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.

翻译：尽管人工智能发展迅速，但由于当前AI技术及其对认知理解的固有局限，其在多个关键方面仍不及人类智能。人类天生具备理解语境、细微差异及沟通中微妙线索的能力，这使我们能领悟笑话、讽刺与隐喻，而机器在准确解读此类语境信息时面临困难。人类拥有庞大的常识知识库，有助于对世界进行逻辑推理与预测，但机器缺乏这种先天理解，常难以应对对人类而言轻而易举的情境。本文综述了有望缩小人机智能差距的候选机器智能方案——包括来自Yann LeCun教授的综述及其他相关研究。具体而言，我们探讨了当前AI技术（如监督学习、强化学习、自监督学习等）的不足之处，进而阐释基于分层规划的方法如何帮助弥合这一差距，并深入剖析基于能量的潜变量方法及联合嵌入预测架构方法。