Social intelligence is the next big thing for AI

Social intelligence is the next big thing for AI

What can’t artificial intelligence do nowadays? With the emergence of large language models (LLMs) like ChatGPT and generative AI models that create eerily beautiful works of art, it seems that AI’s capabilities in some areas have become almost human-like.

However, the AI ​​isn’t entirely foolproof; we’ve seen cases where LLMs have failed spectacularly where they were supposed to provide healthcare resources, and AI models still have trouble accurately producing images of human hands and teeth (with sometimes hilarious results).

But as some experts believe, the next big thing is the development of AI’s social intelligence, which will help it improve the way it interacts with humans. When it comes to being able to decipher non-verbal cues like body language or facial expressions, AI still lacks many of the social skills that many of us humans take for granted. To help AI develop these social skills, new work by Chinese researchers suggests that a multidisciplinary approach will be needed, as tailoring what we know about cognitive science and using computational models would help us better identify disparities between the social intelligence of machine learning models and their human counterparts.

“[Artificial social intelligence or ASI] it is distinct and challenging from our physical understanding of the work; it is highly context-dependent,” said first author Lifeng Fan of the Beijing Institute for General Artificial Intelligence (BIGAI) in a statement. “Here, context could be as large as culture and common sense, or as small as the shared experience of two friends. This unique challenge prevents standard algorithms from tackling ASI problems in real-world environments, which are often complex, ambiguous, dynamic, stochastic, partially observable, and multi-agent.

A multidisciplinary approach is needed

While physical intelligence is relatively easy to measure – like learning to throw a ball or solve an abstract problem – the team’s work points out that much of the knowledge related to the expansion of artificial social intelligence lies in subfields studied in their own silos.

‘In contrast to the mechanistic and abstract nature of physical intelligence, ASI involves many subfields that are currently studied separately, such as social perception, theory of mind (ToM), and social interaction, with varying emphasis on perception, cognitive components, behavior, and even psychometric methods for measuring social skills,’ the team wrote.

However, Fan believes that the unknown factors surrounding artificial social intelligence could be better unveiled using a more comprehensive strategy that focuses on what are currently considered the core characteristics of social intelligence.

“Multidisciplinary research informs and inspires the study of ASI. The study of human social intelligence provides insight into the foundation, curriculum, points of comparison, and benchmarks needed to develop ASI with human-like characteristics,” said Fan. ‘We focus on the three most important and inextricably linked aspects of social intelligence: social perception, theory of mind and social interaction, because they are grounded in well-established cognitive science theories and are readily available tools for developing computational models in these areas.’

In the field of cognitive science, social perception refers to the study of how people form impressions or make inferences about themselves, other individuals, and groups, using social cues to evaluate social roles, rules, and relationships. Social perception forms the basis of theory of mind, which describes a person’s ability to understand other people by attributing mental states to them, thus enabling an informed judgment to be made about what the other person might be feeling or thinking. Most experts believe that developing both social perception and theory of mind in tandem are crucial aspects of successful social interactions.

According to the team, such social instincts may actually be hardwired into humans. For example, as the team describes in a related study, human participants were able to quickly make social judgments about different arrangements of moving shapes shown on a display.

This image shows what is called Heider-Simmel stimuli, which help demonstrate that humans can perceive complex mental states and social interactions based solely on the movement of simple geometric shapes.

“These mental states combine to form a narrative description of the display, like a hero rescuing a victim from a bully. This interpretation of simple moving shapes as animate agents is a remarkable demonstration of how the human visual system can infer complex social relationships and mental states from simple motion cues with minimal visual characteristics. While involving impressions typically associated with higher-level cognitive processing, such interpretations appear to be predominantly perceptual in nature, that is, relatively rapid, automatic, compelling, and highly stimulated.

In general, such complex social judgments are still difficult for machines to make, although some previous work suggests that if artificial intelligence were indeed able to master such skills, it would probably do just as well as humans, especially in situations where compromise and mutual cooperation are required. However, the team’s work underscores how fundamental lessons from neuroscience and cognitive science may lie in the development of emotionally intelligent machines that can accurately judge how humans think and feel.

“To accelerate the future progress of ASI, we recommend taking a more holistic approach just like humans do, to use different learning methods such as lifelong learning, multitasking learning, one-shot and few-shot learning, meta-learning, etc.” said Fan. “We have to define new problems, create new environments and datasets, set up new evaluation protocols and build new computational models. The ultimate goal is to equip AI with high-level ASI and improve human well-being with the help of artificial social intelligence.”

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