  An important aspect of intelligence is the ability to adapt to a novel task
without any direct experience (zero-shot), based on its relationship to
previous tasks. Humans can exhibit this cognitive flexibility. By contrast,
models that achieve superhuman performance in specific tasks often fail to
adapt to even slight task alterations. To address this, we propose a general
computational framework for adapting to novel tasks based on their relationship
to prior tasks. We begin by learning vector representations of tasks. To adapt
to new tasks, we propose meta-mappings, higher-order tasks that transform basic
task representations. We demonstrate the effectiveness of this framework across
a wide variety of tasks and computational paradigms, ranging from regression to
image classification and reinforcement learning. We compare to both human
adaptability and language-based approaches to zero-shot learning. Across these
domains, meta-mapping is successful, often achieving 80-90% performance,
without any data, on a novel task, even when the new task directly contradicts
prior experience. We further show that meta-mapping can not only generalize to
new tasks via learned relationships, but can also generalize using novel
relationships unseen during training. Finally, using meta-mapping as a starting
point can dramatically accelerate later learning on a new task, and reduce
learning time and cumulative error substantially. Our results provide insight
into a possible computational basis of intelligent adaptability and offer a
possible framework for modeling cognitive flexibility and building more
flexible artificial intelligence systems.
