  To achieve human-like common sense about everyday life, machine learning
systems must understand and reason about the goals, preferences, and actions of
other agents in the environment. By the end of their first year of life, human
infants intuitively achieve such common sense, and these cognitive achievements
lay the foundation for humans' rich and complex understanding of the mental
states of others. Can machines achieve generalizable, commonsense reasoning
about other agents like human infants? The Baby Intuitions Benchmark (BIB)
challenges machines to predict the plausibility of an agent's behavior based on
the underlying causes of its actions. Because BIB's content and paradigm are
adopted from developmental cognitive science, BIB allows for direct comparison
between human and machine performance. Nevertheless, recently proposed,
deep-learning-based agency reasoning models fail to show infant-like reasoning,
leaving BIB an open challenge.
