Socially intelligent machines that learn from humans and help humans learn

Abstract

A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human–machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue ‘Cognitive artificial intelligence’.

Introduction to ‘Cognitive artificial intelligence’

“…Gweon et al . [ 8 ] in their paper ‘Beyond imitation: machines that understand and are understood by humans' focus on a particular, and especially fundamental, aspect of explanation: the human ability to infer and reason about the mental states of others from observing their behaviour. Such inferences may be crucial when attempting to learn from another person; and equally is crucial from the point of view of the teacher, attempting to infer what the learner already knows and which actions or words will best help them learn successfully.…”

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

“…DRL from human feedback is when systems or machines learn to behave by using some little assistance from humans, which can actually bring out the best from both humans and the machines [80]. Building AI systems that align with human values, i.e., humancentered AI, in which DRL strategies can be used in the design of social mechanisms preferred by humans, is still an open research topic [81].…”