Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
This addresses the problem of enabling human-like common sense in AI systems for researchers in machine learning and cognitive science, though it is incremental as it builds on existing developmental paradigms.
The paper introduces the Baby Intuitions Benchmark (BIB) to test if machines can achieve generalizable, commonsense reasoning about agents' goals, preferences, and actions, similar to human infants, but current deep-learning models fail to show infant-like performance on this benchmark.
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.