Human memory and AI memory mechanisms both seem to "forget," but the underlying logical differences are enormous.

During a day’s activities, the information being processed in the human brain is like an AI’s short-term context window—accessible at the moment, capable of reasoning, and finding associations, but inherently limited in capacity. The further back the information, the easier it is to accumulate, and attention begins to scatter.

Human short-term memory is more like a workbench, with a limited number of information chunks that can be stably handled—cognitive psychology typically estimates this to fluctuate between 4 and 6 "chunks." This limit is fixed; you cannot break through it. While AI’s context window also has an upper limit, the design logic behind this limit is entirely different—it’s not constrained by biological neural networks but by computational architecture.

Humans forget because the connections between neurons decay, compete, and are overwritten by new memories. AI "forgets" essentially because once the processing window is exceeded, it cannot read the previous information—next session starts anew, and the previous data truly disappears. The mechanisms of these two types of forgetting are fundamentally different.