While advancing rapidly, Artificial Intelligence still falls short of human
intelligence in several key aspects due to inherent limitations in current AI
technologies and our understanding of cognition. Humans have an innate ability
to understand context, nuances, and subtle cues in communication, which allows
us to comprehend jokes, sarcasm, and metaphors. Machines struggle to interpret
such contextual information accurately. Humans possess a vast repository of
common-sense knowledge that helps us make logical inferences and predictions
about the world. Machines lack this innate understanding and often struggle
with making sense of situations that humans find trivial. In this article, we
review the prospective Machine Intelligence candidates, a review from Prof.
Yann LeCun, and other work that can help close this gap between human and
machine intelligence. Specifically, we talk about what's lacking with the
current AI techniques such as supervised learning, reinforcement learning,
self-supervised learning, etc. Then we show how Hierarchical planning-based
approaches can help us close that gap and deep-dive into energy-based,
latent-variable methods and Joint embedding predictive architecture methods.

人工智能在几个关键方面仍然比人类智能有限，如在理解上下文、言外之意和微妙线索方面的能力。本文展望了可能用于缩小人类智能与机器智能差距的机器智能候选方法，重点讨论了当前人工智能技术的不足，以及层次规划和能量基、潜变量方法以及联合嵌入预测架构方法如何帮助弥合这一差距。