Although Siri and Google Assistant have the ability to schedule meetings on request, they do not have the social awareness to independently prioritise the appointments. A team of researchers has argued that the future of AI calls for the implementation of social intelligence to ensure that the growth of the technology is not stunted by a lack of social skills.
“Artificial Intelligence has changed our society and our daily life,” said first author Lifeng Fan, National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI). “What is the next important challenge for AI in the future? We argue that Artificial Social Intelligence (ASI) is the future of AI.”
ASI includes multiple siloed subfields, including social perception, Theory of Mind, and social interaction. The researchers argue that it is important to use cognitive science and computational modelling to identify the gap between AI systems and human social intelligence, as well as current issues and future problems, for future advancements in AI.
“ASI is distinct and challenging compared to our physical understanding of the work; it is highly context-dependent,” Fan said. “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.”
Fan argues that ASI requires a comprehensive approach, as unlike contemporary AI systems, improving specific components of an ASI system may not always result in improved performance. Instead, the future of AI needs technology that has the ability to interpret social cues, such as eye-rolling or yawning, to understand the mental state of other agents, like intent or belief, to co-operate in a shared task.
“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 said.
“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.”
Fan argues that researchers should take a holistic approach to mimic how humans interface with one another and the world around them. For this to occur, an open-ended and interactive environment is required, as well as consideration for how to introduce better human-like biases into ASI models to accelerate the future of AI.
“To accelerate the future progress of ASI, we recommend taking a more holistic approach just as humans do, to utilise different learning methods such as lifelong learning, multi-task learning, one-/few-shot learning, meta-learning, etc.,” Fan said.
“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.”
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