Towards Human-Like Machine Intelligence: Generalizability, Transferability, and Trustworthiness

Abstract

Acquiring human-like machine intelligence is a long-standing goal of machine learning. Thanks to the availability of large-scale datasets and the GPU acceleration, modern learning methods achieve remarkable success. Although it surpasses humans on several tasks, e.g., the game of go, there is still a gap between machine intelligence and human intelligence. The reasons are two-fold. Firstly, how the human brain produces intelligence is still little-known, and how to apply the mechanisms that are discovered in the research of neuroscience to machine intelligence remains unclear. Secondly, human intelligence has been proven to be versatile to a wide variety of capacities, e.g., abstraction, logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, problem-solving, etc. It is unclear how to comprehensively measure human intelligence. There is no evidence thus far that machine intelligence can be a replacement for human intelligence in a wide range of real-world applications. Instead of diving into human brain neurons, Piaget studies human intelligence from the aspect of cognitive development along the key periods of growth. In Piaget's theory, two processes are closely related to human intelligence, that is, assimilation and accommodation. Assimilation aims to fit new information into existing cognitive schemas, while accommodation aims to take new information in one's environment and alter existing schemas to fit in the new information. We focus on three specific characteristics, i.e., generalizability, transferability, and trustworthiness, that center around assimilation and accommodation. Specifically, generalizability is an important yet generic concept in machine learning. Instead, we study the generalizability that takes place in the process of fitting new information associated to unknown classes into the knowledge w.r.t. known classes. Secondly, we explore how to transfer the knowledge learned from the source domain samples to the target domain with very few target-domain examples. Last but not least, there is still a gap between state-of-the-art learning-based approaches and a perfect one. Therefore, there is a critical need to understand the trustworthiness of machine intelligence.