Artificial intelligence (AI) is being promoted as being able to do almost everything – from diagnosing diseases as well as newly graduated physicians to writing speeches and producing art in seconds. Yet a new study from New York University (NYU) shows that infants outperform AI in detecting what motivates other people’s actions and are more adept at spotting motivations that drive human behavior. How can that be?
The study by a team of psychology and data-science researchers highlights fundamental differences between cognition and computation, pointing to shortcomings in today’s technologies, and where improvements are needed for AI to accurately replicate human behavior.
“Adults and even infants can easily make reliable inferences about what drives other people’s actions,” explained Prof. Moira Dillon of NYU’s psychology department and the senior author of the study, which appears in the journal Cognition under the title “Commonsense psychology in human infants and machines.”
“The novel idea of putting infants and AI head-to-head on the same tasks is allowing researchers to better describe infants’ intuitive knowledge about other people and suggest ways of integrating that knowledge into AI,” she added.
“From this rich experimental and theoretical tradition thus arises the need for a comprehensive framework in which to characterize infants' knowledge of agents with results on one task comparable with those on another and with results on the suite of tasks comparable across infants and machines. Such a framework can inform both theories of infants' knowledge and the future of human-like AI,” the researchers wrote.
“If AI aims to build flexible, commonsense thinkers like human adults become, then machines should draw upon the same core abilities infants possess in detecting goals and preferences,” noted Brenden Lake, an assistant professor in NYU’s Center for Data Science and psychology department and one of the paper’s authors.
It’s been well-established that infants are fascinated by other people, as evidenced by how long they look at others to observe their actions and to engage with them socially. In addition, previous studies focused on infants’ “commonsense psychology.”
Why can babies understand what AI cannot?
Their understanding of the intentions, goals, preferences and rationality underlying others’ actions have indicated that infants are able to attribute goals to others and expect others to pursue goals rationally and efficiently. The ability to make these predictions is fundamental for understanding human social intelligence.
“If AI aims to build flexible, commonsense thinkers like human adults become, then machines should draw upon the same core abilities infants possess in detecting goals and preferences.”
Brenden Lake, assistant professor in NYU’s Center for Data Science
Conversely, “commonsense AI” driven by machine-learning algorithms predicts actions directly. This is why, for example, an advertisement touting San Francisco as a travel destination pops up on your computer screen after you read a news story on a newly elected city official. However, what AI lacks is flexibility in recognizing different contexts and situations that guide human behavior.
To develop a foundational understanding of the differences between human and AI abilities, the researchers conducted a series of experiments with 11-month-old infants and compared their responses to those yielded by state-of-the-art learning-driven neural network models.
Machines vs babies
To do so, they used the previously established “Baby Intuitions Benchmark” (BIB) – six tasks probing commonsense psychology. BIB was designed to allow for testing both infant and machine intelligence, allowing for a comparison of performance between infants and machines and, significantly, providing an empirical foundation for building human-like AI.
Specifically, infants on Zoom watched a series of videos of simple animated shapes moving around the screen – similar to a video game. The shapes’ actions simulated human behavior and decision-making through the retrieval of objects on the screen and other movements. Similarly, the researchers built and trained learning-driven neural network models – AI tools that help computers recognize patterns and simulate human intelligence – and tested the models’ responses to the exact same videos.
Their results showed that infants recognize human-like motivations even in the simplified actions of animated shapes. Infants predict that these actions are driven by hidden but consistent goals, for example, the on-screen retrieval of the same object no matter what location it’s in and the movement of that shape efficiently even when the surrounding environment changes.
Infants show such predictions through their longer looking to such events that violate their predictions, which is a common and decades-old measurement for gauging the nature of infants’ knowledge.
Adopting this “surprise paradigm” to study machine intelligence allows for direct comparisons between an algorithm’s quantitative measure of surprise and a well-established human psychological measure of surprise –infants’ looking time.
The models showed no such evidence of understanding the motivations underlying such actions, revealing that they are missing key foundational principles of commonsense psychology that infants possess.
“A human infant’s foundational knowledge is limited, abstract and reflects our evolutionary inheritance, yet it can accommodate any context or culture in which that infant might live and learn,” Dillon concluded.