Emergent Behavior
Unexpected capabilities that appear in AI models at scale without being explicitly programmed or trained for.
Definition
Emergent behavior in AI refers to capabilities that appear spontaneously in large models once they reach a certain scale of parameters or training data. These abilities were not explicitly programmed or targeted during training but arise from the complexity of the learned representations.
Examples include chain-of-thought reasoning, translation between languages not paired in training data, and basic arithmetic. These behaviors often appear abruptly at specific scale thresholds rather than gradually improving, making them difficult to predict.
Why It Matters
Emergent behaviors represent both opportunity and risk. On the positive side, they mean models can surprise us with useful capabilities beyond their original training objectives. On the risk side, unintended behaviors can be difficult to control or anticipate.
For businesses deploying AI, understanding emergence helps set realistic expectations. Current models may develop new capabilities with future updates, but they may also exhibit unexpected behaviors that require monitoring and guardrails.
Examples in Practice
GPT-3 demonstrated emergent few-shot learning abilities that GPT-2 lacked, performing tasks from just a few examples in the prompt without any fine-tuning. This capability appeared at scale and was not directly trained.
Large language models trained only on text have shown the ability to write functional code, despite code not being a primary training objective, an emergent capability that spawned entire product categories like AI coding assistants.