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Study Finds Social Intelligence to be AI’s Next Barrier

While Siri and Google Assistant may be able to organize meetings on demand, they currently lack the social intelligence to prioritize the appointments on their own. Chinese researchers claim that while artificial intelligence (AI) may be intelligent, it is limited by a dearth of social skills.

Three inextricably linked aspects of social intelligence—social perception, Theory of Mind, and social interaction—are the cognitive tools that will help computational science to advance artificial intelligence beyond contemporary models. Image Credit: CAAI Artificial Intelligence Research, Tsinghua University Press

On 10th March 2023, they issued an evaluation of the current situation and a call for future directions in CAAI Artificial Intelligence Research.

Artificial intelligence has changed our society and our daily life. What is the next important challenge for AI in the future? We argue that Artificial Social Intelligence (ASI) is the next big frontier.

Lifeng Fan, Study First Author, National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence

The researchers defined ASI as a collection of siloed subfields that include social perception, Theory of Mind (the understanding that others think from their own point of view), and social interaction.

Fan believes the field will advance if cognitive science and computational modeling are used to discover the gap between AI systems and human social intelligence, in addition to existing difficulties and future prospects.

Fan added, “ASI is distinct and challenging compared to our physical understanding of the work; it is highly context-dependent. Here, context could be as large as culture and common sense or as little as two friends’ shared experience. This unique challenge prohibits standard algorithms from tackling ASI problems in real-world environments, which are frequently complex, ambiguous, dynamic, stochastic, partially observable, and multi-agent.

As a result, ASI calls for an integrated approach because upgrading single components of an ASI system may not necessarily result in enhanced performance—unlike modern AI systems.

Rather, ASI needs the ability to interpret latent social signs like eye-rolling or yawning, to comprehend other agents’ mental states like belief and intent, and to collaborate in a shared activity.

Multidisciplinary research informs and inspires the study of ASI: Studying human social intelligence provides insight into the foundation, curriculum, points of comparison, and benchmarks required to develop ASI with human-like characteristics,” Fan further stated.

He further added, “We concentrate 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.

The optimal approach, according to Fan, is a more holistic one that mimics how individuals interact with one another and the environment around them. This necessitates an open-ended and interactive environment, along with consideration of how to include more human-like biases in ASI models.

Fan stated, “To accelerate the future progress of ASI, we recommend taking a more holistic approach just as humans do, to utilize different learning methods such as lifelong learning, multi-task learning, one-/few-shot learning, meta-learning, etc.

He concluded, “We need 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 lift human well-being with the help of Artificial Social Intelligence.

Manjie Xu, Zhihao Cao, and Song-Chun Zhu of BIGAI, along with Yixin Zhu of Peking University’s Institute for Artificial Intelligence are the other study contributors.

Xu is also associated with the Beijing Institute of Technology’s School of Computer Science and Technology, Cao and Zhu with Tsinghua University’s Department of Automation, and Zhu with Peking University’s Institute for Artificial Intelligence.

This study was supported by the National Key R&D Program of China and the Beijing Nova Program.

Journal Reference

Fan, L., et al. (2023) Artificial Social Intelligence: A Comparative and Holistic View. CAAI Artificial Intelligence Research. doi:10.26599/AIR.2022.9150010

Source: http://www.tup.tsinghua.edu.cn/en/index.html

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