These features or behaviors are not directly specified by the designer, but are gradually generated through the model's own learning process.

For example, in a neural network, the simple computations of each neuron and the connections between neighboring neurons form a large-scale network. When these neurons and connections learn, the network can exhibit complex behaviors that are beyond the capabilities of a single neuron or connection, such as image classification and speech recognition.

Emergence can also refer to some unexpected effects or behaviors that may be difficult to predict or understand directly due to the complexity of the model. For example, in reinforcement learning, the behavior learned by the agent through interaction with the environment may show some unexpected strategies or behaviors, which may be the result of emergent.The emergence of large models andHallucinationsIt's the same principle.

Emergent behavior is not unique to large models and can in fact be seen in many fields, such as physics, evolutionary biology, economics, and dynamical systems. While there is no single definition of emergence used across fields, all definitions boil down to the same basic phenomenon:Small changes in a system's quantitative parameters can lead to large changes in its qualitative behaviorThe qualitative behavior of these systems can be viewed as different “regimes,” where the “rules of the game,” or equations that determine behavior, can vary widely.

References

【1】https://www.assemblyai.com/blog/emergent-abilities-of-large-language-models