  Crowd algorithms often assume workers are inexperienced and thus fail to
adapt as workers in the crowd learn a task. These assumptions fundamentally
limit the types of tasks that systems based on such algorithms can handle. This
paper explores how the crowd learns and remembers over time in the context of
human computation, and how more realistic assumptions of worker experience may
be used when designing new systems. We first demonstrate that the crowd can
recall information over time and discuss possible implications of crowd memory
in the design of crowd algorithms. We then explore crowd learning during a
continuous control task. Recent systems are able to disguise dynamic groups of
workers as crowd agents to support continuous tasks, but have not yet
considered how such agents are able to learn over time. We show, using a
real-time gaming setting, that crowd agents can learn over time, and `remember'
by passing strategies from one generation of workers to the next, despite high
turnover rates in the workers comprising them. We conclude with a discussion of
future research directions for crowd memory and learning.
