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Recently, the Meta AI team launched the video joint embedding prediction architecture (V-JEPA) model, an innovative initiative aimed at advancing machine intelligence. Humans can naturally process information from visual signals and recognize surrounding objects and motion patterns. An important goal of machine learning is to reveal the fundamental principles that drive unsupervised learning in humans. Researchers proposed a key hypothesis—the predictive feature principle—arguing that the representations of continuous sensory inputs should be able to predict each other. Early research methods utilized slow feature analysis.

Recently, a study led by Meta's Chief AI Scientist Yann LeCun revealed how artificial intelligence can develop a basic understanding of physics by watching videos. This research, conducted by scientists from Meta FAIR, Paris University, and EHESS, shows that AI systems can acquire intuitive physical knowledge through self-supervised learning without predefined rules. The research team employed a new method called Video Joint Embedding Predictive Architecture (V-JEPA).