  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.
