  A machine intelligence pipeline usually consists of six components: problem,
representation, model, loss, optimizer and metric. Researchers have worked hard
trying to automate many components of the pipeline. However, one key component
of the pipeline--problem definition--is still left mostly unexplored in terms
of automation. Usually, it requires extensive efforts from domain experts to
identify, define and formulate important problems in an area. However,
automatically discovering research or application problems for an area is
beneficial since it helps to identify valid and potentially important problems
hidden in data that are unknown to domain experts, expand the scope of tasks
that we can do in an area, and even inspire completely new findings.
  This paper describes Problem Learning, which aims at learning to discover and
define valid and ethical problems from data or from the machine's interaction
with the environment. We formalize problem learning as the identification of
valid and ethical problems in a problem space and introduce several possible
approaches to problem learning. In a broader sense, problem learning is an
approach towards the free will of intelligent machines. Currently, machines are
still limited to solving the problems defined by humans, without the ability or
flexibility to freely explore various possible problems that are even unknown
to humans. Though many machine learning techniques have been developed and
integrated into intelligent systems, they still focus on the means rather than
the purpose in that machines are still solving human defined problems. However,
proposing good problems is sometimes even more important than solving problems,
because a good problem can help to inspire new ideas and gain deeper
understandings. The paper also discusses the ethical implications of problem
learning under the background of Responsible AI.
